List of articles (by subject) Journal of Computer & Robotics
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Open Access Article
1 - Determining COVID-19 Tweet Check-Worthiness: Based On Deep Learning Approach
hosniyeh safiarian Mohammad Jafar Tarokh MohammadAli Afshar KazemiWhen, we consider the ubiquity of Facebook, twitter, LinkedIn, it is easy to understand how social media is woven into the fabric of our day-to-day activities. It is a suitable tool to find information about news , events , and different Issues. After corona virus outbr MoreWhen, we consider the ubiquity of Facebook, twitter, LinkedIn, it is easy to understand how social media is woven into the fabric of our day-to-day activities. It is a suitable tool to find information about news , events , and different Issues. After corona virus outbreak, it is inspired users to understand pandemic news, mortality statistics and vaccination news. According to evidence, the diffusion of pandemic news on social medium has increased from 2020 and user face a ton of COVID19 messages. The purpose of this paper is to determine the check-worthiness of news about COVID-19 to identify and priorities news that need fact-checking. We proposed a method that is called CVMD. We extracted the feature of content. We use the deep learning approach for prediction it means that we model this problem with a binary classification problem. Our proposed method is evaluated by different measures on twitter dataset and the results show that CVMD method has a high accuracy in prediction rather than other methods. Manuscript profile -
Open Access Article
2 - IMPTCHA: A Creative Image CAPTCHA
Reza ShaliAbstract We present IMPTCHA, a new CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) as a security measure to recognize human users. The proposed system uses images instead of distorted text to label images as a valuable output. IMPTCH MoreAbstract We present IMPTCHA, a new CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) as a security measure to recognize human users. The proposed system uses images instead of distorted text to label images as a valuable output. IMPTCHA is generated from images on the Web. For passing this CAPTCHA, users must type two words for description of two images. When users pass the challenge, the provided meaningful labels are used to determine the content of images. In addition, semantic graphs for labels and images are created and according of it we’ll able to develop an image semantic search engine. Due to usage of images in this system, and its architecture, it is highly secure compared to its counterparts. In a user study involving 60 participants, IMPTCHA’s word accuracy is measured to be 98.18% while 61.26% of users could pass the challenge.Keywords:CAPTCHA, Ontology, Image labeling, Security Manuscript profile -
Open Access Article
3 - A New Approach to Improve Tracking Performance of Moving Objects with Partial Occlusion.
Zahra Sahraei Amir Masoud Eftekhari Moghadam< p>Tracking objects in video images has attracted much attention by machine vision and image processing researchers in recent years. Due to the importance of the subject, this paper presents a method for improving object tracking tasks with partial occlusion, whi More< p>Tracking objects in video images has attracted much attention by machine vision and image processing researchers in recent years. Due to the importance of the subject, this paper presents a method for improving object tracking tasks with partial occlusion, which increases the efficiency of tracking. The proposed approach first performs a pre-processing and extracts the tracking targets from the image. Then the salient feature points are extracted from the targets that are moving objects. In the next step, the particle filter is used for tracking. The final steps are modifying points and updates. A new approach is used to determine the speed of the feature points because the speed of some points can be out of range and this causes errors in tracking especially when there is occlusion. The location of the new points is corrected and updated using the threshold values in modifying the process as needed. The experiments performed on the video sequence of PETS2000 database show that the precision and recall of the proposed approach are higher than other compared approaches. Manuscript profile -
Open Access Article
4 - Application of Numerical Iterative Methods for Solving Benjamin-Bona-Mahony Equation
Shadan Sadigh behzadiIn this paper, a generalized Benjamin-Bona-Mahony equation ( BBM)is solved by using the Adomian's decomposition method (ADM) ,modified Adomian's decomposition method (MADM), variationaliteration method (VIM), modified variational iteration method (MVIM)and homotopy anal MoreIn this paper, a generalized Benjamin-Bona-Mahony equation ( BBM)is solved by using the Adomian's decomposition method (ADM) ,modified Adomian's decomposition method (MADM), variationaliteration method (VIM), modified variational iteration method (MVIM)and homotopy analysis method (HAM). The approximate solution of thisequation is calculated in the form of series which its componentsare computed by applying a recursive relation. The existence anduniqueness of the solution and the convergence of the proposedmethods are proved. A numerical example is studied to demonstratethe accuracy of the presented methods.The MVIM has been shown to solve effectively, easily and accuratelya large class of nonlinear problems with the approximations whichconvergent are rapidly to exact solutions. Manuscript profile -
Open Access Article
5 - Copy-Move Forgery Detection by an Optimal Keypoint on SIFT (OKSIFT) Method.
Ehsan Amiri Ahmad Mosallanejad Amir SheikhahmadiCopy-Move is a technique widely used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still significant research. This paper proposes an optimal keypoint in SIFT (OKSIFT). The OKSIFT method produces images of different sizes and different sigma& MoreCopy-Move is a technique widely used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still significant research. This paper proposes an optimal keypoint in SIFT (OKSIFT). The OKSIFT method produces images of different sizes and different sigma’s. Then with the help of the Gaussian difference (DoG) method, the maximum and minimum keypoints are calculated. When selecting the optimal keypoints, the absolute value of the second sentence will be used instead of using the Taylor expansion binomial series. First, the keypoints lose their dependence on the blurred regions, and secondly, more keypoints appear at the main edges. In the localization process of the region, considering the cases of multiple copies, method g2NN has been used to compare the keypoints. This method reduces the complexity of keypoint calculations and gives a better answer. Experimental results based on precision, recall, and F1 criteria show that the proposed method, with good robustness, works better than some advanced methods. Manuscript profile -
Open Access Article
6 - Reliability Measurement’s in Depression Detection Using a Data Mining Approach Based on Fuzzy-Genetics
Mohammad Nadjafi Sepideh Jenabi Adel Najafi Ghasem KaheDeveloping a reliable data mining method is one of the most challenging issues in the features of advanced computer-based systems. Model reliability in depression disorder detection is the determining p-value or confidence limit for accuracy score. In this regard, data MoreDeveloping a reliable data mining method is one of the most challenging issues in the features of advanced computer-based systems. Model reliability in depression disorder detection is the determining p-value or confidence limit for accuracy score. In this regard, data mining evaluation metrics provide a path to knowledge discovery and feature extraction is an important process for discovering patterns in data by exploring and modeling big data. The present paper discussed the data mining approach about detection in depression disorder characterized by symptoms such as sadness, feeling empty, anxiety, and sleep symptoms as well as the loss of initiative and interest inactivity. In this survey, a unique dataset containing sensor data collected from patients with depression was used. For each patient, sensor data were measured over several days. In this respect, the represented dataset could be useful for a better understanding of the relationship between depression and motor activity. On the other hand, to overcome the uncertainties raised from wearable sensors (that caused a significant amount of error in similar previous studies using conventional learning methods such as SVM, LR, NB), and also to increase the efficiency and accuracy of the results and to develop a reliable decision-making framework, the evolutionary hybrid machine learning method (fuzzy-genetic algorithm) will be used. The results show the high accuracy of the proposed method compared to other existing methods. Manuscript profile -
Open Access Article
7 - An Optimal Defect-free Synthesis of Four-bar Mechanisms by Using Constrained APT-FPSO Algorithm
SeyedAli MirMohammad Sadeghi Nima Bakhshinezhad Alireza Fathi Hamidreza Mohammadi DanialiFour-bar mechanisms are one of the most common and effective components in the industry. As an example of their applications, they are designed to generate the desired output motion. In this paper, the nonlinear problem of optimal defect-free synthesis of four-bar mecha MoreFour-bar mechanisms are one of the most common and effective components in the industry. As an example of their applications, they are designed to generate the desired output motion. In this paper, the nonlinear problem of optimal defect-free synthesis of four-bar mechanisms is analyzed by using a constrained version of the newly developed adaptive particularly tunable fuzzy particle swarm optimization (APT-FPSO) algorithm. The analyzed case study is designing a four-bar mechanism to generate a path that included three loops and 90 precision points. The results obtained support the superior performance of APT-FPSO compared to the standard PSO in solving the path generation problem. Manuscript profile -
Open Access Article
8 - MS Identification in Brain Magnetic Resonance Images Using Wavelet Transfer Learning
Ali Alijamaat Ali NikravanShalmani Peyman BayatMultiple Sclerosis (MS) is one of the most important diseases of the central nervous system. This disease causes small lesions detectable in Magnetic Resonance Imaging (MRI) images of the patient’s brain. Because of the small size of the lesions, their distributio MoreMultiple Sclerosis (MS) is one of the most important diseases of the central nervous system. This disease causes small lesions detectable in Magnetic Resonance Imaging (MRI) images of the patient’s brain. Because of the small size of the lesions, their distribution, and their similarity to some other diseases, the MS diagnosis can be difficult for specialists and may be mistaken. In this paper, we presented a new method based on deep learning for the automatic classification of MRI images. The proposed method is a combinational architecture from transfer learning and wavelet transform (WT). First, WT was applied to the input MRI image, and its four output sub-bands are used as the input of four fine-tuning networks based on EfficientNet-B3. Transfer learning networks perform feature extraction on all four sub-bands. Then, their outputs are combined, and the result is classified by a fully connected neural network. Due to the feature of WT to extract local features, it was possible to highlight the lesions in the images and subsequently classify it with higher accuracy and precision. Various criteria have been used to evaluate the proposed method. The results of the experiments show that the Values of accuracy, precision, sensitivity, and specificity are 98.91%, 99.20%, 99.20%, and 98.33%, respectively. Manuscript profile -
Open Access Article
9 - Classification of Brain Tumor Grades by MRI Images using Artificial Neural Network
Melika Aboutalebi Rezvan AbbasiIn recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative MoreIn recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative approach of the different classifiers to select the most appropriate classifier for identifying and extracting abnormal tissue and selected the best one by comparing their detection accuracies rate. In this research, GLCM and GLRM methods are used to extracting discriminating features. Thus results in they reduce the computational complexity. fuzzy entropy measurement method is used to determine the optimal properties and finally, we compared the four FFNN, MLP, BPNN, ANFIS neural networks to perform the decision making and classification process. The purpose of these four neural networks are to develop tools for discriminating the malignant tumors from benign ones assisting deciding in clinical diagnosis. Based on the results, we achieved high results among all classifiers. The proposed methodology results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. In our opinion, the use of these classifiers can be very useful in the diagnosis of brain tumors in MRI images. Our other goal is to prove the suitability of the ANN method as a valuable method for statistical methods. The novelty of the paper lies in the implementation of the proposed method for discriminating the malignant tumors from benign which results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. The efficiency of the method is proved through plenty of simulations and comparisons. Manuscript profile -
Open Access Article
10 - Uncertain Fuzzy Time Series: Technical and Mathematical Review
Aref SafariTime series consists of a sequence of observations, measured at moments in time, sorted chronologically, and evenly spaced from each other, so the data are usually dependent on each other. Uncertainty is the consequence of imperfection of knowledge about a state or a pr MoreTime series consists of a sequence of observations, measured at moments in time, sorted chronologically, and evenly spaced from each other, so the data are usually dependent on each other. Uncertainty is the consequence of imperfection of knowledge about a state or a process. The time series is an important class of time-based data objects and it can be easily obtained from scientific and financial applications. Main carrier of time series forecasting is which constitutes the level of uncertainty human knowledge, with its intrinsic ambiguity and vagueness in complex and non-stationary criteria. In this study, a comprehensive revision on the existing time series pattern analysis research is given. They are generally categorized into representation and indexing, similarity measure, uncertainty modeling, visualization and mining. Various Fuzzy Time Series (FTS) models have been proposed in scientific literature during the past decades or so. Among the most accurate FTS models found in literature are the high order models. However, three fundamental issues need to be resolved with regards to the high order models. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall depiction on the current time series prediction and fuzzy time-series models development. Manuscript profile -
Open Access Article
11 - A Fuzzy Fault Tolerant Controller Design Based on Virtual Sensor for a DC Microgrid
Alireza Galavizh Amir Hossein HassanabadiIn this paper, a fault tolerant control (FTC) method is presented for a DC microgrid with constant power loads (CPLs) which is prone to sensor faults. The main idea of this FTC method is based on hiding the sensor faults from the controller point of view using a suitabl MoreIn this paper, a fault tolerant control (FTC) method is presented for a DC microgrid with constant power loads (CPLs) which is prone to sensor faults. The main idea of this FTC method is based on hiding the sensor faults from the controller point of view using a suitable virtual sensor. After presenting the nonlinear model of the system, the model is then converted to a Takagi-Sugeno (TS) fuzzy representation. The nominal controller is designed for the fuzzy model in the form of a state feedback and the states are estimated using a suitable observer. In the event of a sensor fault detection, the effects of the fault in the control loop are compensated by a virtual sensor. The gains of the controller, the virtual sensor and the observer are designed using related linear matrix inequalities (LMIs) and applying some appropriate LMI regions to achieve appropriate performance. The proposed method is an active fault tolerant control (AFTC) strategy in which the virtual sensor hides the sensor faults from the controller and the observer. In this method, from the controller's point of view, the faulty system plus the virtual sensor acts like a healthy system, and the nominal controller continues to its work without the need to be reconfigured. The efficiency of the proposed method is demonstrated in a constant-load DC microgrid modelled using electrical elements. Manuscript profile -
Open Access Article
12 - Ensemble Learning Improvement through Reinforcement Learning Idea
Mohammad Savargiv Behrooz Masoumi Mohammadreza KeyvanporEnsemble learning is one of the learning methods to create a strong classifier through the integration of basic classifiers that includes the benefits of all of them. Meanwhile, weighting classifiers in the ensemble learning approach is a major challenge. This challenge MoreEnsemble learning is one of the learning methods to create a strong classifier through the integration of basic classifiers that includes the benefits of all of them. Meanwhile, weighting classifiers in the ensemble learning approach is a major challenge. This challenge arises from the fact that in ensemble learning all constructor classifiers are considered to be at the same level of distinguishing ability. While in different problem situations and especially in dynamic environments, the performance of base learners is affected by the problem space and data behavior. The solutions that have been presented in the subject literature assumed that problem space condition is permanent and static. While for each entry in real, the situation has changed and a completely dynamic environment is created. In this paper, a method based on the reinforcement learning idea is proposed to modify the weight of the base learners in the ensemble according to problem space dynamically. The proposed method is based on receiving feedback from the environment and therefore can adapt to the problem space. In the proposed method, learning automata is used to receive feedback from the environment and perform appropriate actions. Sentiment analysis has been selected as a case study to evaluate the proposed method. The diversity of data behavior in sentiment analysis is very high and it creates an environment with dynamic data behavior. The results of the evaluation on six different datasets and the ranking of different values of learning automata parameters reveal a significant difference between the efficiency of the proposed method and the ensemble learning literature. Manuscript profile -
Open Access Article
13 - Soft Error Rate Estimation of Logic Circuits Using Recurrent Neural Networks
Rasoul Farjaminezhad saeed safari Amir Masoud Eftekhari MoghadamNano-scale technology has brought more susceptibility to soft errors for the generation of complicated and state of the art devices. Soft errors are the impacts of radiation of the particles like a neutron, alpha, and ions on the surface of the circuits. To tackle the s MoreNano-scale technology has brought more susceptibility to soft errors for the generation of complicated and state of the art devices. Soft errors are the impacts of radiation of the particles like a neutron, alpha, and ions on the surface of the circuits. To tackle the system malfunctions and provide a reliable device, studying the transient fault effects on the logic circuits can be a more significant issue. This paper presents a new approach based on Recurrent Neural Networks (RNNs) to estimate ICs' Soft Errors Rate (SER). As RNN can be deployed for signal processing and time series, we applied it to investigate transient fault effects while propagating through the combinational and sequential parts of a test chip and compute its SER by simulating and analyzing the circuit outputs. In this paper, the results of utilizing the proposed RNN model to estimate the SER of the ISCAS-85 benchmark circuits have been provided. Manuscript profile -
Open Access Article
14 - Improving Face Recognition Rate Based on Histogram of Oriented Gradients and Difference of Gaussian
Sahar Iranpour Mobarakeh Mehran EmadiFace recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Ma MoreFace recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Mainly, these challenges are divided into local and global categories. Local challenges can be addressed using sustainable methods against change while global challenges such as illumination challenges require powerful pre-processing methods. Therefore, in this study, a sustainable method against light changes has been proposed. In this method, two stages of the Difference of Gaussian have been utilized for the illumination normalization. Then, the features of the normalized image are extracted using Histogram of Oriented Gradient (HOG) and the feature vectors are classified using 3 k-nearest neighbor classifiers and the support vector machine with linear kernel, and the support vector machine with Radial Basis Function (RBF) kernel. Testing the proposed method on Computer Vision and Biometric Laboratory (CVBL) data indicated that the recognition rate, at best for the illumination challenge in the whole face and a part of the face is 98.6 % and 97.9% respectively. Manuscript profile -
Open Access Article
15 - An Adaptive Data Hiding Method for Compressed Videos in HEVC Standard
Mozhgan Zamani Mohammadreza RamezanpourHigh efficiency video coding (HEVC) standard is highly contributive in data hiding. Combining the HEVC standard with data hiding methods is a complex task, and because the highly efficient coding process is a powerful attack, eliminating some of the original video data MoreHigh efficiency video coding (HEVC) standard is highly contributive in data hiding. Combining the HEVC standard with data hiding methods is a complex task, and because the highly efficient coding process is a powerful attack, eliminating some of the original video data would not have a significant effect on obtaining the best compression rates and transmission efficiency. The data embedded in the pixels is lost when compressed with the HEVC standard. In order to solve this problem, we need to use features other than the pixel value .In this paper, an efficient data hiding method is proposed through intra prediction modes in HEVC, where the intra prediction modes is applied as the secret data carrier obtained from the N smallest prediction units. The experimental results show that the average PSNR decreased by 0.11 db, while the bit rate increased by on average 0.25%. Manuscript profile -
Open Access Article
16 - Induced Voltage and Current of Electrical Power Line on Adjacent Buried Pipeline
Mohammad Reza NasiriAn effective approach is introduced to apply Transmission Line Model (TLM) to calculate induced voltage and current on a buried pipeline due to electrical power line. The approach can be applied to the parallel or nonparallel pipelines. The method also extended for any MoreAn effective approach is introduced to apply Transmission Line Model (TLM) to calculate induced voltage and current on a buried pipeline due to electrical power line. The approach can be applied to the parallel or nonparallel pipelines. The method also extended for any complexity system with different conditions and locations of the pipeline. A two ports circuitry network or model is achieved for a parallel pipeline. This two ports network is then modified for each small length, straight and ramped pipeline relative to the power line, which are used for circuitry modelling a complicated configuration of the pipeline. Two numerical services is proceeded to show the simplicity and effectiveness. Manuscript profile -
Open Access Article
17 - SMAK-IOV: Secure Mutual Authentication Scheme and Key Exchange Protocol in Fog Based IoV
Yashar Salami Vahid KhajehvandInternet of Vehicles (IOV) is a section of the Internet of Things (IoT) which makes road transportation smart and provides security for the passengers traveling along the roads. Fog computation can be considered as a complement for IOV because it is close to the user an MoreInternet of Vehicles (IOV) is a section of the Internet of Things (IoT) which makes road transportation smart and provides security for the passengers traveling along the roads. Fog computation can be considered as a complement for IOV because it is close to the user and can communicate with Road Side Units (RSU) and process information with low latency. IOV employs a wireless network for message exchange which is a security flaw and an opportunity for the adversaries since that can modify the transmitted data. Thus, data authentication between the transmitter and the receiver has become a challenge in this context. We propose a secure mutual authentication protocol with the ability to key exchange in this paper, which does not use the hash function. We compared this design with other protocols in terms of security requirements and communication and processing costs. To the security analysis of the proposed Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used. The results show that the proposed protocol is more resistant to other methods of active and passive attacks but Computation and communication costs have increased. Manuscript profile -
Open Access Article
18 - Energy-Efficient Cloud Servers: an Overview of Solutions and Architectures
Adnan NasriBecause of the changing from traditional paper-based systems to a digital systems and the evolution of online storage and cloud computing, datacenters are becoming fundamental to almost every sector of the economy and the main energy consumers in the universe. With the MoreBecause of the changing from traditional paper-based systems to a digital systems and the evolution of online storage and cloud computing, datacenters are becoming fundamental to almost every sector of the economy and the main energy consumers in the universe. With the acceptance of High Performance Computing (HPC) and cloud computing, the area and number of cloud datacenter grow quickly; hence, it has become significant to optimize datacenter energy consumption. With modern energy efficient design in cloud datacenter infrastructure and cooling devices, active items like servers and cooling devices consume most of the power. In many researches, it was shown that cloud datacenters consume enormous energy; therefore researchers are looking for metrics of energy efficiency. The goal of energy efficient researches is to sufficiently take benefit of reachable resources such as processors and network devices, or to reduce thermal cooling expenses and energy consumption. In this paper, we discuss the state of the art researches and provide an overview of energy efficient solutions and architectures for cloud servers in processor design, power distribution unit, and server cooling management. Manuscript profile -
Open Access Article
19 - Weighted-HR: an Improved Hierarchical Grid Resource Discovery
Mohammad mehdi Gilanian sadeghi Mahdi MollaMotalebiGrid computing environments include heterogeneous resources shared by a large number of computers to handle the data and process intensive applications. The required resources must be accessible for Grid applications on demand, which makes the resource discovery a criti MoreGrid computing environments include heterogeneous resources shared by a large number of computers to handle the data and process intensive applications. The required resources must be accessible for Grid applications on demand, which makes the resource discovery a critical service in Grid environments. In recent years, diverse techniques are provided to index and discover the Grid resources. The response time and message load during the search process highly affect the efficiency of resource discovery. This paper proposes a new technique to forward the queries based on the resource types which are accessible through each branch in hierarchical Grid resource discovery approaches. The proposed technique is simulated in GridSim and the experimental results indicated that it is able to reduce the response time and message load during the search process especially when the Grid environment contains a large number of nodes. Manuscript profile -
Open Access Article
20 - A Way to Reduce Effects of Packet Loss in Video Streaming Using Multiple Description Coding
Mahboobe Shabanyan Ehsan AkhtarkavanMultiple description (MD) coding has appeared to be an attractive technique to decrease impact of network failures and increase the robustness of multimedia communications. Very common model of this technique is multiple-description lattice vector quantization, which is MoreMultiple description (MD) coding has appeared to be an attractive technique to decrease impact of network failures and increase the robustness of multimedia communications. Very common model of this technique is multiple-description lattice vector quantization, which is the best choice for robust data transmission over the unreliable network channels. However, MD coinciding lattice vector quantizer (MDCLVQ) is not considered discrete network conditions, so in this scheme, all videos are received or are not received. In this paper, this scheme is implemented in real network environment. So, raw video will be send in various packet, packets send independently and packets lose independently. The possibility of lossing all packets together is close to zero. Our object for increasing of resistance transfer in error- prone communication channels are used. This technique has been tested for standard videos "Akiyo", "Carphone", "Miss-America" and "Foreman". This results show that the quality of the reconstructed videos from the average PSNR values of the central decoder and the side decoders has been reached to grate degree, so increases error resilience over error-prone communication channels. Manuscript profile -
Open Access Article
21 - A MAPE-K Loop Based Model for Virtual Machine Consolidation in Cloud Data Centers
Negin Najafizadegan Eslam Nazemi Vahid KhajehvandToday, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an auto MoreToday, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an autonomous model with predictive capability is needed to do virtual machine consolidation at runtime effectively. In fact, using the feedback system of autonomous systems can make this process simpler and more optimized. The goal of this research is to propose a cloud resource management model that makes the virtual machine consolidation process autonomous, and by using a prediction method compromises between service level agreement violations and energy consumption reduction. In this research, an autonomous model is presented which detects overloaded servers in the analysis phase by a prediction algorithm. Also, at the planning phase, a multi heuristic algorithm based on learning automata is proposed to find proper servers for virtual machine placement. Cloudsim version 3.0.3 was used to evaluate the proposed model. The results show that the proposed model has reduced averagely the service level agreement violations, energy and migration counts by 67.08%, 11.61% and 70.64% respectively, compared to other methods. Manuscript profile -
Open Access Article
22 - Multi-Instance Learning (MIL) by Finding an Optimal set of Classification Exemplars (OSCE) Using Linear Programming
Mohammad Khodadadi Azadboni Abolfazl LakdashtiThis paper describes how to classify a data set by using an optimum set of exemplar to determine the label of an instance among a set of data for solving classification run time problem in a large data set. In this paper, we purposely use these exemplars to classify pos MoreThis paper describes how to classify a data set by using an optimum set of exemplar to determine the label of an instance among a set of data for solving classification run time problem in a large data set. In this paper, we purposely use these exemplars to classify positive and negative bags in synthetic data set.There are several methods to implement multi-instance learning (MIL) such as SVM, CNN, and Diverse density. In this paper, optimum set of classifier exemplar (OSCE) is used to recognize positive bag (contains tumor patches). The goal of this paper is to find a way to speed up the classifier run time by choosing a set of exemplars. We used linear programming problems to optimize a hinge loss cost function, in which estimated label and actual label is used to train the classification. Estimated label is calculated by measuring Euclidean distance of a query point to all of its k nearest neighbors and an actual label value. To select some exemplars with none zero weights, Two solutions is suggested to have a better result. One of them is choosing k closer neighbors. The other one is using LP and thresholding to select some maximum of achieved unknown variable which are more significant in finding a set of exemplar. Also, there is trade-off between classifier run time and accuracy. In large data set, OSCE classifier has better performance than ANN and K-NN cluster. Also, OSCE is faster than NN classifier. After describing OSCE method, we used it to recognize a data set which contains cancer in synthetic data points. In deed, we define OSCE to apply for MIL for cancer detection. Manuscript profile -
Open Access Article
23 - Black Widow Optimization (BWO) Algorithm in Cloud Brokering Systems for Connected Internet of Things
Nasim Jelodari Ali AsgharPourhaji KazemThe Internet of Things (IoT) now connects over nine billion devices. This number is predicted to approach 20 billion in the near future, and the number of things is rapidly expanding, implying that a large amount of data will be created. To handle the connected things, MoreThe Internet of Things (IoT) now connects over nine billion devices. This number is predicted to approach 20 billion in the near future, and the number of things is rapidly expanding, implying that a large amount of data will be created. To handle the connected things, an infrastructure must be built. Cloud computing (CC) has become necessary in the analysis and data storage for IoT. A cloud broker, which is an intermediate in the infrastructure that controls connected things in cloud computing, is discussed in this study. An optimization problem is examined for maximizing the broker's profit and system availability while minimizing request response time and energy consumption. For this purpose, an objective function is proposed and solved using the Black Widow Optimization (BWO) algorithm. Subsequently, the obtained results are compared with the particle swarm optimization (PSO) algorithms. The results indicate that the BWO algorithm could outperform the PSO algorithm, and it can provide much better results considering different scenarios. Manuscript profile -
Open Access Article
24 - Increasing Lifetime Using Whale Optimization Routing Algorithm in Wireless Sensor Networks
Hassan Nouri Esmaeil ZeinaliFollowing the development of wireless sensor networks, the need to design a low-waste, scalable, and long-life network is felt more than ever. Clustering and routing are widely used to minimize energy consumption and increase network lifetime, as important issues in wir MoreFollowing the development of wireless sensor networks, the need to design a low-waste, scalable, and long-life network is felt more than ever. Clustering and routing are widely used to minimize energy consumption and increase network lifetime, as important issues in wireless sensor networks. Since, in these networks, the largest amount of energy is spent on sending and receiving the data, the clustering technique done by collecting data on cluster heads has been found to influence the overall network performance; along with this, routine and efficient routing has also found to improve the network throughput. Therefore, multi-hop routing can increase the network lifetime and reduce the energy consumption by sensor nodes. In this paper, the main approach was using the mobile sinks attached to the public transportation vehicles, such as the bus to collect data in wireless sensor networks. The proposed protocol used multi-hop routing as well as Whale Optimization Algorithm to select cluster heads based on a fitness function, in which the amount of the remaining energy of the sensor nodes and the sum of the remaining energy of the adjacent sensor nodes were taken into account. Adopting this approach created a balance in the amount of energy consumption in sensor nodes. The proposed protocol was studied to validate the results obtained for the network lifetime and energy consumption. Independent and consecutive simulation results and statistical analysis indicates the superiority of the proposed protocol compared to other protocols. Also, the network lifetime improved by averagely 20% and the energy consumption reduced about 25% during the network activity. Manuscript profile -
Open Access Article
25 - A Method for Multi-text Summarization Based on Multi-Objective Optimization use Imperialist Competitive Algorithm
Amir Shahab Shahabi Mohammad Reza Kangavari Amir Masoud RahmaniIn this research, we discuss the methods that have been proposed so far to solve automatic summarization, in which both single-text and multi-text are summarized with emphasis on experimental methods and text extraction techniques. In multi-text summarization, retrievin MoreIn this research, we discuss the methods that have been proposed so far to solve automatic summarization, in which both single-text and multi-text are summarized with emphasis on experimental methods and text extraction techniques. In multi-text summarization, retrieving redundant information that is readable and coherent and contains maximum information from the original text and minimum redundancy has made research in this field very important. An extraction approach based on several methods for identifying sentence similarities and a meta-heuristic optimization algorithm that has been modified and optimized for faster convergence is presented. In this algorithm, changes are made based on density detection through the probability distribution function to avoid being placed in local optimization and try to search more extensively for the response space. The experimental results obtained from the implementation of the algorithm show that the efficiency on criteria such as ROUGE and the accuracy of the proposed method is effectively increased. Manuscript profile -
Open Access Article
26 - Hardware Implementation of dynamic S-BOX to use in AES Cryptosystem.
