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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Liu, Ziye Chen, Chen Zhang, Qizhong Wang, Zhiyi Choon Chang, Yoong Liu, Lei Pei, Qingqi Wan, Shaohua School of Telecommunications Engineering Xidian University Xi’an China State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China Lee Kong Chian Faculty of Engineering & Science Universiti Tunku Abdul Rahman Kajang Bandar Sungai Long Malaysia Xidian Guangzhou Institute of Technology Xidian University Guangzhou China Xidian Hangzhou Institute of Technology Xidian University Hangzhou China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9789819628292

In response to the burgeoning interest in Vehicle-to-Everything (V2X) communications and its pivotal role in enhancing road transportation efficiency and safety, this paper presents a novel scheme for the formation of vehicle platoons, addressing the critical need for realistic implementation strategies. Recognizing the limitations of existing research that often relies on idealized assumptions, this work proposes a platoon formation method utilizing the real-world dataset. The method combines the robustness of Random Forest algorithms for feature selection with the adaptability of Fuzzy K-Means (FKM) clustering to effectively group vehicles with similar characteristics into platoons. This approach ensures the consistency of speed and acceleration within platoons, a crucial aspect for maintaining platoon integrity and stability. Validated through the Kaggle dataset, the scheme not only showcases the feasibility of forming efficient vehicle platoons in realistic scenarios but also introduces a velocity and acceleration-based Cooperative Adaptive Cruise control (CACC) strategy to prove the formatted platoon stability. © IFIP International Federation for Information Processing 2025.

关键词： K-means clustering

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A Novel Circularly Polarized Filtering Reflectarray by Manipulating the Multimode Reflective Phase of Orthogonal Polarizations

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IEEE Transactions on Antennas and Propagation 2025年

作者： Fang, Chang Zhang, Xiao Li, Yin Zhu, Lei Wong, Sai-Wai Quan, Zhi China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Peng Cheng Laboratory Shenzhen518038 China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering Macau China

This paper presents a novel design methodology for circularly polarized (CP) filtering reflectarray (RA). Unlike conventional filtering techniques based on magnitude response, the proposed filtering RA solely manipulates the phase responses of the reflected wave under the total reflection condition. An isosceles triangle patch radiator loaded with slots is employed as the unit cell. Two sets of orthogonal resonant modes of the triangular patch are exploited to produce a phase difference between the u- and v-polarized components. By adopting slot loading, first- and third-order resonant states are induced in the two orthogonal directions. Next, the phase difference is fully controlled by adjusting the slope of the reflection phase. In the passband, the phase difference is maintained at 180°, while outside the band, it rapidly rolls down (up) to 0° (360°), enabling steep band edges. Finally, a CP filtering RA using the optimized element is constructed for the first time, which verifies the validity of the proposed method. The fabricated RA exhibits satisfactory performance, including a 3-dB gain bandwidth of 25.7%, 3-dB axial ratio (AR) bandwidth of 29.8%, and the out-of-band suppression levels of 33.5 dB (22.8 dB) in the low (high) frequency band. © 1963-2012 IEEE.

关键词： Circular polarization

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Brain Region Knowledge Based Dual-Stream Transformer for EEG Emotion Recognition

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IEEE Transactions on Consumer Electronics 2025年

作者： Lin, Yichuan Xu, Geng-Xin Liang, Haotian Wang, Yishan Wan, Feng Li, Ye Southern University of Science and Technology Shenzhen518055 China Research Center for Biomedical Information Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hangzhou Institute of Advanced Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering The Faculty of Science and Technology The Centre for Cognitive and Brain Sciences The Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau China

In consumer electronics, affective computing allows devices to recognize and respond to the emotional states of individuals, thereby enhancing personalization and satisfaction. Electroencephalogram (EEG)-based emotion recognition methods have garnered attention for their capability to provide real-time, objective insights into emotional and cognitive states. Existing research mainly focuses on extracting temporal and spectral features from EEG signals for emotion recognition. However, electrode shifts may occur across different experiments during data acquisition, resulting in models with limited robustness and constrained classification performance. To address this challenge, this paper introduces a Brain Region Knowledge based Dual-Stream network (BRKDSnet) for emotion recognition. It employs a dual-stream architecture to effectively fuse the temporal, spectral, and spatial features of EEG signals. By incorporating prior brain region partition knowledge, it efficiently aggregates channel features into distinct brain region features. Such a brain region based partition mitigates noise issues caused by electrode shifts during user wearing in consumer electronic applications, and leading to learn more robust feature representations. The experiments conducted on the widely used emotion recognition datasets SEED and SEED-IV shows that BRKDSnet achieves state-of-the-art results, with accuracies of 97.81% (improved by 0.64%-3.57% compared to existing works) and 90.76% (improved by 3.13%-11.39%) respectively. Finally, through analysis of the visualization results, it is found that EEG channels located in the temporal lobe contributed the most to emotion recognition classification, which also provides the potential for future applications in wearable devices. © 2025 IEEE.

