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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8379-8384页

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Hybrid pruning for nonlinear equalization in short-reach high-speed optical links

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Optics Express 2025年第6期33卷 13087-13100页

作者： Widhianto, Benedictus Yohanes Bagus Chen, Jyehong Taiwan Department of Photonics College of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu300 Taiwan

Nonlinear impairments from transceiver components in short-reach optical communication systems, such as drivers, TIAs, and external modulators, pose significant challenges for achieving high data rates with low latency. Volterra Nonlinear Equalization (VNLE) can mitigate these impairments but suffers from higher computational complexity, rendering it impractical for energy-efficient and low-cost data center applications. We propose a Hybrid Pruned VNLE (HP-VNLE) that combines structured and unstructured kernel reduction to significantly reduce complexity while maintaining low bit error rate (BER) performance. This study reveals that most non-significant taps in the third-order unpruned VNLE consist of at least three different index terms, justifying the necessity of pruning to remove taps with minimal contribution to nonlinearity mitigation. Experimental results demonstrate that the HP-VNLE’s performance highly depends on the initial tap configuration derived from the proposed structured kernel reduction so-called center-spread (CS)VNLE. Moreover, combining the third-order HP-VNLE with a linear equalizer and a 1-tap decision feedback equalizer (DFE) yields superior BER results compared to unpruned VNLE. This hybrid approach reduces the computational complexity and enhances system performance, offering a range of promising solutions for future optical communication systems where energy efficiency, low complexity, and high data rates must be optimized for various application scenarios. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Optical links

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Glass Packaging for 6G Applications

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IEEE Microwave Magazine 2025年第6期26卷 46-64页

作者： Swaminathan, Madhavan Li, Xingchen Vijay Kumar, Lakshmi Narasimha Jia, Xiaofan Erdogan, Serhat Rehman, Mutee ur Huang, Kai-Qi Kim, Joon Woo Al-Juwhari, Mohammed Ahamed, Mahin Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Penn State University Department of Electrical Engineering State CollegePA16801 United States

With the increasing demand for channel capacity, higher data rate, and wider bandwidth for wireless communication and sensing, the focus of millimeter-wave (mmWave) technology development is moving toward higher frequencies. For example, the 5G standard maps the frequency range 2 (FR2) bands into 24.25–52.6 GHz to satisfy the need for higher bandwidth, while the upcoming 6G communications is considering W-band (75–110 GHz), D-band (110–170 GHz), and even G-band (110–300 GHz) as the targeted operating frequencies. This evolution is driving mmWave systems toward the miniaturization of devices and components. This trend advocates the need for integrating compact passive and active components into the package, leading to complex heterogeneous integration platforms. © 2000-2012 IEEE.

关键词： Wireless communication Fabrication 6G mobile communication Wireless sensor networks Bandwidth Sensors Millimeter wave communication Substrates Standards Channel capacity

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Single-Frequency Self-Alignment RF Resonant Beam for Information and Power Transfer

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IEEE Internet of Things Journal 2025年第11期12卷 16622-16636页

作者： Jiang, Qingwei Liu, Mingqing Xu, Mengyuan Fang, Wen Xiong, Mingliang Liu, Qingwen Zhou, Shengli Tongji University College of Computer Science and Technology Shanghai201804 China Tongji University College of Electronics and Information Engineering Shanghai201804 China University of Connecticut Department of Electrical and Computer Engineering CT06250 United States

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective ways to enhance transmission efficiency. However, when the target is an array or in the near field, the RD-BF system (RD-BFS) cannot radiate more energy to the target due to phase irregularities in the target region, resulting in challenges in achieving higher efficiency. To address this issue, we propose the RF-based resonant beam system (RF-RBS), which adaptively optimizes phase and power distribution between transmitting and receiving arrays by leveraging the resonance mechanism to achieve higher transmission efficiency. We analyze the system structure and develop an analytical model to evaluate power flow and resonance establishment. Numerical analysis demonstrates that the proposed RF-RBS achieves self-alignment without beam control and provides higher transmission efficiency compared to RD-BFS, with improvements of up to 16%. This self-alignment capability allows the system to effectively transfer power and information across varying distances and offsets. The numerical results indicate the capability to transmit watt-level power and achieve 21 bps/Hz of downlink spectral efficiency in indoor settings, highlighting the advantages of RF-RBS in information and power transfer for mobile applications. © 2025 IEEE.