sahar darvish motevali kooroush kooroush< p>One of the major cipher symmetric algorithms is AES. Its main feature is to use S-BOX step, which is the only non-linear part of this standard possessing fixed structure. During the previous studies, it was shown that AES standard security was increased by cha More< p>One of the major cipher symmetric algorithms is AES. Its main feature is to use S-BOX step, which is the only non-linear part of this standard possessing fixed structure. During the previous studies, it was shown that AES standard security was increased by changing the design concepts of S-BOX and production of dynamic S-BOX. In this paper, a change of AES standard security is studied by production of dynamic and key-dependent S-BOX. Also the LFSR random number generation hardware algorithm is applied in order to produce the dynamic S-BOX. In order to produce a dynamic and key-dependent S-BOX, the field bits of key are divided into separated bits at first and then a byte is selected by LFSR algorithm randomly. The number of selected bit is considered as the repeating number of LFSR algorithm and is applied in order to produce dynamic S-BOX. In the evaluation step, we compared the proposed model with fixed S-BOX model in the original AES algorithm. It was shown that the proposed implementation could increase the security as about 0.2%, 0.017% and 0.19%, 0.04 in the case of avalanche effect, output bits dependence criteria, compared with the strict avalanche criteria and in the case of linear criteria, respectively. Manuscript profile -
Open Access Article
27 - Providing a Technique Based on Tree and Super-Peer Structures for Resource Discovery in Grid Environment
Behnam Norouzi Mahdi MollaMotalebiGrid is a new generation of distributed networks and allows users to share files like the Internet. With regard to the specific features of Grid environments, such as high dynamicity and resource/members heterogeneity, there are some challenges dealing to it. One of the MoreGrid is a new generation of distributed networks and allows users to share files like the Internet. With regard to the specific features of Grid environments, such as high dynamicity and resource/members heterogeneity, there are some challenges dealing to it. One of the most important services in grid environments is the resource discovery. The purpose of resource discovery is to identify a list of available resources for assigning to tasks. In this paper, using the assignment of prime numbers as the weight for tree nodes, and combining the hierarchical and super-peer structure, a new algorithm is presented with multiple trees. The results of the experiments and comparison with the previous methods indicate the improvement of the proposed method in terms of the number of visited nodes during the search process and the reduction of processing load caused by the smaller number of weights in the indexing tree. Manuscript profile -
Open Access Article
28 - Experimental Object Manipulation of Assistive Robotic Arm for Pick and PlaceTask
Mahdi Yousefi Mohgaddam Farzad Cheraghpour SamavatiFor people that need total or partial assistance to perform daily tasks, assistive robots are one of the solutions. Force control of these robots when interact with human or manipulate objects, is one the challenging problems in this area. In this paper a ROS-based forc MoreFor people that need total or partial assistance to perform daily tasks, assistive robots are one of the solutions. Force control of these robots when interact with human or manipulate objects, is one the challenging problems in this area. In this paper a ROS-based force control system is implemented on a JACO assistive robot for grasping tasks. This robot is specifically designed for people with upper body disorders. However, the robot can be used for public use and used for specific tasks that require high precision. To do this, we need to know exactly how the robot's internal performance works and how it is structured. It is usually achieved by designing sophisticated control techniques that meet these criteria. Advanced control architectures such as torque computation control allow tracking of desired paths with high accuracy, however, the need to integrate robotic models remains. The work presented in this study provides a basis for applying these techniques to the JACO robotic arm. The calculation is based on the Euler-Lagrange method of calculating the internal energy. The results are then analyzed to ensure the models estimated with control schemes. Therefore, more advanced analysis and control techniques can be implemented on this robotic arm. Finally, this study can be controlled by PID with respect to the torque entered to the end effector by the object so that the robotic arm can move from the initial position to the secondary position with optimum capture and torque control of all robot joints. The experimental results showed the effectiveness of proposed method to perform grasping and manipulation scenario successfully. Manuscript profile -
Open Access Article
29 - Types of Communication in Vehicular Ad-Hoc Networks: Various Techniques and Current Challenges in this Field
Sanaz Afshari Javad Akbari Torkestani Mehdi FartashWith the development of technology and the growing number of vehicles, vehicular ad-hoc networks were introduced in order to reduce road accidents and increase the level of safety and comfort of passengers. In these networks, vehicles can communicate with each other, ro MoreWith the development of technology and the growing number of vehicles, vehicular ad-hoc networks were introduced in order to reduce road accidents and increase the level of safety and comfort of passengers. In these networks, vehicles can communicate with each other, roadside equipment, and other network components. Communications in VANET have been established for the rapid and accurate delivery of integrated information from vehicles and infrastructure to other vehicles and infrastructures through wireless networks, which play an important role in network performance, service quality, safety, and traffic congestion rate. In this article, we have categorized the types of communications in this type of network. We have also categorized and reviewed the works done in this field as a table form based on four parameters: safety, convenience, driver and passenger entertainment, and developing the structure of these communications. Finally, we presented certain aspects and challenges of research in this area that need to be resolved. Manuscript profile -
Open Access Article
30 - Diagnosis of anomalies in Thyroid Gland Images Based on Feature Extraction from Capsule Network Architecture.
Mahin Tasnimi Hamid Reza GhaffariDiagnosing benign and malignant glands in thyroid ultrasound images is considered as a challenging issue. Recently, deep learning techniques have significantly resulted in extracting features from medical images and classifying them. Convolutional networks ignore the hi MoreDiagnosing benign and malignant glands in thyroid ultrasound images is considered as a challenging issue. Recently, deep learning techniques have significantly resulted in extracting features from medical images and classifying them. Convolutional networks ignore the hierarchical structure of entities within images and do not pay attention to spatial information as well as the need for a large number of training samples. Capsule networks consist of different hierarchical capsules equivalent to the same layers in the CNN neural network. This study tried to extract textural features using a deep learning model based on a capsule network. Thyroid ultrasound images were given to the capsule network as input data, and finally the features learned in the capsule network were used to teach the Support Vector Machine classifier, in order to diagnose thyroid cancer. Experimental results showed that the proposed method with 98% accuracy has achieved better results compared to convolutional networks. Manuscript profile -
Open Access Article
31 - Visual Torch Position Control Using Fuzzy-Servoing Controller for Arc Welding Process.
Reza Babazadeh Tili Fereshteh Akbarnejad Vahid RostamiIn this paper, we propose a fuzzy-servoing controller method for automatic welding. The proposed method uses a vision based arc tracking to find the initial points of the weld seam and to track them without a prior knowledge. Due to a serious melt down in the weld pool MoreIn this paper, we propose a fuzzy-servoing controller method for automatic welding. The proposed method uses a vision based arc tracking to find the initial points of the weld seam and to track them without a prior knowledge. Due to a serious melt down in the weld pool during the welding process, the method requires to control the welding torch in two directions, up-down and left-right directions. To perform these, an IR, two CCD cameras and two stepper motors by inference of fuzzy rules are used to control the movement of the welding torch tip. Therefore, the proposed method canaccomplish different tasks such as welding a curved seam or moving into multi directions, while majority of autonomous arc-welding approaches are single-purpose so that they are designed to accomplish only one task, like welding in a direct line or a predefined arc seam. This method is applied on several workpieces and then the maximum error in both directions is shifted to zero in 30ms. This time is proper for workpieces with thickness in the range of [1-5] mm. Manuscript profile -
Open Access Article
32 - Design and Simulation of Adaptive Neuro Fuzzy Inference Based Controller for Chaotic Lorenz System.
Mehran Adibzadeh Ahmad Fakharian< p>Chaos is a nonlinear behavior that shows chaotic and irregular responses to internal and external stimuli in dynamic systems. Chaos appears in the system which is very sensitive to initial condition. Study of chaos dynamic systems has quickly spread in the las More< p>Chaos is a nonlinear behavior that shows chaotic and irregular responses to internal and external stimuli in dynamic systems. Chaos appears in the system which is very sensitive to initial condition. Study of chaos dynamic systems has quickly spread in the last three decades, and it has become a very attractive area of research to remove dynamic chaotic behaviors and make nonlinear systems stable. Stabilization has been considered as a high usage tool to eliminate aberrant behaviors of chaotic system and can be divided into two categories, regulation and tracking. In regulation stabilizing, system becomes stable by designing proper control signals to one of the available balance points or one of the alternate unstable paths on strange absorbers in chaos system. Another set of chaos systems stabilizing is tracking. In this type of stabilization, a reference signal varying with time and a control frame are considered in the way the system responses follow that signal. In this thesis, both regulation and tracking stabilizing are considered, first without chaos and then with chaos. For this purpose, smart and powerful adaptive neuro fuzzy inference system (ANFIS) technic is used. The proposed method is examined by a famous example of a chaos system called the Lorenz system. The simulation results show the ability of the proposed method. Our proposed approach is ANFIS which is designed for Lorenz chaotic system. it is compare with PID controller in the system . Manuscript profile -
Open Access Article
33 - Optimal State Feedback Control for Bicycle Stabilization using APT-FPSO Algorithm
Mana Azim Araghi Seyed Mohammad Nami Mir Mohammad Sadeghi Seyed Ali Mir Mohammad SadeghiAdvanced control systems are required to maintain bicycle stability due to its unstable open-loop behaviour. This work is aimed at designing an optimal state feedback control system for bicycle stabilization. The performance index of the optimal control system is minimi MoreAdvanced control systems are required to maintain bicycle stability due to its unstable open-loop behaviour. This work is aimed at designing an optimal state feedback control system for bicycle stabilization. The performance index of the optimal control system is minimized using the newly developed adaptive particularly tunable fuzzy particle swarm optimization algorithm. The states of the system are estimated using a state observer. The obtained results are compared with those of the linear–quadratic regulator (LQR). The main advantage of the developed control system is that, unlike the LQR controller that is limited to linear systems, it can be extended to nonlinear control systems. Manuscript profile -
Open Access Article
34 - Taxonomy of Threats and Attacks in IoT
Maryam Shamsadini Ali GhaffariThe Internet of everything that is often known as The Internet of Things (IoT), is the next generation internet network that is created by intelligent objects with software and sensors, where machines can communicate with various machines and humans. The IoT industry do MoreThe Internet of everything that is often known as The Internet of Things (IoT), is the next generation internet network that is created by intelligent objects with software and sensors, where machines can communicate with various machines and humans. The IoT industry does not have one clear set of security standards for developers and manufactures to build in consistent security. The data collected and stored with these devices such as name, age, health data, location and more can aid cyber-attack activity. The first step to face these threats is to classify it and determine the risk of attacks and threats according to different classes of layers. The present study discusses about various IoT attacks happening, classify them, its countermeasures and finding the most prominent attacks in IoT in different layers. Manuscript profile -
Open Access Article
35 - A New Multi-Wave Cellular Learning Automata and Its Application for Link Prediction Problem in Social Networks
Mozhdeh Khaksar Manshad Mohammad Reza Meybodi Afshin SalajeghehLink Prediction (LP) is one of the main research areas in Social Network Analysis (SNA). The problem of LP can help us understand the evolution mechanism of social networks, and it can be used in different applications such as recommendation systems, bioinformatics, and MoreLink Prediction (LP) is one of the main research areas in Social Network Analysis (SNA). The problem of LP can help us understand the evolution mechanism of social networks, and it can be used in different applications such as recommendation systems, bioinformatics, and marketing. Social networks can be shown as a graph, and LP algorithms predict future connections by using previous network information. In this paper, a multi-wave cellular learning automaton (MWCLA) is introduced and used to solve the LP problem in social networks. The proposed model is a new CLA with a connected structure and a module of LAs in each cell where a cell module’s neighbors are its successors. In the MWCLA method for improving convergence speed and accuracy, multiple waves have been used parallelly in the network. By using multiple waves, different information of the network can be considered for predicting links in the social network. Here we show that the model converges upon a stable and compatible configuration. Then for the LP problem, it has been demonstrated that MWCLA produces much better results than other approaches compared to some state-of-the-art methods. Manuscript profile -
Open Access Article
36 - An Improved Real-Time Noise Removal Method in Video StreamBased on Pipe-and-Filter Architecture
Vahid Fazel Asl Babak Karasfi Behrooz Masoumi Mohamadreza KeyvanporAutomated analysis of video scenes requires the separation of moving objects from the background environment, which could not separate moving items from the background in the presence of noise. This paper presents a method to solve this challenge; this method uses the D MoreAutomated analysis of video scenes requires the separation of moving objects from the background environment, which could not separate moving items from the background in the presence of noise. This paper presents a method to solve this challenge; this method uses the Directshow framework based on the pipe-and-filter architecture. This framework trace in three ways. In the first step, the values of the MSE, SNR, and PSNR criteria calculate. In this step, the results of the error criteria are compared with applying salt and pepper and Gaussian noise to images and then applying median, Gaussian, and Directshow filters. In the second step, the processing time for each method check in case of using median, Gaussian, and Directshow filter, and it will result that the used method in the article has high performance for real-time computing. In the third step, error criteria of foreground image check in the presence or absence of the Directshow filter. In the pipe-and-filter architecture, because filters can work asynchronously; as a result, it can boost the frame rate process, and the Directshow framework based on the pipe-and-filter architecture will remove the existing noise in the video at high speed. The results show that the used method is far superior to existing methods, and the calculated values for the MSE error criteria and the processing time decrease significantly. Using the Directshow, there are high values for the SNR and PSNR criteria, which indicate high-quality image restoration. By removing noise in the images, you could also separate moving objects from the background appropriately. Manuscript profile -
Open Access Article
37 - Optimization of the DFIG Wind Turbine Controller Parameters by the Gray Wolf Algorithm
Mahyar Abbaszadeh Rezvan AbbasiThe increase in the power generated by the wind hashad effects on the performance of the power system incases such as power quality, safety, stability, andvoltage control. The wind turbines are used to generateelectrical energy from wind. They can work in fixedor variab MoreThe increase in the power generated by the wind hashad effects on the performance of the power system incases such as power quality, safety, stability, andvoltage control. The wind turbines are used to generateelectrical energy from wind. They can work in fixedor variable speeds. The asynchronous generator isdirectly connected to the grid for the fixed-speed windturbines. In order to connect the DFIG (Doubly-FedInduction Generator) to the grid, this machine must beable to integrate its generated power into the grid in aspecific voltage (the grid voltage level). The mainDFIG controlling method is the use of field-orientedvector control for regulating the rotor flux. The DFIGvector control consists of two main parts as grid sideconverter control and rotor side converter control. Therotor side converter is used to control the grid outputpower. This converter regulates the power factors inthe terminals, and actually restores the generatedpower deviation from the reference power through thePID controllers, besides guaranteeing the stability ofthe induction generator. In the current study, the powerwas controlled through the determination of the PIDoptimal coefficient of the rotor and grid sidescontrollers and the gray wolf algorithm in theMATLAB software. In addition, the stability of thesmall signal of the grid equipped with the doubly-fedwind generator in the wind speed turbulenceconditions was optimized to satisfy the requiredcriteria in output active and reactive power of a DFIG.From the simulation results it is observed that theproposed controller yields better results whencompared to other methods in literature in terms ofperformance index. Manuscript profile -
Open Access Article
38 - Green Reverse Supply Chain on the Way of Optimization: A Case of Dairy Sector
zeinab zarrat dakheli parast hassan haleh soroush avakh darestani hamzeh amin tahmasbiThe achievement of chain greening objectives, besides costs minimization, can be realized when both reverse and forward flows are taken into account in the design of the supply chain network. It is possible to decrease the chain costs and have a greener chain by means o MoreThe achievement of chain greening objectives, besides costs minimization, can be realized when both reverse and forward flows are taken into account in the design of the supply chain network. It is possible to decrease the chain costs and have a greener chain by means of different strategies like vehicular routing, hub location, inventory management, and simultaneous pickup and delivery. The development of green reverse supply chains and the practice of the above-mentioned strategies are becoming increasing important with the appearance of perishable product chains. Considering the mentioned points, the current study attempts to design a green reverse supply chain network for the purpose of distributing dairy items such as yogurt drink where the strategy of simultaneous pickup and delivery under uncertainty is taken into consideration. This model focuses on the simultaneous costs reduction and also decrease of lost demands and presents a fuzzy solution for solving the bi-objective model. Manuscript profile -
Open Access Article
39 - CKD-PML: Toward an Effective Model for Improving Diagnosis of Chronic Kidney Disease
Razieh Asgarnezhad Karrar Ali Mohsin AlhameedawiChronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to pr MoreChronic Kidney Disease is one of the most common metabolic diseases. The challenge in this area is a pre-processing problem. Artificial Intelligence techniques have been implemented over medical disease diagnoses successfully. Classification systems aim clinicians to predict the risk factors that cause Chronic Kidney Disease. To address this challenge, we introduce an effective model to investigate the role of pre-processing and machine learning techniques for classification problems in the diagnosis of Chronic Kidney Disease. The model has four stages including, Pre-processing, Feature Selection, Classification, and Performance. Missing values and outliers are two problems that are addressed in the pre-processing stage. Many classifiers are used for classification. Two tools are conducted to reveal model performance for the diagnosis of Chronic Kidney Disease. The results confirmed the superiority of the proposed model over its counterparts. Manuscript profile -
Open Access Article
40 - Selecting Optimal k in the k-means Clustering Algorithm
Mojtaba Jahanian Abbas Karimi Faraneh ZarafshanClustering is one of the essential machine learning algorithms. Data is not labeled in clustering. The most fundamental challenge in clustering algorithms is to choose the correct number of clusters at the beginning of the algorithm. The proper performance of the cluste MoreClustering is one of the essential machine learning algorithms. Data is not labeled in clustering. The most fundamental challenge in clustering algorithms is to choose the correct number of clusters at the beginning of the algorithm. The proper performance of the clustering algorithm depends on selecting the appropriate number of clusters and selecting the optimal right centers. The quality and an optimal number of clusters are essential in algorithm analysis. This article has tried to distinguish our work from other writings by carefully analyzing and comparing existing algorithms and a clear and accurate understanding of all aspects. Also, by comparing other methods using three criteria, the minimum internal distance between points of a cluster and the maximum external distance between clusters and the location of a cluster, we have presented an intelligent method for selecting the optimal number of clusters. In this method, clusters with the lowest error and the lowest internal variance are chosen based on the results obtained from the research. Manuscript profile -
Open Access Article
41 - Design and Implementation of Quadrotor Guidance and Detection System Hardware for Passing Through Window Based on Machine Vision
Sahar Azizi Mohammad Menhaj Mohammad NorouziQuadrotor is one of the types of flying robots that has attracted the attention of researchers due to its simple structure and perpendicular flight capability. This paper presents a new method based on machine vision for correct window detection, in smoothly unknown env MoreQuadrotor is one of the types of flying robots that has attracted the attention of researchers due to its simple structure and perpendicular flight capability. This paper presents a new method based on machine vision for correct window detection, in smoothly unknown environments. One of the challenges of controlling the Quadrotor path in unknown environments is actually accurate window identification for passing through it. In this study, quadrotor Parrot Bebop2 is used which is equipped with a camera. Also, an algorithm is proposed to perform image processing to identify the window in the environment and control the quadrotor's trajectory, which is implemented on the quadrotor. This method consists of three parts: preprocessing, diagnosis and identification. First, by applying image processing algorithms, we improve the image and delete the data unrelated to the target, and then we use a smart machine vision algorithm to extract information. Furthermore, to control the quadrotor route, a proportional-integral-derivative controller is designed and implemented using Ziegler and Nichols method, which will take place during a real indoor flight in an automated tracking. According to the obtained results, it can be concluded that the use of flying robots can have positive results in military processes and assistance to people in a short time. Manuscript profile -
Open Access Article
42 - RGB-D SLAM Technique for an Indoor UAV Robot using Levenberg-Marquardt Optimization Approach
Navid Dinarvand Mohammad Norouzi Mohammad Dosaranian MoghadamSimultaneous localization and mapping (SLAM) technique is a practical approach for unmanned aerial vehicles (UAVs) to position themselves in unknown zones. In a structured arena with sufficient landmarks and enough lighting, the performance of the existing algorithms is MoreSimultaneous localization and mapping (SLAM) technique is a practical approach for unmanned aerial vehicles (UAVs) to position themselves in unknown zones. In a structured arena with sufficient landmarks and enough lighting, the performance of the existing algorithms is satisfactory. But in a typical indoor field and in absence of GPS signal and poor texture and insufficient lighting, the SLAM would be unstable for navigation owing to the lack of features. In this article's suggested technique, the accuracy and resilience in many unknown situations (including dynamic and static ones) are enhanced by extracting edge and corner features instead of lone point features. A pre-processing block is intended to improve picture frames captured by the RGB-D sensor put on a robot with subpar characteristics. Using a predefined distance function, we filter out dynamic characteristics and solve dynamic issues in the same manner as static problems. Real-time use of our suggested strategy effectively reduces the influence of outliers and moving objects on the SLAM. This improves the accuracy of the procedure's computing output significantly. We validated our findings using data from the Technical University of Munich (TUM) to evaluate the proposed method. Additionally, our developed UAV is utilized for testing as well. The results of the trials indicate that the suggested approach is more precise and less susceptible to changes and system noise than the existing methods. Manuscript profile -
Open Access Article
43 - Non-deterministic Optimal Pricing of VMs in Cloud Environments: An IGDT-based Method
Mona Naghdehforoushha Mehdi Dehghan Takht Fooladi Mohammad Hossein Rezvani Mohammad Mehdi Gilanian SadeghiToday, cloud markets, especially Amazon, have attracted a lot of attention from users due to the provision of Spot Virtual Machines (SVMs). It has several advantages for both sides of the market. On the one hand, Amazon can generate revenue from its underutilized virtua MoreToday, cloud markets, especially Amazon, have attracted a lot of attention from users due to the provision of Spot Virtual Machines (SVMs). It has several advantages for both sides of the market. On the one hand, Amazon can generate revenue from its underutilized virtual machines. On the other hand, the customer can get the SVM as needed at a dynamic price through an auction method. Providing optimal bidding strategies in such a market is a crucial challenge. The bidding price is affected by uncertain parameters such as the price of SVMs, the number of available SVMs, the number of current customers, and their bidding values. In this paper, we use Information Gap Decision Theory (IGDT) to determine the best bidding strategy. Our proposed method includes both risk-averse and risk-neutral strategies. The evaluation results based on historical Amazon EC2 prices confirm the effectiveness of the proposed method in the presence of uncertain prices. It has high performance compared to the baseline methods in terms of robustness cost, uncertainty budget, and execution time. Manuscript profile -
Open Access Article
44 - CHB STATCOM Utilization for Smoothing Power Oscillations of Fixed Speed Wind Farms
Mohammad Reza NasiriAbstract— Electrical output power fluctuations of wind farms inject poor quality of power into the grid. This problem is more remarkable for the wind farms utilizing fixed speed wind turbine generators (WTGs). In this paper the output power of a 10MW wind farm wit MoreAbstract— Electrical output power fluctuations of wind farms inject poor quality of power into the grid. This problem is more remarkable for the wind farms utilizing fixed speed wind turbine generators (WTGs). In this paper the output power of a 10MW wind farm with 24 fixed speed WTGs, is investigated. The most appropriate location for power smoothing based on the short-term power recording and effective power oscillation frequency is determined. A transformerless cascaded H-bridge STATCOM (CHB STATCOM) combined with a mechanically switched capacitor (MSC) is proposed to compensate variable reactive power of the wind farm, as well as to smooth the short-term active power fluctuations. The active power flattening is accomplished by proper sizing of the CHB dc link capacitors according to necessary energy exchange. By comparing several distributed and centralized schemes, a 2MVar CHB STATCOM, which is economically justified, is proposed. The STATCOM performance for improving power quality of the wind farm is investigated by applying several power profiles acquired from the wind farm using simulink MATLAB environment. Manuscript profile -
Open Access Article
45 - Global Path Planning of Quadrotor Using Reinforcement Learning
Mehdi Khakbaz Majid AnjidaniThis paper aims to improve the trajectory by an extended reinforcement learning based method in which a new tracking algorithm is used for mobile robot applications with low-rate control command. There are some trajectories that underactuated robots, like quadrotors, ar MoreThis paper aims to improve the trajectory by an extended reinforcement learning based method in which a new tracking algorithm is used for mobile robot applications with low-rate control command. There are some trajectories that underactuated robots, like quadrotors, are unable to track; hence a suitable trajectory should be designed with respect to the robot's dynamics. In this paper, the initial trajectory is generated by Rapidly-exploring Random Tree Star algorithm which is not suitable for quadrotor application. Then, the initial trajectory is improved by an extension of Path Integral Policy Improvement with Covariance Matrix Adaption (PI2-CMA) algorithm. The extension includes improving tracking algorithm and controller performance considering low-rate control command. Our proposed algorithm succeeded to reduce the cost of tracking by designing safer and shorter trajectories which are more suitable for real robots. Furthermore, the results show that the proposed tracking algorithm and controller improve the performance of tracking. The hardware requirements for implementing our proposed method are a webcam and a personal computer; therefore with a low-cost implementation of the proposed method, a suitable trajectory is designed. In this paper, the initial trajectory is improved by an extension of PI2-CMA algorithm in which the trajectory tracking is performed such that reciprocating motions are avoided. Also, desired velocity and acceleration are used by controller for better tracking. Manuscript profile -
Open Access Article
46 - Providing a Recommendation System for Recommending Articles to users using Data Mining Methods
