XML
-
-
List of Articles
-
Open Access Article
1 - Using the Electrocardiogram Signal to Identify and Detection Heart Diseases by Combining Time and Frequency Characteristics
Mohamad Reza Yousefi Zahra Khodadadi Amin DehghaniOne highly valuable tool for diagnosing heart diseases is the Electrocardiogram (ECG). This method involves recording the electrical signals emitted by the heart, using electrodes placed on the chest and various organs. The primary objective of this project is to employ MoreOne highly valuable tool for diagnosing heart diseases is the Electrocardiogram (ECG). This method involves recording the electrical signals emitted by the heart, using electrodes placed on the chest and various organs. The primary objective of this project is to employ digital signal pro-cessing of ECG signals to classify and diagnose heart diseases. The conditions that can be classi-fied through this digital processing of ECG signals encompass arrhythmia, atrioventricular block, cardiomyopathy, bundle branch block, and more. Therefore, this study primarily focuses on the classification and diagnosis of some of these heart diseases. The Pan-Tompkins algorithm is em-ployed in this study to detect the QRS complex in the ECG signals. Various classification algo-rithms, such as K-nearest neighbor, support vector machine, decision tree, and neural network, have been utilized to classify these signals. The digital processing of ECG signals is conducted using MATLAB software. The ECG signals utilized in this project were sourced from the PTB Diagnostic database available at physionet.org. Ultimately, the K-NN classifier with an F-criterion of 0.88 and a K-value of 20 demonstrated the most robust performance in classifying these heart diseases. Manuscript profile -
Open Access Article
2 - Miniaturized Ultra-wide Stopband Bandpass Filter for WiMAX Applications
Saeid Jafari Farzin Shama Abbas Ghadrdan Mohammad Saeed Feali Mohammad Amir Korani Mohammad Amir SattariIn this research, a miniaturized narrow-band bandpass filter is presented. The design procedure has been started using a coupled structure and the final circuit has been bent to reduce the circuit size. The operational frequency is located at 3.6 GHz with an acceptable MoreIn this research, a miniaturized narrow-band bandpass filter is presented. The design procedure has been started using a coupled structure and the final circuit has been bent to reduce the circuit size. The operational frequency is located at 3.6 GHz with an acceptable sharpness in the transition bands. In the passband, both the insertion and return loss are 0.6 and 27 dB, respectively. The obtained fractional bandwidth of the filter is about 2%, which is highly appropriate for a special-purpose narrow-band BPF. The presented BPF has been fabricated on an RT/Dourid5880 substrate. The obtained results of the fabricated circuit are consistent with the simulation one. Manuscript profile -
Open Access Article
3 - Robust PID Optimized Load-Frequency Controller of a Two-Area Power System Considering Systems Uncertainties
Ali Naderi Saatlo Maryam AshooryThe Load Frequency Control (LFC) has been a major subject in electric power system and is be-coming more significant system in recent decades. This paper targets to investigate the problem of LFC in interconnected power systems in order to obtain robust state. In this p MoreThe Load Frequency Control (LFC) has been a major subject in electric power system and is be-coming more significant system in recent decades. This paper targets to investigate the problem of LFC in interconnected power systems in order to obtain robust state. In this paper, a design method for a robust controller, based on PID, has been presented to overcome the robustness against uncertainties. To achieve optimal PID, Particle Swarm Optimization (PSO) has been em-ployed to obtain coefficients of the SMC. Variations of uncertain parameters are considered be-tween -40% and +40% of nominal values. The simulation results show that the system response with the proposed PID is better than the conventional PID controller. It is also shown that the transient response of the tie line power can be improved. Manuscript profile -
Open Access Article
4 - Classification of brain tumors using GoogleNet feature set and machine learning
Mahdi Eslami SANA golmarziasaslIn healthcare research, the Internet of Medical Things (IoMT) is transforming how healthcare operates and introducing a new era of the internet. IoMT enables computer-aided diagnosis (CAD) systems, storing health data online and providing patients with valuable informat MoreIn healthcare research, the Internet of Medical Things (IoMT) is transforming how healthcare operates and introducing a new era of the internet. IoMT enables computer-aided diagnosis (CAD) systems, storing health data online and providing patients with valuable information and support. Connected smart devices communicate over the Internet, enabling patients to communicate with medical professionals through IoMT-based the care systems, especially for critical conditions such as brain tumors, which are often precursors to cancer with low survival rates. Are. Early tumor detection and classification is crucial to save human lives, and IoMT-enabled CAD systems are emerging as indispensable solutions. Deep learning, especially Convolutional Neural Net-works (CNN), has gained a lot of interest in this field in recent years. In this research, we classify most common three types of brain tumors, namely, glioma, meningioma, pituitary and use AlexNet, GoogleNet, ResNet18, and VGG16 networks to check their correct diagnosis. Manuscript profile -
Open Access Article
5 - Revolutionizing Brain MRI Analysis: Advanced Deep Learning Techniques for Cutting-Edge Classification
Mahdi Eslami Hamideh Barghamadi montather__alwan__kadem montather__alwan__kademUsing advanced classification techniques in MRI imaging significantly enhances the accuracy of brain tumor diagnoses. Prior research predominantly concentrated on differentiating between normal (non-tumor) and abnormal (tumor) brain MRIs through machine learning or arti MoreUsing advanced classification techniques in MRI imaging significantly enhances the accuracy of brain tumor diagnoses. Prior research predominantly concentrated on differentiating between normal (non-tumor) and abnormal (tumor) brain MRIs through machine learning or artificial intelligence approaches. This article, however, advances the field by employing deep learning architectures to categorize brain MRI images into four distinct classes: healthy, meningioma, pituitary, and glioma. To achieve a more precise and meaningful classification, the study incorporates gender and age as critical features. A convolutional neural network (CNN)-based method is proposed for this effective classification. To compare their effectiveness, the study meticulously implements and analyses various designed architectures of deep learning networks, including LeNet, AlexNet, ResNet, and an innovative CNN-DNN network. A notable finding of this research is the impressive accuracy rate of 98.70% for the test data in this 4-class classification, which is a remarkable achievement. This high level of accuracy underscores the efficacy of the proposed method. Furthermore, the results compellingly demonstrate that the inclusion of age and gender information significantly enhances the classification process, playing a crucial role in the accuracy of the diagnoses. In summary, this study presents a highly accurate deep learning-based approach for classifying brain MRI images and highlights the importance of incorporating demographic features like age and gender in medical image analysis. Manuscript profile -
Open Access Article
6 - The Structure of Advanced Converters in Rechargeable Hybrid Electric Vehicles
Naghmeh Malekiye Mahdiyeh Eslami mehdi jafariThe main goal of this article was to compare advanced converters in plug-in hybrid electric vehicles. In this article, there are two examples of integrated chargers, the first charger integrates two DC/DC converters and uses the obtained converter in the structure of th MoreThe main goal of this article was to compare advanced converters in plug-in hybrid electric vehicles. In this article, there are two examples of integrated chargers, the first charger integrates two DC/DC converters and uses the obtained converter in the structure of the charger, and the second charger integrates two power converters and inverters. It introduces a second integrated charger. These two chargers were simulated using valid references and compared with each other and finally the results showed that the second structure is a better structure in the car. The second structure reduced the volume, consumption, charging cost and losses by reducing the power electronic elements. This structure also had a better and higher capacity than the first structure. When it comes to getting the battery voltage to a high enough level to power the motor, the performance of the two structures was not much different, but in all other cases, the second structure performed better than its counterpart. Therefore, it is better and more economical to use this converter in rechargeable hybrid cars. Manuscript profile
-
The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024