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An Efficient and Generalized Auto-Design Methodology for Complicated Beamforming networks

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年第5期73卷 2776-2791页

作者： Liu, Anlan Yang, Yimin Yu, Ming Chinese Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab EM Informat Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China

To deal with different beamforming network (BFN) design tasks, a generalized auto-design methodology is developed by the following three steps. First, a structure reconfiguration is performed by using a binary design matrix assisted with different reconfiguration functions. Then, to speed up the component design, the homotopy continuation (HC) method is exploited to form a partial database. A mapping technique ensures the partial database to be expanded to the complete database, which covers the whole feature space. An inverse model is well trained based on the complete database to generate the physical parameters of multiple couplers. Finally, the optimal distributed phase compensation is decided by minimizing the total phase shift value of phase shifters. For the demonstration, two different waveguide BFNs are designed. In addition, this work discussed the potential of using the proposed methodology to design BFNs with a more complex configuration or BFNs in other materials. The results prove that the proposed methodology allows an automatic and simultaneous generation of multiple physical structures, which can improve the BFN's electrical performance or meet different physical specifications (PS). Considering many possible problems in a BFN design, the proposed methodology is generalized to different classes of BFNs. Moreover, the component design can be finished within a shorter time than existing algorithms.

关键词： Couplers Topology Transmission line matrix methods Databases network topology Butler matrices Matrix decomposition Artificial neural networks Array signal processing Artificial neural network (ANN) beamforming network (BFN) computer-aided design (CAD) design automation directional coupler homotopy continuation (HC) phase shifter (PSF) phase shifter (PSF)

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Fusing temporal-frequency information with Contrast Learning on Graph Convolution network to decoding EEG

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BIOMEDICAL SIGNAL processing AND CONTROL 2025年 100卷

作者： Yu, Minghao He, Qing Wang, Yuji Du, Nisuo Guizhou Univ Coll Big Data & Informat Engn Guiyang 550025 Guizhou Peoples R China Guizhou Aerosp Fenghua Precis Equipment Co Ltd Guiyang 550009 GuiZhou Peoples R China Guizhou Univ Guizhou Big Data Acad Guiyang 550025 GuiZhou Peoples R China

Decoding human electroencephalogram (EEG) signals and identifying the brain's response to external stimuli is a challenging task, but it is crucial for understanding the brain's information processing mechanisms and developing brain like intelligent computers. Previous studies have used neural networks to analyze the spatiotemporal features of various EEG regions for EEG emotion recognition, but there have been few studies characterizing different frequency bands of EEG signals, and little attention has been paid to the issue of fuzzy emotion labeling in continuous emotion models. Based on these two issues, this study proposes a Graph Convolutional network (FCLGCN) method to collect time and frequency band information of EEG, and solves the problem of fuzzy emotional boundaries through contrastive learning. FCLGCN achieved high recognition accuracy on DEAP and SEED datasets. According to the experimental results, the connection between the frontal and temporal lobes on both sides of the brain becomes tighter during emotional changes.

关键词： EEG emotion recognition Graph Convolutional network Contrast learning Brain connection

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Leveraging Spark-TensorFlow Distributor for distributed Deep Convolutional neural networks: Accelerating large-scale COVID-19 Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第17期49卷 173-188页

作者： Mezzoudj, Saliha Khelifa, Meriem Saadna, Yasmina Department of Computer Science University of Algiers Algiers Algeria Artificial Intelligence of Information Technologies Department of Computer Science and Information Technologies University of Kasdi Merbah Ouargla Algeria Labstic laboratory Batna 2 University Batna Algeria

The COVID-19 epidemic has been a critical global challenge due to its high mortality rate and rapid spread. Initial diagnostic methods, such as chest X-rays and reverse transcriptase polymerase chain reaction (RT-PCR), can be time-consuming and require enhanced efficiency, especially in scenarios where large-scale testing is necessary. Combining deep learning (DL) and big data analytics tools is crucial for developing accurate and fast systems to detect COVID-19 infections and prevent further transmission. In this study, we introduce a distributed Deep Convolutional neural network (DCNN) framework designed for the detection of COVID-19, utilizing a comprehensive dataset of chest X-ray images. Our approach leverages the spark-tensorflow-distributor, which integrates TensorFlow 2.x with Apache Spark 3.x, enabling distributed training within the Spark framework. The system utilizes Spark’s barrier mode to execute distributed training tasks, significantly enhancing the speed of both training and testing phases. We classify chest X-ray images into two categories: COVID-19 and non-COVID-19. The experimental setup involves processing the large-scale dataset of chest X-ray images in a parallelized manner using the Spark framework. Our results demonstrate that the proposed DCNN model achieves a classification accuracy of 95.53%, with a testing time of 0.06 seconds and a training time of 18,909 seconds. Furthermore, when compared to other state-of-the-art methods for COVID-19 classification, our approach proves to be more efficient and effective. This study highlights the potential of combining distributed computing frameworks with deep learning techniques to address largescale medical image analysis challenges, particularly in the context of COVID-19 detection. © 2025 Slovene Society Informatika. All rights reserved.