Reza Molaee fard Payam YarahmadiDue to the growing number of articles and books available on the web, it seems necessary to have a system that can extract users' articles and books from the vast amount of information that is increasing day by day. One of the best ways to do this is to use referral sys MoreDue to the growing number of articles and books available on the web, it seems necessary to have a system that can extract users' articles and books from the vast amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of article recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the firefly algorithm, then the genetic algorithm is used to predict the data, and finally the recommender system based on participatory filtering provides a list of different articles that can be of interest to the user. Be him. The results of the evaluation of the proposed method indicate that this recommending system has a score of 94% in the accuracy of the system. And in the call section, it obtained a score of 91%, which according to the obtained statistics, it can be said that this system can correctly suggest up to 90% of the user's favorite articles to the user. Manuscript profile -
Open Access Article
47 - RCMS: Requirements Conflict Management and Overlapping Control Strategy in CSOP+RP using Pearson Correlation Coefficient
Soheil Afraz Hassan Rashidi Nasser MikaeilvandRequirement engineering is one of the critical phases in the software development process. Functional Requirements (FR) and Non-Functional Requirements(NFR) are two of the fundamental requirements in software projects that are observed in the classifications of most res MoreRequirement engineering is one of the critical phases in the software development process. Functional Requirements (FR) and Non-Functional Requirements(NFR) are two of the fundamental requirements in software projects that are observed in the classifications of most researchers in the software engineering field. Conflicting and overlapping among the requirements in both intra and extra communications levels are one of the main challenges in the elicitation and prioritization phases. This paper presents a decision strategy to respond to this challenge called requirements conflicts management strategy (RCMS). This strategy is defined to manage conflict and overlap of NFRs in the prioritization of the constraints satisfaction model for requirements prioritization, known as "CSOP + RP" model, to which the necessary constraints are applied. RCMS is applied to the "CSOP+RP" model as a pre-processing phase by the requirement analyzer and the results are delivered to the system manager. RCMS is founded on several components: the conflicts catalog among NFRs, the mapping model of NFRs to the domain of software systems, and the calculation of Pearson correlation coefficients in NFRs. The negative, positive, and zero values of the correlation coefficients are calculated on the importance of the requirements, which mean conflict, overlap and neutral, respectively. RCMS was implemented on Police Command-and-Control System(PCCS) as a designed case study with specific NFRs and FRs. Therefore, the statistical analysis of the experimental results shows that the proposed strategy increases the accuracy of the input values of the prioritization model and better decision-making in managing conflicts and controlling overlaps. Furthermore, RCMS help to reduce the ambiguities between NFRs and FRs and also influences of NFRs in requirement ranking by the search-based prioritization approach. Manuscript profile -
Open Access Article
48 - Optimization and Improvement of Spam Email Detection using Deep Learning Approaches
Mohsen Nooraee Hamid Reza GhaffariToday, one of the widely used fields in artificial intelligence is text mining methods, which due to the expansion of virtual space and the increase in the use of media and social messengers, and on the other hand, the ability of these methods to extract the desired inf MoreToday, one of the widely used fields in artificial intelligence is text mining methods, which due to the expansion of virtual space and the increase in the use of media and social messengers, and on the other hand, the ability of these methods to extract the desired information from a very large volume of Unstructured text files have a special place. for example, one of its applications can be mentioned in spam detection. Nowadays, the presence of spam content in social media is increasing drastically, and therefore spam detection has become critical. Users receive many text messages through social networks. These messages contain malicious links, programs, etc., and it is necessary to identify and control spam texts and emails to improve social media security. There are various techniques for this, among which neural networks have shown more effective results. In this article, an approach based on deep learning using an LSTM neural network and Glove word embedding method is introduced to display text word vectors to detect spam emails. The results of the proposed model have been evaluated using accuracy criteria. This model has shown successful and acceptable performance by achieving 98.39% and 99.49% accuracy on two different data sets. Manuscript profile -
Open Access Article
49 - A Reinforcement Learning Method for Joint Minimization of Energy Consumption and Delay in Fog Computing
Reza Besharati Mohammad Hossein Rezvani Mohammad Mehdi Gilanian Sadeghinowadays, there is a growing demand for the use of fog computing in applications such as e-health, agriculture, industry, and intelligent transportation management. In fog computing, optimal offloading is of crucial importance due to the limited energy of mobile devices Morenowadays, there is a growing demand for the use of fog computing in applications such as e-health, agriculture, industry, and intelligent transportation management. In fog computing, optimal offloading is of crucial importance due to the limited energy of mobile devices. In this regard, using machine learning methods has recently attracted much attention. This paper presents a reinforcement learning-based approach to motivate users to offload their tasks. We propose a self-organizing algorithm for offloading based on Q-learning theory. Performance evaluation of the proposed method against traditional and state-of-the-art methods shows that it consumes less energy. It also reduces the execution time of tasks and results in less consumption of network resources. Manuscript profile -
Open Access Article
50 - A Model for Predicting Building Energy Consumption Based on the Stacking of Machine Learning Regression Models
MohammadHosein Khodadadi Ladan Riazi Samaneh YazdaniIn different societies, buildings are considered one of the main energy consumers in the world, and accordingly, they are responsible for a significant percentage of greenhouse gas emissions. Due to the upward growth of the population, the demand for energy consumption MoreIn different societies, buildings are considered one of the main energy consumers in the world, and accordingly, they are responsible for a significant percentage of greenhouse gas emissions. Due to the upward growth of the population, the demand for energy consumption is increasing day by day. In such a situation, the prediction of energy consumption has become a vital issue to control the efficiency of energy consumption. To obtain an effective solution to solve this problem, a number of machine learning methods were examined and Xgboost and MLP methods were selected as the best available methods. In order to obtain more suitable results in this research, a system based on stacking was proposed. In the proposed method based on stacking, XGBoost and MLP methods were used in the first level so that the advantages of both methods can be used. The predictions made by each of these methods, in the second level, were used as input to another XGBoost algorithm, which was used as a meta-learner. To obtain better results, the hyperparameters of the basic techniques were optimized using the successive halving search. For a better comparison, machine learning regression techniques were implemented to solve the problem of energy consumption intensity prediction, and the results obtained from them were analyzed on WiDS Datathon. The results showed that the proposed system has improved the MAE, MAPE, and R2 criteria by 0.6, 0.03, and 0.07, respectively, compared to the best existing method.Keywords: Energy Consumption, Stacking, Regression, XGBoost, MLP. Manuscript profile -
Open Access Article
51 - LA-Based Approaches to Infer Urban Structure from Traffic Dynamics Considering Costs
Hamid Yasinian Mansour EsmaeilpourSuccessful future urban planning is highly dependent on optimal connectivity between important areas of cities. Discovering essential latent links will optimize the urban structure. Moving towards a better structure requires some information. There are a lot of sources MoreSuccessful future urban planning is highly dependent on optimal connectivity between important areas of cities. Discovering essential latent links will optimize the urban structure. Moving towards a better structure requires some information. There are a lot of sources of information for urban structure inferring, including the current structure, the time-varying traffic dynamics, and the construction costs, which are the basics of the optimization problem formulation. This paper presents a new formulation for the problem. The model problem to be solved tries to utilize all data sources needed for inferring. There are some methods for solving the formulated problem. The methods need some development to apply to the model. Methods utilizing learning automata (LA) are very favorable in this field due to the interaction with the environment. This paper presents two LA-based approaches for the model: Distributed Learning Automata (DLA) and Cellular Learning Automata (CLA). The algorithms result in an optimal connectivity matrix considering urban structure, traffic dynamics, and costs, where the matrix must include the current urban structure and some new reasonable necessary links. Moreover, comparisons are possible because the model has a fitness value for evaluating the provided connectivity matrix. The CLA-based proposed method performed better than the others in most experiments. Manuscript profile -
Open Access Article
52 - On Complex Systems and Structure of Emergence in Games- A Survey
Hassan Rashidi Latifeh Pour Mohammad BagherMany different complex systems display emergent behavior, and quite a few of these systems have been studied in the past. The science of complexity, popularly known as chaos theory, deals with emergent systems in other fields. Designing emergence is something of a parad MoreMany different complex systems display emergent behavior, and quite a few of these systems have been studied in the past. The science of complexity, popularly known as chaos theory, deals with emergent systems in other fields. Designing emergence is something of a paradoxical task because one of the defining aspects of emergent behavior is that it occurs only after a system is put into motion. In this paper, we begin with the definition of complex systems. Then, we describe the continuum between strictly ordered systems and entirely chaotic ones and show that emergence takes place somewhere between the two. After that, we survey and show how gameplay emerges from the complex system. Our survey points out that three structural features of complex systems contribute to emergence: (a) active and interconnected elements; (b) feedback loops; and (c) interaction at different scales. To show the active and interconnected elements, we explain cellular automata as an example of simple systems that can produce emergence in games. Moreover, we described how a system can be stabilized/destabilized by feedback loops and how different behaviors may emerge in a system at different scales, along with particular games. In this survey, we identified seven classes of emergence that can be considered in games. These classes are Simple, Weak, Multiple, Strong, Cluster, Hub, and Complex Emergence. These classes are produced by different combinations of feedback loops and interactions among the elements of a system at different scales. Manuscript profile -
Open Access Article
53 - An Interval Type-2 Fuzzy-Markov Model for Prediction of Urban Air Pollution
Aref Safari Rahil Hosseini Mahdi MazinaniPrediction is a very important problem that appears in many disciplines. Better weather forecasts can save lives in the event of a catastrophic hurricane; better financial forecasts can improve the return on an investment. The increasing rate of industrial development a MorePrediction is a very important problem that appears in many disciplines. Better weather forecasts can save lives in the event of a catastrophic hurricane; better financial forecasts can improve the return on an investment. The increasing rate of industrial development and urbanization, especially in developing countries, has led to increased levels of air pollution along with increased concern about air pollution effect on human health. This has taken about a diversity of strategies for air quality management, prediction and pollution control. Today’s applications of fuzzy systems are emerging in uncertain environments such as air quality assessments. A fuzzy system that accounts for all of the uncertainties that are present, namely, rule uncertainties due to training with noisy data and measurement uncertainties due to noisy measurements that are used during actual forecasting. The performance results on real data set show the superiority of the fuzzy-markov model in the prediction process with an average accuracy of 94.79% compared to other related works. These results are promising for early prediction of the natural disasters and prevention of its side effects Manuscript profile -
Open Access Article
54 - A Technical Review on Unsupervised Learning of Graph and Hypergraph Pattern Analysis
Aref SafariGraph and hypergraph matching are fundamental problems in pattern analysis problems. They are applied to various tasks requiring 2D and 3D feature matching, such as image alignment, 3D reconstruction, and object or action recognition. Graph pattern analysis considers pa MoreGraph and hypergraph matching are fundamental problems in pattern analysis problems. They are applied to various tasks requiring 2D and 3D feature matching, such as image alignment, 3D reconstruction, and object or action recognition. Graph pattern analysis considers pairwise constraints that usually encode geometric and appearance associations between local features. On the other hand, hypergraph matching incorporates higher-order relations computed over sets of features, which could capture both geometric and appearance information. Therefore, using higher-order constraints enables matching that is more robust (or even invariant) to changes in scale, non-rigid deformations, and outliers. Many objects or other entities such as gesture recognition and human activities in the spatiotemporal domain can be signified by graphs with local information on nodes and more global information on edges or hyperedges. In this research, and essential review have been done on the unsupervised methods to explore and communicate meta-analytic data and results with a large number of novel graphs proposed quite recently. Manuscript profile -
Open Access Article
55 - A Review of Feature Selection Method Based on Optimization Algorithms
Zohre Sadeghian Ebrahim Akbari Hossein Nematzadeh Homayun MotameniFeature selection is the process of identifying relevant features and removing irrelevant and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal loss of efficiency. One of the feature selection approaches i MoreFeature selection is the process of identifying relevant features and removing irrelevant and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal loss of efficiency. One of the feature selection approaches is using optimization algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2021 that were designed and implemented on a wide range of different data. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. Manuscript profile -
Open Access Article
56 - Epileptic Seizure Prediction using Multi-Channel Raw EEGs with Convolutional Neural Network
Jamal Nazari Ali Motie Nasrabadi Mohamad Bager Menhaj Somayeh RaiesdanaEpileptic seizure prediction has been one of the interesting topics among researchers in recent years. Recent evidence suggests that, in many seizures, changes in the preictal signal begin minutes before the ictal begins, raising hopes of predicting the seizure onset be MoreEpileptic seizure prediction has been one of the interesting topics among researchers in recent years. Recent evidence suggests that, in many seizures, changes in the preictal signal begin minutes before the ictal begins, raising hopes of predicting the seizure onset before it occurs. Convolutional neural network (ConvNet) is a powerful computational tool with deep learning capacity which is able to detect complex structures in data. In this study, we employed a ConvNet and a set of techniques to make optimal use of the existing data for an end-to-end learning. Multi-channel non-invasive raw EEGs from the CHB-MIT database were used for training of the proposed model. The proposed method resulted in sensitivity of 92.05% and false prediction rate of 0.073/h with the cross-validation approach in distinguishing preictal and ictal. We obtained a 10-minute seizure prediction horizon that is relatively higher than the values obtained in other researches. This longer time period can give the patient more opportunity for preventive actions. Seizure occurrence period was computed nearly 20 minutes which lets the patient wait less for the seizure to occur and this in turn makes him have less anxiety. Furthermore, a feature map visualizing method was employed in the present work to decode the employed deep network and to understand how it learns and what it learns when trying to solve the seizure prediction task. By investigating feature maps of the used ConvNet’s middle layer, we observed that the proposed network retains most of the beta and gamma band properties in layers. Manuscript profile -
Open Access Article
57 - Fuzzy Clustering Algorithm to Identify Sybil Attacks in Vehicular ad Hoc Networks
Mahdi Maleknasab Ardekani Mohammad Tabarzad Mohammad Amin ShayeganAbstract: Due to the increasing use of VANET networks and the use of smart systems in these types of networks, their challenges have been the focus of researchers. One of the important challenges of such networks is the security issues that threaten this category of net MoreAbstract: Due to the increasing use of VANET networks and the use of smart systems in these types of networks, their challenges have been the focus of researchers. One of the important challenges of such networks is the security issues that threaten this category of networks. In this article, the Sybil attack, which is one of the security challenges in VANET networks, has been investigated and identified. In a Sybil attack, a node threatens VANET networks by stealing the identity of other nodes or creating a virtual identity, by making incorrect decisions and sending false information. In this article, the clustering method is used to avoid the overhead of identification nodes in centralized methods and avoid delay in distributed methods. RSU determines the cluster head with the help of fuzzy logic. The cluster head creates moving clusters by placing similar nodes in terms of direction, speed, and distance in separate clusters while moving. The cluster head performs malicious node detection using a directional antenna and a fuzzy system. The first fuzzy system places the cluster head in the best possible place of the cluster. The cluster head identifies the malicious nodes in each cluster locally, while the second fuzzy system interferes in determining the validity of the cluster members. In the proposed plan, in addition to optimizing the sending and receiving of messages, The simulation results show that the proposed method has improved by 1.2% in detecting the malicious node, 0.4% in the number of a false positive detection, 0.6% in the lost packet, and 0.1% in the delay compared to the previous methods. Manuscript profile -
Open Access Article
58 - Cryptographic Algorithms: A Review of the Literature, Weaknesses and Open Challenges
Yashar Salami Vahid Khajevand Esmaeil ZeinaliInformation security has become an important issue in the modern world due to its increasing popularity in Internet commerce and communication technologies such as the Internet of Things. Future media actors are considered a threat to security. Therefore, the need to us MoreInformation security has become an important issue in the modern world due to its increasing popularity in Internet commerce and communication technologies such as the Internet of Things. Future media actors are considered a threat to security. Therefore, the need to use different levels of information security in different fields is more needed. Advanced information security methods are vital to prevent this type of threat. Cryptography is a valuable and efficient component for the safe transfer or storage of information in the cyber world. Familiarity with all types of encryption models is an essential need for cybersecurity experts. This paper separates Cryptographic algorithms into symmetric (SYM) and asymmetric (ASYM) categories based on the type of cryptographic structure. SYM algorithms mostly use the Feistel network (FN) structure, Substitution-Permutation Network (SPN), and the ASYM algorithms follow the mathematical structures. Based on this, we examined different encryption methods in terms of performance and detailed comparison of key size, block size, and the number of rounds. In continuation of the weakness of each algorithm against attacks and open challenges in each category, to study more is provided. Manuscript profile -
Open Access Article
59 - Valuation of Companies Providing Digital Platform Services in Iran
Farhad Asghari Estiar Amir Mohammadzadeh Ebrahim AbbasiIntangible assets are defined as non-monetary assets that do not have physical substance but possess economic features that grant rights and advantages to their owner. The role of digital applications in this century can be compared with the function of oil in the past MoreIntangible assets are defined as non-monetary assets that do not have physical substance but possess economic features that grant rights and advantages to their owner. The role of digital applications in this century can be compared with the function of oil in the past century with was the driving force for growth, wealth, and change. The Covid-19 pandemic has led to the rapid growth of digital services in Iran, and many companies have included digital development in their plans. However, the valuation of these companies poses many difficulties, and introducing the national information network in Iran will add to the importance of evaluation even further.This may lead to an underestimating of the book value of enterprises with extensive intangible assets. Intangible assets are usually difficult to evaluate, and the International Valuation Standard 210 recommends three approaches: (a) an income approach; (b) a market approach; and (c) a cost approach. However, generating accurate results can be challenging. This study innovatively apply traditional approaches to digital intangible assets and combines them with a customer-perspective value to provide more precise results for decision-making and suggest new valuation pattern. To this end, one of the large companies providing digital services in Iran was selected for evaluation, and the results are presented. This pattern is practical and can be implemented for all companies providing digital services in Iran. Manuscript profile -
Open Access Article
60 - Prediction of Digital Governance in the Direction of Urban Smartness with Sustainability Approach (Case Study: Tehran)
Bahram Parvin Ali Shayan Alireza Poorebrahimi Reza RadfarObjectives: In this research, the researchers seek to present a mechanism for digital governance foresight in the direction of urban smartness with a sustainability approach based on scenario writing in the city of Tehran.Tools and methods:The research method is mixed i MoreObjectives: In this research, the researchers seek to present a mechanism for digital governance foresight in the direction of urban smartness with a sustainability approach based on scenario writing in the city of Tehran.Tools and methods:The research method is mixed in terms of how to check the data; Because it uses both quantitative research strategies (in expert data) and qualitative method strategy (in interview content analysis). In terms of the nature of the data, the current research uses both quantitative and qualitative methods. This article is included in basic-applied research. Because the research is exploratory and its main purpose is to identify the environmental drivers related to the subject of the research, therefore the research is of a fundamental type; At the same time, its achievements are included as a benchmark for urban management, especially relevant organizations including the municipality, so it is also considered practical. The statistical population of the research includes elites, managers, and senior experts, whose opinions can be used in the field of digital governance and urban smartness with a sustainable approach.Finding :Based on the results, the first scenarios in the areas of intelligence, participation, transparency, structural arrangements, -integration, culture and stabilization of the best scenario and the sixth scenario, and to some extent scenario 5, the worst possible scenarios are the worst. The second to fourth scenarios are based on the least changes in the main factors and showed improvement in one factor and in one factor the regression was shown. Resulting :The results showed that capacity-building to create the right to access information, increase law-abiding, discipline urban management mechanisms, and strengthen internal platforms for networking and securing information in line with urban intelligence can be implemented through the implementation of digital governance requirements. Manuscript profile -
Open Access Article
61 - The Effect of using Virtual Reality Game on Health and Fitness
Seyed Amirhossein Mousavi Ehsan Tahami Majid Zare BidakiDuring the corona days, the body's activities are very reduced due to the quarantine, and after that, many people still have little activity. Nearly two billion people in the world are overweight. In other words, more than 30% of the world's population is obese or overw MoreDuring the corona days, the body's activities are very reduced due to the quarantine, and after that, many people still have little activity. Nearly two billion people in the world are overweight. In other words, more than 30% of the world's population is obese or overweight. In this study, a solution for fitness and weight loss at home has been proposed. 2 groups participated in this study, the first group consisted of 20 people in a traditional way and the second group included 20 people under virtual reality, all of whom were undergraduate students, for 4 weeks and 3 sessions per week participated in this study and none of them had experience using virtual reality. The results show that fitness parameters include waist circumference, weight, BMI and the distance traveled in the Cooper test have improved. The motivation of people to continue this study was more in the virtual reality group than in the normal group. Manuscript profile -
Open Access Article
62 - A Novel Classification Method: A Hybrid Approach Based on Large Margin Nearest Neighbor Classifier
Alieh Ashoorzadeh Abbas Toloie Eshlaghy Mohammad Ali Afshar KazemiClassification is the operation of dividing various data into multiple classes where they share quantitative and qualitative similarities. Classification has many use cases in engineering fields such as cloud computing, power distribution, and remote sensing. The accura MoreClassification is the operation of dividing various data into multiple classes where they share quantitative and qualitative similarities. Classification has many use cases in engineering fields such as cloud computing, power distribution, and remote sensing. The accuracy of many classification techniques such as k-nearest neighbor (k-NN) is highly dependent on the method used in the calculation of distances between samples. It is assumed that samples close to each other belong to the same class while samples that belong to different classes have a large distance between them. One of the popular distance calculation methods is the Mahalanobis distance. Many methods, including large margin nearest neighbor (LMNN), have been proposed to improve the performance of k-NN in recent years. Our proposed method aims to introduce a cost function to calculate data similarities while solving the local optimum pitfall of LMNN and optimizing the cost function determining distances between instances. Although k-NN is an efficient classification technique that is simple to comprehend and use, it is costly to compute for large datasets and sensitive to outlier data. Another difficult feature of k-NN is that it can only measure distance in Euclidean space. The distance metric should ideally be modified to fit the specific needs of the application. Due to the disadvantages in k-NN and LMNN methods, to optimize the objective function to calculate distances for the test data and to improve classification accuracy, we initially use the genetic algorithm to reduce the range of the solution space and then by using the gradient descent the optimal values of parameters in the cost function is obtained. Our method is carried out on different benchmark datasets with varying numbers of attributes and the results are compared to k-NN and LMNN methods. Misclassification rate, precision, f1 score, and kappa score are calculated for different values of k, mutation rate, and crossover rate. Overall, our proposed method shows superior performance with an average accuracy rate of 87.81% which is the highest among all methods. The average precision, f1 score, and kappa score of our method are 0.8453, 0.8513, and 0.6976 respectively. Manuscript profile -
Open Access Article
63 - Identifying and Ranking the Criteria of Outsourcing Capabilities of Maintenance Activities and Analyzing the Profitability of Outsourcing Using Bayesian BWM
Hamid Esmaili Hossein Kaveh PishghadamOutsourcing of corporate activities by suppliers has long been done in the oil and gas industry. Outsourcing is known as a tool to gain strategic advantages. Outsourcing maintenance is also a common practice in many industries, including producing chemicals, petroleum, MoreOutsourcing of corporate activities by suppliers has long been done in the oil and gas industry. Outsourcing is known as a tool to gain strategic advantages. Outsourcing maintenance is also a common practice in many industries, including producing chemicals, petroleum, petrochemicals, and medical equipment. However, this process involves many risks, with their extent and nature still unclear. There are strong reasons for outsourcing some of the most important economic concepts. Determining the effective indicators in this selection and the importance and priority of each of them has always been the subject of intense research. In this paper, we examined the effects of these variables and assessed their relationship with decision-making outsourcing maintenance at gas refineries. First, the effective variables were identified by reviewing the literature and based on experts’ opinions. Next, it was tried to prioritize the indicators identified from previous studies using the relatively new Bayesian Best-Worst method (BWM). The results are then compared using one of the most recent decision-making methods, i.e., the Ordinal Priority Approach. Comparing the results of these two models shows that in both models, the cost of technology modernization and upgrades, the cost of emergency repairs and production stops, the cost of depreciation of equipment and machinery, and the cost of major repairs are the top four significant criteria among all the examined ones. However, the first and second methods consider “cost of maintenance” and “cost of productivity” more significant, respectively. It is worth noting that other differences were also identified in this study. Manuscript profile -
Open Access Article
64 - Enhanced Self-Attention Model for Cross-Lingual Semantic Textual Similarity in SOV and SVO Languages: Persian and English Case Study
