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Patient-independent epileptic seizure detection using weighted visibility graph features and wavelet decomposition

Multimedia Tools and Applications 2025年第6期84卷 3197-3221页

作者： Mohammadpoory, Zeynab Nasrolahzadeh, Mahda Amiri, Sekineh Asadi Department of Biomedical Engineering Shahrood University of Technology Shahrood Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Computer Engineering University of Mazandaran Babolsar Iran

Epileptic seizure detection has been a complex task due to the chaos and non-stationariness observed in the electroencephalogram (EEG) signals. Most existing EEG-based seizure detection algorithms are patient-dependent and train a detection model for each patient. This study investigates the patient-independent detection of seizure events using the big dataset CHB-MIT Scalp EEG. This paper proposes a new method based on weighted visibility graph (WVG) features to identify seizures from EEG signals. EEG signals from 22 channels and delta, theta, alpha, beta, and gamma sub-bands of EEGs are mapped into the WVG, and then WVG features are calculated from these WVGs. Then more informative features were selected using a combination of some feature selection methods and were given to five classifiers to investigate the performance of these features to classify the brain signals into seizure free (interictal) and during a seizure (ictal) groups. After that, the post-processing step was used to promote the performance of the method and also detect the seizure onset and offset times detection. The proposed method could detect 163 seizures out of 184, considering patient-independent situations for training classifiers and it could obtain an accuracy, sensitivity, and specificity of 94.02%, 92.31%, and 94.12% respectively. It also could obtain an FDR equal to 0.12/h and an ADL of 5.11 Seconds. Then, the proposed method has promising results in detection of seizures and it may use in online seizure detection applications such as closed-loop therapies. This study proposed extracting some WVG-based features from the signal and its sub-band with a feature selection scheme combining some feature selection methods and using some classifiers. Then a post-processing proposed to process the classifiers output and promote the performance of the proposed method. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Electroencephalography

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Resolution-aware beam scanning for joint detection and communication in ISAC systems

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Science China(Information Sciences) 2025年第5期68卷 250-266页

作者： Yiming XU Dongfang XU Zhanyuan XIE Zheng JIANG Shenghui SONG Derrick Wing Kwan NG Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Telecom Research Institute School of Electrical Engineering and Telecommunications University of New South Wales

Beam scanning for joint detection and communication in integrated sensing and communication(ISAC) systems plays a critical role in continuous monitoring and rapid adaptation to dynamic environments. However, the design of sequential scanning beams for target detection with the required sensing resolution has not been tackled in the *** bridge this gap, this paper introduces a resolution-aware beam scanning design. In particular, the transmit information beamformer, the covariance matrix of the dedicated radar signal, and the receive beamformer are jointly optimized to maximize the average sum rate of the system while satisfying the sensing resolution and detection probability requirements.A block coordinate descent(BCD)-based optimization framework is developed to address the non-convex design problem. By exploiting successive convex approximation(SCA), S-procedure, and semidefinite relaxation(SDR), the proposed algorithm is guaranteed to converge to a stationary solution with polynomial time complexity. Simulation results show that the proposed design can efficiently handle the stringent detection requirement and outperform existing antenna-activation-based methods in the literature by exploiting the full degrees of freedom(DoFs) brought by all antennas.

关键词： integrated sensing and communication (ISAC) detection beam scanning resolution beamwidth

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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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IEEE Access 2025年 13卷 87753-87776页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Department of Electrical Engineering Penghu880011 Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu880011 Taiwan

In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior;however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%. © 2013 IEEE.

关键词： Convolutional neural networks

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Real-Time Electrochemical Model-Based BMS Control for Mitigating Li-Plating and Extending Battery Life

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7403-7419页

作者： Qasem, Mohammad Haddadin, Mariana Yassin, Yazan Ratrout, Sadam Chen, Chengxiu Stoyanov, Stoyan Al-Hallaj, Said Krishnamurthy, Mahesh Southern Methodist University Department of Electrical and Computer Engineering DallasTX75205 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States Beam Global BroadviewIL60155 United States

It is well-known that lithium plating significantly reduces the capacity of Li-ion batteries, particularly at elevated charging rates, high state of charge (SoC), and low temperatures. This study presents a simplified electrochemical battery model incorporating side reactions for real-time (RT) applications. The proposed model is implemented on a battery management system (BMS) using the TI TMS320F28335 DSP, the model’s internal and external states are validated through extensive experimental tests against the high-fidelity P2D model by Gamma technology. The results demonstrated the model’s ability to accurately depict the behavior of a commercially available type 21 700 cylindrical cell while minimizing execution power consumption. The lithium plating/stripping model is examined and compared with the actual voltage plateau characteristics observed in practical testing. Finally, various controllers are designed and implemented into the BMS to optimize the charging current and eliminate plating without significantly increasing charging duration. The battery demonstrated an increase in capacity of 8.3% when using the proposed charging control algorithm compared with the common constant current (CC)–constant voltage (CV) technique, with a charging time increase of less than 30 min, resulting in a 17% improvement in SoH. This demonstrates the effectiveness of enhancing battery performance and longevity with minimal additional charging time. © 2015 IEEE.

