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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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Fast alternating minimization algorithm for coded aperture snapshot spectral imaging based on sparsity and deep image priors

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Applied Optics 2025年第14期64卷 4121-4131页

作者： Qile Zhao Xianhong Zhao Xu Ma Xudong Chen Gonzalo R Arce Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and PhotonicsBeijing Institute of Technology Beijing 100081 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Department of Electrical and Computer Engineering University of Delaware Newark Delaware 19716 USA

Coded aperture snapshot spectral imaging is a computational imaging technique used to reconstruct three-dimensional hyperspectral images from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels lead to a severely ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors of natural images. By integrating deep image prior into the principle of compressive sensing reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that the Fama-SDIP method significantly outperforms prevailing methods on simulation and real HSI datasets.

关键词： Biomedical imaging Computational imaging Deep learning Imaging systems Imaging techniques Three dimensional imaging

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FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph Convolutional Network for Traffic Forecasting

FDDSGCN: Fractional Decoupling Dynamic Spatiotemporal Graph ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinpeng Xu Chunna Zhao Jing Yang Yaqun Huang Yaoyuan Yang Por Lip Yee School of Information Science & Engineering Yunnan University Kunming China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Urban traffic flow management faces increasing challenges due to accelerating urbanization. Traffic data collected from roadside sensors contain complex temporal and spatial dependencies that interact simultaneously. Although Graph Neural Networks and Recurrent Neural Networks have been successful in capturing these dependencies, two critical issues remain: 1) Treating all traffic signals equally fails to capture the nuanced spatiotemporal dependencies hidden in time series data; 2) Dynamic traffic conditions hinder the accurate capture of local spatial dependencies, thereby limiting prediction accuracy and reliability. To address these challenges, we propose an innovative model, FDDSGCN. To resolve the first issue, we introduce Fractional Residual Decomposition, which effectively separates traffic data into spatial and temporal signals. For the second issue, we employ Dynamic Spatial-Temporal Graph Convolution with fractional-order weight adjustments to dynamically capture local dependencies. Additionally, the Long Short-Term Dependency module analyzes both long-term and short-term dependencies. Extensive experiments on three public datasets demonstrate the superior performance and practical value of our model.

关键词： Accuracy Convolution Urban areas Time series analysis Spatial databases Spatiotemporal phenomena Sensors Speech processing Spatial resolution Signal resolution

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Design and Implementation of an On-Board system for a Spider Robot 1st

Design and Implementation of an On-Board System for a Spider...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Nauman, Nasir Mustafa, Hirra Ashraf, Abrar Khalil, Hisham Department of Computer Science Superior University Lahore Pakistan Department of Computer Science Sharif College of Engineering and Technology Lahore Lahore Pakistan Embedded System and Robotics Lab Department of Technology The University of Lahore Lahore Pakistan

ISBN: (纸本)9783031776199

This paper presents the design of a system onboard spider robot for rescue services. The spider has the six-leg walking pattern that contains the total number of 18 servo motors. In order to reduce the payload, the abdomen of the spider is made with RF4 printed circuit board (PCB). Whereas, the abdomen is also used as the main structure of the robot to attach the rest of the system. Wherein, two pulse-width modulations (PWM) module are controlled by Arduino Nano, the Bluetooth module is used for communication link and bulk converter for the power supply. The spider robot is operated with an in-house developed Android application. It has been observed that the spider robot has a stable walking response along with the multiple excise performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Machine design

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Trajectory Control and Fair Communications for Multi-UAV Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

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IEEE Transactions on Wireless Communications 2025年

作者： Tarekegn, Getaneh Berie Tesfaw, Belayneh Abebe Juang, Rong-Terng Saha, Dola Tarekegn, Robel Berie Lin, Hsin-Piao Tai, Li-Chia Nycu Department of Electrical and Computer Engineering 1001 University Road Hsinchu300 Taiwan Nycu Institute of Electrical and Control Engineering 1001 University Road Hsinchu300 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Institute of Aerospace and System Engineering Taipei10608 Taiwan University at Albany Suny Department of Electrical and Computer Engineering AlbanyNY12222 United States

