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Devices for Integrated Circuit (DevIC)

作者： Sheela D Bhavani N. P.G Prameeladevi C Sarada Devi CH Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

The rapid growth in demand for high-capacity telecommunication links, and the speed limitation of single-wavelength links, has resulted in an extraordinary increase in the use of wavelength-division multiplexing (WDM) in advanced lightwave networks. The existing research focuses on waveguide tapering, with limited attention given to tapering effects in WDM phasor arrays. This study introduces a tapered WDM phasor array and investigates how the tapering ratio influences the output field. A WDM phasor array with linear tapering is designed with uniform separation. The performance is analyzed across various tapering ratios and compared with the non-tapered structure. The ouput is analyzed in terms of optical field, electric field and effective refractive index. Results show that lower tapering ratios improve output compared to non-tapered designs, while higher tapering ratios lead to reduced output field strengths.

关键词： Couplings Integrated circuits Refractive index Wavelength division multiplexing Optical variables control Optical coupling Communications technology Optical refraction Electric fields Optical waveguides

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Solving Multiobjective Combinatorial Optimization via Learning to Improve Method

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第3期9卷 2122-2136页

作者： Ye, Te Zhang, Zizhen Zhang, Qingfu Chen, Jinbiao Wang, Jiahai Sun Yat-sen University School of Computer Science and Engineering Guangzhou510275 China Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510275 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

Recently, neural combinatorial optimization (NCO) methods have been prevailing for solving multiobjective combinatorial optimization problems (MOCOPs). Most NCO methods are based on the "Learning to Construct"(L2C) paradigm, where the trained model(s) can directly generate a set of approximate Pareto optimal solutions. However, these methods still suffer from insufficient proximity and poor diversity towards the true Pareto front. In this paper, following the "Learning to Improve"(L2I) paradigm, we propose weight-related policy network (WRPN), a learning-based improvement method for solving MOCOPs. WRPN is incorporated into multiobjective evolutionary algorithm (MOEA) frameworks to effectively guide the search direction. A shared baseline for proximal policy optimization is presented to reduce variance in model training. A quality enhancement mechanism is designed to further refine the Pareto set during model inference. Computational experiments conducted on two classic MOCOPs, i.e., multiobjective traveling salesman problem and multiobjective vehicle routing problem, indicate that our method achieves remarkable results. Notably, our WRPN module can be easily integrated into various MOEA frameworks such as NSGA-II, MOEA/D and MOGLS, providing versatility and applicability across different problem domains. © 2025 IEEE.

关键词： Deep reinforcement learning

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Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition

arXiv

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arXiv 2025年

作者： Zhang, Jie Ni, Yiyang Li, Jun Chen, Guangji Wang, Zhe Shi, Long Jin, Shi Chen, Wen Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China University Jiangsu Institute of Educational Science Research Nanjing210094 China National Mobile Communications Research Laboratory Southeast University China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Enhancing Ieee 1588 Ptp Security for Iiot Networks: A Lightweight Attack Detection and Mitigation Framework

SSRN

引用

SSRN 2025年

作者： Idrees, Zeba Latif, Shahid Tahir, Hira Zheng, Liron Electrical and Computer Engineering Department University of Alberta Canada School of Computing and Creative Technologies University of the West of England United Kingdom Interdisciplinary Research Centre for Smart Mobility and Logistics King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia School of Information Science and Technology Fudan University China

Highly precise clock synchronization is an important aspect of the Industrial Internet of Things (IIoT) network because desynchronized clocks among nodes in IIoT can degrade system performance. IEEE 1588 Precision Time Protocol (PTP) is widely used in such time-sensitive networks. Resource efficiency and security have become the most important concerns in designing PTP for IIoT applications. PTP provides unified and high-precision time, whereas it is resource inefficient and insecure in its current form, particularly for resource-constrained IoT devices, such as battery powered sensing nodes. This paper aims to advance the existing PTP to improve security for IIoT networks without involving complex and power-consuming cryptographic algorithms. We study and analyze the potential cyber-attacks that can affect the security and synchronization of the PTP network. Considering the limitations of the PTP security defined by IEEE 1588 in its Annex K, we propose a security extension to the PTP algorithm. This security model covers the full PTP attack surface and allows the detection of attacks on all the PTP nodes in a timely manner. Along with the attack detection, we establish an attack mitigation model to mitigate the attack effects on Master PTP nodes. The proposed secure PTP model was evaluated under different network conditions and with varying important parameters. It was observed that newly introduced functions do not compromise synchronization accuracy. All the experimental evaluations demonstrate that the proposed approach is more secure and robust to cyber-attacks and does not affect the operation of PTP devices in all considered network configurations. © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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An Adaptive Deep Learning Framework for Human Activity Recognition using Video Data

An Adaptive Deep Learning Framework for Human Activity Recog...