关键词： Lithium-ion batteries

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MediVerse: A Secure and Scalable IoT-MR Framework for Real-Time Health and Performance Monitoring

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IEEE Access 2025年 13卷 97511-97528页

作者： Regalado, Pedro H. Han, Tao New Jersey Institute of Technology Electrical and Computer Engineering Department NewarkNJ07102 United States

Recent advancements in wearable and Internet of Things (IoT) technologies have yet to be fully realized in combination with Mixed Reality (MR) for comprehensive real-time health monitoring systems. This paper introduces MediVerse, a secure and scalable IoT-MR framework designed to enhance proactive health management through immersive, context-aware data visualization and real-time feedback. MediVerse employs a three-tiered architecture integrating intelligent sensors, wearable technologies, and MR interfaces to facilitate dynamic prediction and decision-making during critical healthcare scenarios. The framework addresses key challenges in latency, data throughput, interoperability, and user engagement by leveraging edge computing, adaptive compression, and AI-driven analytics. Extensive evaluations demonstrate MediVerse’s superiority, achieving a 70% latency reduction, 51% higher data throughput, and 69% lower error rates compared to conventional systems. Additionally, usability studies highlight significant improvements in user satisfaction and interaction clarity. These findings position MediVerse as a transformative solution for next-generation real-time healthcare monitoring, with adaptability to domains such as sports and education. © 2013 IEEE.

关键词： eHealth

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Feasibility of a novel beamforming algorithm via retrieving spatial harmonics

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Journal of Systems engineering and Electronics 2022年第1期33卷 38-46页

作者： NOROLAHI Jafar AZMI Paeiz NASIRIAN Mahdi Electrical and Computer Engineering Department Tarbiat Modares UniversityTehran 14115Iran Department of Electrical and Computer Engineering Sharif University of TechnologyTehran 11365Iran

This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval(MHR).This algorithm resolves problems,removes limitations of sampling and provides a more robust beamformer.A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer(SHRB).Simulation results show that SHRB has a better performance,accuracy,and applicability and more powerful eigenvalues than conventional beamformers.A simple mathematical proof is *** changing the number of harmonics,as a degree of freedom that is missing in conventional beamformers,SHRB can achieve more optimal outputs without increasing the number of spatial or temporal *** will demonstrate that SHRB offers an improvement of 4 dB in signal to noise ratio(SNR) in bit error rate(BER) of 10~(-4) over conventional *** the case of direction of arrival(DOA) estimation,SHRB can estimate the DOA of the desired signal with an SNR of-25 dB,when conventional methods cannot have acceptable response.

关键词： multi-input-multi-output(MIMO) beamforming spatial and temporal signal processing multidimensional harmonic retrieval(MHR) space-time signal processing array signal processing

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Quad-Mode H-Beam Metasurface-Embedded Dual-Band Circularly-Polarized Multi-Tiered Antenna Scheme With 5G (n79) and Wi-Fi 6E Capabilities

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IEEE Access 2025年 13卷 89844-89857页

作者： Supreeyatitikul, N. Chomtong, P. Konpang, J. Thitimahatthanakusol, P. Akkaraekthalin, P. Rajamangala University of Technology Krungthep Faculty of Engineering Department of Electrical and Telecommunication Engineering Bangkok10120 Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok10800 Thailand Rajamangala University of Technology Isan Faculty of Engineering and Technology Department of Telecommunications Engineering and Digital Innovation Nakhon Ratchasima30000 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

This research proposes a quad-mode H-beam metasurface (MTS)-embedded dual-band circularly-polarized (CP) multi-tiered antenna scheme with 5G (n79) and Wi-Fi 6E capabilities. The proposed antenna scheme consists of three layers of corner-truncated FR-4 substrates without an air gap. The upper substrate contains an array of 3× 4 H-beam MTS elemental units tilted at 49°. The middle substrate is a copper plate with a horizontal Z-shaped slot at its center functioning as the ground plane, and the lower substrate contains a microstrip feed line. Characteristic mode analysis (CMA) is employed to optimize the circular polarization of the MTS structure. Results show that Modes 1 & 2 at 4.81 GHz and Modes 4 & 9 at 6.2 GHz contribute to CP radiation. The measured return loss bandwidths of the antenna are 19.37% (4.63 - 5.56 GHz) and 9.51% (5.97 - 6.56 GHz) for the 5G (n79) and Wi-Fi 6E bands. The measured axial ratio bandwidths are 13.33% (4.6 - 5.24 GHz) and 1.77% (6.15 - 6.26 GHz), with maximum gains of 5.2 and 3.9 dBic at 5.5 and 6.2 GHz, respectively. Unlike conventional MTS-based CP antennas which employ rectangular- or square-shaped MTS elements for single-band operation, this work utilizes H-beam-shaped MTS elemental units to achieve dual-band CP capability. Additionally, rather than traditional full-wave methods, CMA is used to optimize circular polarization. Essentially, the proposed antenna scheme is suitable for 5G (n79) and Wi-Fi 6E communications. © 2013 IEEE.

