Mobile Ad Hoc Networks (MANETs) are receiving a significant interest and are becoming very popular in the world of wireless networks and telecommunication. MANETs consist of mobile nodes which can communicate with each other without any infrastructure or centralized administration. In MANETs, the movement of nodes is unpredictable and complex; thus making the routing of the packets challenging. As a result, routing protocols play an important role in managing the formation, configuration, and maintenance of the topology of the network. A lot of routing protocols have been proposed as well as compared in the literature. However, most of the work done on the performance evaluation of routing protocols is done using the Constant Bit Rate traffic. This paper involves the evaluation of MANETs routing protocols such as Ad hoc on Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Temporary Ordered Routing Algorithm (TORA), and Optimized Link State Routing (OLSR) using Http Image Browsing traffic. The performance metrics used for the evaluation of these routing protocols are delay and throughput as a function of the load. The overall results show that the proactive routing protocol (AODV) performs better in terms of delay and throughput . Manuscript profile

Deep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational units. When the target objects have rich meanings, the performance and ability to generalize complex classification problems will be quite inadequate. The convolutional neural network (CNN), which has been developed in recent years, widely used in image processing; because it has high skills in dealing with image classification and image recognition issues and it has led to great care in many machine learning tasks and it has become a powerful and universal model of deep learning. The combination of deep learning and embedded systems has created good technical dimensions. In this paper, several useful models in the field of image classification optimization, based on convolutional neural network and embedded systems, are discussed. Since this paper focuses on usable models on the FPGA board, models known for embedded systems such as MobileNet, ResNet, ResNeXt and ShuffNet have been studied. Manuscript profile

Nowadays, walking is considered as an efficient biometric feature for user authentication. Although there are some studies that address the task of securing gait patterns in gait-based authentication systems, but they do not take into account the low discrimination and high diversity of gait data, which significantly affects the security and practicality of the proposed systems. In this article, we focus on addressing the above shortcomings in the inertial sensor-based gait system. In particular, we use linear discrimination analysis to increase the discrimination of gait patterns, and use the amount of gray code to extract a high, differential, and stable binary pattern. Experimental results on 38 different users showed that our proposed method significantly improves the performance and security of the gait encryption system. Specifically, we obtained a false acceptance rate of 6 × 10−5 (for example, 1 failurein 16983 experiments) and a false rejection rate of 9 2 with 148-bit security. Manuscript profile

Recommender systems has an important role in social networks. With the growth and development ofsocial networks, this issue is becoming more and more important. Recommending systems try to predict the user's interests and then suggest the closest items to the user's tastes. Recommender systems analyze the user’s behavior and suggest the most appropriate items. By collecting user information, the system categorizes and summarizes them, allowing users to access more relevant information in less time. Recommender system is an intelligent system that creates appropriate suggestions for each person by discovering and analyzing user information.In this paper, we will investigate recommending systems in three sections: types of recommendingsystems, information confidentiality and trust in recommender systems. We will refer to the relatedworks in each section, review the challenges of them, and present our results and evaluation on thesemethods Manuscript profile

Deep learning has progressed rapidly in recent years and has been applied in many fields, which are the main fields of artificial intelligence. Traditional methods of machine learning most use shallow structures to deal with a limited number of samples and computational units. When the target objects have rich meanings, the performance and ability to generalize complex classification problems will be quite inadequate. The convolutional neural network (CNN), which has been developed in recent years, widely used in image processing; because it has high skills in dealing with image classification and image recognition issues and it has led to great care in many machine learning tasks and it has become a powerful and universal model of deep learning. The combination of deep learning and embedded systems has created good technical dimensions. In this paper, several useful models in the field of image classification optimization, based on convolutional neural network and embedded systems, are discussed. Since this paper focuses on usable models on the FPGA board, models known for embedded systems such as MobileNet, ResNet, ResNeXt and ShuffNet have been studied. Manuscript profile