关键词： COVID-19

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SHEATH: Defending Horizontal Collaboration for distributed CNNs Against Adversarial Noise

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 4353-4368页

作者： Asif, Muneeba Kazmi, Mohammad Kumail Rahman, Mohammad Ashiqur Hasan, Syed Rafay Homsi, Soamar Florida Int Univ Analyt Cyber Def ACyD Lab Miami FL 33199 USA Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA Air Force Res Lab Informat Warfare Div Rome NY 13441 USA

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC) emerges as a distributed data processing solution for resource-constrained devices. In particular, a convolutional neural network (CNN) model can be deployed on multiple IoT devices, allowing distributed inference execution for image recognition while ensuring model and data privacy. Yet, this distributed architecture remains vulnerable to adversaries who want to make subtle alterations that impact the model, even if they lack access to the entire model. Such vulnerabilities can have severe implications for various sectors, including healthcare, military, and autonomous systems. However, security solutions for these vulnerabilities have not been explored. This paper presents a novel framework for Secure Horizontal Edge with Adversarial Threat Handling (SHEATH) to detect adversarial noise and eliminate its effect on CNN inference by recovering the original feature maps. Specifically, SHEATH aims to address vulnerabilities without requiring complete knowledge of the CNN model in HC edge architectures based on sequential partitioning. It ensures data and model integrity, offering security against adversarial noise in diverse HC environments. Our evaluations demonstrate SHEATH's adaptability and effectiveness across diverse CNN configurations.

关键词： Noise Internet of Things Computational modeling Security Image edge detection Collaboration Data models Convolutional neural networks Privacy Edge computing Secure horizontal collaboration adversarial noise detection decentralized CNNs

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Empirical Study on Myopia Identification Using CNN Hereditary Modelfor Resource Constrained Ophthalmology

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International Journal Bioautomation 2025年第1期29卷 19-32页

作者： Nazifa, Aqila Joshi, Manisha Shivaram Ramani, Soumya Medical Electronics Engineering Department BMS College of Engineering Karnataka Bengaluru560019 India Ophthalmology Department Ramaiah Memorial Hospital Karnataka Bengaluru560019 India

Refractive errors, which include myopia, hyperopia, presbyopia, and astigmatism, are common vision problems that result in blurred vision when light rays are not focused correctly on the retinal plane. Diagnosis and classification of refractive errors are essential for providing appropriate corrective measures such as glasses or contact lenses. The key objective of this research is to establish an efficient and fast approach to identifying a refractive defect and categorizing them. Leveraging the capabilities of modern technology, we utilize a smartphone’s camera to capture pictures of the red reflex in the eye. During capturing, the photos are processed using recent image processing techniques to identify any irregularities or asymmetries that may indicate refractive errors. By comparing our method to other current models, we hope to illustrate the advantage of our Hereditary model, which combines a random forest and a convolutional neural network, in accurately diagnosing and classifying refractive errors. Additionally, the proposed approach can serve as a foundation in order to do additional research and development in machine learning and image processing methods improvements for the classification of ocular disorders. © 2025 by the authors. Licensee Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://***/licenses/by/4.0/).

关键词： Convolutional neural networks

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Hadoop neural network for parallel and distributed feature selection

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neural networkS 2016年 78卷 24-35页

作者： Hodge, Victoria J. O'Keefe, Simon Austin, Jim Univ York Dept Comp Sci Adv Comp Architecture Grp York YO10 5GH N Yorkshire England

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license.

关键词： Hadoop MapReduce distributed Parallel Feature selection Binary neural network

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Response Time Analysis and Optimal Priority Assignment for Global Non-Preemptive Fixed-Priority Rigid Gang Scheduling

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2025年第3期36卷 455-470页

作者： Sun, Binqi Kloda, Tomasz Chen, Jiyang Lu, Cen Caccamo, Marco Tech Univ Munich D-85748 Munich Germany Univ Toulouse AAS CNRS INSA F-31000 Toulouse France

