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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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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos

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ACM Transactions on Multimedia computing, Communications and Applications 2025年第4期21卷

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Chengchi University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science Dire Dawa University Dire Dawa Ethiopia Social Networks and Human-Centered Computing Taiwan International Graduate Program Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan

Deep-fake videos, generated through AI face-swapping techniques, have garnered considerable attention due to their potential for impactful impersonation attacks. While existing research primarily distinguishes real from fake videos, attributing a deep-fake to its specific generation model or encoder is crucial for forensic investigation, enabling precise source tracing and tailored countermeasures. This approach not only enhances detection accuracy by leveraging unique model-specific artifacts but also provides insights essential for developing proactive defenses against evolving deep-fake techniques. Addressing this gap, this article investigates the model attribution problem for deep-fake videos using two datasets-Deepfakes from Different Models (DFDM) and GANGen-Detection, which comprise deep-fake videos and images generated by GAN models. We select only fake images from the GANGen-Detection dataset to align with the DFDM dataset, which specifies the goal of this study, focusing on model attribution rather than real/fake classification. This study formulates deep-fake model attribution as a multiclass classification task, introducing a novel Capsule-Spatial-Temporal (CapST) model that effectively integrates a modified VGG19 (utilizing only the first 26 out of 52 layers) for feature extraction, combined with Capsule Networks and a Spatio-Temporal attention mechanism. The Capsule module captures intricate feature hierarchies, enabling robust identification of deep-fake attributes, while a video-level fusion technique leverages temporal attention mechanisms to process concatenated feature vectors and capture temporal dependencies in deep-fake videos. By aggregating insights across frames, our model achieves a comprehensive understanding of video content, resulting in more precise predictions. Experimental results on the DFDM and GANGen-Detection datasets demonstrate the efficacy of CapST, achieving substantial improvements in accurately categorizing deep-fake videos

关键词： computer forensics

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Load Balancing to Optimize MPEG-DASH Video Streaming

Load Balancing to Optimize MPEG-DASH Video Streaming

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International Conference on Information Networking

作者： Edenilson Jônatas Dos Passos Adriano Fiorese Department of Computer Science (DCC) Graduate Program in Apllied Computing (PPGCAP) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Recent advancements in on-demand multimedia streaming have revealed the potential of this business model, driven by the convenience of accessing content anytime and anywhere. However, delivering a seamless user experience remains challenging due to the demanding nature of audiovisual streams, despite significant resource allocation. A promising solution involves using load balancers to distribute workloads evenly among processing units. This study proposes optimizing the processing and transmission resources by means of Software-Defined Networks (SDN). By continuously monitoring performance metrics like CPU usage and throughput, the SDN network controller determines the best server for handling new connections, even allowing seamless server migration during playback. The results show up to 80% faster response times and more consistent video quality, with reduced latency and uninterrupted playback. The approach also confirms the feasibility of SDN in streaming services, providing a foundation for future network improvements.

关键词： Transform coding Streaming media Load management User experience Quality assessment Servers Time factors Software defined networking Video recording Monitoring

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The Effect of WebRTC Jitter Buffer on Cloud Streaming Game QoE

The Effect of WebRTC Jitter Buffer on Cloud Streaming Game Q...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Naoyasu Kaneko Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper considers a cloud streaming game using WebRTC. Since the cloud streaming game consists of a server and a client, the network quality between them affects the game. Among the network quality degradation factors, we focus on delay jitter. We reduce the effect of the jitter by buffering. We conduct a subjective experiment of a single-player action game and evaluate application-level QoS and QoE. We investigate the impact of buffering on the video and audio quality of the user playing the game.

关键词： Degradation Games Quality of service Jitter Streaming media Cloud gaming Delays Quality of experience Servers WebRTC

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A Parallel JPEG Pleno Baseline Block-Based Profile Light Field Encoder Using OpenMP

A Parallel JPEG Pleno Baseline Block-Based Profile Light Fie...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Ismael Seidel André Filipe da Silva Fernandes José Luis Güntzel Dept. of Computer Science and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

Light Fields (LFs) are a plenoptic image modality that provides more information on light rays, making them an excellent representation for immersive media. To compress such a modality, the Joint Photographic Experts Group (JPEG) committee created the JPEG Pleno Part 2 standard with two profiles. This work focuses on the reference encoder implementation for the Baseline Block-Based Profile (BBBP), called JPEG Pleno Model (JPLM). Our main contribution lies in the proposal and analysis of a parallel implementation of JPLM using OpenMP. We show that it is possible to accelerate encoding from nearly 2 to 10 times when using 2 to 16 threads, with a memory overhead ranging from 15% up to 78%, depending on the LF size. Moreover, the speedup comes with no cost in terms of coding efficiency, i.e., the LFs encoded with the proposed parallel version are bit-exact matches to ones encoded with the sequential version.

关键词： Costs Instruction sets Transform coding Transforms Encoding Light fields Software Decoding Proposals Standards

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The institute of Electrical Engineers of Japan.

关键词： Capacitance

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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RBLJAN: Robust Byte-Label Joint Attention Network for Network Traffic Classification

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2161-2178页

作者： Xiao, Xi Wang, Shuo Hu, Guangwu Li, Qing Mao, Kelong Luo, Xiapu Zhang, Bin Xia, Shutao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen Guangdong518055 China Peng Cheng Laboratory Shenzhen Guangdong518038 China Key Laboratory of Cyberspace Security Ministry of Education of China Zhengzhou Henan450001 China School of Computer Science Shenzhen Institute of Information Technology Shenzhen Guangdong518172 China Gaoling School of Artificial Intelligence Renmin University Beijing100086 China Department of Computing Hong Kong Polytechnic University Hong Kong

Network traffic classification plays a crucial role in network management and cyberspace security. As the Internet evolves with new applications and protocols, traditional machine learning-based methods relying on feature mining have become obsolete. Instead, deep learning-based methods are becoming more popular in the field of traffic classification due to their end-to-end processing approach. However, the vulnerability of neural networks to adversarial examples significantly compromises their performance. In this paper, we propose Robust Byte-Label Joint Attention Network (RBLJAN), an efficient and robust deep learning-based framework for encrypted network traffic classification at both the packet-level and the flow-level. RBLJAN comprises a classifier and an adversarial traffic generator. The classifier utilizes mechanisms such as header-payload parallel processing and byte-label joint attention learning to capture implicit correlations between bytes and labels, enabling the construction of powerful packet representations. The generator produces adversarial examples that are fed to the classifier to enhance its robustness. Experimental results demonstrate that RBLJAN achieves over 99% average F1-score on real-world legitimate traffic datasets and achieves 97.86% average F1-score on malware identification. Moreover, RBLJAN exhibits superior performance in terms of detection speed and robustness compared to state-of-the-art methods in real-world scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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