Ebrahim Ganjalipour Amir Hossein Refahi Sheikhani Sohrab Kordrostami Ali Asghar HosseinzadehSemantic Textual Similarity (STS) is considered one of the subfields of natural language processing that has gained extensive research attention in recent years. Measuring the semantic similarity between words, phrases, paragraphs, and documents plays a significant role MoreSemantic Textual Similarity (STS) is considered one of the subfields of natural language processing that has gained extensive research attention in recent years. Measuring the semantic similarity between words, phrases, paragraphs, and documents plays a significant role in natural language processing and computational linguistics. Semantic Textual Similarity finds applications in plagiarism detection, machine translation, information retrieval, and similar areas. STS aims to develop computational methods that can capture the nuanced degrees of resemblance in meaning between words, phrases, sentences, paragraphs, or even entire documents which is a challenging task for languages with low digital resources. This task becomes intricate in languages with pronoun-dropping and Subject-Object-Verb (SOV) word order specifications, such as Persian, due to their distinctive syntactic structures. One of the most important aspects of linguistic diversity lies in word order variation within languages. Some languages adhere to Subject-Object-Verb (SOV) word order, while others follow Subject-Verb-Object (SVO) patterns. These structural disparities, compounded by factors like pronoun-dropping, render the task of measuring cross-lingual STS in such languages exceptionally intricate. In the context of low-resource languages like Persian, this study proposes a customized model based on linguistic properties. Leveraging pronoun-dropping and SOV word order specifications of Persian, we introduce an innovative enhancement: a novel weighted relative positional encoding integrated into the self-attention mechanism. Moreover, we enrich context representations by infusing co-occurrence information through pointwise mutual information (PMI) factors. This paper introduces a cross-lingual model for semantic similarity analysis between Persian and English texts, utilizing parallel corpora. The experiments show that our proposed model achieves better performance than other models. Ablation study also shows that our system can converge faster and is less prone to overfitting. The proposed model is evaluated on Persian-English and Persian-Persian STS-Benchmarks and achieved 88.29% and 91.65% Pearson correlation coefficients on monolingual and cross-lingual STS-B, respectively. Manuscript profile -
Open Access Article
65 - Semiautomatic Image Retrieval Using the High Level Semantic Labels
Shabnam Asbaghi Mohammad Reza KeyvanpourContent-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user MoreContent-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of query presenting, query by keyword and query by sample image. The proposed system, after the first result retrieval, does an interactive retrieval process semantically based on user's relevance feedbacks and related high level semantic labels to the images semi-automatically. This system can reply different requests in the image retrieval domain based on a hierarchical semantic network and doing a kind of learning process by the feedbacks given by user. According to experiments, the proposed approach concludes acceptable accuracy for retrieval results Manuscript profile -
Open Access Article
66 - Semantic Preserving Data Reduction using Artificial Immune Systems
Seyed Amir Ehsani Amir Masood Eftekhari MoghadamArtificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature MoreArtificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and semantic based image retrieval. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. In this paper we introduce the capability of AIS for semantic preserving data reduction (SPDR). For this purpose a complete survey is done on artificial immune systems. Then a case study is selected to represent the capability of semantic preserving data reduction of AIS. Experimental results subjectively show and verify the proposed idea. Manuscript profile -
Open Access Article
67 - Relational Databases Query Optimization using Hybrid EvolutionaryAlgorithm
Ali Safari Mamaghani Kayvan Asghari Farborz Mahmoudi Mohammad Reza MeybodiOptimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the MoreOptimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, because of their efficiency and strength, has been changed in to a suitable research area in the field of optimizing the database queries. In this paper, a hybrid evolutionary algorithm has been proposed for solving the optimization of Join ordering problem in database queries. This algorithm uses two methods of genetic algorithm and learning automata synchronically for searching the states space of problem. It has been showed in this paper that by synchronic use of learning automata and genetic algorithms in searching process, the speed of finding an answer has been accelerated and prevented from getting stuck in local minimums. The results of experiments show that hybrid algorithm has dominance over the methods of genetic algorithm and learning automata Manuscript profile -
Open Access Article
68 - Solving linear and nonlinear optimal control problem using modified adomian decomposition method
Ahmad Fakharian Mohammad Taghi Hamidi BeheshtiFirst Riccati equation with matrix variable coefficients, arising in optimal and robust control approach, is considered. An analytical approximation of the solution of nonlinear differential Riccati equation is investigated using the Adomian decomposition method. An app MoreFirst Riccati equation with matrix variable coefficients, arising in optimal and robust control approach, is considered. An analytical approximation of the solution of nonlinear differential Riccati equation is investigated using the Adomian decomposition method. An application in optimal control is presented. The solution in different order of approximations and different methods of approximation will be compared respect to accuracy. Then the Hamilton-Jacobi-Belman (HJB) equation, obtained in nonlinear optimal approach, is considered and an analytical approximation of the solution of it using the Adomian decomposition method is presented. Manuscript profile -
Open Access Article
69 - Persian Printed Document Analysis and Page Segmentation
Ali Broumandnia Jamshid ShanbehzadehThis paper presents, a hybrid method, low-resolution and high-resolution, for Persian page segmentation. In the low-resolution page segmentation, a pyramidal image structure is constructed for multiscale analysis and segments document image to a set of regions. By hig MoreThis paper presents, a hybrid method, low-resolution and high-resolution, for Persian page segmentation. In the low-resolution page segmentation, a pyramidal image structure is constructed for multiscale analysis and segments document image to a set of regions. By high-resolution page segmentation, by connected components analysis, each region is segmented to homogeneous regions and identifying them as texts, images, and tables/drawings. The proposed method was experiment with the Persian documents. The result of these tests have shown that the proposed method provide more accurate and speed results. Manuscript profile -
Open Access Article
70 - An Improved Standard Cell Placement Methodology using Hybrid Analytic and Heuristic Techniques
Ali Jahanian Morteza Saheb Zamani Esmaeil KhorramIn recent years, size of VLSI circuits is dramatically grown and layout generation of current circuits has become a dominant task in design flow. Standard cell placement is an effective stage of physical design and quality of placement affects directly on the performanc MoreIn recent years, size of VLSI circuits is dramatically grown and layout generation of current circuits has become a dominant task in design flow. Standard cell placement is an effective stage of physical design and quality of placement affects directly on the performance, power consumption and signal immunity of design. Placement can be performed analytically or heuristically. Analytical placers generate optimal or near-optimal solution but they are not usable for large circuits due to large computation time. In contrast, Heuristic placers can be used to place large circuits with more poor quality rather than analytical ones. In this paper, a hybrid analytical and heuristic approach for standard-cell placement is proposed. In this approach, cell rows are arranged heuristically but the location of cells inside each row are determined analytically. Experimental results show that general metric of placement (total wire length) is improved by 28.6% and this improvement will be more considerable for more large circuits. However, total wire length reduction is gained with a little computation overhead (about 0.01%). Manuscript profile -
Open Access Article
71 - Using BELBIC based optimal controller for omni-directional threewheelrobots model identified by LOLIMOT
Maziar Ahmad Sharbafi Caro Lucas Aida MohammadinejadIn this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. The MoreIn this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional learning is based on a computational model of limbic system in the mammalian brain. The Brain Emotional Learning Based Intelligent Controller (BELBIC), using the concept of LQR control is adopted for the omni-directional robots. The performance of this multi objective control is illustrated with simulation results based on real world data. This approach can be utilized directly to the robots in the future. Manuscript profile -
Open Access Article
72 - QEA: A New Systematic and Comprehensive Classification of Query Expansion Approaches
Fatemeh Serpush Mohammadreza KeyvanpourA major problem in information retrieval is the difficulty to define the information needs of user and on the other hand, when user offers your query there is a vast amount of information to retrieval. Different methods , therefore, have been suggested for query expansi MoreA major problem in information retrieval is the difficulty to define the information needs of user and on the other hand, when user offers your query there is a vast amount of information to retrieval. Different methods , therefore, have been suggested for query expansion which concerned with reconfiguring of query by increasing efficiency and improving the criterion accuracy in the information retrieval system. Accordingly in this paper, in addition to propose a new coherent categorization for approaches, we proceed to detailed identify them, and proper functional criteria to evaluate each of these approaches are suggested. Manuscript profile -
Open Access Article
73 - Cluster-Based Image Segmentation Using Fuzzy Markov Random Field
Peyman Rasouli Mohammad Reza MeybodiImage segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and MoreImage segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural information at the same time. Fuzzy Markov random field (FMRF) is a MRF in fuzzy space which handles fuzziness and randomness of data simultaneously. This paper propose a new method called FMRF-C which is model clustering using FMRF and applying it in application of image segmentation. Due to the similarity of FMRF model structure and image neighbourhood structure, exploiting FMRF in image segmentation makes results in acceptable levels. One of the important tools is Cellular learning automata (CLA) for suitable initial labelling of FMRF. The reason for choosing this tool is the similarity of CLA to FMRF and image structure. We compared the proposed method with several approaches such as Kmeans, FCM, and MRF and results demonstratably show the good performance of our method in terms of tanimoto, mean square error and energy minimization metrics. Manuscript profile -
Open Access Article
74 - Directional Stroke Width Transform to Separate Text and Graphics in City Maps
Ali Ghafari-Beranghar Ehsanollah Kabir Kaveh KangarlooOne of the complex documents in the real world is city maps. In these kinds of maps, text labels overlap by graphics with having a variety of fonts and styles in different orientations. Usually, text and graphic colour is not predefined due to various map publishers. In MoreOne of the complex documents in the real world is city maps. In these kinds of maps, text labels overlap by graphics with having a variety of fonts and styles in different orientations. Usually, text and graphic colour is not predefined due to various map publishers. In most city maps, text and graphic lines form a single connected component. Moreover, the common regions of text and graphic lines have similar features; hence, the separation of text and graphic lines is a challenging task in document analysis. Generally, these text labels could not be recognized efficiently by current commercial OCR systems in city map processing. In this paper, we propose an image decomposition approach based on stroke width feature to extract text labels from city maps. In our approach, we assign to each pixel of image a local stroke width based on minimum distance from borders in four directional borders. This mapping generates a suitable representation to distinguish text and non-text pixels. The experimental results on several varieties of city maps are promising Manuscript profile -
Open Access Article
75 - This number will be updated
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Open Access Article
76 - An Integrated Temporal Partitioning and Mapping Framework for Improving Performance of a Reconfigurable Instruction Set Processor
Farhad Mehdipour Hamid Noori Morteza Saheb Zamani Hiroaki Honda Koji Inoue Kazuaki MurakamiReconfigurable instruction set processors allow customization for an application domain by extending the core instruction set architecture. Extracting appropriate custom instructions is an important phase for implementing an application on a reconfigurable instruction s MoreReconfigurable instruction set processors allow customization for an application domain by extending the core instruction set architecture. Extracting appropriate custom instructions is an important phase for implementing an application on a reconfigurable instruction set processor. A custom instruction (CI) is usually extracted from critical portions of applications and implemented on a reconfigurable functional unit. In this paper, our proposed RFU architecture for a reconfigurable instruction set processor is introduced. As the main contribution of this work, an integrated framework of temporal partitioning and mapping is introduced that partitions and maps CIs on the RFU. Temporal partitioning iterates and modifies partitions incrementally to generate CIs. The proposed framework improves the timing performance particularly for the applications comprising a considerable amount of CIs that could not be implemented on the RFU due to architectural limitations. Furthermore, exploiting similarity detection and merging as two complementary techniques for the integrated framework brings about reduction in the configuration memory size. Manuscript profile -
Open Access Article
77 - Nonlinear Modeling and Optimal Output Control of Two Wheeled Balancing Transporter
Reza Babazadeh Ataollah Gogani Khiabani Hadi AzmiIn this paper an optimal controller is proposed for a self-balancing electrical vehicle called Segway PT. This vehicle has one platform and two wheels on the sides and the rider stands on the platform. A handlebar, as a navigator, is attached to the body of Segway, with MoreIn this paper an optimal controller is proposed for a self-balancing electrical vehicle called Segway PT. This vehicle has one platform and two wheels on the sides and the rider stands on the platform. A handlebar, as a navigator, is attached to the body of Segway, with which the rider controls the vehicle. Since Segway uses electrical energy produced by batteries, resource consumption management is of utmost importance. On the other hand, complex nonlinear dynamics cause difficulties in controlling the vehicle. Our proposed controller reduces energy consumption and enhance response speed of system instead of classic PID controller which proposed before. Simulation results show the desired performance of the proposed controller. Manuscript profile -
Open Access Article
78 - Coordination Approach to Find Best Defense Decision with Multiple Possibilities among Robocup Soccer Simulation Team
Ashkan Keshavarzi Nader ZareIn 2D Soccer Simulation league, agents will decide based on information and data in their model. Effective decisions need to have world model information without any noise and missing data; however, there are few solutions to omit noise in world model data; so we should MoreIn 2D Soccer Simulation league, agents will decide based on information and data in their model. Effective decisions need to have world model information without any noise and missing data; however, there are few solutions to omit noise in world model data; so we should find efficient ways to reduce the effect of noise when making decisions. In this article we evaluate some simple solutions when making defense decisions and try to find a solution based on message-passing to coordinating agents in defense situations. Our experimental results showed that in each situation one of the agents has a better view than others, so that agent can send messages to the others and provide needed information for doing defense behavior(ex: block behavior or clear ball behavior). Finally, we implement our solution based on Agent2D, version 3 and compare that with other solutions implemented in Cyrus2014 and Marlik2013 Soccer 2D simulation teams. Manuscript profile -
Open Access Article
79 - A Novel Robust Adaptive Trajectory Tracking in Robot Manipulators
Shaghayegh Gorji Mohammad Javad YazdanpanahIn this paper, a novel adaptive sliding mode control for rigid robot manipulators is proposed. In the proposed system, since there may exist explicit unknown parameters and perturbations, a Lyapunov based approach is presented to increase system robustness, even in pres MoreIn this paper, a novel adaptive sliding mode control for rigid robot manipulators is proposed. In the proposed system, since there may exist explicit unknown parameters and perturbations, a Lyapunov based approach is presented to increase system robustness, even in presence of arbitrarily large (but not infinite) discontinuous perturbations. To control and track the robot, a continuous controller is designed with two phases of adaptation. The first phase is related to the robot parameters and the other one is accounted for perturbation estimating. We investigated the stability in the sense of Lyapunov with derive adaptive laws and uniform ultimate boundedness in the applied worst condition. The simulation results for two degrees of freedom rigid robot manipulator effectively demonstrate capability of the mentioned approach. Moreover, the results show that the domain of attraction is so vast and a global uniform ultimate boundedness could be expected. Manuscript profile -
Open Access Article
80 - An Enhanced MSS-based checkpointing Scheme for Mobile Computing Environment
Mobile computing systems are made up of different components among which Mobile Support Stations (MSSs) play a key role. This paper proposes an efficient MSS-based non-blocking coordinated checkpointing scheme for mobile computing environment. In the scheme suggested ne MoreMobile computing systems are made up of different components among which Mobile Support Stations (MSSs) play a key role. This paper proposes an efficient MSS-based non-blocking coordinated checkpointing scheme for mobile computing environment. In the scheme suggested nearly all aspects of checkpointing and their related overheads are forwarded to the MSSs and as a result the workload of Mobile Hosts (MHs) will reduce substantially. Moreover, the total amount of exchanging checkpoint requests will be decreased in order to have a batch transmission of such requests. The scheme is also enhanced using a simple data structure to have fewer propagating checkpoint requests and avoid the avalanche effect in the system. Simulation results show that compared to other existing algorithms, in the proposed scheme the average number of propagating requests and checkpoints, the average elapsed time for each checkpointing process, and the size of system messages are significantly lower and smaller, respectively. Thus considering its distinguishing features, the proposed approach would be efficient and suitable for mobile computing environment. Manuscript profile -
Open Access Article
81 - A Rssi Based Localization Algorithm for WSN Using a Mobile Anchor Node
Fereydoon Abdi Abolfazl Toroghi HaghighatWireless sensor networks attracting a great deal of research interest. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. In recent years, several techniques have been proposed for localization in wireless sensor networks. In t MoreWireless sensor networks attracting a great deal of research interest. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. In recent years, several techniques have been proposed for localization in wireless sensor networks. In this paper we present a localization scheme with using only one mobile anchor station and received signal strength indicator technique, which reduces average localization errors and execution time. Satisfactory simulation results and also comparison of localization errors and execution time between our scheme and similar previous schemes depicts the efficiency of proposed method against previous schemes. Manuscript profile -
Open Access Article
82 - MHIDCA: Multi Level Hybrid Intrusion Detection and Continuous Authentication for MANET Security
Soheila Mirzagholi Karim FaezMobile ad-hoc networks have attracted a great deal of attentions over the past few years. Considering their applications, the security issue has a great significance in them. Security scheme utilization that includes prevention and detection has the worth of considerati MoreMobile ad-hoc networks have attracted a great deal of attentions over the past few years. Considering their applications, the security issue has a great significance in them. Security scheme utilization that includes prevention and detection has the worth of consideration. In this paper, a method is presented that includes a multi-level security scheme to identify intrusion by sensors and authenticates using biosensors. Optimizing authentication and intrusion detection combination, we formulate the problem as a partially observable distributed stochastic system. In order to reduce the computation time, the parallel forward algorithm of Hidden Markov Model has been used. Due to the possibility of misdetection of the sensor and in order to increase the accuracy of observations, more than one sensor is selected in every step, the observations obtained from the sensors are combined for more accurate identification, and the system decides about the security status based on combined observations of the sensors. Bayesian theory has been used in sensors evidence fusion brought by increased accuracy and network security, which will be observed in the simulations. The use of this theory causes the increase of accuracy and security on networks. Manuscript profile -
Open Access Article
83 - A Novel Ensemble Approach for Anomaly Detection in Wireless Sensor Networks Using Time-overlapped Sliding Windows
Zahra Malmir Mohammad Hossein RezvaniOne of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine le MoreOne of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data mining. Our proposed algorithm, at first applies a fuzzy clustering approach using the well-known C-means clustering method to create the clusters. In the classification step, we created some base classifiers, each of which utilizes the data of overlapping windows to utilize the correlation among data over time by creating time-overlapped batches of data. By aggregating these batches, the classifier proceeds to find an appropriate label for future incoming instance. The concept of “Ensemble of Classifiers” with majority voting scheme has been used in order to combine the judgment of all classifiers. The results of our implementation with MATLAB toolboxes shows that the proposed majority-based ensemble learning method attains more efficiency compared to the case of the single classifier method. Our proposed method enhances the performance of the system in terms of major criteria such as False Positive Rate, True Positive Rate, False Negative Rate, True Negative Rate, Sensitivity, Specificity and also the ROC curve. Manuscript profile -
Open Access Article
84 - Augmented Downhill Simplex a Modified Heuristic Optimization Method
Mohsen Jalaeian-FAugmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorith MoreAugmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, random search is considered as a global exploration operator in combination with DSM as a local exploitation method. Thus, presented algorithm is a derivative-free, fast, simple and nonlinear optimization method that is easy to be implemented numerically. Efficiency and reliability of the presented algorithm are compared with several other optimization methods, namely traditional downhill simplex, random search and steepest descent. Simulations verify the merits of the proposed method. Manuscript profile -
Open Access Article
85 - Simulation of Fabrication toward High Quality Thin Films for Robotic Applications by Ionized Cluster Beam Deposition
Esmaeil ZaminpaymaThe most commonly used method for the production of thin films is based on deposition of atoms or molecules onto a solid surface. One of the suitable method is to produce high quality metallic, semiconductor and organic thin film is Ionized cluster beam deposition (ICBD MoreThe most commonly used method for the production of thin films is based on deposition of atoms or molecules onto a solid surface. One of the suitable method is to produce high quality metallic, semiconductor and organic thin film is Ionized cluster beam deposition (ICBD), which are used in electronic, robotic, optical, optoelectronic devices. Many important factors such as cluster size, cluster energy, impact angle and substrate temperature have important effects on the quality of final thin film such as cluster implanted atoms, substrate sputtering atoms and surface roughness. In this paper, molecular dynamics (MD) simulation of nano-Si cluster impact on Si(100) substrate surface has been carried out for energies of 1-5 eV/atom. The 3-body Stillinger-Weber potential (SW) was used in this simulation. Si cluster sizes of 30, 70, and 160 atoms were deposited on a Si (100) substrate whose temperatures were set around 300 K. Our results illustrate that the maximum substrate temperature, heat transferred time, the cluster implantation and sputtering atoms from the surface increase with increasing the cluster size and energy of the clusters. We found that small nano-clusters with high kinetic energy can produce flatter surface. Manuscript profile -
Open Access Article
86 - Fuzzy PD Cascade Controller Design for Ball and Beam System Based on an Improved ARO Technique
Mojtaba Kazemi Jalal Najafi Mohamad Bagher MenhajThe ball and beam system is one of the most popular laboratory setups for control education. In this paper, we design a fuzzy PD cascade controller for a ball and beam system using Asexual Reproduction Optimization (ARO) technique. The ball & beam system consists of MoreThe ball and beam system is one of the most popular laboratory setups for control education. In this paper, we design a fuzzy PD cascade controller for a ball and beam system using Asexual Reproduction Optimization (ARO) technique. The ball & beam system consists of a servo motor, a grooved beam, and a rolling ball. This system utilizes a servo motor to control ball’s position on the beam. Changing the angle of servo motor results in the movement of the beam and, subsequently, the ball rolling on it. We designed a fuzzy PD cascade and a PD cascade controller scheme which consists of the two controller loops. The first (outer) controller and the second (inner) controller are organized in a cascaded construction. Manuscript profile -
Open Access Article
87 - Neuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion
Farhad Abedini Mohammad Bagher Menhaj Mohammad Reza KeyvanpourIn this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not poss MoreIn this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, the NTN is modeled as a multi-layer perceptron (MLP) network and the tensor parameter can be distributed into the new network neurons. Moreover, it is suggested that the inputs can be converted into one vector rather than the inputs of NTN are two correlated vectors at the same time. The results approve that the NTN does not indeed represent a new neural network and the implementation results easily confirm it can be considered as another representation of the MLP network. So, the first idea is representation of a neuron based mathematical model for the NTN through the ordinary and yet well-defined neural network concepts and next contribution will be equivalency proof of the two NTN and suggested MLP networks. Manuscript profile -
Open Access Article
88 - Adaptive Sliding Mode Tracking Control of Mobile Robot in Dynamic Environment Using Artificial Potential Fields
Abolfath Nikranjbar Masoud Haidari Ali Asghar AtaiSolution to the safe and collision-free trajectory of the wheeled mobile robot in cluttered environments containing the static and/or dynamic obstacle has become a very popular and challenging research topic in the last decade. Notwithstanding of the amount of publicati MoreSolution to the safe and collision-free trajectory of the wheeled mobile robot in cluttered environments containing the static and/or dynamic obstacle has become a very popular and challenging research topic in the last decade. Notwithstanding of the amount of publications dealing with the different aspects of this field, the ongoing efforts to address the more effective and creative methods is continued. In this article, the effectiveness of the real-time harmonic potential field theory based on the panel method to generate the reference path and the orientation of the trajectory tracking control of the three-wheel nonholonomic robot in the presence of variable-size dynamic obstacle is investigated. The hybrid control strategy based on a backstepping kinematic and regressor-based adaptive integral sliding mode dynamic control in the presence of disturbance in the torque level and parameter uncertainties is employed. In order to illustrate the performance of the proposed adaptive algorithm, a hybrid conventional integral sliding mode dynamic control has been established. The employed control methods ensure the stability of the controlled system according to Lyapunov’s stability law. The results of simulation program show the remarkable performance of the both methods as the robust dynamic control of the mobile robot in tracking the reference path in unstructured environment containing variable-size dynamic obstacle with outstanding disturbance suppression characteristic. Manuscript profile -
Open Access Article
89 - An Approach to Reducing Overfitting in FCM with Evolutionary Optimization
Seyed Mahmood HashemiFuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly MoreFuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the fuzzy model. Two new cost functions are developed to set the parameters of FCM algorithm properly and the two evolutionary optimization algorithms, i.e. the multi-objective simulated annealing and the multi-objective imperialist competitive algorithm, are employed to optimize the parameters of FCM according to the proposed cost functions. The multi-objective imperialist competitive algorithm is the proposed algorithm. Manuscript profile -
Open Access Article
90 - Weighted-HR: An Improved Hierarchical Grid Resource Discovery
Mahdi Mollamotalebi Mohammad Mehdi Gilanian SadeghiGrid computing environments include heterogeneous resources shared by a large number of computers to handle the data and process intensive applications. In these environments, the required resources must be accessible for Grid applications on demand, which makes the res MoreGrid computing environments include heterogeneous resources shared by a large number of computers to handle the data and process intensive applications. In these environments, the required resources must be accessible for Grid applications on demand, which makes the resource discovery as a critical service. In recent years, various techniques are proposed to index and discover the Grid resources. The response time and message load during the search process could highly affect the efficiency of resource discovery. This paper proposes a new technique in order to forward the queries based on the resource types which are accessible through each branch in hierarchical Grid resource discovery approaches. To evaluate the proposed technique, it is simulated in GridSim. The experimental results showed reducing the response time and message load during the search process especially when the Grid environment contains a large number of nodes. Manuscript profile -
Open Access Article
91 - A New Model for Best Customer Segment Selection Using Fuzzy TOPSIS Based on Shannon Entropy