引用

Emerging Systems and Intelligent computing (ESIC), International Conference on

作者： Chejarla Raja S D. Arivazhagan S Lavanya Rajeswari C Banu S Rahmaan K Faculty of Information and Communication Engineering Anna University Chennai Tamil Nadu India AMET Business School Academy of Maritime Education and Training Deemed to be University Chennai India Department of Computer Science and Engineering KCG College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This research presents a dual-pathway SlowFast network in order to provide a novel approach for the recognition of human actions. The method has the ability to capture low-intensity as well as high-intensity activities efficiently. The model is trained on two different datasets: one on the routine activities, such as sitting and walking, and the other on complex sports and leisure activities. The SlowFast network increases the accuracy in a wide variety of scenarios by incorporating high-level spatial context along with fast motion characteristics using a dual-path architecture. The proposed model outperforms the conventional HAR methods on different grounds such as adaptability and accuracy, and thus aptly useful for applications such as security surveillance, healthcare, and sports analysis. This approach brings forth a robust system able to identify a range of human actions in static as well as dynamic environments by removing some of the limitations from existing HAR models.

关键词： Adaptation models Analytical models Accuracy Medical services Video surveillance Real-time systems Motion capture Human activity recognition Security Sports

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ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis

arXiv

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arXiv 2025年

作者： Wu, Yuqi Wan, Guangya Li, Jingjing Zhao, Shengming Ma, Lingfeng Ye, Tianyi Pop, Ion Zhang, Yanbo Chen, Jie Zhang, Mike Department of Electrical and Computer Engineering University of Alberta Canada School of Data Science University of Virginia United States McIntire School of Commerce University of Virginia United States Department of Psychiatry University of Alberta Canada Academy for Engineering Fudan University China

Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction. However, many real-world applications—such as psychiatric diagnosis, consulting, and interviews—require AI to take a proactive role, asking the right questions and steering conversations toward specific objectives. Using mental health differential diagnosis as an application context, we introduce ProAI, a goal-oriented, proactive conversational AI framework. ProAI integrates structured knowledge-guided memory, multi-agent proactive reasoning, and a multi-faceted evaluation strategy, enabling LLMs to engage in clinician-style diagnostic reasoning rather than simple response generation. Through simulated patient interactions, user experience assessment, and professional clinical validation, we demonstrate that ProAI achieves up to 83.3% accuracy in mental disorder differential diagnosis while maintaining professional and empathetic interaction standards. These results highlight the potential for more reliable, adaptive, and goal-driven AI diagnostic assistants, advancing LLMs beyond reactive dialogue systems. © 2025, CC BY-NC-SA.

关键词： Chatbots

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The Effects of Heat Treatment on the Corrosion Behavior of Cold-Sprayed 304l Stainless Steel

SSRN

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SSRN 2025年

作者： Chiang, Richard Roper, Christopher Steiner, Matthew A. Allison, Paul G. Brewer, Luke N. Vasudevan, Vijay K. Department of Mechanical and Materials Engineering University of Cincinnati CincinnatiOH United States Department of Metallurgical and Materials Engineering University of Alabama TuscaloosaAL United States School of Engineering and Computer Science Baylor University WacoTX United States Department of Materials Science and Engineering University of North Texas DentonTX United States

Electrochemical analysis was utilized in this study to investigate the effects of recrystallization and sensitization heat treatment on the passivity and corrosion behavior of cold-sprayed 304L stainless steel. Results from both cyclic polarization and EIS indicate the cold-sprayed specimen to have poor passivity because of its high defect density that hinders film stability. Recrystallization heat treatment was observed to significantly improve the passivity, though localized corrosion still occurs at the particle boundaries due to the presence of porosities. Sensitization of recrystallized cold-sprayed 304L stainless steel was shown to have no significant effect. This was attributed to the low carbon content and ultrafine grain size that mitigated the growth of the carbides from reaching a critical size for chromium depletion. © 2025, The Authors. All rights reserved.

