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11th International Symposium on Telecommunication, IST 2024

作者： Zamani, Mohammad Ali Amiri, Fatemeh Jamshidi, Pooya Hosseini, Seyed Mahdi Yazdani, Nasser University of Tehran Electrical and Computer Engineering Faculty Tehran Iran Hamedan University of Technology Department of Computer Engineering Hamedan Iran

ISBN: (纸本)9798350356250

Federated Learning (FL) has emerged as a key enabler for secure and decentralized model training while preserving user data privacy. Nonetheless, FL is vulnerable to poisoning attacks;in which a malicious participant tries to corrupt the global model by sending manipulated updates. One common attack is label-flipping, where attackers intentionally mess up labels for training data on their end. Current defenses against these threats treat scenarios where a single adversarial group is cooperating in coordination. This misses the modern real-world scenario in which different adversarial groups have differing, conflicting objectives and act independently. This paper introduces FL-ProtectX, a defense mechanism designed to combat label-flipping attacks targeting heterogeneous multi-adversary environments. Our model also detects the adversary (one that pursues a set of attack goals) behind and can differentiate from other adversaries using their unique target attacks. FL-ProtectX: by utilizing last-layer gradient analysis and cosine similarity computation along with PCA for compression, provides accurate separation and ensures that malicious participants are detected. To facilitate global model aggregation, our method adaptively adjusts trust factors according to the calculated angular similarities between updates. We evaluate the effectiveness of FL-ProtectX on defending against adversary groups by experimentally studying label-flipping attacks on the CIFAR10 dataset and show that for most situations, it effectively neutralizes these attacks, reducing attack success rate from 17.12 to 11.03 while maintaining high accuracy in non-attack-related classes in multi-agent configurations. © 2024 IEEE.

关键词： Federated learning

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Contactless monitoring of human behaviors in bed using RSSI signals

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Medical and Biological engineering and Computing 2023年第10期61卷 2561-2579页

作者： Thammachote, Peeradon Intongkum, Chawakorn Sengchuai, Kiattisak Jindapetch, Nattha Phukpattaranont, Pornchai Saito, Hiroshi Booranawong, Apidet Department of Electrical Engineering Faculty of Engineering Prince of Songkla University Songkhla90110 Thailand Division of Computer Engineering The University of Aizu Aizu-Wakamatsu965-8580 Japan

In this paper, contactless monitoring and classification of human activities and sleeping postures in bed using radio signals is presented. The major contribution of this work is the development of a contactless monitoring and classification system with a proposed framework that uses received signal strength indicator (RSSI) signals collected from only one wireless link, where different human activities and sleep postures, including (a) no one in the bed, (b) a man sitting on the bed, (c) sleeping on his back, (d) seizure sleeping, and (e) sleeping on his side, are tested. With our proposed system, there is no need to attach any sensors or medical devices to the human body or the bed. That is the limitation of the sensor-based technology. Additionally, our system does not raise a privacy concern, which is the major limitation of vision-based technology. Experiments using low-cost, low-power 2.4 GHz IEEE802.15.4 wireless networks have been conducted in laboratories. Results demonstrate that the proposed system can automatically monitor and classify human sleeping postures in real time. The average classification accuracy of activities and sleep postures obtained from different subjects, test environments, and hardware platforms is 99.92%, 98.87%, 98.01%, 87.57%, and 95.87% for cases (a) to (e), respectively. Here, the proposed system provides an average accuracy of 96.05%. Furthermore, the system can also monitor and separate the difference between the cases of the man falling from his bed and the man getting out of his bed. This autonomous system and sleep posture information can thus be used to support care people, physicians, and medical staffs in the evaluation and planning of treatment for the benefit of patients and related people. Graphical Abstract: The proposed system for non-invasive monitoring and classification of human activities and sleeping postures in bed using RSSI signals [Figure not available: see fulltext.] © 2023, International Federation for Med

关键词： Patient treatment

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan Ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Software design and FPGA implementation of optimized medical image fusion techniques

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Multimedia Tools and Applications 2025年 1-31页