Naime Ranjbar Kermany Sasan H. AlizadehIn today’s competitive market, for a business firm to win higher profit among its rivals, it is of necessity to evaluate, and rank its potential customer segments to improve its Customer Relationship Management (CRM). This brings the importance of having more effi MoreIn today’s competitive market, for a business firm to win higher profit among its rivals, it is of necessity to evaluate, and rank its potential customer segments to improve its Customer Relationship Management (CRM). This brings the importance of having more efficient decision making methods considering the current fast growing information era. These decisions usually involve several criteria, and it is often necessary to compromise among possibly conflicting factors. In this paper a new extension of fuzzy Techniques for Order Preferences by Similarity to Ideal Solution (TOPSIS) based on Shannon entropy concept for customer segment selection is proposed. Fuzzy set theories are also employed due to the presence of vagueness and imprecision of information. The contribution of this paper is that it provides a framework for MCDM which considers vagueness and ambiguity as well as allowing to set multiple aspiration levels for customer segment selection problems in which ‘‘the more/higher is better’’ (e.g., benefit criteria) or ‘‘the less/lower is better’’ (e.g., cost criteria).At the end, a numerical example of this approach is shown to illustrate its effectiveness. Manuscript profile -
Open Access Article
92 - A New Intrusion Detection System to deal with Black Hole Attacks in Mobile Ad Hoc Networks
Maryam Fathi Ahmadsaraei Abolfazl Toroghi HaghighatBy extending wireless networks and because of their different nature, some attacks appear in these networks which did not exist in wired networks. Security is a serious challenge for actual implementation in wireless networks. Due to lack of the fixed infrastructure and MoreBy extending wireless networks and because of their different nature, some attacks appear in these networks which did not exist in wired networks. Security is a serious challenge for actual implementation in wireless networks. Due to lack of the fixed infrastructure and also because of security holes in routing protocols in mobile ad hoc networks, these networks are not protected against attacks. For example in black hole attack, an attacker catches packets and throw them away, instead of forwarding them to their destinations. By using wireless intrusion detection systems, wireless networks can be protected. In this study, we introduce a new intrusion detection system to encounter black hole attack. This system is based on a combination of anomaly based intrusion detection (ABID) and specification based intrusion detection (SBID), we also use a new intrusion response. The analysis of simulation results (with NS-2) show that our method is success by using three measures: throughput, packet loss rate and packet delivery rate in comparing with ABID and SBID. Manuscript profile -
Open Access Article
93 - Protein Secondary Structure Prediction: a Literature Review with Focus on Machine Learning Approaches
Leila Khalatbari Mohammad Reza KangavariDNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological inter MoreDNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functional responsibilities. Consequently protein function prediction is a momentous task in bioinformatics. Protein function can be elucidated from its structure. Protein secondary structure prediction has attracted great attention since it’s the input feature of many bioinformatics problems. The variety of proposed computational methods for protein secondary structure prediction is very extensive. Nevertheless they couldn’t achieve much due to the existing obstacles such as abstruse protein data patterns, noise, class imbalance and high dimensionality of encoding schemes of amino acid sequences. With the advent of machine learning and later ensemble approaches, a considerable elevation was made. In order to reach a meaningful conclusion about the strength, bottlenecks and limitations of what have been done in this research area, a review of the literature will be of great benefit. Such review is advantageous not only to wrap what has been accomplished by far but also to cast light for the future decisions about the potential and unseen solutions to this area. Consequently in this paper it’s aimed to review different computational approaches for protein secondary structure prediction with the focus on machine learning methods, addressing different parts of the problem’s area. Manuscript profile -
Open Access Article
94 - A Novel Approach to Background Subtraction Using Visual Saliency Map
Soheil Tehranipour Hamidreza Rashidy KananGenerally human vision system searches for salient regions and movements in video scenes to lessen the search space and effort. Using visual saliency map for modelling gives important information for understanding in many applications. In this paper we present a simple MoreGenerally human vision system searches for salient regions and movements in video scenes to lessen the search space and effort. Using visual saliency map for modelling gives important information for understanding in many applications. In this paper we present a simple method with low computation load using visual saliency map for background subtraction in video stream. The proposed technique is based on finding image segments whose intensity values can be distinguished accurate. The practical implementation uses a sliding window approach, where the distributions of the objects and surroundings are estimated using semi-local intensity histograms. This introduced method requires no training so it can be used in embedded systems like cameras due to low load in calculation. So with our background subtraction algorithm we can detect pre-defined targets. Also the automatically video regions detected by proposed model are consistent with the ground truth saliency maps of eye movement data. Comparisons with state-of-the-art background subtraction techniques indicate that the introduced approach results in high performance and accuracy. Manuscript profile -
Open Access Article
95 - A W-band Simultaneously Matched Power and Noise Low Noise Amplifier Using CMOS 0.13µm
Mahmoud Mohammad-TaheriA complete procedure for the design of W-band low noise amplifier in MMIC technology is presented. The design is based on a simultaneously power and noise matched technique. For implementing the method, scalable bilateral transistor model parameters should be first extr MoreA complete procedure for the design of W-band low noise amplifier in MMIC technology is presented. The design is based on a simultaneously power and noise matched technique. For implementing the method, scalable bilateral transistor model parameters should be first extracted. The model is also used for transmission line utilized in the amplifier circuit. In the presented method, input/output matching networks and transistor gate width have been optimized for simultaneous maximum gain and minimum noise figure. It is easily shown that due to the low gain property of amplifier at high frequency, it is unconditionally stable; so, the common source topology has superior performance compared to other topologies. In addition, better noise figure, lower size and higher gain with the same power consumption can be achieved compared with those of the cascode topology. The simulation results show a gain of better than 18dB and noise figure of 7.4dB at 94GHz while input/output return losses are better than 20dB Manuscript profile -
Open Access Article
96 - A Cluster-Based Hybrid broadcasting mechanism for Quantum Systems with Power Management and delay Constraint in the MAC sub-layer
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Open Access Article
97 - A Supervised Method for Constructing Sentiment Lexicon in Persian Language
Faranak Ebrahimi Rashed Neda AbdolvandDue to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has att MoreDue to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the important fields of study in this domain. The main researches in the area of sentiment analysis have focused on English language and few works considered the sentiment analysis in Persian language due to the lack of resources. This paper aims to introduce a supervised method for creating a sentiment dictionary in Persian language with extracting linguistic features in reviews and statistical mutual information to determine the sentiment orientation and sentistrength of words. To evaluate the proposed method, a set of existing reviews in the online retail site is used in various domains and the present dictionary is compared with Sentiwordnet. The results show the proposed method achieves an accuracy of 80% in determining the orientation of sentiment word. Manuscript profile -
Open Access Article
98 - Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Mojgan Elikaei Ahari Babak NasersharifDifferent approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions ar MoreDifferent approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measures like inter-class distance, features statistical independence or information theoretic measures. Even though, wrapper methods use a classifier to evaluate features subsets by their predictive accuracy (on test data) by statistical resampling or cross-validation. Filter methods usually use only one measure for feature selection that does not necessarily produce the best result. In this paper, we proposed to use the classification error measures besides to filter measures where our classifier is support vector machine (SVM). To this end, we use multi objective genetic algorithm. In this way, one of our feature selection measure is SVM classification error. Another measure is selected between mutual information and Laplacian criteria which indicates informative content and structure preserving property of features, respectively. The evaluation results on the UCI datasets show the efficiency of this method. Manuscript profile -
Open Access Article
99 - Solving Path Following Problem for Car-Like Robot in the Presence of Sliding Effect via LMI Formulation
Masoud Emam Ahmad FakharianOne of the main problems of car-like robot is robust path following in the presence of sliding effect. To tackle this problem, a robust mix H2/H∞ static state feedback control method is selected. This method is the well-known linear robust controller which is robu MoreOne of the main problems of car-like robot is robust path following in the presence of sliding effect. To tackle this problem, a robust mix H2/H∞ static state feedback control method is selected. This method is the well-known linear robust controller which is robust against external disturbance as well as model uncertainty. In this paper, the path following problem is formulated as linear matrix inequality for the kinematic model of car-like robot, which includes sliding effect. The robustness and path following performance of the proposed controller are investigated based on the comparison of suggested controller with an optimal proportional integral controller. The simulation results, which have been performed by MATLAB Simulink, shows the presented controller is able to follow various paths including simple, linear and more complex function path like square, and sine function path even in the presence of sliding effect. In addition, the robustness and correctness of closed-loop system in the simulations are demonstrated based on the nonlinear analysis of equilibrium point. Manuscript profile -
Open Access Article
100 - Reducing the Consumption Power in Flash ADC Using 65nm CMOS Technology
Nafise Haji-Karimi Mohamad Dosaranian-MoghadamThis paper presents a new method to reduce consumption power in flash ADC in 65nm CMOS technology. This method indicates a considerable reduction in consumption power, by removing comparators memories. The simulations used a frequency of 1 GHZ, resulting in decreased co MoreThis paper presents a new method to reduce consumption power in flash ADC in 65nm CMOS technology. This method indicates a considerable reduction in consumption power, by removing comparators memories. The simulations used a frequency of 1 GHZ, resulting in decreased consumption power by approximately 90% for different processing corners. In addition, in this paper the proposed method was designed using interpolation technique for purpose of promoting the performance as well as decreasing the class of chip. The simulation results indicate that the consumption power for interpolation technique was decreased by approximately 5% compared to the proposed method. Also, we compare the results of the proposed technique with those of convertors frequently referred in other studies. The results show that the consumption power is considerably decreased, using the proposed technique. Manuscript profile -
Open Access Article
101 - Designing and Optimizing the Fetch Unit for a RISC Core
Bahman Javadi Mojtaba Shojaei Mohammad Kazem Akbari Farnaz IrannejadDespite the extensive deployment of multi-core architectures in the past few years, the design and optimization of each single processing core is still a fresh field in computing .On the other hand, having a design procedure (used to solve the problems related to the de MoreDespite the extensive deployment of multi-core architectures in the past few years, the design and optimization of each single processing core is still a fresh field in computing .On the other hand, having a design procedure (used to solve the problems related to the design of a single processing core )makes it possible to apply the proposed solutions to specific-purpose processing cores .The instruction fetch, which is one of the parts of the architectural design, is considered to have the greatest effect on the performance .RISC processors, which have architecture with a high capability for parallelism, need a high instruction width in order to reach an appropriate performance .Accurate branch prediction and low cache miss rate are two effective factors in the operation of the fetching unit .In this paper, we have designed and analyzed the fetching unit for a 4-way( 4-issue )superscalar processing core .We have applied the cost per performance design style and quantitative approach to propose this fetch unit .Moreover, timing constrains are specially analyzed for instruction cache to enable the proposed fetch unit to be in a superpipeline system .In order to solve the timing problem, we have applied the division method to the branch prediction tables and the wave pipelining technique to the instruction cache. Manuscript profile -
Open Access Article
102 - Energy and Reserve Market Clearing to Consider Interruptible Loads
Toktam Nikfarjam Karim AfsharThis paper demonstrates a method to how reserve capacity and cost allocation could be determined in a pool-based and disaggregated market model. The method considers both the spinning reserve and interruptible loads as the operating reserve services. In the proposed mar MoreThis paper demonstrates a method to how reserve capacity and cost allocation could be determined in a pool-based and disaggregated market model. The method considers both the spinning reserve and interruptible loads as the operating reserve services. In the proposed market, generators and consumers (including participation of interruptible loads) submit offers and bids to the independent system operator. Firstly, the energy market is cleared according to GENCOs' offers and customers' energy requirements. To make; the more competitive market, interruptible customers participate in reserve market and supply operating reserve. It is assumed that the operating reserve market structure cleared in two-stages. Based on the reliability evaluation of the generators, market operator (MO) clears the reserve market. According to the contribution of generation units to the system expected energy not supplied, reserve cost of this level is allocated among them. In the second section of the reserve market clearing, customers can choose their desired reliability requirements. The independent system operator is cleared reserve market such that the required reliability levels of the customers are met. Reserve cost associated with this part is allocated among customers that are willing to have a higher reliability level than the standard level. To determine the share of each consumer from a shortage in the real time operation, Deficiency Factor is introduced. Finally, numerical results are presented to illustrate the impact of the reserve cost allocation and effectiveness of participations’ demand side on the operating reserve market. Manuscript profile -
Open Access Article
103 - An LPV Approach to Sensor Fault Diagnosis of Robotic Arm
Amir Hossein Sabbaghan Amir Hossein HassanabadiOne of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using MoreOne of the major challenges in robotic arms is to diagnosis sensor fault. To address this challenge, this paper presents an LPV approach. Initially, the dynamics of a two-link manipulator is modelled with a polytopic linear parameter varying structure and then by using a descriptor system approach and a robust design of a suitable unknown input observer by means of pole placement method along with linear matrix inequalities, in addition to providing an estimate of state variables for using in state feedback, the detection, isolation, and identification of sensor faults in the manipulator are addressed. The proposed observer provides a robust estimate of the faults along with attenuating the disturbance effects. Further, the desired angles of the joints are calculated for achieving the desired trajectory of the robot’s end-effector using the inverse kinematics and by designing a suitable state feedback law with integral mode, the reference signals are tracked. The sufficient condition for stability of the closed-loop system is obtained as a set of linear matrix inequalities at the vertices of the system. The efficiency and effectiveness of the control system, along with the designed fault diagnosis unit, are shown using numerical simulations. Manuscript profile -
Open Access Article
104 - Utilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs
Samaneh Assar Behrooz MasoumiMulti agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a g MoreMulti agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP problem is described as a directed graph in which the nodes are the states of the problem, and the directed edges represent the actions that result in transition from one state to another. Each state of the environment is equipped with a generalized learning automaton whose actions are moving to different adjacent states of that state. Each agent moves from one state to another and tries to reach the goal state. In each state, the agent chooses its next transition with help of the generalized learning automaton in that state. The experimental results have shown that the proposed algorithm have better learning performance in terms of the speed of reaching the optimal policy as compared to existing learning algorithms. Manuscript profile -
Open Access Article
105 - Query Architecture Expansion in Web Using Fuzzy Multi Domain Ontology
Shaghayegh Rabiee Kenari Eslam NazemiDue to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any dat MoreDue to the increasing web, there are many challenges to establish a general framework for data mining and retrieving structured data from the Web. Creating an ontology is a step towards solving this problem. The ontology raises the main entity and the concept of any data in data mining. In this paper, we tried to propose a method for applying the "meaning" of the search system, But the problem for these methods is building a knowledge base that can be used for semantic search. The previous work interprets the query in three ways:'semantic relation in ontology', 'co-occurrence in the document', and 'semantic relation from Thesaurus'. The proposed method has two parts. The first part, using domain ontology for classified web pages based on keyword and the concept in each domain and builds Fuzzy ontology as Knowledge Base and the next section offers a method for expanding the query using built fuzzy ontology. In this paper, we tried to create knowledge base with WordNet as a comprehensive dictionary and extracted Sub string (phrases include multi words) from WordNet for each keyword in each domain ontology. The created Search engine was applied to an experimental system to evaluate the "precision – Recall” and it was revealed that applying the proposed method can improve query expansion 11%better in our experiments for precision. Manuscript profile -
Open Access Article
106 - Self-Cleaning Measurement of Nano-Sized Photoactive TiO2
Majid FarahmandjouTitanium dioxide (TiO2)nanoparticles have been frequently employed in the environmental treatment and purification purposes as a cheap and highly efficient photocatalyst. A photocatalyst can facilitate the breakdown and removal of a variety of environmental pollutants a MoreTitanium dioxide (TiO2)nanoparticles have been frequently employed in the environmental treatment and purification purposes as a cheap and highly efficient photocatalyst. A photocatalyst can facilitate the breakdown and removal of a variety of environmental pollutants at room temperature. TiO2 photocatalyst is the best candidatebecause of its strong oxidized ability, non-toxicity and longthermal photostability. The TiO2 is also importantand need deep studies because it can be used as self-cleaningand anti-fogging glass in future.In this paper, TiO2 nanoparticles were synthesized by liquid phase method. The samples were characterized by x-ray diffraction (XRD) and transmission electron microscopy (TEM) analyses after heat treatments. The XRD results show the sharp picks after annealing process. The TEM results reveal that the size of nanoparticles is in the range of 20-40 nm in diameter. Raman scattering pattern of the TiO2 nanoparticles confirm the TEM analysis and indicate the anatase phase Manuscript profile -
Open Access Article
107 - A New Approach to Software Cost Estimation by Improving Genetic Algorithm with Bat Algorithm
Sakineh Asghari Agcheh Dizaj Farhad Soleimanian GharehchopoghBecause of the low accuracy of estimation and uncertainty of the techniques used in the past to Software Cost Estimation (SCE), software producers face a high risk in practice with regards to software projects and they often fail in such projects. Thus, SCE as a complex MoreBecause of the low accuracy of estimation and uncertainty of the techniques used in the past to Software Cost Estimation (SCE), software producers face a high risk in practice with regards to software projects and they often fail in such projects. Thus, SCE as a complex issue in software engineering requires new solutions, and researchers make an effort to make use of Meta-heuristic algorithms to solve this complicated and sensitive issue. In this paper, we propose a new method by improving Genetic Algorithm (GA) with Bat Algorithm (BA), considering the effect of qualitative factors and false variables in the relations concerning the total estimation of the cost. The proposed method was investigated and assessed on four various datasets based on seven criteria. The experimental results indicate that the proposed method mainly improves accuracy in the SCE and it reduced errors' value in comparison with other models. In the results obtained, Mean Magnitude of Relative Error (MMRE) on NASA60, NASA63, NASA93, and KEMERER is 17.91, 34.80, 41.97, and 95.86, respectively. In addition, the experimental results on datasets show that the proposed method significantly outperforms GA and BA and also many other recent SCE methods. Manuscript profile -
Open Access Article
108 - Pseudo Zernike Moment-based Multi-frame Super Resolution
Sara Salkhordeh Hamidreza Rashidy KananThe goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion MoreThe goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted averaging all its neighboring pixels in all LR images which carries the degree of similarity between image blocks centered on two pixels. Since in case of rotation between LR images, comparing the gray level of blocks around the pixels is not a suitable criterion for calculating weight, so, magnitude of Zernike Moments (ZM) has been used as a rotation invariant feature. Due to the lower sensitivity of Pseudo Zernike Moments (PZM) to noise and the higher discrimination capability of it for the same order compared to ZM, in this paper, we propose a new method based on magnitude of PZM of the blocks as a rotation invariant descriptor for representation of pixels in weight calculation. Experimental results on several image sequences show that the performance of the proposed algorithm is better than the existing and new techniques from the aspect of PSNR and visual image quality. Manuscript profile -
Open Access Article
109 - Study on Unit-Selection and Statistical Parametric Speech Synthesis Techniques
Mohammad Savargiv Azam BastanfardOne of the interesting topics on multimedia domain is concerned with empowering computer in order to speech production. Speech synthesis is granting human abilities to the computer for speech production. Data-based approach and process-based approach are the two main ap MoreOne of the interesting topics on multimedia domain is concerned with empowering computer in order to speech production. Speech synthesis is granting human abilities to the computer for speech production. Data-based approach and process-based approach are the two main approaches on speech synthesis. Each approach has its varied challenges. Unit-selection speech synthesis and statistical parametric speech synthesis are two dominant speech synthesizer techniques. The naturalness is the main challenge of all speech synthesis approaches. The Intonation, speech style and emotional state are included in naturalness factor and all of them are considered as suprasegmental features. Equipped synthesized speech with paralinguistic information is more believable from the perceptual aspect. Prosody information plays an important role on the synthesized speech quality of text to speech systems. The first purpose of modern speech synthesizer systems is text to speech conversion and the second purpose is transferring the emotional states of text in the voice form. In this paper two main speech synthesis approaches and their challenges are investigated in detail. Manuscript profile -
Open Access Article
110 - Use of the Improved Frog-Leaping Algorithm in Data Clustering
Sahifeh Poor Ramezani Kalashami Seyyed Javad Seyyed Mahdavi ChabokClustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and MoreClustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. K-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. In recent years, several algorithms are provided based on evolutionary algorithms for clustering, but unfortunately they have shown disappointing behavior. In this study, a shuffled frog leaping algorithm (LSFLA) is proposed for clustering, where the concept of mixing and chaos is used to raise the accuracy of the algorithm. Because the use of concept of entropy in the fitness functions, we are able to raise the efficiency of the algorithm for clustering. To perform the test, the four sets of real data are used which have been compared with the algorithms K-menas, GA, PSO, CPSO. The results show better performance of this method in the clustering. Manuscript profile -
Open Access Article
111 - Coverage Quality in Visual Sensor Networks
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Open Access Article
112 - Text Summarization Using Cuckoo Search Optimization Algorithm
Seyed Hossein Mirshojaei Behrooz MasoomiToday, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of sel MoreToday, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extractive-based text summarization seems to be an unsolvable problem. Therefore, to deal with such problems, meta-heuristic techniques are applied as a solution. In this paper, we used Cuckoo Search Optimization Algorithm (CSOA) to improve performance of extractive-based summarization method. The proposed approach is examined on Doc. 2002 standard documents and analyzed by Rouge evaluation software. The obtained results indicate better performance of proposed method compared with other similar techniques. Manuscript profile -
Open Access Article
113 - Interference-Aware and Cluster Based Multicast Routing in Multi-Radio Multi-Channel Wireless Mesh Networks
Rasoul Behravesh Mohsen JahanshahiMulticast routing is one of the most important services in Multi Radio Multi Channel (MRMC) Wireless Mesh Networks (WMN). Multicast routing performance in WMNs could be improved by choosing the best routes and the routes that have minimum interference to reach multicast MoreMulticast routing is one of the most important services in Multi Radio Multi Channel (MRMC) Wireless Mesh Networks (WMN). Multicast routing performance in WMNs could be improved by choosing the best routes and the routes that have minimum interference to reach multicast receivers. In this paper we want to address the multicast routing problem for a given channel assignment in WMNs. The channels that are assigned to the network graph are given to the algorithm as an input. To reduce the problem complexity and decrease the problem size, we partition the network to balanced clusters. Fuzzy logic is used as a tool for clustering in our method. After clustering and electing most suitable nodes as cluster head, we take a mathematical method to solve the multicast tree construction problem. We conducted several simulations to verify the performance of our method and the simulation results demonstrated that our proposed method outperforms CAMF algorithm in terms of throughput and end to end delay. Manuscript profile -
Open Access Article
114 - Optimized Joint Trajectory Model with Customized Genetic Algorithm for Biped Robot Walk
Mostafa Salehi Mostafa Azarkaman Mohammad AghaabbaslooBiped robot locomotion is one of the active research areas in robotics. In this area, real-time stable walking with proper speed is one of the main challenges that needs to be overcome. Central Pattern Generators (CPG) as one of the biological gait generation models, ca MoreBiped robot locomotion is one of the active research areas in robotics. In this area, real-time stable walking with proper speed is one of the main challenges that needs to be overcome. Central Pattern Generators (CPG) as one of the biological gait generation models, can produce complex nonlinear oscillation as a pattern for walking. In this paper, we propose a model for a biped robot joint trajectory in order to be able to walk straight, exploiting polynomial equations for the support leg’s joints and Truncated Fourier (TFS) Series equations for the swing leg’s joints in the sagittal plane and frontal plane. Four customized genetic algorithms (GA-1 to GA-4) with different implementations for the crossover steps are used as evolutionary algorithms to optimize equation parameters and achieve the best speed and performance in walking motion. These four GAs differ in crossover step and parent selection parts. After a primary evaluation to make sure the next generation is better off than before, we consider a clever comparison feature between the best of two generations (parent and child) in GA-4. The algorithms have been tested on the Darwin humanoid robot in the Webots simulator environment where the results show that the GA-4 model has the best performance and achieves the desired fitness value. Manuscript profile -
Open Access Article
115 - Fast Cellular Automata Implementation on Graphic Processor Unit (GPU) for Salt and Pepper Noise Removal
Afsaneh Jalalian Babak Karasfi Khairulmizam Samsudin M.Iqbal Saripan Syamsiah MashohorNoise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of the s MoreNoise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt and pepper noise. In order to enhance the performance of the designed CA for noise removal, a parallel programming approach has been adopted and implemented on GPU. The results obtained show that the proposed CA models implemented on general purpose processor and GPU are able to suppress noise in high noise intensity up to 90 percents. The proposed CA implemented on GPU has successfully outperformed the method implemented on CPU by factor of 2 for gray scale image and factor of 10 for color images. Manuscript profile -
Open Access Article
116 - A New System of Contactless Power Transfer with Low Voltage Stress and Parasitic Capacitors Effect
Mohammad Reza Yousefi Hossein TorkamanIn this paper, a high frequency contactless power transfer (CPT) system is designed with ∅2 inverter drive. This system works in 30MHz frequency and 380W power with low voltage stress and considers the inductive link parasitic capacitor effect. In the design, we f MoreIn this paper, a high frequency contactless power transfer (CPT) system is designed with ∅2 inverter drive. This system works in 30MHz frequency and 380W power with low voltage stress and considers the inductive link parasitic capacitor effect. In the design, we formulated the inverter equations first and then suggested another design for the transmitter and the receiver coils as the energy transfer medium. Following the inverter equations, structures of proper coil are designed for a CPT system. The results of the coil and ∅2 inverter designs are implemented as a bipolar circuit model which is equal to considering the inductive link parasitic capacitors. The system characteristics such as the stress, efficiency, mutual induction, field scattering, magnetic field distribution and the parameters’ variations are evaluated along with analysis. The results demonstrate that the presented CPT system has high efficiency, low switching voltage stress, small passive energy storage elements and fast dynamic response. Manuscript profile -