关键词： Electroencephalography

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Construction and optimization of a computational holographic lithography system 8

Construction and optimization of a computational holographic...

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Chen, Rui Lin, Angyi You, Jianfeng Lin, Huixiang Zhao, Wenqing Zhang, Fucai School of Control and Computer Engineering North China Electric Power University Baoding071003 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education North China Electric Power University China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Traditional lithography techniques are currently facing challenges, including high costs and the susceptibility of mask plates to damage. This research aims to elucidate the feasibility and technical constraints of a novel diffraction lithography approach. This method can reduce the size and cost of lithography machines, break through existing technological barriers, and offer new possibilities for the future development of lithography machines. We have built a basic computational holographic lithography system utilizing a 405nm laser and validated the system using holograms obtained with both classical GS algorithm and a newly proposed method. With the GS holograms, patterns with 700nm features have been successfully exposed on the photoresist. With our new algorithms, multiple holograms for the same target pattern can be generated with different settings in the algorithm. Sequential exposure of those holograms resulted in continuous lines 500 nm wide with reduced speckle artifacts, which was about a 1.4-time improvement over the result from the GS algorithm. Future research will focus on optimizing and enhancing the experimental system and the hologram calculation methods to reduce the line widths further and improve the exposure pattern quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Interplay between cerebral and muscular activations during motor tasks: An fNIRS-sEMG study

Interplay between cerebral and muscular activations during m...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ruisen Huang Wenze Shang Yongji Lin Fengyan Liang Ming Yin Xiao-Ping Lu Xinyu Wu Fei Gao Institute of Advanced Integration Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering Hainan University Hainan China School of Computer Science and Engineering Macau University of Science and Technology Macau China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Understanding the intricate relationship between brain activations and muscle activations during motor tasks is crucial for elucidating motor control mechanisms and designing effective rehabilitation strategies. In this study, we investigated this relationship using functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG) technologies. Three healthy male subjects performed lower-limb motor tasks while their brain activations and muscle activations were recorded simultaneously. The fNIRS data were converted to optical densities and filtered by lowpass filter, detrending, and short-separation regression to remove artifacts. The filtered signals were fed to a general linear model with the desired hemodynamic responses (dHRFs) constructed with Balloon model and simulated physiological noises reconstructed with the parameters obtained from the spectrum analysis of baseline signals. Despite the extracted hemodynamic responses from fNIRS data, sEMG signals were lowpass and analyzed to detect muscle activations. Results revealed that cerebral activations during lateral leg flexion tasks were similar yet only partially consistent with those observed in stand-up tasks. Significant correlations between brain and muscle activations were also observed, highlighting the complex interplay between central and peripheral neural mechanisms during motor control. These findings provide valuable insights into the neural basis of motor control and have implications for developing personalized rehabilitation interventions and assistive technologies to enhance motor function in clinical populations.

关键词： Optical filters Motor drives Analytical models Biological system modeling Muscles Motors Brain modeling Electromyography Hemodynamics Functional near-infrared spectroscopy

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Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity

arXiv

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arXiv 2025年

作者： Chen, Xiaolu Huang, Chenghao Zhang, Yanru Wang, Hao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Data Science and AI Faculty of IT Monash Energy Institute Monash University MelbourneVIC3800 Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Shenzhen Institute for Advanced Study of UESTC Shenzhen China

The rapid expansion of distributed photovoltaic (PV) installations worldwide, many being behind-the-meter systems, has significantly challenged energy management and grid operations, as unobservable PV generation further complicates the supply-demand balance. Therefore, estimating this generation from net load, known as PV disaggregation, is critical. Given privacy concerns and the need for large training datasets, federated learning becomes a promising approach, but statistical heterogeneity, arising from geographical and behavioral variations among prosumers, poses new challenges to PV disaggregation. To overcome these challenges, a privacy-preserving distributed PV disaggregation framework is proposed using Personalized Federated Learning (PFL). The proposed method employs a two-level framework that combines local and global modeling. At the local level, a transformer-based PV disaggregation model is designed to generate solar irradiance embeddings for representing local PV conditions. A novel adaptive local aggregation mechanism is adopted to mitigate the impact of statistical heterogeneity on the local model, extracting a portion of global information that benefits the local model. At the global level, a central server aggregates information uploaded from multiple data centers, preserving privacy while enabling cross-center knowledge sharing. Experiments on real-world data demonstrate the effectiveness of this proposed framework, showing improved accuracy and robustness compared to benchmark methods. © 2025, CC BY.