作者： Elshazly, E.A. El-Shafai, Walid El-Hoseny, Heba El-Rabaie, El-Sayed M Zahran, O. Abdelwahab, Safey A. S. El-Halawany, M.M. Fikry, R.M. Elaraby, S.M. Faragallah, Osama S. Mohamed, Wael A. Mahmoud, Korany R. Abd El-Samie, Fathi E. Cairo Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Faculty of Computer Studies Arab Open University 11681 Riyadh Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University 11099 Taif21944 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Benha University Benha Egypt Electronics and Communications Engineering Department Faculty of Engineering Helwan University Helwan Egypt Giza Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Nowadays, medical image fusion plays a crucial role in enhancing the diagnosis accuracy and the clinical decision-making process in various healthcare applications. This research work presents a comprehensive study of the design and implementation of optimized medical image fusion techniques using a combination of software and Field-Programmable Gate Array (FPGA) technologies. The proposed medical image fusion strategy is based on the utilization of Discrete Wavelet Transform (DWT) and Modified Central Force Optimization (MCFO). The implementation of the proposed technique as well as the traditional medical image fusion techniques is considered using an appropriate software design and FPGA. The presented techniques aim to overcome the limitations of traditional fusion techniques by integrating advanced image processing algorithms, optimization algorithms, and parallel computing capabilities offered by FPGA platforms. The first step in the proposed framework is to match the histogram of one of the images with that of the other, so that both images will have the same dynamic range. After that, the DWT is used to decompose the images that should be fused together. Based on some constraints, the MCFO optimization algorithm is used to evaluate the optimum level of decomposition and the optimum parameters for the best fusion quality. Finally, to improve the obtained visual quality and reinforce the information in the fusion result, an additional contrast enhancement step using adaptive histogram equalization is applied to the fusion result. Comparative study between the proposed optimized DWT-based fusion framework, the traditional Principal Component Analysis (PCA), Additive Wavelet Transform (AWT), and DWT-based fusion techniques is presented. Various metrics of fusion quality are considered, including average gradient, standard deviation, local contrast, entropy, edge strength, Peak Signal-to-Noise Ratio (PSNR), Qab/f, and processing time. The proposed optimized DWT-ba

关键词： MATLAB

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Review and Comparative Analysis of Deep Learning Techniques for Smart Grid Load Forecasting 20

Review and Comparative Analysis of Deep Learning Techniques ...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Shahinzadeh, Hossein Sadrarhami, Hamidreza Hayati, Mohammad Mohsen Majidi-Gharehnaz, Hassan Abapour, Mehdi Gharehpetian, Gevork B. Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Islamic Azad University Faculty of Computer Engineering Najafabad Branch Najafabad Iran University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798350383942

In the last decade, the water and electricity industry has experienced significant investments in smart grid technologies. Within a smart grid framework, information and energy engage in bidirectional transmission, opening up diverse applications for artificial intelligence, including artificial neural networks, machine learning, and deep learning. This comprehensive review investigates the dynamic landscape of deep learning methodologies applied to load forecasting within smart grids, spanning short-term (STLF), medium-term (MILF), and long-term (LTLF) Forecasting horizons. We scrutinize a range of techniques, encompassing Auto-Encoder Method, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Graph Neural Networks (GNNs), Attention Mechanisms, and Hybrid Models. This article introduces and reviews common deep-learning algorithms used in load forecasting for smart grids and power systems. It also offers a comparative assessment based on the reduction percentage in four indicators: accuracy, speed, mean absolute error (MAPE), and root mean square error (RMSE). The research aims to provide valuable insights into the strengths and weaknesses of each deep learning method, guiding researchers and practitioners in making informed decisions when selecting the most suitable approach for diverse load forecasting scenarios in smart grid environments. © 2024 IEEE.

关键词： Smart power grids

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Data-driven control for nonlinear automated vehicles with multi-description coding for handling data dropouts 63

Data-driven control for nonlinear automated vehicles with mu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Shuhua Ma, Lifeng Charalambous, Themistoklis Nanjing University of Science and Technology School of Automation Nanjing210094 China University of Cyprus School of Electrical Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo Finland FinEst Centre for Smart Cities Tallinn Estonia

ISBN: (纸本)9798350316339

This paper proposes a data-driven control (DDC) strategy for nonlinear automated vehicles, employing a multidescription coding (MDC) mechanism based on scalar quantization to address the challenges of data dropouts and limited bandwidth in networked communication environments. The developed MDC-based communication protocol enhances system robustness by reducing the probability of data dropout. It achieves this by transmitting multiple descriptions of source data through diverse channels, incorporating quantization and index reassignment to efficiently alleviate bandwidth constraints. Based on the reconstructed real-time data, a novel data-driven controller and a parameter estimation algorithm are designed, offering adaptability to varying driving conditions and rapid response in dynamic environments. A numerical simulation showcases the potential of the proposed approach as a reliable and efficient solution for the speed problem in nonlinear automated vehicles with challenging communication environments. © 2024 IEEE.