Non-preemptive rigid gang scheduling combines the efficiency of parallel execution with the reduced overhead of non-preemptive scheduling. This approach is particularly advantageous for parallel hardware accelerators, such as Google's Edge Tensor processing Unit (TPU), which is widely used for deep neural network (DNN) inference on embedded systems. This paper studies sporadic global non-preemptive fixed-priority (NP-FP) rigid gang scheduling, which is well-suited for DNN applications in Edge TPU pipelines. Each gang task spawns a fixed number of threads that must execute concurrently across distinct processing units. We introduce the first carry-in limitation technique specifically designed for gang task response time analysis, addressing the unique challenges posed by intra-task parallelism. This technique is formulated as a generalized knapsack problem, and we develop both a linear programming relaxation and a dynamic programming approach to solve it under different time complexities. Additionally, we propose the first optimal priority assignment policy for NP-FP gang schedulability tests. Our proposed schedulability analysis and optimal priority assignment policy are evaluated through extensive experiments, including both synthetic task sets and a case study using DNN benchmarks on commercial off-the-shelf Edge TPU accelerators. The results demonstrate that the proposed approaches effectively enhance the state-of-the-art global NP-FP gang schedulability tests, achieving improvements of up to 57.9% for synthetic task sets and 76.7% for Edge TPU benchmarks. Furthermore, we conduct an ablations study to examine the impact of different algorithmic components in the proposed technique, providing valuable insights for future research.

关键词： Optimal scheduling Artificial neural networks Scheduling Time factors Pipeline processing Scheduling algorithms Real-time systems Interference Benchmark testing Sun Gang scheduling response time analysis optimal priority assignment Edge TPU real-time system

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A Discrete-Time Collaborative Neurodynamic Approach to distributed Global Optimization 13

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

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13th International Conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results. © 2025 IEEE.

关键词： neural network models

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Unsupervised Deep Learning for DAS-VSP Denoising Using Attention-Based Deep Image Prior

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Cui, Yang Bin Waheed, Umair Chen, Yangkang King Fahd Univ Petr & Minerals Coll Petr Engn & Geosci Dhahran 31261 Saudi Arabia Univ Texas Austin John A & Katherine G Jackson Sch Geosci Bur Econ Geol Austin TX 78712 USA

distributed acoustic sensing (DAS) has emerged as a widely used technology in various applications, including borehole microseismic monitoring, active source exploration, and ambient noise tomography. Compared with conventional geophones, the fiber optic cable has unique characteristics that allow it to withstand high-temperature and high-pressure environments. However, due to its high sensitivity, the obtained seismic records are often corrupted with unavoidable background noise, which introduces more uncertainty in the subsequent seismic data processing and interpretation. Thus, the development of robust denoising techniques for DAS data is crucial to minimize the impact of noise and enhance the reliability of seismic data processing and interpretation. In this work, we propose a ground-truth-free method for strong background noise suppression in DAS vertical seismic profiling (DAS-VSP) data. Compared to existing deep learning (DL) methods, the proposed approach demonstrates promising generalizability in handling field examples across different surveys. The proposed method consists of four stages: training set extension with a patching scheme, feature selection with a kurtosis-based method, denoising with a deep image prior (DIP)-based unsupervised neural network, and an unpatching approach for denoised data reconstruction. Numerical experiments conducted on synthetic data and several profiles from the Utah FORGE project and the Gro ss Sch & ouml;nebeck site demonstrate that the proposed method can effectively suppress most of the background noise while preserving hidden signals. Furthermore, the unsupervised learning (USL) approach is unconditionally generalizable when applied to vastly different field data because it does not require pre-labeled datasets for training. The codes related to this article are fully open-source via https://***/cuiyang512/Unsupervised-DAS-Denoising.

关键词： Noise Noise reduction Training Kurtosis Feature extraction Background noise Noise measurement Training data Optical fibers Data processing Denoising distributed acoustic sensing (DAS) seismic data processing unsupervised learning (USL)

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State-Augmented Opportunistic Routing in Wireless Communication Systems with Graph neural networks

State-Augmented Opportunistic Routing in Wireless Communicat...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Das, Sourajit NaderiAlizadeh, Navid Ribeiro, Alejandro University of Pennsylvania United States Duke University United States

ISBN: (纸本)9798350368741

In this study, we address the challenge of packet based information routing in large-scale wireless communication networks. We approach this scenario by framing the problem as a statistical learning problem, where each node in the network relies only on the local data. Our exploration focuses on the idea of opportunistic routing, which exploits the broadcast nature of wireless communication to select the optimal relay node and transmit information packets to the destination node via multiple relay nodes. We present a distributed optimization method based on state augmentation (SA) that aims to maximize the total information on different source nodes of the network. Our formulation of the problem deploys graph neural networks (GNNs) to perform graph convolution on the topological connections between the network nodes. Using unsupervised learning, we derive optimal routing policies for the source nodes across multiple flows from the GNN output. Numerical results show the superiority of our proposed method by comparing a GNN model trained against standard algorithms. © 2025 IEEE.

关键词： Graph neural networks Opportunistic routing State augmentation Unsupervised learning Wireless communication networks

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