Open Access Article
117 - Detection of Breast Cancer Progress Using Adaptive Nero Fuzzy Inference System and Data Mining Techniques
Hengameh MahdaviPrediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. Nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the prev MorePrediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. Nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. In this paper has been used adaptive nero fuzzy inference system and data mining techniques for processing of input data and the educational combined algorithm for arranging of parameters of input functions. It has used also the downward gradient algorithm for arranging of unlined input parameters and the algorithm of the least of squares for arranging of lined output parameters. It has been used the data the institute of oncology Ljubljana of Yugoslavia that contain the information of 1090 the breast cancer patients. The results show the suggesting system has 89% accuracy in the diagnosis of progressing the breast cancer, which has improved by compared with neural network classification method. Manuscript profile -
Open Access Article
118 - Facial Expression Recognition Based on Structural Changes in Facial Skin
Zeynab Shokoohi Karim FaezFacial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services ar MoreFacial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services are keen on expanding on the potential advantages of facial recognition technology. As machine vision and artificial intelligence advances, facial recognition has become more accessible and is now a key technique to be employed and used in creating more natural man-machine interactions, Computer vision, and health care. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. In this paper, we proposed a face expression detection method based on the difference of a face expression andthe allocated special pattern to each expression. The analysis of the image detection system locally and through a sliding window (sliding) at multiple scales, are estimated. Multiple scales are extracted aslocally binary features. Through using the change point between windows, points of face are getting a directional movement. Through using points movement of whole facial expressions and rating system that is created thesuperfluouspoints are eliminated. The classifications are taken based on the nearest neighbor.To sum up this paper, the proposed algorithms are tested on Cohn-Kanade data set and the results showed the best performance and reliability into other algorithms. We investigated LBP features for the facial skin structural changes, which is seldom addressed in the existing literature. Manuscript profile -
Open Access Article
119 - Sales Budget Forecasting and Revision by Adaptive Network Fuzzy Base Inference System and Optimization Methods
Kaban Koochakpour Mohammad Jafar TarokhThe sales proceeds are the most important factors for keeping alive profitable companies. So sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, poor forecasting can lead to great loses in MoreThe sales proceeds are the most important factors for keeping alive profitable companies. So sales and budget sales are considered as important parameters influencing all other decision variables in an organization. Therefore, poor forecasting can lead to great loses in organization caused by inaccurate and non-comprehensive production and human resource planning. In this research a coherent solution has been proposed for forecasting sales besides refining and revising it continuously by ANFIS model with consideration of time series relations. The relevant data has been collected from the public and accessible annual financial reports being related to a famous Iranian company. Moreover, for more accuracy in forecasting, solution has been examined by Back Propagation neural Network (BPN) and Particle swarm Optimization (PSO). The comparison between prediction taken and real data shows that PSO can optimize some parts of prediction in contrast to the rest which is more coincident to the output of BPN analysis with more precise results relatively. Manuscript profile -
Open Access Article
120 - A Comprehensive Model Driven ‘Secure Mobile Application for KFU Email System’ (SMAKE)
Ayat Bu-Suhail Al-Jwharah Al-Hulaibi Zainab Al-Khalaf Noor A. Jebril Qasem Abu Al-HaijaNowadays, the development of innovative technology has emerged, particularly in mobile phones. People are often using smartphones daily in almost every aspect of their lives to use different applications and share various types of information quickly while moving anywhe MoreNowadays, the development of innovative technology has emerged, particularly in mobile phones. People are often using smartphones daily in almost every aspect of their lives to use different applications and share various types of information quickly while moving anywhere. Mobile’s email applications are classified as one of the important applications to communicate ubiquitously since the use of email is considered as the best formal way for communication inside any organization. Due to this importance of e-mail and the daily needs of using it especially for faculty members and students, we propose to develop a mobile application for KFU E-Mail system with secure data transmission. The proposed application has encryption and decryption features to ensure security. As a result, the students and faculty members can communicate via the email application in a safer and more comfortable way. Manuscript profile -
Open Access Article
121 - A Novel Approach to Determine Dosage in Treatment of AIDS Based on Direct Self Tuning Regulator (STR) Theory and Linearization Around Equilibrium Condition
Mohammad Fiuzy Sayed Kamaledin Mousavi Mashhadiin this study, a new approach for control (treatment) of AIDS based on patient entry drug. One of the main problems related to AIDS, is lack of control, lack of identification and early treatment of this disease. Today physicians more than anything, control the disease Morein this study, a new approach for control (treatment) of AIDS based on patient entry drug. One of the main problems related to AIDS, is lack of control, lack of identification and early treatment of this disease. Today physicians more than anything, control the disease relying on their experience and knowledge and time-consuming and complex experiments, nevertheless, human errors are inevitable. Now this study investigated dynamic and mathematical model of AIDS. Then 2 inputs (RTI1 and PI2) simplified based on a 1 input. Then, using the linearization on operating point or equilibrium, dynamic linearized by 1 input. Finally, by applying control signal to the disease by using direct adaptive control (STR 3), the disease can be controlled adaptively as the patient's condition is not worse and controlled. The proposed system by combining these methods is able to reach small amounts and a substantial sum of squared errors SSE4, mean absolute error and mean square error MAE5 MSE6. Relying on the dynamic characteristics, in terms of composition and interaction to high matching accuracy. The present methods, despite on high precision, are time-consuming and expensive. By comparing this method and those methods, we will discover accuracy and efficiency of these methods. Manuscript profile -
Open Access Article
122 - A Survey of Solutions to Protect Against All Types of Attacks in Mobile Ad Hoc Networks
Maryam Fathi Ahmadsaraei Abolfazl Toroghi HaghighatIn recent years mobile networks have expanded dramatically, compared with other wireless networks. Routing protocols in these networks are designed with the assumption that there is no attacker node, so routing protocols are vulnerable to various attacks in these networ MoreIn recent years mobile networks have expanded dramatically, compared with other wireless networks. Routing protocols in these networks are designed with the assumption that there is no attacker node, so routing protocols are vulnerable to various attacks in these networks. In this paper, we review the network layer attacks and then we simulate the impact of black hole attack on ad hoc on demand distance vector routing protocol with NS-2 simulation. Then we review all kinds of intrusion detection systems (IDS) in large and small mobile ad hoc networks. We simulate these networks when they are under single black hole attack and with the existence of IDS byNS-2 simulator software. Finally, we compared the results according to throughput, packet loss ratio and packet delivery rate with each other. Manuscript profile -
Open Access Article
123 - BQPS: A Broadcast Mechanism for Asynchronous Quorum-based Power Saving Protocols in Ad-Hoc Networks
Shahrzad ShirazipourazadQuorum-based power saving (QPS) protocols allow “asynchronous” wireless hosts, operating in a multi-hop ad-hoc network, to tune to the low power mode conceived in IEEE 802.11 MAC standard. QPS schemes guarantee that the wake-up schedule for every two neighbo MoreQuorum-based power saving (QPS) protocols allow “asynchronous” wireless hosts, operating in a multi-hop ad-hoc network, to tune to the low power mode conceived in IEEE 802.11 MAC standard. QPS schemes guarantee that the wake-up schedule for every two neighboring hosts would ultimately overlap within a bounded latency so as to be able to accomplish their reciprocal “unicast” communications. A major drawback in quorum-based rendezvous schemes, however, lies in the absence of an efficient mechanism for enabling the simultaneous re-activation of all PS neighbors to receive “broadcast” messages. In this paper, a novel asynchronous wake-up scheduling mechanism is proposed, which specifically tackles the broadcast transmission problem in QPS systems. We introduce a special control packet at the MAC layer through which a sending host notifies its neighbors of forthcoming broadcast traffic, allowing the receivers to estimate the approximate re-activation time for ensuring the successful delivery of the messages. We will investigate, analytically, the optimum frequency with which to emit notifications so that the energy overhead induced is minimized in both single-hop broadcasting as well as network-wide flooding scenarios. Evaluation results derived from our simulation experiments reveal that the proposed mechanism can effectively improve the performance of an asynchronous QPS system in terms of both throughput as well as energy saving ratio; for instance, when operating with a wake-up ratio of 16%, network throughput will be enhanced by at least 60% in comparison with the existing schemes. Manuscript profile -
Open Access Article
124 - Control of a Hyperchaotic System Via Generalized Backstepping Method
Zinat Asadi Ahmad FakharianThis paper investigates on control and stabilization of a new hyperchaotic system. The hyperchaotic system is stabilized using a new technique which called Generalized Backstepping Method (GBM). Because of its similarity to Backstepping approach, this method is called G MoreThis paper investigates on control and stabilization of a new hyperchaotic system. The hyperchaotic system is stabilized using a new technique which called Generalized Backstepping Method (GBM). Because of its similarity to Backstepping approach, this method is called GBM. But, this method is more applicable in comparison with conventional Backstepping. Backstepping method is used only for systems with strictly feedback form, but GBM works for a wide range form of the nonlinear dynamical systems. In Design procedure, two cases is considered that their difference is in the number of control inputs. Numerical simulation results are presented to show the effectiveness of the proposed controller. Manuscript profile -
Open Access Article
125 - An Improved RNS Reverse Converter in Three-Moduli Set
Navid Habibi Mohammad Reza SalehnamadiResidue Number System (RNS) is a carry-free and non-weighed integer system. In this paper an improved three-moduli set in reverse converter based on CRT algorithm is proposed. CRT algorithm can perform a better delay and hardware implementation in modules via other algo MoreResidue Number System (RNS) is a carry-free and non-weighed integer system. In this paper an improved three-moduli set in reverse converter based on CRT algorithm is proposed. CRT algorithm can perform a better delay and hardware implementation in modules via other algorithms. This moduli is based on p that covers a wide range on modules and supports the whole range of its modules in dynamic range. With growth in moduli, many types of modules have been proposed. By using dynamic range we can solve many problems in Residue Number System (RNS) by just one three moduli. In proposed moduli set of this paper in Residue Number System (RNS), the internal circuit is improved and thus, complexity of circuit, energy consumption and power consumption in our proposed design is improved. These improvements are shown in evaluation in terms of CSA adders, CPA adders and delay. Manuscript profile -
Open Access Article
126 - Applications of Fuzzy Program Graph in Symbolic Checking of Fuzzy Flip-Flops
Gholamreza Sotudeh Ali MovagharAll practical digital circuits are usually a mixture of combinational and sequential logic. Flip–flops are essential to sequential logic therefore fuzzy flip–flops are considered to be among the most essential topics of fuzzy digital circuit. The concept of MoreAll practical digital circuits are usually a mixture of combinational and sequential logic. Flip–flops are essential to sequential logic therefore fuzzy flip–flops are considered to be among the most essential topics of fuzzy digital circuit. The concept of fuzzy digital circuit is among the most interesting applications of fuzzy sets and logic due to the fact that if there has to be an ultimate fuzzy computer then fuzzy circuitry is inevitable. In this research field, hardware realization of fuzzy negation, t–norms and t–conorms have been well studied in details meanwhile no formal model is introduced for more complex fuzzy circuitry such as combinational circuits, sequential circuits or memory modules. The lack of a formal model checker indicates flaws and design deficiencies are usually remain out of sight therefore validating fuzzy logic circuits was impossible to this date. In this paper we are elaborating the application of Fuzzy Program Graph in symbolic checking of fuzzy flip–flops; thus, the content is mainly focused on formal modelling of fuzzy flip–flops and investigating their correctness. To this purpose we investigated design deficiencies of a multivalued D flip–flop and found a dynamic hazard then we proposed a formal model toward fuzzy J–K flip–flops to further elaborate applications of proposed formal model and model checking approach in detecting design phase deficiencies. Manuscript profile -
Open Access Article
127 - An Adaptive Approach to Increase Accuracy of Forward Algorithm for Solving Evaluation Problems on Unstable Statistical Data Set
Omid SojodiShijani Nader RezazadehNowadays, Hidden Markov models are extensively utilized for modeling stochastic processes. These models help researchers establish and implement the desired theoretical foundations using Markov algorithms such as Forward one. however, Using Stability hypothesis and the MoreNowadays, Hidden Markov models are extensively utilized for modeling stochastic processes. These models help researchers establish and implement the desired theoretical foundations using Markov algorithms such as Forward one. however, Using Stability hypothesis and the mean statistic for determining the values of Markov functions on unstable statistical data set has led to a significant reduction in the accuracy of Markov algorithms including Forward algorithm used in solving Evaluation problems. The model’s parameters such as the occurrence probability of observation symbol being produced by state, varies directly among the successive events. Since the probability value of the above-mentioned parameter plays an important role in the accurate Evaluation and assessment of the probability of observations’ occurrence in the Evaluation problem by Forward algorithm, the variations between events and observations generated by the States should be automatically extracted. In order to achieve this, the current paper proposes an adaptive parameter for event probability in order to match and adjust the variations in the parameter after each event during the lifetime of Forward algorithm. The results of the experiments on a real set of data indicates the superior performance of the proposed method compared to other conventional methods regarding their accuracy. Manuscript profile -
Open Access Article
128 - Advertising Keyword Suggestion Using Relevance-Based Language Models from Wikipedia Rich Articles
Amir H. Jadidinejad Fariborz MahmoudiWhen emerging technologies such as Search Engine Marketing (SEM) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. Keyword suggestion for search en MoreWhen emerging technologies such as Search Engine Marketing (SEM) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. Keyword suggestion for search engine advertising is an important problem for sponsored search and SEM that requires a goldmine repository of knowledge. A recent strategy in this area is bidding on non-obvious yet relevant keywords, which are economically more viable. In this paper, we exploited a modified relevance-based language model for keyword suggestion problem using Wikipedia as our knowledge base. Huge amounts of clean information in Wikipedia allowed us to uncover important relations between concepts and suggest excessive low volume, inexpensive keywords. Also, we will show the viability of our approach by comparing its results to recent proposed systems. Compared to previous researches, our proposed approach have many advantages, namely, being language independent, being well-grounded, containing expert keywords and being more computationally efficient. Manuscript profile -
Open Access Article
129 - Identification and Robust Fault Detection of Industrial Gas Turbine Prototype Using LLNF Model
Leila Shahmohamadi Mahdi AliyariShoorehdeli Sharareh TalaieIn this study, detection and identification of common faults in industrial gas turbines is investigated. We propose a model-based robust fault detection(FD) method based on multiple models. For residual generation a bank of Local Linear Neuro-Fuzzy (LLNF) models is used MoreIn this study, detection and identification of common faults in industrial gas turbines is investigated. We propose a model-based robust fault detection(FD) method based on multiple models. For residual generation a bank of Local Linear Neuro-Fuzzy (LLNF) models is used. Moreover, in fault detection step, a passive approach based on adaptive threshold is employed. To achieve this purpose, the adaptive threshold band is made by a sliding window technique to make decision whether a fault occurred or not. In order to show the effectiveness of proposed FD method, it is used to identify a simulated single-shaft industrial gas turbine prototype model, which works in various operation points. This model is a reference simulation which is used in many similar researches with the aim of fault detection in gas turbines. Manuscript profile -
Open Access Article
130 - A Method to Reduce Effects of Packet Loss in Video Streaming Using Multiple Description Coding
Mahboobe Shabaniyan Ehsan AkhtarkavanMultiple description (MD) coding has evolved as a promising technique for promoting error resiliency of multimedia system in real-time application programs over error-prone communicational channels. Although multiple description lattice vector quantization (MDCLVQ) is a MoreMultiple description (MD) coding has evolved as a promising technique for promoting error resiliency of multimedia system in real-time application programs over error-prone communicational channels. Although multiple description lattice vector quantization (MDCLVQ) is an efficient method for transmitting reliable data in the context of potential error channels, this method doesn’t consider discreteness of network so that losing all descriptions is highly possible. It means all videos may be removed. In this study, we have implemented scheme of MDCLVQ in real-time environment of network, in a method that, raw video (i.e. video with no standard encoding (like MPEG)) is transmitted through independent packets inside of network. This technique leads in low or close to zero loss of all packets. Our purpose is to increase error resiliency and reliable data transmission in error-prone channels. The technique has been tested on some videos sources of Akiyo, Carphone, Foreman and Miss-America. The experimental results indicate that quality of reconstructed videos are substantially improved in terms of central and side PSNR. Manuscript profile -
Open Access Article
131 - Abnormality Detection in a Landing Operation Using Hidden Markov Model
Hasan Keyghobadi Alireza SeyedinThe air transport industry is seeking to manage risks in air travels. Its main objective is to detect abnormal behaviors in various flight conditions. The current methods have some limitations and are based on studying the risks and measuring the effective parameters. T MoreThe air transport industry is seeking to manage risks in air travels. Its main objective is to detect abnormal behaviors in various flight conditions. The current methods have some limitations and are based on studying the risks and measuring the effective parameters. These parameters do not remove the dependency of a flight process on the time and human decisions. In this paper, we used an HMM-based method which is among the main methods of situation assessment in data fusion. This method includes two clustering levels based on data and model. The experiments were conducted with B_777 flight data and the variables considered in the next generation of ADS_B. According to the results of this study, our method has high speed and sensitivity in detection of abnormal changes which are effective in the flight parameters when landing. With the dynamic modelling, there is no dependency on time and conditions. The adaptation of this method to other air traffic control systems makes its extension possible to cover all flight conditions. Manuscript profile -
Open Access Article
132 - Back-Stepping Sliding Mode Controller for Uncertain Chaotic Colpitts Oscillator with no Chattering
Maryam Ghorbani Hamid GhadiriBy introducing Colpitts oscillator as a chaotic system, this paper deals with back-stepping control method and investigates the restrictions and problems of the controller where non-existence of a suitable response in the presence of uncertainty is the most important pr MoreBy introducing Colpitts oscillator as a chaotic system, this paper deals with back-stepping control method and investigates the restrictions and problems of the controller where non-existence of a suitable response in the presence of uncertainty is the most important problem to note. In this paper, the back-stepping sliding mode method is introduced as a robust method for controlling nonlinear Colpitts oscillator system with chaotic behavior. Thereafter, we simulated the proposed method and compared its advantages with that of the previous method. The experimental results show that the most important advantages of the proposed method are making system robust in case of uncertainties and disturbances, and also having a fast response. Manuscript profile -
Open Access Article
133 - Kinematic and Gait Analysis Implementation of an Experimental Radially Symmetric Six-Legged Walking Robot
Mohammadali Shahriari Kambiz Ghaemi OsguieAs a robot could be stable statically standing on three or more legs, a six legged walking robot can be highly flexible in movements and perform different missions without dealing with serious kinematic and dynamic problems. An experimental six legged walking robot with MoreAs a robot could be stable statically standing on three or more legs, a six legged walking robot can be highly flexible in movements and perform different missions without dealing with serious kinematic and dynamic problems. An experimental six legged walking robot with 18 degrees of freedom is studied and built in this paper. The kinematic and gait analysis formulations are demonstrated by an experimental hexapod robot. The results show that the robot walks well as it was simulated. Manuscript profile -
Open Access Article
134 - Modified Convex Data Clustering Algorithm Based on Alternating Direction Method of Multipliers
Tahereh Esmaeili Abharian Mohammad Bagher MenhajKnowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering in which there is no MoreKnowing the fact that the main weakness of the most standard methods including k-means and hierarchical data clustering is their sensitivity to initialization and trapping to local minima, this paper proposes a modification of convex data clustering in which there is no need to be peculiar about how to select initial values. Due to properly converting the task of optimization to an equivalent convex optimization problem, the proposed data clustering algorithm can be indeed considered as a global minimizer. In this paper, a splitting method for solving the convex clustering problem is used called as Alterneting Direction Method of Multipliers (ADMM), a simple but powerful algorithm that is well suited to convex optimization. We demonstrate the performance of the proposed algorithm on real data examples. The simulation result easily approve that the Modified Convex Data Clustering (MCDC) algorithm provides separation more than the Convex Data Clustering (CDC) algorithm. Furthermore, complexity of solving the second part of MCDC problem is reduced from O(n2) to O(n). Manuscript profile -
Open Access Article
135 - A Novel Multicast Tree Construction Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
Rasoul Behravesh Mohsen JahanshahiMany appealing multicast services such as on-demand TV, teleconference, online games and etc. can benefit from high available bandwidth in multi-radio multi-channel wireless mesh networks. When multiple simultaneous transmissions use a similar channel to transmit data p MoreMany appealing multicast services such as on-demand TV, teleconference, online games and etc. can benefit from high available bandwidth in multi-radio multi-channel wireless mesh networks. When multiple simultaneous transmissions use a similar channel to transmit data packets, network performance degrades to a large extant. Designing a good multicast tree to route data packets could enhance the performance of the multicast services in such networks. In this paper we want to address the problem of multicast routing in multi-radio multi-channel wireless mesh networks aiming at minimizing intermediate nodes. It is assumed that channel assignment is known at prior and channels are assigned to the links in advance. Aiming at constructing multicast tree with minimum number of intermediate nodes and minimum number of interfered nodes we propose a heuristic algorithm called Maximum Multicast Group Nodes (MMGN). Simulation results demonstrated that our proposed method outperforms LC-MRMC algorithm in terms of throughput and packet delivery ratio. Manuscript profile -
Open Access Article
136 - A new Approach to the Multi-Objective Control of Linear Singular Perturbation Systems
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Open Access Article
137 - A New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Sama Jamalzehi Mohammad Bagher MenhajRecommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems MoreRecommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem and sparse data conditions, this paper makes some contributions. Firstly, we provide an exposition of all-distance sketch (ADS) node labelling which is an efficient algorithm for estimating distance distributions; also we show how the ADS node labels can support the approximation of shortest path (SP) distance. Secondly, we extract items’ features and accordingly we describe an item proximity measurement using ochiai coefficient. Third, we define an estimation of closeness similarity, a natural measure that compares two items based on the similarity of their features and their rating correlations to all other items, then we describe our user similarity model. Finally, we show the effectiveness of collaborative filtering recommendation based on the proposed similarity measure on two datasets of MovieLens and FilmTrust, compared to state-of-the-art methods. Manuscript profile -
Open Access Article
138 - MMDT: Multi-Objective Memetic Rule Learning from Decision Tree
Bahareh Shaabani Hedieh SajediIn this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individ MoreIn this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This article proposed a way to handle imbalance classes’ distribution. We introduce Multi-Objective Memetic Rule Learning from Decision Tree (MMDT). This approach partially solves the problem of class imbalance. Moreover, a MA is proposed for refining rule extracted by decision tree. In this algorithm, a Particle Swarm Optimization (PSO) is used in MA. In refinement step, the aim is to increase the accuracy and ability to interpret. MMDT has been compared with PART, C4.5 and DTGA on numbers of data sets from UCI based on accuracy and interpretation measures. Results show MMDT offers improvement in many cases. Manuscript profile -
Open Access Article
139 - A Survey on Multicast Routing Approaches in Wireless Mesh Networks
Shiva Zendehdelan Reza Ravanmehr Babak VaziriWireless mesh networks (WMNs) which mediates the broadband Internet access, have been recently received many attentions by the researchers. In order to increase capacity in these networks, nodes are equipped with multiple radios tuned on multiple channels emerging multi MoreWireless mesh networks (WMNs) which mediates the broadband Internet access, have been recently received many attentions by the researchers. In order to increase capacity in these networks, nodes are equipped with multiple radios tuned on multiple channels emerging multi radio multi-channel WMNs (MRMC WMNs). Therefore, a vital challenge that poses in MRMC WMNs is how to properly assign channels to the radios. On the other hand, multicast routing lets the delivery of data possibly from one source to several destinations which makes it suitable for multimedia applications such as video conferencing and distant learning. In this paper, different methods of multicast routing in WMNs are investigated. Moreover, the existing methods are classified from the viewpoints of management style (centralized / decentralized) and achieving optimal solution (heuristic / meta-heuristic / operation research). Manuscript profile -
Open Access Article
140 - Nonlinear Stabilizing Controller for a Special Class of Single Link Flexible Joint Robots
Neda Nasiri Houman Sadjadian Alireza Mohammad ShahriJoint flexibility is a very important factor to consider in the controller design for robot manipulators if high performance is expected. Most of the research works on control of flexible-joint robots in literature have ignored the actuator dynamics to avoid complexity MoreJoint flexibility is a very important factor to consider in the controller design for robot manipulators if high performance is expected. Most of the research works on control of flexible-joint robots in literature have ignored the actuator dynamics to avoid complexity in controller design. The problem of designing nonlinear controller for a class of single-link flexible-joint robot manipulators whose model incorporates the effect of the electrical actuator is considered in this paper. The main control purpose followed in this research is stabilization of the system states and backstepping approach is employed to achieve this goal and find control law. The global asymptotic stabilization of the closed-loop system is achieved in the sense of Lyapunov. Finally, to demonstrate the efficiency of the designed controller in stabilization the system states, the simulation results for system dynamics and closed-loop system, are compared in different initial conditions without and in the presence of external disturbances. Manuscript profile -
Open Access Article
141 - Cluster-head Election in Wireless Sensor Networks Using Fuzzy Logic