关键词： Bandwidth

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Shuffled multi-evolutionary algorithm with linear population size reduction

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Soft Computing 2024年第21期28卷 12671-12688页

作者： Ahandani, Morteza Alinia Kharrati, Hamed Rahimi, Afshin Department of Electrical Engineering Langarud Branch Islamic Azad University Langarud Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Mechanical Automotive and Materials Engineering University of Windsor WindsorONN9B 3P4 Canada

The evolutionary algorithms with shuffling concept divide a population into several groups and then each group try to evolve its members in an independent evolutionary process. In an attempt to increase and diversify search moves and preventing the premature convergence of such algorithms, this paper proposes a meta-heuristic technique by employing several evolutionary processes for different groups. In this strategy, different groups can be evolved using a randomly selected evolutionary process. In accordance with this purpose and to provide an ability of jumping out of local optima, which can be useful for finding global optima in complex optimization problems, four different evolutionary strategies inspired from shuffled frog leaping (SFL), shuffled complex evolution (SCE), shuffled differential evolution (SCE) and opposition-based learning (OBL) are incorporated into a new structure called the shuffled multi-evolutionary algorithm (SMEA). The SMEA is extended with linear population size reduction (LPSR) known as L-SMEA, which continually decreases the population size and number of groups according to a linear function. The SMEA is evaluated on CEC2014 and CEC2017 benchmark functions. The obtained results are compared with SFL, SCE and SDE and other state-of-the-art algorithms proposed in the literatures. Performing the statistical analysis of the obtained results demonstrate that the proposed L-SMEA is superior to other three algorithms including SFL, SCE and SDE and some other state-of-the-art algorithms. Also, the performed experiments indicate the contribution of employing multiple evolutionary strategies beside of linear population size reduction leads to achieving a more effective and robust algorithm than those used single evolutionary strategy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary algorithms

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Enhanced Modified Contrast Enhancement and Exposure Fusion: a visionary dehazing approach for clarity in hazy imagery

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Multimedia Tools and Applications 2025年 1-24页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In the field of image processing, haze is a type of atmospheric scattering that reduces contrast and clarity of images, frequently masking small details and far-off objects. It is mostly caused by microscopic airborne particles like smoke, dust, and water droplets that scatter incoming light. The scattered light, producing a hazy image, diminishes the direct light that reaches the camera sensor. Many image processing methods have been presened to solve this problem and lessen the impacts of haze. Usually, these methods work by determining how much haze is there in an image, and then using this information to get the image back to its assumed previous quality. In this work, we present the proposed Modified Contrast Enhancement and Exposure Fusion (MCEEF) technique. The MCEEF dehazing technique falls under the umbrella of enhancement-based dehazing techniques. In this technique, hazy frames or images undergo sharpening through a Smoothing-Sharpening Image Filter (SSIF) designed to accentuate the disparity between the haze and objects in images. Subsequently, Gamma Correction (GC) and Color Preserving Adaptive Histogram Equalization (CP-AHE) enhance the sharpened hazy images by augmenting their contrast. Dehazed images are then obtained by fusing the results of the CP-AHE- and GC-enhanced images. The MCEEF dehazing technique is aided with enhanced hazy images or frames as input for the dehazing process. Moreover, essential enhancement tools precede the MCEEF dehazing technique. These tools include homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), which are instrumental in controlling the dynamic range before the dehazing phase. In the proposed approach, a hazy image or frame is initially subjected to homomorphic processing, followed by the application of CLAHE, and finally, the MCEEF dehazing technique is employed. To demonstrate the effectiveness of the proposed approach, we apply it on both visible and Near Infrared (NIR) frames. We

关键词： Demulsification

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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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