关键词： Circular polarization

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A hybrid feature weighting and selection-based strategy to classify the high-dimensional and imbalanced medical data

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Neural Computing and Applications 2024年第20期36卷 12299-12316页

作者： Singh, Harpreet Kaur, Manpreet Singh, Birmohan Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal Sangrur148106 India

Machine learning algorithms generally assume that the data are balanced in nature. However, medical datasets suffer from the curse of dimensionality and class imbalance problems. The medical datasets are obtained from the patient information which creates an imbalance in class distribution as the number of normal persons is more than the number of patients and contains a large number of features to represent a sample. It tends to the machine learning algorithms biased toward the majority class which degrades their classification performance for minority class samples and increases the computation overhead. Therefore, oversampling, feature selection and feature weighting-based four strategies are proposed to deal with the problems of class imbalance and high dimensionality. The key idea behind the proposed strategies is to generate a balanced sample space along with the optimal weighted feature space of the most relevant and discriminative features. The Synthetic Minority Oversampling Technique is utilized to generate the synthetic minority class samples and reduce the bias toward the majority class. An Improved Elephant Herding Optimization algorithm is applied to select the optimal features and weights for reducing the computation overhead and improving the interpretation ability of the learning algorithms by providing weights to relevant features. In addition, thirteen methods are developed from the proposed strategies to deal with the problems of high-dimensionality and imbalanced data. The optimized k-Nearest Neighbor (k-NN) learning algorithm is utilized to perform classification. The performance of the proposed methods is evaluated and compared for sixteen high-dimensional imbalanced medical datasets. Further, Freidman’s mean rank test is applied to show the statistical difference between the proposed methods. Experimental and statistical results show that the proposed Feature Weighting followed by the Feature Selection (FW–FS) method performed significantly b

关键词： Feature Selection

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RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 908-927页

作者： Ismail, Mohamed I. Shaheen, Abdullah M. Fouda, Mostafa M. Alwakeel, Ahmed S. Modern Academy for Engineering and Technology Department of Electronic Engineering and Communication Technology Cairo4410242 Egypt Suez University Faculty of Engineering Department of Electrical Engineering Suez8151650 Egypt Idaho State University College of Science and Engineering Department of Electrical and Computer Engineering PocatelloID83209 United States

The Integrated Sensing and Communication (ISAC) system merged with Reconfigurable Intelligent Surface (RIS) has recently received much attention. This paper proposes an intelligent metaheuristic version of Enhanced Artificial Ecosystem Optimizer (EAEO) for a suggested beamforming optimization framework in ISAC systems with RIS. Two RIS are utilized in the presented model to enhance the received signal-to-noise ratio (SNR) for multiple-input multiple-output (MIMO) communication systems. Also, each element of each RIS scatters the incoming signal with a controllable phase-shift, without increasing its power. The signal is transmitted by a Dual-Function Base Station (DFBS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. In the proposed EAEO version, a Fitness-Distance-Balance Model (FDBM) is combined with the standard Artificial Ecosystem Optimizer (AEO) to improve the quality of the solutions in multidimensional and nonlinear optimization scenarios. The simulation results show that the proposed EAEO algorithm improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements. Moreover, the proposed EAEO is tested on the IEEE Congress on Evolutionary Computation 2017 (IEEE CEC'17) test suite. A comparative analysis is conducted compared to the standard AEO and several recent algorithms. The proposed EAEO derives great effectiveness and robustness over the others as it provides a higher rate of success in achieving the global optimum point. © 2020 IEEE.

关键词： Signal to noise ratio

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Uncertainty quantification in large language models through convex hull analysis

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Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Catak, Ferhat Ozgur Kuzlu, Murat Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. The responses are transformed into high-dimensional embeddings via a BERT model and subsequently projected into a two-dimensional space using Principal Component Analysis (PCA), Isomap, Multidimensional Scaling (MDS). The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the embeddings and compute the convex hull for each selected cluster. The experimental results indicate that the uncertainty of the model for LLMs depends on the prompt complexity, the model, and the temperature setting. © The Author(s) 2024.

关键词： Embeddings

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