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FusionGCNN: An IoT-Based Novel Spatiotemporal Graph Convolutional Network for ECG Arrhythmia Detection

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IEEE Internet of Things Journal 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Alhussein, Musaed Wu, Zongze Aurangzeb, Khursheed Liu, Weixiang Zhang, Yudong Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Henan Polytechnic University School of Computer Science and Technology Henan Jiaozuo China

Electrocardiogram (ECG) arrhythmia identification is critical for early cardiovascular disease diagnosis and monitoring in Internet of Things (IoT) industry. Still, it is difficult due to complicated waveforms, individual variability, and the requirement for real-time analysis on resource-limited equipment. Traditional approaches sometimes fail to detect complicated spatial-temporal correlations in ECG data, limiting their efficiency in identifying arrhythmias. Furthermore, deploying these models in tinyML contexts, such as edge and IoT devices limited by large computational and memory needs, emphasizes the importance of lightweight, accurate models for real-time applications. Our suggested solution consists of three main components: SigNet, DualGCNN, and FusionGCNN. SigNet uses Separable Convolution layers to effectively extract local spatial features, making it ideal for IoT-based healthcare deployment. DualGCNN combines dual Graph Convolutional layers with spatial attention, allowing the model to capture local and global dependencies for better classification of arrhythmia. FusionGCNN combines the capabilities of GCN and SigNet with an effective feature fusion technique to improve feature representation while remaining computationally economical. Ablation tests show that FusionGCNN improves performance considerably, with greater accuracy (0.9641), lower training error (0.0004), and a higher F1 Score (0.9645) across a variety of ECG patterns. FusionGCNN, with its low training error, high stability, and computational economy, is well-suited to tinyML requirements, allowing implementation on edge and IoT devices for scalable, real-time ECG monitoring in healthcare. © 2014 IEEE.

关键词： Electrocardiograms

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and Tracking

A Novel Deep Learning Approach Featuring Graph-Based Algorit...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Keliang Niyogisubizo, Jovial Xiao, Linxia Pan, Yi Wei, Dongqing Rosiyadi, Didi Wei, Yanjie Chinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences College of Computer Science and Control Engineering Shenzhen Institutes of Advanced Technology Shenzhen518055 China Shanghai Jiao Tong University State Key Laboratory of Microbial Metabolism School of Life Sciences and Biotechnology Shanghai200240 China National Research and Innovation Agency Research Center for Artificial Intelligence and Cybersecurity Bandung Indonesia

ISBN: (纸本)9798350337488

The precise segmentation and tracking of cells in microscopy image sequences play a pivotal role in biomedical research, facilitating the study of tissue, organ, and organism development. However, manual segmentation and tracking of cells is time-consuming and often require professional experiences. Besides, segmenting cells in the images with a low signal-to-noise ratio remains difficult. While deep learning (DL) has become a common method for cell segmentation, few DL- based methods address concurrent cell segmentation and tracking. In this paper, we propose a novel DL approach featuring graph-based tracking for cell segmentation and tracking in microscopy images. We combine Deeplabv3+ for semantic segmentation and ResNet50 for enhanced feature extraction, enabling comprehensive cell detection and instance segmentation. Post-processing, involving non-maxima suppression and outlier detection, refines predictions and produces final segmentation. The tracking method is based on the relative position of graph nodes to track segmented cells, encompassing cell division and apoptosis. We conduct our experiments on the induced pluripotent stem (iPS) cell datasets, and the results show that the segmentation and tracking performance of our method yields superior performance compared to the benchmark models. More specifically, our approach achieved DET values of 0.955 and 0.913, TRA values of 0.951 and 0.906, and SEG values of 0.690 and 0.665 on two iPS dataset videos, respectively. Additionally, the performance assessment encompasses four real microscopy datasets from the Cell Tracking Challenge (CTC). Our method greatly reduces the cost of manual labeling and labor-intensive costs. © 2023 IEEE.

关键词： cell segmentation cell tracking deep learning Deeplabv3+ instance segmentation iPS cell reprogramming

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