关键词： Inductive power transmission

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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Advancing Federated Learning: A Comprehensive Solution for Model Aggregation, Heterogeneity, Privacy, and Security

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SN computer Science 2025年第5期6卷 1-36页

作者： Bari, Bifta Sama Yelamarthi, Kumar Department of Electrical and Computer Engineering College of Engineering Tennessee Tech University Cookeville 38505 TN United States

Secure vehicular communication is one of the challenges that is crucial for Intelligent Transportation Systems (ITS). Although Federated Learning (FL) enhances data privacy compared to Centralized Learning (CL), it faces challenges with data heterogeneity and resource allocation. To address these challenges, the Hybrid Federated-Centralized Learning (HFCL) framework improves efficiency and reduces communication overhead but still lacks comprehensive solutions to existing key issues including FL model aggregation, client heterogeneity, privacy-preserving communication, and enhanced security protocols. This paper proposes a unified HFCL framework incorporating the existing key issues that aims to develop and analyze model aggregation ensuring equitable client contributions, secure communications, and protect against various security threats. The implementation with FedAvg aggregation algorithm on diverse clients and datasets ensures robust and efficient communication, while Differential Privacy (DP) in HFCL enhances security and resilience against adversaries including poisoning attacks. Performance analysis reveals that HFCL maintains high accuracy and stability across varying learning rates and client configurations. Notably, the proposed unified privacy preserving HFCL achieves nearly 99.15% accuracy on the MNIST dataset and 68.4% on the CIFAR10 dataset, demonstrating its efficacy in different scenarios. By addressing the existing critical challenges, the proposed framework enhances the scalability, performance, and security of distributed ML systems, with a primary focus on vehicular communication. It highlights the significant advantages of HFCL for data processing and decision-making in applications like collision avoidance and autonomous driving. Additionally, the framework’s applicability could be extended to healthcare and finance, enabling secure and efficient analysis of sensitive data. © The Author(s), under exclusive licence to Springer Nature Singapore P

关键词： Centralized machine learning Convolutional neural networks Edge computing Federated learning Vehicular communication

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Examining AI-Powered ADHD Diagnosis: Current Trends, Key Challenges, and Future Directions in the Field

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IEEE Access 2025年 13卷 93148-93177页

作者： Ain, Qurat Ul Jawed, Soyiba Subhani, Ahmad Rauf Akram, Muhammad Usman Butt, Wasi Haider College of E&ME National University of Sciences and Technology Department of Computer &Software Engineering Islamabad Pakistan College of E & ME National University of Sciences and Technology Department of Electrical Engineering Islamabad Pakistan

Attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disease affecting the mental health of children and adults. Individuals with ADHD show various symptoms such as inattention, hyperactivity, and impulsivity. Early diagnosis of ADHD helps to alter neural connections and refine symptoms. The clinical practice to diagnose ADHD is through subjective measures and does not significantly capture the underlying structural and functional mechanisms of the brain. Therefore, it is crucial to explore other approaches such as Artificial Intelligence (AI) to improve the accuracy and efficacy of ADHD diagnosis. Consequently, in this article we systematically investigate various Machine Learning (ML) and Deep Learning (DL) approaches as well as different diagnostic tools or modalities employed for the identification of ADHD. Particularly, a Systematic Literature Review (SLR) is conducted to review and analyze 98 selected studies published from 2021 to 2024. Subsequently, the selected studies are grouped into five categories based on the modalities utilized in these studies: physiological signals (37), magnetic resonance imaging (31), questionnaires (11), motion data (8), and others (11). We also analyze AI models which indicates that 45 studies utilized ML models, 33 studies employed DL models, and 20 studies used both. However, there are still some gaps in current research such as a lack of publicly available datasets except MRI and EEG. Although datasets for MEG and actigraphy exist, but they are underexplored and have been utilized in only a few studies. While DL models like CNNs and ANNs have been increasingly applied in recent years for ADHD diagnosis, there is a shortage of advanced DL models, including transfer learning approaches like ResNet and VGG. Additionally, there is a lack of interpretability in AI models, particularly DL models. Furthermore, most studies focus on individual modalities for ADHD diagnosis, and despite many studies showing

关键词： Electrocardiography

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Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes

Kernel-Based Anomaly Detection Using Generalized Hyperbolic ...

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

作者： Bourigault, Pauline Mandic, Danilo P. Department of Computing Imperial College London United Kingdom Department of Electronic and Electrical Engineering Imperial College London United Kingdom

ISBN: (纸本)9798350368741

We present a novel approach to anomaly detection by integrating Generalized Hyperbolic (GH) processes into kernel-based methods. The GH distribution, known for its flexibility in modeling skewness, heavy tails, and kurtosis, helps to capture complex patterns in data that deviate from Gaussian assumptions. We propose a GH-based kernel function and utilize it within Kernel Density Estimation (KDE) and One-Class Support Vector Machines (OCSVM) to develop anomaly detection frameworks. Theoretical results confirmed the positive semi-definiteness and consistency of the GH-based kernel, ensuring its suitability for machine learning applications. Empirical evaluation on synthetic and real-world datasets showed that our method improves detection performance in scenarios involving heavy-tailed and asymmetric or imbalanced distributions. https://***/paulinebourigault/GHKernelAnomalyDetect. © 2025 IEEE.

关键词： anomaly detection generalized hyperbolic processes kernel

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