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IEEE Electron Device Letters 2025年第5期46卷 705-708页

作者： Chen, Yi-Ho Chu, Fu-Chuan Aslam, Muhammad Lee, Yao-Jen Li, Yiming Samukawa, Seiji National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Graduate Degree Program of College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung-Shan Institute of Science and Technology Solid State Devices Section Electronic System Research Division Taoyuan325 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Recess gate etching is a critical technique for achieving enhancement-mode (E-mode) AlGaN/GaN high-electron mobility transistors (HEMTs) because the interface is susceptible to the etching damage. This study fabricates recess gates using the neutral beam etching (NBE) technique. By adjusting the aperture thickness in the NBE apparatus, we simulate both NB-mode and plasma-mode etching. The electrical characteristics of E-mode HEMTs fabricated using these two modes are analyzed and compared through DC, noise, and pulsed IV measurements. The results demonstrate that NB-recessed HEMTs exhibit superior performance. © 1980-2012 IEEE.

关键词： Aluminum gallium nitride

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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On Strong Secrecy for Multiple Access Channels With States and Causal CSI

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IEEE Transactions on Information Theory 2025年第4期71卷 3070-3099页

作者： Chen, Yiqi Oechtering, Tobias J. Skoglund, Mikael Luo, Yuan Technical University of Munich Chair of Theoretical Information Technology Munich80333 Germany Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China KTH Royal Institute of Technology Division of Information Science and Engineering School of Electrical Engineering and Computer Science Stockholm100 44 Sweden Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai200240 China

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases. © 1963-2012 IEEE.

关键词： Markov processes

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6DMA-Aided Cell-Free Massive MIMO Communication

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IEEE Wireless Communications Letters 2025年第5期14卷 1361-1365页

作者： Shi, Xiaoming Shao, Xiaodan Zheng, Beixiong Zhang, Rui School of Science and Engineering Guangdong Shenzhen518172 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada South China University of Technology School of Microelectronics Guangzhou511442 China School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Lower Kent Ridge Road 117583 Singapore

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit (CPU) via fronthaul links, 6DMA-APs can optimize their combining vectors for decoding the users' information based on either local channel state information (CSI) or global CSI shared among them. We aim to maximize the average achievable sum-rate via jointly optimizing the rotation angles of all 6DMA surfaces at all APs, based on the users' spatial distribution. Since the formulated problem is non-convex and highly non-linear, we propose a Bayesian optimization-based algorithm to solve it efficiently. Simulation results show that, by enhancing signal power and mitigating interference through reduced channel cross-correlation among users, 6DMA-APs with optimized rotations can significantly improve the average sum-rate, as compared to the conventional cell-free network with fixed-position antennas and that with only a single centralized AP with optimally rotated 6DMAs, especially when the user distribution is more spatially diverse. © 2012 IEEE.

关键词： Convex optimization

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Explainable AI in Blockchain System for Decentralized Governance

Explainable AI in Blockchain System for Decentralized Govern...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798331508685

Decentralized governance is going through radical changes due to the innovative use of blockchain which allows decisions to be made in a transparent, secure and more im- portantly, immutable way, without the authority of a single individual or party. Thus, the introduction of artificial intelligence (AI) in blockchain governing systems complicates such thing as transparency and trust, especially in the case of deep AI systems. But more on this later - this is where Explainable AI or XAI comes into the picture. XAI methods enable understanding of the reasons behind the choices made by AI models and solves the "black box"problem that is predominant in machine learn- ing. When applied to decentralised governance, XAI provides clarifications of AI's decision making in such areas as voting, resource distribution and the enactment of smart contracts. This way, the stakeholders are confident that the decision-making is fair and impartial and this is made possible by the use of XAI. This paper aims to find out more about the applicability of XAI concept in blockchain -based governance systems in terms of bias and fairness in automated decisions and explanation of the decisions made. The reasons why interpretability plays the crucial role in decentralized systems are explained with the help of the comparison between the classical artificial intelligence and the Xin Intelligent. The study also looks at various use cases such as, Decentralized Autonomous Organizations (DAOs), and voting systems implemented on blockchain platforms and how XAI can enhance the performance of the governance systems. As this study shows, XAI is vital in building trust, improving responsibility, and guaranteeing balanced majorities in decentralized blockchain governance structures. © 2025 IEEE.

关键词： Smart contract

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Wireless Sensing in Artificial Intelligence of Things: A General Quantum Machine Learning Framework

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IEEE Network 2025年第3期39卷 207-214页

作者： Liao, Peng Wang, Xuyu Shan, Yingxin An, Lingling Mao, Shiwen Xidian University Guangzhou Institute of Technology Guangzhou China Florida International University Knight Foundation School of Computing and Information Sciences MiamiFL United States Xidian University School of Computer Science and Technology Xi'an China Auburn University Department of Electrical and Computer Engineering Auburn United States

With the emergence of the 5G and beyond, wireless networks have transformed from a simple communication medium to ubiquitous versatile platforms. This trend has enabled numerous device-free and non-contact applications. As computing power and machine learning algorithms continue to improve, deep learning techniques are increasingly used in wireless sensing applications. However, the limits of deep learning-centered wireless sensing approaches are still being explored. Concurrently, research in quantum computing is advancing rapidly, prompting researchers to explore the burgeoning field of quantum machine learning, which combines quantum computing and machine learning, for its boundless potential. In this article, we propose a general quantum machine learning framework for wireless sensing applications in the Artificial Internet of Things (AIoT). The proposed framework provides a systematic approach for designing deeply interpreted wireless sensing models based on quantum machine learning. We then present several representative applications and case studies, and conclude this article with a discussion of the challenges and future research directions in this exciting area. © 2025 IEEE.

关键词： Adversarial machine learning

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, R. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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Edge-Driven Industrial computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model 1

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Sneha, B.J. Shriyashreshta Kodipalli, Ashwini Martis, Roshan Joy Ushasree, A. Devamane, Shridhar B. Global Academy of Technology Department of Ai & Data Science Bangalore India Manipal Institute of Technology Department of Electronics & Communication Engineering Bangalore India Gokaraju Lailavathi Engineering College Department of Computer Science & Engineering Hyderabad India Amrita Vishwavidyapeetam School of Computing Department of Cs Mysuru India

ISBN: (纸本)9798331529246

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer. © 2025 IEEE.

关键词： ANN dropouts HIV/AIDS optimizers

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Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

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3rd International Conference on Intelligent and Innovative Technologies in computing, electrical and Electronics, IITCEE 2025

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 IEEE.

关键词： Phishing

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