Due to their highly flexible deployment and agility features, unmanned aerial vehicles (UAVs) serving as aerial base stations are increasingly being used in challenging environments, including emergency communication, traffic offloading, and failures of existing communications infrastructure. A reliable and effective communication service requires 3D deployment and autonomous UAV trajectory optimization in each time slot. While most prior approaches focus on trajectory design that maximizes communication coverage or network throughput without considering fairness as well as computation time. This paper presents a multi-UAV trajectory control and fair communication (TCFC) scheme that maximizes ground user data rate and communication coverage while ensuring service fairness in a UAV-aided wireless communications system. The proposed TCFC scheme employs a two-stage learning approach. Firstly, it develops a gated recurrent unit-based link quality estimation model to assess each user's link quality over time. Then, a federated multi-agent deep reinforcement learning (FedMADRL) algorithm is utilized to continuously adjust the trajectory of the UAVs, optimizing communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. The results show that the proposed TCFC scheme reduces computation time by 26.91% and provides comparable network performance with the baseline methods while improving DRL agents' privacy. © 2025 IEEE.

关键词： Deep reinforcement learning

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Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images 8th

Compressed VGGNet for Automatic COVID-19 Disease Detection f...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Vinothini, R. Niranjana, G. Dhamanti, Inge Lailiyah, Syifa’ul Yakub, Fitri Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur 603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur 603203 India Department of Health Policy and Administration Faculty of Public Health Universitas Airlangga Surabaya Indonesia Faculty of Health Medicine and Life Sciences Universitas Airlangga East Java Indonesia Electronic System Engineering Department Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783031736162

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics. © IFIP International Federation for Information Processing 2025.

关键词： COVID-19

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Prasanna Kumar Sushma S A K Chandrasekaran Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Cloud and Smart System Services Lab Siddaganga Institute of Technology Tumakuru

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms.

关键词： Costs Heuristic algorithms Dynamics Collaboration Quality of service Multitasking Minimization Delays Servers Vehicle dynamics

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Online Flow Scheduling in Virtualized Time-Sensitive Networks: A Joint Admission Control and VNF Embedding Approach

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4651-4667页

作者： Zhang, Yajing Chen, Cailian Li, Mingyan You, Chaoqun Guan, Xinping Quek, Tony Q. S. Shanghai Satellite Network Research Institute Co. Ltd. Shanghai200120 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Chongqing University College of Computer Science Chongqing400044 China Fudan University School of Computer Science Shanghai200433 China Singapore University of Technology and Design 487372 Singapore

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as the static stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as the dynamic stage, fine-tunes VNE solutions obtained from the static stage to adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies. © 2002-2012 IEEE.

关键词： Budget control

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Conversational Voice User Interfaces Supporting Individuals with Down Syndrome: A Literature Review

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ACM Transactions on Accessible Computing 2025年第1期18卷 1-24页

作者： Franceli L. Cibrian Concepción Valdez Lauren Min Vivian Genaro Motti Fowler School of Engineering Chapman University System Orange California USA Department of Computer Science CICESE Ensenada Baja California Mexico Department of Information Sciences and Technology George Mason University Fairfax Virginia USA

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with Down syndrome, showing that (1) the most used form factor for voice assistants are displays with or without speakers; (2) although most of the papers related to speech-to-text technologies are about datasets aimed at supporting speech recognition, most of them are created involving a small sample size; and (3) there is high interest in including individuals with Down syndrome in evaluation phases mainly to address their communication skills, therapy, and general assistance. We discuss the design and evaluation stages, as well as how to make CVUIs more accessible and inclusive for individuals with Down syndrome in particular and neurodiverse users in general.

关键词： Conversational Voice User Interfaces Down syndrome Accessibility Literature review

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