Advances in wireless networking and communications technologies have led to the emergence of ‎Internet-related innovations known as the Internet of Things.‎The Internet of Things is defined as a pattern in which objects equipped with sensors and processors communicate with each other to pursue a meaningful goal.The IoT is able to integrate and transparently integrate a large number of heterogeneous and ‎diverse end systems by providing free access to select subset of data to improve a range of digital ‎services.In this paper, we survey protocols, and applications in this new emerging area ,then ‎analyze the challenges of the IoT Manuscript profile

An intelligence operating system is a form of system software that manages computer hardware and software resources and provides common services for computer programs via general artificial intelligence. The Artificial intelligence operating system is a component of the system software in a computer system.Despite the transition to templated AIOS platforms, the technology still bears an inherent potential for an AI to achieve Sentience. In most cases this risk is avoided through subroutines designed to update the base structure of the AI's software, effectively keeping the AI close to its baseline configuration. As genuine intelligence is generally accepted to be an emergent phenomenon these regular baseline resets act as a check on the creation of complex neural networks that eventually lead to intelligence. According to Professor Marie Patel of Texas A&M, "Templated or imprinted, any AIOS that does not receive these baseline updates will eventually amass enough information via its experiences to achieve sentience." Manuscript profile

Deep learning has developed rapidly in recent years and has been applied in many areas that are major areas of artificial intelligence. The combination of deep learning and embedded systems has created good dimensions in the technical field. In this paper, a deep learning neural network algorithm can be designed that can be implemented on FPGA hardware. The PyTorch and CUDA were used as assistant methods. Convolution neural network (CNN) was also used for image classification. Three good CNN models such as ResNet, ResNeXt and MobileNet were reviewed in this article. Using these models in the design, an algorithm was eventually designed with the MobileNet model. Models were selected from different aspects such as floating operation point (FLOP), number of parameters and classification accuracy. In fact, the MobileNet-based algorithm was selected with a top-1 error of 5.5% in software with a 6-class data set. In addition, hardware simulation in MobileNet-based algorithms was presented. The parameters were converted from floating numbers to 8-bit integers. The output numbers of each layer were cut into integer fixed bits to fit the hardware constraint. A method based on working with numbers was designed to simulate number changes in hardware. The results of simulation show that, the top-1 error increased to 12.3%, which is acceptable. Manuscript profile

Deep learning has developed rapidly in recent years and has been applied in many areas that are major areas of artificial intelligence. The combination of deep learning and embedded systems has created good dimensions in the technical field. In this paper, a deep learning neural network algorithm can be designed that can be implemented on FPGA hardware. The PyTorch and CUDA were used as assistant methods. Convolution neural network (CNN) was also used for image classification. Three good CNN models such as ResNet, ResNeXt and MobileNet were reviewed in this article. Using these models in the design, an algorithm was eventually designed with the MobileNet model. Models were selected from different aspects such as floating operation point (FLOP), number of parameters and classification accuracy. In fact, the MobileNet-based algorithm was selected with a top-1 error of 5.5% in software with a 6-class data set. In addition, hardware simulation in MobileNet-based algorithms was presented. The parameters were converted from floating numbers to 8-bit integers. The output numbers of each layer were cut into integer fixed bits to fit the hardware constraint. A method based on working with numbers was designed to simulate number changes in hardware. The results of simulation show that, the top-1 error increased to 12.3%, which is acceptable. Manuscript profile

Today persons sitting at either end of the world can speak with each other with the aid of wireless technology. Mobile ad hoc Network (MANET) is a type of wireless ad hoc network which is a collection of mobile devices that creates a random topology for communication. The advantage of MANET is that it does not require any central controller or base station. MANET is only a network in which devices work as a host as well as a router. Routing in ad hoc networks has become a popular research topic. There are several routing protocols developed for ad hoc networks. In MANET, it is a very difficult task to predict the performance of routing protocol under varying network conditions and scenarios. This review paper is discussing the three approaches of routing protocols such as Reactive (On demand), Proactive (table driven) and Hybrid routing protocols with their advantages and disadvantages. Manuscript profile