Hamid Reza Bakhshi Maryam BenabbasA wireless sensor network consists of many inexpensive sensor nodes that can be used toconfidently extract data from the environment .Nodes are organized into clusters and in each cluster all non-cluster nodes transmit their data only to the cluster-head .The cluster-he MoreA wireless sensor network consists of many inexpensive sensor nodes that can be used toconfidently extract data from the environment .Nodes are organized into clusters and in each cluster all non-cluster nodes transmit their data only to the cluster-head .The cluster-head transmits all received data to the base station .Because of energy limitation in sensor nodes and energy reduction in each data transmission, appropriate cluster-head election can significantly reduce energy consumption and enhance the life time of the network .In the proposed algorithm, a modified fuzzy logic approach is presented in order to improve the cluster-head election based on four descriptors energy, concentration, centrality and distance to base station .Cluster-head is elected by the base station in each round by calculating the chance each node has to elect as a cluster-head by considering descriptors .Network life time is evaluated based on first node dies metric, so energy depletion of one node causes the network to die .Simulation shows that theproposed algorithm can effectively increase the network life time .Sensor network is also simulated when sensor nodes move with random velocity in random direction in each round .Simulation shows that network life time is increased by considering this assumption in the proposed algorithm and can develop a better performance. Manuscript profile -
Open Access Article
142 - Design of a Multiplier for Similar Base Numbers Without Converting Base Using a Data Oriented Memory
Majid Jafari Ali Broumandnia Navid Habibi Shahab ForganiOne the challenging in hardware performance is to designing a high speed calculating unit. The higher of calculations speeds ina computer system will be pointed out in terms of performance. As a result, designing a high speed calculating unit is of utmost importance. In MoreOne the challenging in hardware performance is to designing a high speed calculating unit. The higher of calculations speeds ina computer system will be pointed out in terms of performance. As a result, designing a high speed calculating unit is of utmost importance. In this paper, we start design whit this knowledge that one multiplier made of several adder and one divider made of several sub tractor. Therefore, if the fast adder or fast multiplier designed, performance will be improved. In this design, a circuit is designed in a manner that without a need for transforming numbers or letters from the given bases into binary bases, the multiplication of two numbers for the same base is done in that base. Reduction in the number of conversions in the calculating unit, causes reduction in the consumption power and an increase in the operating speed of the system. In this design, a very small Data Oriented Memory is used to save numerical and character data. Manuscript profile -
Open Access Article
143 - An Overview of Group Key Management Issues in IEEE 802.16e Networks
Mohammad Mehdi Gilanian SadeghiThe computer industry has defined the IEEE 802.16 family of standards that will enable mobile devices to access a broadband network as an alternative to digital subscriber line technology. As the mobile devices join and leave a network, security measures must be taken t MoreThe computer industry has defined the IEEE 802.16 family of standards that will enable mobile devices to access a broadband network as an alternative to digital subscriber line technology. As the mobile devices join and leave a network, security measures must be taken to ensure the safety of the network against unauthorized usage by encryption and group key management. IEEE 802.16e uses Multicast and Broadcast Service (MBS) as an efficient mechanism to distribute the same data concurrently to Multiple mobile Stations (MSs) through one Base Station (BS). To generate, update and distribute the group keys, the MBS applies Multicast and Broadcast Rekeying Algorithm (MBRA). The main performance parameters of group key management schemes are typically communications, computation and storage cost as well as scalability. The purpose of this paper is to review and investigate the challenges and security issues of performance parameters in different group key managements in IEEE 802.16e. Manuscript profile -
Open Access Article
144 - A Fuzzy Logic Control System for Quadcopter by Human Voluntary-Physical Movements
Shayan Mesdaghi Mohamad Dosaranian-MoghadamIn recent years, many scientists in universities and research centers focused on quadcopters. One of the problems with quadcopters is the complexity of its manual control system. In a typical system, the user is the observer of robot in addition to controlling the radio MoreIn recent years, many scientists in universities and research centers focused on quadcopters. One of the problems with quadcopters is the complexity of its manual control system. In a typical system, the user is the observer of robot in addition to controlling the radio controller. In this paper, using a fuzzy logic algorithm, a robot control system for main and subsidiary movements by human head or wrist voluntary-physical movements is considered. In this case, without looking at control board the user can control the robot only with changing the head control voluntary or physical movements. Simulation results show that using fuzzy algorithm for determining the bending scale in different angles can decrease the human errors and processor computations. Also using fuzzy logic algorithm in the designed system the robot can track the user voluntary-physical movements optimally. In addition, the system output noises adjust due to involuntary user movements. Manuscript profile -
Open Access Article
145 - A Novel Method for Selecting the Supplier Based on Association Rule Mining
Ali Molaali Mohammad Jafar TarokhOne of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some me MoreOne of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analytic hierarchy process (AHP), analytic network process model, TOPSIS, etc. Past research gaps are lack of attention to enterprise historical data and extract knowledge from them, review the past performance of suppliers and use effect of the their past performance to their future work. The aim of this paper is to solve supplier selection problem based on historical data by a novel model. The proposed model has tried to uncover hidden relation in massive unstructured industrial data and has used them to extract knowledge for optimizing decision making and predicting in supply chain management by BI tools. The model is based on FP-Growth algorithm integrated with AHP. Moreover, the proposed model is a multi-criteria decision making model (MCDM) with four criteria: quality, priority, delay on delivery and cost that have chosen from literature review. The criteria have been weighed by AHP and finally the model has been validated by industrial group’s historical data. Manuscript profile -
Open Access Article
146 - A Power Aware Cache and Register File Design Space Exploration
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Open Access Article
147 - Compressed Domain Scene Change Detection Based on Transform Units Distribution in High Efficiency Video Coding Standard
Navid Dorfeshan Mohammadreza RamezanpourScene change detection plays an important role in a number of video applications, including video indexing, searching, browsing, semantic features extraction, and, in general, pre-processing and post-processing operations. Several scene change detection methods have bee MoreScene change detection plays an important role in a number of video applications, including video indexing, searching, browsing, semantic features extraction, and, in general, pre-processing and post-processing operations. Several scene change detection methods have been proposed in different coding standards. Most of them use fixed thresholds for the similarity metrics to determine if there was a change or not. These thresholds are obtained empirically or they must be calculated before the scene change detection after the whole sequence is obtained. Efficiency of scene change detectors decreases considerably for videos with high scene complexity and variation. In this paper, we propose a novel scene change detection algorithm in the HEVC compressed domain. In the proposed method, we have developed an efficient method based on the analysis of the Transform Units distribution in HEVC standard. In order to enhance the accuracy of detecting the scene changes, we have also defined an automated, dynamic threshold model which can efficiently trace scene changes. The experimental results on UHD videos indicate a higher performance with significantly improved accuracy combined with minimum complexity. Manuscript profile -
Open Access Article
148 - Contours Extraction Using Line Detection and Zernike Moment
Vahid Rostami Mahdieh RaesiMost of the contour detection methods suffers from some drawbacks such as noise, occlusion of objects, shifting, scaling and rotation of objects in image which they suppress the recognition accuracy. To solve the problem, this paper utilizes Zernike Moment (ZM) and Pseu MoreMost of the contour detection methods suffers from some drawbacks such as noise, occlusion of objects, shifting, scaling and rotation of objects in image which they suppress the recognition accuracy. To solve the problem, this paper utilizes Zernike Moment (ZM) and Pseudo Zernike Moment (PZM) to extract object contour features in all situations such as rotation, scaling and shifting of object inside the image. The proposed method consist of three steps: first step employs Line Detection with Contours (LDC) in order to find the object region based on the connected components objects inside the image. In the second step, PZM is applied on the detected object regions to extract feature vector. Regarding to investigate the effectiveness of classifier at the final stage, the SVM and KNN classifiers are employed to extract final object contours. Experimental results on Caltech-101 dataset shows that classification rate is improved to 96.46%. In comparison to the former contour detectors, that proves the ability of the proposed method to detect object boundary in the most of the contour’s changes such as rotation or scaling. Manuscript profile -
Open Access Article
149 - Merging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems
Leily Sheugh Sasan H. AlizadehIn recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded user MoreIn recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the development of social network, trust measure introduced as a new approach to overcome the CF problems. On the other hand, trust-aware recommender systems are techniques to make use of trust statements and user personal data in social networks to improve the accuracy of rating prediction for cold start users. In addition, clustering-based recommender systems are other kind of systems that to be efficient and scalable to large-scale data sets but these systems suffer from relatively low accuracy and especially coverage too. Therefore to address these problems, in this paper we proposed a multi-view clustering based on Euclidean distance by combination both similarity view and trust relationships that is including explicit and implicit trusts. In order to analyze the effectiveness of the proposed method we used the real-world FilmTrust dataset. The experimental results on this data sets show that our approach can effectively improve both the accuracy and especially coverage of recommendations as well as in the cold start problem. Manuscript profile -
Open Access Article
150 - Optimization of Non-volatile Memory Cell and Energy Consumption in Robot Systems by Synthesized Silicon Nanoparticles via Electrical Discharge
Mehdi MardanianIn this paper, we propose to optimize manufacturing methods of memory cells by produced silicon nanoparticles via electrical spark discharge of silicon electrodes in water to reduce the energy consumption for low power applications. The pulsed spark discharge with the p MoreIn this paper, we propose to optimize manufacturing methods of memory cells by produced silicon nanoparticles via electrical spark discharge of silicon electrodes in water to reduce the energy consumption for low power applications. The pulsed spark discharge with the peak current of 60 A and a duration of a single discharge pulse of 60 µs was used in our experiment. The structure, morphology, and average size of the resulting nanoparticles were characterized by means of X-Ray Diffraction (XRD), Raman spectroscopy and transmission electron microscopy (TEM). TEM images illustrated nearly spherical and isolated Si nanoparticles with diameters in the 3-8 nm range. The optical absorption spectrum of the nanoparticles was measured in the violet-visible (UV-V is) spectral region. By measuring of the band gap we could estimate the average size of the prepared particles. The silicon nanoparticles synthesized exhibited a photoluminescence (PL) band in the violet- blue region with a double peak at around 417 and 439 nm. It can be attributed to oxide-related defects on the surface of silicon nanoparticles, which can act as the radiative centers for the electron-holepairsre combination. Manuscript profile -
Open Access Article
151 - A Link Prediction Method Based on Learning Automata in Social Networks
Sara YounessZadeh Mohammad Reza MeybodiNowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probabi MoreNowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electronic commerce and recommender systems or identification of terroristic relations in social networks. In this article, a new idea is presented for the prediction. It is an integration of the two methods of prediction of similarity score based link and prediction of probabilistic link, which is placed in a new category of link prediction methods. This idea acquires the similarity score between nodes from probabilistic techniques and through using learning automata, and provides better results compared to other criteria methods on standard datasets. Manuscript profile -
Open Access Article
152 - A New Content Based Image Retrieval Method Using Contourlet Transform
Farzad Zargari Ali MoslehOne of the challenging issues in managing the existing large digital image libraries and databases is Content Based Image Retrieval (CBIR). The accuracy of image retrieval methods in CBIR is subject to effective extraction of image features such as color, texture, and s MoreOne of the challenging issues in managing the existing large digital image libraries and databases is Content Based Image Retrieval (CBIR). The accuracy of image retrieval methods in CBIR is subject to effective extraction of image features such as color, texture, and shape. In this paper, we propose a new image retrieval method using contourlet transform coefficients to index texture of the images. We employ the properties of contourlet coefficients to model the distribution of coefficients in each sub-band using the normal distribution function. The assigned normal distribution functions are used effectively at the next stage to extract the texture feature vector. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based CBIR method. Manuscript profile -
Open Access Article
153 - Multi-Step-Ahead Prediction of Stock Price Using a New Architecture of Neural Networks
Mohammad Talebi Motlagh Hamid KhaloozadehModelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a MoreModelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of different stocks prices. Several factors, such as input variables, preparing data sets, network architectures and training procedures, have huge impact on the accuracy of the neural network prediction. The purpose of this paper is to predict multi-step-ahead prices of the stock market and derive the method, based on Recurrent Neural Networks (RNN), Real-Time Recurrent Learning (RTRL) networks and Nonlinear Autoregressive model process with exogenous input (NARX). This model is trained and tested by Tehran Securities Exchange data. Manuscript profile -
Open Access Article
154 - PSO-Based Path Planning Algorithm for Humanoid Robots Considering Safety
Roham Shakiba Mostafa E. SalehiIn this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, w MoreIn this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. Ferguson splines create preliminary paths using random generated parameters. The random parameters are then iteratively fed into the PSO for optimization and converging to optimal path. Our proposed method makes a balance between the path shortness and the safety which makes it more efficient for humanoid soccer playing robots and also for any other crowded environment with various moving obstacles. Experimental results show that our proposed algorithm converges in at most 60 iterations with the average accuracy of 92% and the maximum path length overhead of 14% for planning the shortest and yet safest path. Manuscript profile -
Open Access Article
155 - A New Multi-Agent Bat Approach for Detecting Community Structure in Social Networks
Saeed Alidoost Behrooz MasoumiThe complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden MoreThe complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden social structures in these networks is extremely valuable because of the perception and exploitation of their secret knowledge. The community structure is a great topological property of social networks, and the process to detect this structure is a challenging problem. In this paper, a new approach is proposed to detect non-overlapping community structure. The approach is based on multi-agents and the bat algorithm. The objective is to optimize the amount of modularity, which is one of the primary criteria for determining the quality of the detected communities. The results of the experiments show the proposed approach performs better than existing methods in terms of modularity. Manuscript profile -
Open Access Article
156 - Gesture Recognition Using the Linear Combination of Membership Degrees of Observations
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Open Access Article
157 - An Improved Token-Based and Starvation Free Distributed Mutual Exclusion Algorithm
Om-Kolsoom Shahryari Ali BroumandniaDistributed mutual exclusion is a fundamental problem of distributed systems that coordinates the access to critical shared resources. It concerns with how the various distributed processes access to the shared resources in a mutually exclusive manner. This paper presen MoreDistributed mutual exclusion is a fundamental problem of distributed systems that coordinates the access to critical shared resources. It concerns with how the various distributed processes access to the shared resources in a mutually exclusive manner. This paper presents fully distributed improved token based mutual exclusion algorithm for distributed system. In this algorithm, a process which has owing token, could enter to its critical section. The processes communicate to each other in an asynchronous message passing manner. We assume the distributed processes are organized in a wraparound two dimensional array. Also, the communication graph of the network is supposed to be a complete graph. The proposed algorithm uses three types of messages, namely ReqMsg, InfoMsg and RelMsg. Beside token-holding node, there are some nodes, we call them informed-nodes, which can know token-holding node and transmit request message to it directly. The number of messages, which are exchanged per each critical section entrance, is a key parameter to avoid posing additional overhead to the distributed system. In this paper, we obtain to messages per critical section access where N is the number of nodes in the system. The proposed algorithm outperforms other token based algorithms whilst fairness is kept and the proposed algorithm is starvation free. Manuscript profile -
Open Access Article
158 - Comparison of Different Linear Filter Design Methods for Handling Ocular Artifacts in Brain Computer Interface System
Sahar Seifzadeh Karim Faez Mahmood AmiriBrain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI MoreBrain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic lateral sclerosis, stroke, or spinal cord injury. One of the focal points in Brain-Computer Interface (BCI) systems is physiological artifacts handling. Physiological artifacts such as Electrooculography (EOG) and Electrooculography (EMG) are considered among the most important sources of physiological artifacts in BCI systems. Pre-processing is considerable step by means of next steps such as feature extraction and classification that we need clean signals without undesirable artifacts to have better classification rate. Using a linear filter to remove these artifacts is like a dime a dozen due to their acceptable results in recent BCI pre-processing researches. Although this method has different options, Forasmuch as the mu (8–13 Hz) and beta (16–25 Hz) frequency bands play a key role in classification of motor imagery we have decided to design two band pass filters with Elliptic and Butterworth Infinite impulse response designing methods in 8 to 40 Hz frequencies. Our results in Graz 2a dataset in BCI Competition IV indicates that, Elliptic band-pass filter has better performance for EOG removing in this specific dataset. Manuscript profile -
Open Access Article
159 - Disguised Face Recognition by Using Local Phase Quantization and Singular Value Decomposition
Fatemeh Jafari Hamidreza Rashidy KananDisguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is MoreDisguised face recognition is a major challenge in the field of face recognition which has been taken less attention. Therefore, in this paper a disguised face recognition algorithm based on Local Phase Quantization (LPQ) method and Singular Value Decomposition (SVD) is presented which deals with two main challenges. The first challenge is when an individual intentionally alters the appearance by using disguise accessories, and the second one is when gallery images are limited for recognition. LPQ has been used for extraction of the statistical feature of the phase in windows with different sizes for each pixel of the image. SVD is used to cope with the challenge of the gallery images limitation and also with the help of original images extracted from that, every single image turns to three renovated images. In this study, disguise is intended as a blur in the image and Local phase quantization method is robust against the disguised mode, due to the use of the statistical feature of the Fourier transform phase. Also the use of different-sized window instead of fixed window in feature extraction stage, the performance of the proposed method has increased. The distance of images from each other is computed by using Manhattan and Euclidean distance for recognition in the proposed method. The Performance of the proposed algorithm has been evaluated by using three series of experiments on two real and synthesized databases. The first test has been performed by evaluating all the possible combinations of the different-sized windows created in the feature extraction stage, and the second experiment has been done by reducing the number of gallery images and then the third one has been carried out in different disguise. In all cases, the proposed method is competitive with to several existing well-known algorithms and when there is only an image of the person it even outperforms them. Manuscript profile -
Open Access Article
160 - A Hybrid Geospatial Data Clustering Method for Hotspot Analysis
Mohammad Reza Keyvanpour Mostafa Javideh Mohammad Reza EbrahimiTraditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. To eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks wit MoreTraditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. To eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. Fortunately, geospatial data is considered a proper subject for leveraging data mining techniques. The main purpose of this paper is presenting a hybrid geospatial data clustering mechanism in order to achieve a high performance hotspot analysis method. The method basically works on 2 or 3-dimensional geographic coordinates of different natural and unnatural phenomena. It uses the systematic cooperation of two popular clustering algorithms: the AGlomerative NEStive, as a hierarchical clustering method and κ-means, as a partitional clustering method. It is claimed that the hybrid method will inherit the low time complexity of the κ-means algorithm and also relative independency from user’s knowledge of the AGNES algorithm. Thus, the proposed method is expected to be faster than AGNES algorithm and also more accurate than κ-means algorithm. Finally, the method was evaluated against two popular clustering measurement criteria. The first clustering evaluation criterion is adapted from Fisher’s separability criterion, and the second one is the popular minimum total distance measure. Results of evaluation reveal that the proposed hybrid method results in an acceptable performance. It has a desirable time complexity and also enjoys a higher cluster quality than its parents (AGNES and κ-means). Real-time processing of hotspots requires an efficient approach with low time complexity. So, the problem of time complexity has been taken into account in designing the proposed approach. Manuscript profile -
Open Access Article
161 - Improving Energy-Efficient Target Coverage in Visual Sensor Networks
Behrooz Shahrokhzadeh Mehdi Dehghan MohammadReza ShahrokhzadehTarget coverage is one of the important problems in visual sensor networks. The coverage should be accompanied with an efficient use of energy in order to increase the network lifetime. In this paper, we address the maximum lifetime for visual sensor networks (MLV) prob MoreTarget coverage is one of the important problems in visual sensor networks. The coverage should be accompanied with an efficient use of energy in order to increase the network lifetime. In this paper, we address the maximum lifetime for visual sensor networks (MLV) problem by maximizing the network lifetime while covering all the targets. For this purpose, we develop a simulated annealing (SA) algorithm that divides the sensors’ Field-of-View (FoV) to a number of cover sets and then applies a sleep-wake schedule for cover sets. We also identify the best possible FoV of sensors according to the targets’ location using rotating cameras, to reduce the solution space and approaching to a near-optimal solution. Our proposed energy and neighbor generating functions of the SA result in a balanced distribution of energy consumption as well as escaping from local optima. We conduct some simulation experiments to evaluate the performance of our proposed method by comparing with some well-known solutions in the literature. Manuscript profile -
Open Access Article
162 - Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models
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Open Access Article
163 - Improving Agent Performance for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning
Monireh Haghighatjoo Behrooz Masoumi Mohamad Reza MeybodiIn electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an int MoreIn electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other.In recent years, negotiation has been employed to allocate resources in multi-agent systems. Yet, in most of the conventional methods, negotiation is done without considering past experiments. In this paper, in order to use experiments of agents, a hybrid method is used which employed case-based reasoning andlearning automata in negotiation. In the proposed method, the buyer agent would determine its seller and its offered price based on the passed experiments and then an offer would be made. Afterwards, the seller would choose one of the allowed actions using learning automata. Results of the experiments indicated that the proposed algorithm has caused an improvement in some performance measures such as success rate. Manuscript profile -
Open Access Article
164 - A Back-Stepping Controller Scheme for Altitude Subsystem of Hypersonic Missile with ANFIS Algorithm
Davood Allahverdy Ahmad FakharianIn this paper, we propose a back-stepping controller scheme for the altitude subsystem of hypersonic missile of which model is nonlinear, non-minimum phase, uncertain, and highly coupled. In the scheme, the guidance law is selected as a desired flight path angle that de MoreIn this paper, we propose a back-stepping controller scheme for the altitude subsystem of hypersonic missile of which model is nonlinear, non-minimum phase, uncertain, and highly coupled. In the scheme, the guidance law is selected as a desired flight path angle that derived from the sliding mode control method. The back-stepping technique is designed and analyzed for the altitude dynamics of hypersonic missiles for maneuvering targets. Additionally, the algorithm of adaptive neuro-fuzzy inference system (ANFIS) is used for estimating the uncertainty of model parameters and Lyapunove theorem is used to examine the stability of closed-loop systems. The simulation indicates that the proposed scheme has shown effectiveness of the control strategy, high accuracy, stability of states, and low-amplitude control inputs in the presence of uncertainties with external disturbance. Manuscript profile -
Open Access Article
165 - Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW)
Mojtaba Gholamian Mohammad Reza MeybodiSo far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improvi MoreSo far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of inefficiency of PSO algorithm in high-dimensional search space, some algorithms such as Cooperative PSO offered. Accordingly, in the present article, we intend, in order to develop and improve PSO algorithm take advantage of some optimization methods such as Cooperatives PSO, Comprehensive Learning PSO and fuzzy logic, while enjoying the benefits of some functions and procedures such aslocal search function and Coloning procedure, propose the Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW) algorithm. By proposing this algorithm we try to improve mentioned deficiencies of PSO and get better performance in high dimensions. Manuscript profile -
Open Access Article
166 - Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Rasool Azimi Hedieh SajediIdentifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a c MoreIdentifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K-Means, which alters the convergence method of K-Means algorithm to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters’ coherence. Persistent K-Means uses an iterative approach to discover the best result for consecutive iterations of K-Means algorithm. Manuscript profile -
Open Access Article
167 - Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms
Maryam Ghodsi Mohammad Saniee AbadehThe aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this stu MoreThe aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in order to reach a more efficient and accurate algorithm. By combining evolutionary algorithms with ANFIS, the optimal tuning of ANFIS parameters is achieved by the Teaching-Learning-Based Optimization (TLBO) and the Particle Swarm Optimization (PSO). The aim of using this approach is to improve the network performance and to reduce calculation complexities compared to gradient descent and least square methods. The proposed algorithm is implemented and evaluated on credit cards data to detect fraud. The results demonstrate superior performance of the designed scheme compared to other intelligent identification methods. Manuscript profile -
Open Access Article
168 - Visual Tracking using Learning Histogram of Oriented Gradients by SVM on Mobile Robot
Iman Zabbah Shima Foolad Ali Maroosi Alireza PourrezaThe intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. MoreThe intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. Tracking algorithms suffer from sequence challenges such as illumination variation, occlusion, and background clutter, so an accurate tracker should employ the appropriate visual features to identify target. In this paper, we propose using the histogram of oriented gradient (HOG), as an important descriptor. The descriptor simulates the performance of the complex cells in the primary visual cortex (V1) and it has low sensitivity to the illumination changes. In the proposed method, firstly, an object model is generated by training the HOG of multi first frames via an SVM classifier. Then, in order to track a new frame, the HOG descriptors are extracted from the surrounding areas of the target in the previous frame and convolved with the object model. Finally, the location with the highest score is defined as the target. The experimental results demonstrate the proposed method has significant performance compare to the state-of-the-art methods. Furthermore, we apply our algorithm to the mobile robot built by the robotics team to ensure its performance in a real environment. Manuscript profile -
Open Access Article
169 - Overlapping Community Detection in Social Networks Based on Stochastic Simulation
Hadi Zare Mahdi HajiabadiCommunity detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bi MoreCommunity detection is a task of fundamental importance in social network analysis. Community structures enable us to discover the hidden interactions among the network entities and summarize the network information that can be applied in many applied domains such as bioinformatics, finance, e-commerce and forensic science. There exist a variety of methods for community detection based on different metrics and domain of applications. Most of these methods are based on the existing of the non-overlapping or sparse overlapping communities. Moreover, the experimental analysis showed that, overlapping areas of communities become denser than non-overlapping area of communities. In this paper, significant methods of overlapping community detection are compared according to well-known evaluation criteria. The experimental analyses on artificial network generation have shown that earlier methods of community detection will not discover overlapping communities properly and we offered suggestions for resolving them. Manuscript profile -