关键词： Steel corrosion

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Deep Learning-Driven Throughput Maximization in Covert Communication for UAV-RIS Cognitive Systems

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2025年 6卷 4140-4155页

作者： Nhan Vo, Van Quoc Long, Nguyen Dang, Viet-Hung Dac Ho, Tu Tran, Hung Chatzinotas, Symeon Tran, Dinh-Hieu Sanguanpong, Surasak So-In, Chakchai Duy Tan University Faculty of Information Technology Da Nang550000 Viet Nam Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Norwegian University of Science and Technology Faculty of Information Technology and Electrical Engineering Department of Information Security and Communication Technology Trondheim7034 Norway National Economics University DATCOM Lab Faculty of Data Science and Artificial Intelligence College of Technology Hanoi12116 Viet Nam University of Luxembourg Interdisciplinary Centre for Security Reliability and Trust Esch-sur-Alzette4365 Luxembourg Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok10900 Thailand Khon Kaen University Applied Network Technology College of Computing Khon Kaen40002 Thailand

This paper examines a cognitive radio (CR) nonorthogonal multiple access (NOMA) system in which an unmanned aerial vehicle equipped with a reconfigurable intelligent surface (UAV-RIS) plays two roles: relaying and friendly jamming. The communication protocol has two phases. The first is an energy harvesting phase in which the UAV harvests radio frequency energy from a power beacon. In the second phase, a secondary transmitter (ST) simultaneously sends superimposed signals to secondary receivers (SRs) (a public SR and a covert SR) via NOMA with the assistance of the UAV-RIS. Then, a UAV warden and a UAV jammer launch a cooperative attack, in which the first adversary wiretaps the signals from the ST and UAV-RIS, whereas the second interferes with the SRs to force the ST to increase its transmit power. For improved secrecy, the UAV-RIS uses its harvested energy to combat the UAV warden. For this system, the secrecy performance is evaluated on the basis of the concept of covert communication. In particular, optimization algorithms are employed to maximize the covert SR throughput under outage probability and security constraints. A deep neural network model is subsequently trained to discover the relationships between the environmental parameters and optimized parameters to enable rapid adaptation to environmental conditions. © 2020 IEEE.

关键词： Drones

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Real-time raw signal genomic analysis using fully integrated memristor hardware

arXiv

引用

arXiv 2025年

作者： He, Peiyi Wang, Shengbo Mao, Ruibin Siegel, Sebastian Pedretti, Giacomo Ignowski, Jim Strachan, John Paul Luo, Ruibang Li, Can Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Forschungszentrum Jülich GmbH Jülich Germany Hewlett Packard Labs Hewlett Packard Enterprise MilpitasCA United States RWTH Aachen University Aachen Germany Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analysis due to prohibitive time and energy costs. These technologies generate a massive amount of noisy analog signals that traditionally require basecalling and digital mapping, both demanding frequent and costly data movement on von Neumann hardware. To overcome these challenges, we present a memristor-based hardware-software co-design that processes raw sequencer signals directly in analog memory, effectively combining the separated basecalling and read mapping steps. Here we demonstrate, for the first time, end-to-end memristor-based genomic analysis in a fully integrated memristor chip. By exploiting intrinsic device noise for locality-sensitive hashing and implementing parallel approximate searches in content-addressable memory, we experimentally showcase on-site applications including infectious disease detection and metagenomic classification. Our experimentally-validated analysis confirms the effectiveness of this approach on real-world tasks, achieving a state-of-the-art 97.15% F1 score in virus raw signal mapping, with 51× speed up and 477× energy saving compared to implementation on a state-of-the-art ASIC. These results demonstrate that memristor-based in-memory computing provides a viable solution for integration with portable sequencers, enabling truly real-time on-site genomic analysis for applications ranging from pathogen surveillance to microbial community profiling. © 2025, CC BY-NC-SA.

关键词： Analog storage

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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