Open Access Article
170 - Designing a Coreless High-Speed Axial-Flux PM Generator for Microturbines
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Open Access Article
171 - A Combination of Genetic Algorithm and Particle Swarm Optimization for Power Systems Planning Subject to Energy Storage
Mohsen Mohammadhosseini Hamid GhadiriWith the ever-increasing growth of electrical energy consumption in different fields of a power plant, expanding strategies in power plants is a vital, important and inevitable action. Generally, greenhouse gas emissions can be reduced by replacing wind energy instead o MoreWith the ever-increasing growth of electrical energy consumption in different fields of a power plant, expanding strategies in power plants is a vital, important and inevitable action. Generally, greenhouse gas emissions can be reduced by replacing wind energy instead of using fossil fuels in power plants for electricity generation. A physical system that is capable of harnessing energy for distribution and compensation electricity at a desired and determined later time is called a typical energy storage system. In this paper, a proper optimization method for expansion planning of electrical energy storage is presented. Since the meta-heuristic algorithms are a very suitable tool for optimization purposes, a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) technique are used in this research. The main objective of the optimization problem is to increase the energy storage. The implementation of the proposed method is performed using MATLAB and GAMS tools. The simulation results strongly validate the correctness and effectiveness of the proposed method. Manuscript profile -
Open Access Article
172 - Motion detection by a moving observer using Kalman filter and neuralnetwork in soccer robot
Sanaz Taleghani Siavash Aslani Saeed ShiryIn many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we MoreIn many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. Duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. In thispaper, we propose a novel method to effectively overcome this problem using Neural Networks and Kalman Filtering theory. Thistechnique uses movement parameters of camera to resolve problems caused by error in image processing outputs. The technique issuccessfully applied in the MRL Middle Size Soccer Robots where ball motion detection has an especial importance in their decisionmaking. Experimental results are presented and 2.2% achieved error suggests that the combined approach performs significantly better thantraditional techniques. Manuscript profile -
Open Access Article
173 - Two New Methods of Boundary Correction for Classifying Textural Images
Amin Akbari Hassan RashidiWith the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. Among these systems are machine vision systems which are based on image processing and analysis. One of the importan MoreWith the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. Among these systems are machine vision systems which are based on image processing and analysis. One of the important tasks of image processing is classification of images into desirable categories for the identification of objects or their specific areas. One of the common methods is using an edge finder in image classification. Due to the lack of definite edges in many images obtained from various sciences and industries such as textural images, the topic of textural image classification has recently become of interest in the science of machine vision. Thus, in this article, two methods are proposed to detect edges and eliminate blocks with non-connected classes based on fuzzy theory and weighted voting concepts in classifying textural images. In the proposed methods, the boundaries are corrected using fuzzy theory and weighted voting concepts. Using the proposed methods can help improve the definition of boundaries and classification accuracy. Manuscript profile -
Open Access Article
174 - Negative Selection Based Data Classification with Flexible Boundaries
Lena Nemati Mojtaba ShakeriOne of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the n MoreOne of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two negative selection based algorithms are proposed for two-class and multi-class classification problems; using a Gaussian mixture model which is fitted on normal space to create a flexible boundary between self and non-self-spaces, by determining the dynamic subsets of effective detectors to solve the problem of data classification. Initialization of effective parameters such as the detection threshold, the maximum number of detectors etc. for each dataset, is one of the challenges in negative selection based classification algorithms, which affects the precision and accuracy of the classification; therefore, an adaptive and optimal calculation of these parameters is necessary. To overcome this problem, the particle swarm optimization algorithm has been used to properly set the parameters of the proposed methods. The experimental results showed that using a Gaussian mixture model and dynamic adjustment of parameters such as optimum number of Gaussian components according to the shape of the boundaries, creation of appropriate number of detectors, and also automatic adjustment of the training and testing thresholds, using particle swarm optimization algorithm as well as utilization of a combinatorial objective function has led to a better classification with fewer detectors. The proposed algorithms showed competitive performance compared with some of the existing classification algorithms, including several immune-inspired models, especially negative selection ones, and other traditional classification methods. Manuscript profile -
Open Access Article
175 - Improving the Performance of RPL Routing Protocol for Internet of Things
Zahra Aslani Hadi SargolzaeyThe emerging Internet of Things (IoT) connects the physical world to the digital one and composes large networks of smart devices to support various applications. In order to provide a suitable communication in such networks, a reliable routing protocol is needed. In th MoreThe emerging Internet of Things (IoT) connects the physical world to the digital one and composes large networks of smart devices to support various applications. In order to provide a suitable communication in such networks, a reliable routing protocol is needed. In this paper, a modified version of an IPv6 Routing Protocol for Low-Power and Lossy networks (RPL), which has been standardized by IETF is proposed. It is used in Low power and Lossy Networks (LLNs) that consist of lossy links and electronic devices use a set of novel Internet of Things technologies. RPL protocol is based on the constructional concept of Directed Acyclic Graphs (DAGs) that is constructed using a scalar value called rank. The default metric which is commonly used in low power and lossy networks to compute rank of Expected Transmission Count (ETX) based on the number of re-transmission. While the results represent that this method of calculation is not effective enough. Therefore, we introduce a new method of ETX computation which is used to construct the DAGs with better rank computation and selected routes. The simulation results show that our proposed idea has better performance in contrast with the basic RPL and AODV protocols in terms of Packet Delivery Ratio (PDR), number of re-transmission, end to end delay, and throughput. Manuscript profile -
Open Access Article
176 - A Passive-Based Force Reflecting Algorithm for a Piezo-Actuated Macro-Micro Telemanipulation System
M. Zareinejad Saeed Shiry Ghidary S. M. RezaeiPiezoelectric actuators are widely used in micro manipulation applications. However hysteresis nonlinearity limits accuracy of these actuators. This paper presents a novel approach utilizing a piezoelectric nano-stage as slave manipulator of a teleoperation system. The MorePiezoelectric actuators are widely used in micro manipulation applications. However hysteresis nonlinearity limits accuracy of these actuators. This paper presents a novel approach utilizing a piezoelectric nano-stage as slave manipulator of a teleoperation system. The Prandtl-Ishlinskii (PI) model is used to model actuator hysteresis in feedforward scheme to cancel out this nonlinearity. A passive coordination control which uses the new outputs to state synchronize the master and slave robots in free motion is extended to achieve position coordination in contact tasks. The proposed approach uses force feedback using a passivity of the systems and Lyapunov stability methods; the asymptotic stability of force reflecting teleoperation with communication delay and position/force scaling is proven. Performance of the proposed controllers is verified through experiments. Manuscript profile -
Open Access Article
177 - A Knowledge Management Approach to Discovering Influential Users in Social Media
Hosniyeh Safi Arian Mohammad Jafar TarokhA key step for success of marketer is to discover influential users who diffuse information and their followers have interest to this information and increase to diffuse information on social media. They can reduce the cost of advertising, increase sales and maximize di MoreA key step for success of marketer is to discover influential users who diffuse information and their followers have interest to this information and increase to diffuse information on social media. They can reduce the cost of advertising, increase sales and maximize diffusion of information. A key problem is how to precisely identify the most influential users on social networks. In this paper, we propose a method to discover influential users based on knowledge management cycle that is called KMIU. The knowledge management cycle consists of several stages including capture, organize, storage, retrieval and mining stages. We try to analyze influential users in two micro bloggings networks as Facebook and twitter by KMIU method. The experimental results showed the proposed method maximize diffusion and has an accuracy 0.55. These maximization and accuracy are more than those of the previous methods. Manuscript profile -
Open Access Article
178 - Study of Evolutionary and Swarm Intelligent Techniques for Soccer Robot Path Planning
Mostafa Salehi Elahe MansuryFinding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points MoreFinding an optimal path for a robot in a soccer field involves different parameters such as the positions of the robot, positions of the obstacles, etc. Due to simplicity and smoothness of Ferguson Spline, it has been employed for path planning between arbitrary points on the field in many research teams. In order to optimize the parameters of Ferguson Spline some evolutionary or intelligent algorithms are proposed in the literature. In this paper, we present a comparative study on different evolutionary and swarm algorithms as solutions to the problem of robot path planning. We optimize the parameters of Ferguson Spline and find the best path between two arbitrary points, studying Differential Evaluation (DE), Genetic Algorithm (GA), Evolutionary Strategies (ES), Artificial Bee Colony (ABC), and Particle Swarm optimization (PSO) algorithms. Firstly, a path for robot movement is describe by Ferguson splines and then these algorithms are used to optimize the parameters of splines to find an optimal path between the starting and the goal point considering the obstacles between them. The experimental results show the performance and effectiveness of the studied solutions in comparison with other swarm intelligent algorithms. Manuscript profile -
Open Access Article
179 - A New Approach to Promote Safety in the Software Life Cycle
Shahrzad Oveisi Mohammad Ali Farsi Mohammad Nadjafi Ali MoeiniDeveloping a reliable and safe system is one of the most important features of advanced computer-based systems. The software is often responsible for controlling the behavior of mechanical and electrical components as well as interactions between components in systems. MoreDeveloping a reliable and safe system is one of the most important features of advanced computer-based systems. The software is often responsible for controlling the behavior of mechanical and electrical components as well as interactions between components in systems. Therefore, considering software safety and fault detection are essential in software development. This paper introduces an approach to engineering evidence that examines the software in its lifecycle according to the principles of software safety and system safety engineering. This approach ensures that software risks are identified and documented in the software lifecycle, after which the risks are reduced to an acceptable level in terms of safety according to the proposed methods. The presented approach was applied to a real master case with positive results, namely the Data and Command Unit. Manuscript profile -
Open Access Article
180 - Control of Wheeled Mobile Manipulators with Flexible Suspension Considering Wheels Slip Effects
Rambod Rastegari Khalil AlipourWheeled mobile manipulators utilize both the locomotion capabilities of the wheeled platform and manipulation capacity of the arm. While the modelling and control of such systems have previously been studied, most of them have considered robots with rigid suspension and MoreWheeled mobile manipulators utilize both the locomotion capabilities of the wheeled platform and manipulation capacity of the arm. While the modelling and control of such systems have previously been studied, most of them have considered robots with rigid suspension and their wheels are subject to pure rolling conditions. To relax the aforementioned limiting assumptions, this research addresses modelling and control of a mobile manipulator with flexible suspension while considers the frictional effects. To this end, the Newton-Euler approach is employed and the modelling process is elaborated. To control the system, a two degree-of-freedom policy is suggested at two levels. In the first level, the Multiple Impedance Control (MIC) algorithm is modified and then is effectively utilized. Next, in the second level, the wheels actuating torques are adjusted such that the required forces/torques of the platform resulted from the first level be realized. The obtained simulation results support the proficiency of the suggested control scenario to control of wheeled mobile robots with flexible suspension and pneumatic tires. Manuscript profile -
Open Access Article
181 - Soccer Goalkeeper Task Modeling and Analysis by Petri Nets
Azadeh Gholami Bahram Sadeghi BighamIn a robotic soccer team, goalkeeper is an important challenging role, which has different characteristics from the other teammates. This paper proposes a new learning-based behavior model for a soccer goalkeeper robot by using Petri nets. The model focuses on modeling MoreIn a robotic soccer team, goalkeeper is an important challenging role, which has different characteristics from the other teammates. This paper proposes a new learning-based behavior model for a soccer goalkeeper robot by using Petri nets. The model focuses on modeling and analyzing, both qualitatively and quantitatively, for the goalkeeper role so that we have a model-based knowledge of the task performance in different possible situations. The different primitive actions and behaviors as well as the events to switch between them, and also environment models were designed and implemented. For this purpose, a modeling and analysis framework based on Petri nets is used, which enables modeling a robot task, analyzing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The proposed model building blocks and some tasks of robot are detailed. The novelty of approach is considering some alternatives through tasks execution, which are implemented by conflicts in their Petri net models, and also Q_learning employment in these decision points in order to learn the best policy. Therefore, the execution of actions in different tasks will be controlled effectively. The results of theoretical analysis of some case studies show impressive performance improvement in goalkeeper task execution. Manuscript profile -
Open Access Article
182 - Delay Model Estimation in RC-tree Circuits Based on the Power-lognormal Distribution
Farshad SafaeiComputation of the second order delay in RC-tree based circuits is important during the design process of modern VLSI systems with respect to having tree structure circuits. Calculation of the second and higher order moments is possible in tree based networks. Because o MoreComputation of the second order delay in RC-tree based circuits is important during the design process of modern VLSI systems with respect to having tree structure circuits. Calculation of the second and higher order moments is possible in tree based networks. Because of the closed form solution, computation speed and the ease of using the performance optimization in VLSI design methods such as floor planning, placement and routing, the Elmore delay metric is widely implemented for past generation circuits. However, physical and logical synthesis optimizations require fast and accurate analysis techniques of the RC networks. Elmore first proposed matching circuit moments to a probability density function (PDF), which led to the widespread implementation of it in many networks. But the accuracy of Elmore metric is sometimes unacceptable for the RC interconnect problems in today’s CMOS technologies. The main idea behind our approach is based on the moment matching technique with the power-lognormal distribution and proposing the closed form formula for the delay evaluation of the RC-tree networks. The primary advantages of our approach over the past proposed metrics are the ease of implementation, reduction of the complexity and proposing an efficiency formula without referring to lookup tables. Simulation results confirmed that our method illustrates a good degree of accuracy and the relative average of errors is less than 20%. Manuscript profile -
Open Access Article
183 - Turning P2P Networks into DDoS Engines: A Survey
Hamid Farhadi Behzad Akbari Shahab Rajaee Mohammad FarahaniRecently, Peer-to-Peer (P2P) networks contribute to a large fraction of the Internet backbone traffic. Consequently, misusing such networks for malicious purposes is a potential side effect. In this review article, we investigate different techniques of misusing P2P ove MoreRecently, Peer-to-Peer (P2P) networks contribute to a large fraction of the Internet backbone traffic. Consequently, misusing such networks for malicious purposes is a potential side effect. In this review article, we investigate different techniques of misusing P2P overlay networks to launch large-scale next-generation Distributed Denial of Service (DDoS) attacks. In particular, we investigate representative systems of the structured (Overnet), unstructured (Gnutella) and hybrid (BitTorrent) P2P overlay networks. Real world experiments indicate the high performance, difficulty in detection and tracking, and the low cost of launching such attacks. Manuscript profile -
Open Access Article
184 - MAC-layer Acknowledgment as a Tool to Detect Routing Misbehavior
Mehdi KeshavarzThe establishment as well as the survival of mobile ad-hoc networks relies on the cooperation of nodes for performing network operations such as routing and packet forwarding. In these networks, misbehaving nodes can severely degrade network’s performance by not c MoreThe establishment as well as the survival of mobile ad-hoc networks relies on the cooperation of nodes for performing network operations such as routing and packet forwarding. In these networks, misbehaving nodes can severely degrade network’s performance by not cooperating in networking operations. In this paper, we study the issue of node misbehavior in packet forwarding. To counter this type of misbehavior, we propose a scheme based on the overhearing of MAC-layer acknowledgements. Our main idea centers on the exploitation of the fact that the impartial nodes within the intersection of the transmission zones of the ACK-transmitter and its successor overhear the transmitted acknowledgments by these two nodes. Therefore, if an ACK-transmitter emits an ACK for an in-transit packet, but on a timeout, no ACK is sensed from its successor, acknowledging the receipt of the packet, the misbehavior of the ACK-transmitter will be noticed by the impartial overhearing nodes and reported to the original data packet transmitter, i.e. to the node preceding the ACK-transmitter. We have conducted a series of NS-2 simulation experiments to evaluate the performance of our scheme. Manuscript profile -
Open Access Article
185 - Quality of Service Improvement for Voice Streaming over WirelessAd-hoc Networks using an Adaptive Playout Adjustment Algorithm
Maral Salehi Mehdi DehghanProviding a high-quality service for transmission and playing real-time voice conversations (voice streaming) over wireless ad-hoc networks is no mean feat. Buffering together with adjusting the playout time of the packets is a receiver-side solution to overcome this ch MoreProviding a high-quality service for transmission and playing real-time voice conversations (voice streaming) over wireless ad-hoc networks is no mean feat. Buffering together with adjusting the playout time of the packets is a receiver-side solution to overcome this challenge. In this paper, a new adaptive playout adjustment algorithm is proposed to stream the voice conversations over wireless ad-hoc networks. This algorithm always tries to be aware of the network's conditions, adapts itself with these conditions and adjusts the playout time of the voice packets as efficiently as possible. It is required that not only most of the packets be received before their playout time, as scheduled in the receiver, but also that the playout time not be too long so as to adversely affect the interactivity between the sender and the receiver. The main features of the presented method are: adjusting the threshold adaptively with respect to the varying conditions of the network in order to determine the state of system; calculating the mean network jitter dynamically based on the current conditions of the network in order to calculate the playout delay for the current packet; being optimistic about the future state of the network and not using the delay history in order to calculate the mean network delay. Simulation results show that the proposed algorithm adapts itself with the network's dynamics and adjusts the playout delay for voice packets better than the other algorithms. Manuscript profile -
Open Access Article
186 - A Sliding Mode Controler of Hips Actuated for Passive Walking Robots
Sina Bakhtiari Mahdi Razzaghi Ahmad SamieeThis paper addresses the application of using pneumatic force actuators at the hips of a five-link robotic system to provide a controllable input torque. The goal of this research is to provide a base to build upon to eventually produce an ” active” biped wa MoreThis paper addresses the application of using pneumatic force actuators at the hips of a five-link robotic system to provide a controllable input torque. The goal of this research is to provide a base to build upon to eventually produce an ” active” biped walking robot that utilizes the benefits of the passive walking cycle. A reduced-order mathematical model of the system consisting of the pneumatic proportional valve and actuators is utilized in designing the force controller. The model takes into account tube links, valve friction, piston friction, and valve mechanics. The five-link robot is also modeled, including moments of inertia, masses, and centers of mass to design the trajectory controller. The mathematical models provide the equations necessary to develop the nonlinear control laws based on Sliding Mode Control Theory for both the force and trajectory controller. The controllers receive input signals from both pressure and position sensors located at the hips and position sensors at the knees. These signals are then converted into digital signals and processed by the computer using numerical analysis to obtain ethical values. Once the signals are input into the controllers, the experimental results of the actual system track the desired force and position trajectories defined for each controller within desired limits. Manuscript profile -
Open Access Article
187 - A Novel Eye Gaze Estimation Method Using Ant Colony Optimizer
Mina Etehadi AbariThis paper addresses the eye gaze estimation problem in low-resolution images, using the low-cost camera in order to eliminate problems caused by infrared high-resolution imaging such as needing an expensive camera, complex setup, special light sources, and being limite MoreThis paper addresses the eye gaze estimation problem in low-resolution images, using the low-cost camera in order to eliminate problems caused by infrared high-resolution imaging such as needing an expensive camera, complex setup, special light sources, and being limited in lab research environments. In the proposed method, the human face is detected with Ant Colony Optimization (ACO) algorithm, and then the Kirsch compass mask is utilized to detect the position of humans’ eyes. For iris detection, a novel strategy based on ACO algorithm, which has been rarely used before, is applied. The pupil is recognized by morphological processing. Finally, the extracted features, obtained from the radius and position of the irises of the pupils, are given to the Support Vector Machine (SVM) classifier to detect the gaze pointing. In order to receive assurance of the reliability and superiority of the newly designed ACO algorithm, some other metaheuristic algorithms such as (GA, PSO, and BBO) are implemented and evaluated. Additionally, a novel dataset, comprising 700 images gazing at seven different major orientations, is created in this research. The extensive experiments are performed on three various datasets, including Eye-Chimera with 92.55% accuracy, BIOID dataset with 96% accuracy, and the newly constructed dataset with 90.71% accuracy. The suggested method outperformed the state of the art gaze estimation methods in terms of the robustness and accuracy. Manuscript profile -
Open Access Article
188 - Reduction of Cramer-Rao Bound in Arbitrary Pre-designed ArraysUsing Altering an Element Position
Hamidreza Bakhshi Mohsen AbediniSimultaneous estimation of the range and the angle of close emitters usually requires a multidimensional search. This paper proposes analgorithm to improve the position of an element for arrays designed on the basis of some certain or random rules. In the proposed metho MoreSimultaneous estimation of the range and the angle of close emitters usually requires a multidimensional search. This paper proposes analgorithm to improve the position of an element for arrays designed on the basis of some certain or random rules. In the proposed method,one element moves along the same previous direction, maintaining its vertical distance from each source, to reach a constellation with lessCramer-Rao Bound (CRB). The efficiency of this method has been demonstrated through simulation and a comparative study has beenconducted, contrasting both the CRB and the determinant of the received signal’s covariance matrix before and after applying our proposedscheme. Manuscript profile -
Open Access Article
189 - A Secure Channel to Improve Energy Cost in Internet of Things
Mohammad Mehdi Gilanian Sadeghi Kobra Karkhaneh -
Open Access Article
190 - Using Chip Master Planning in Automatic ASIC Design Flow to Improve Performance and Buffer Resource Management
Ali Jahanian Morteza Saheb ZamaniModern integrated circuits consist of millions of standard cells and routing paths. In nano-scale designs, mis-prediction is a dominant problem that may diminish the quality of physical design algorithms or even result in the disruption of the convergence of the design MoreModern integrated circuits consist of millions of standard cells and routing paths. In nano-scale designs, mis-prediction is a dominant problem that may diminish the quality of physical design algorithms or even result in the disruption of the convergence of the design cycle. In this paper, a new planning methodology is presented in which a master-plan of the chip is constructed at the early levels of the physical design, preparing for the operation of the subsequent physical design stages. As a proof of concept study, the proposed planning design flow is applied to both wire planning and buffer resource planning, and the outcomes are compared against conventional contributions. Experimental results reveal considerable improvements in terms of performance, timing yield and buffer usage. Manuscript profile -
Open Access Article
191 - Exploring the VLIW Architecture Space for Network Applications
Mostafa E. Salehi Ali Torabi Abolfazl SalarianThe increasing diversity in packet-processing applications together with the rapid increase in channel bandwidth has brought about greater complexity in communication protocols. Also influenced by these factors is the computational load for packet-processing engines, de MoreThe increasing diversity in packet-processing applications together with the rapid increase in channel bandwidth has brought about greater complexity in communication protocols. Also influenced by these factors is the computational load for packet-processing engines, demanding high performance microprocessor designs as an indispensable solution. This paper reports on extensive simulation experiments carried out for exploring the performance of instruction-level parallel Very Long Instruction Word (VLIW) processors executing packet-processing applications. On the grounds of the experimental results, a design space exploration has been used to derive an efficient application-specific VLIW processor architecture based on the VEX instruction set architecture. The VEX simulator toolset has been used for design space exploration, and a number of networking applications have been chosen to serve in guiding the architectural exploration. The optimization measures achieve up to 60% improvement in performance for the most representative packet-processing applications. Manuscript profile -
Open Access Article
192 - Switching H2/H∞ Controller Design for Linear Singular Perturbation Systems
Ahmad FakharianThis paper undertakes the synthesis of a logic-based switching H2/H∞ state-feedback controller for continuous-time LTI singular perturbation systems. Our solution achieves a minimum bound on the H2 performance level, while also satisfying the H∞ performance MoreThis paper undertakes the synthesis of a logic-based switching H2/H∞ state-feedback controller for continuous-time LTI singular perturbation systems. Our solution achieves a minimum bound on the H2 performance level, while also satisfying the H∞ performance requirements. The proposed hybrid control scheme is based on a fuzzy supervisor managing the combination of two controllers. A convex LMI-Based formulation of two fast and slow subsystem controllers leads to a structure which ensures a good performance in both transient and steady-state phases. The stability analysis leverages on the Lyapunov technique, inspired from the switching system theory, to prove that a system with the proposed controller remains globally stable in the face of changes in configuration (controller). Manuscript profile -
Open Access Article
193 - Sub-band Information Fusion Based on Wavelet Thresholding for Robust Speech Recognition
Babak Nasersharif Ahamd AkbariIn recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. In sub-band speech recognition, the full band speech is divided into several fre MoreIn recent years, sub-band speech recognition has been found useful in addressing the need for robustness in speech recognition, especially for the speech contaminated by band-limited noise. In sub-band speech recognition, the full band speech is divided into several frequency sub-bands, with the result of the recognition task given by the combination of the sub-band feature vectors or their likelihoods as generated by the corresponding sub-band recognizers. In this paper, we draw on the notion of discrete wavelet transform to divide the speech signal into sub-bands. We also make use of the robust features in sub-bands in order to obtain a higher sub-band speech recognition rate. In addition, we propose a likelihood weighting and fusion method based on the wavelet thresholding technique. The experimental results indicate that the proposed weighting methods for likelihood combination and classifiers fusion improve the sub-band speech recognition rate in noisy conditions. Manuscript profile