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Consumer Communications and Networking Conference, CCNC IEEE

作者： Rei Nakagawa Satoshi Ohzahata Ryo Yamamoto Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

Today, information centric networking enables adaptive video streaming clients to further improve QoE by applying flexible content-based control. However, an adaptive bitrate algorithm makes a client occupy the bottleneck link at excessively high bitrate, reducing the QoE fairness to other clients sharing the bottleneck link. Then, we propose fairAccel, a method of accelerating bitrate-based feedback control for achieving QoE fairness. fairAccel assigns more bandwidth to clients selecting the lower bitrate while suppressing content requests from clients selecting the highest bitrate on the bottleneck link. In addition, to further improve QoE fairness, fairAccel exploits the symmetric routing of ICN content request / response and applies bidirectional feedback control to the content request / response path. Thus, fairAccel accelerates feedback control by mitigating router queues under control of suppressing content requests before excessive traffic is delivered to the response path. Through simulation experiments, fairAccel improves the average bitrate and further improves QoE fairness for representative ABR algorithms.

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Micro-Expression Recognition by 3D-CNN in Combination with GRU

Micro-Expression Recognition by 3D-CNN in Combination with G...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chun-Ting Fang Tsung-Jung Liu Kuan-Hsien Liu Dept. of Electrical Engineering Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Dept. of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Micro expressions are fleeting emotional expressions that manifest within a brief time frame, often characterized by subtle facial muscle movements. Despite their brevity and imperceptibility to the naked eyes, these micro expressions encapsulate valuable emotional cues. The objective of micro expression recognition is to leverage AI technology to effectively detect and analyze these nuanced facial changes, thereby enhancing our ability to gain deeper insights into an individual’s underlying emotional state. In this paper, we introduce an approach featuring a three-layer 3D-CNN architecture combined with GRU. Experimental results demonstrate that our method yields favorable outcomes despite its relatively straightforward architecture.

关键词： Gold Emotion recognition Face recognition Facial muscles Artificial intelligence Consumer electronics Videos

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A solution for resource allocation through complex systems in fog computing for the internet of things

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Computing 2024年第7期106卷 2107-2131页

作者： Faraji, Fatimah Javadpour, Amir Sangaiah, Arun Kumar Zavieh, Hadi Department of Computer Engineering Yazd University Safaeih Yazd100190 Iran Harbin Institute of Technology Shenzhen10587 China ADiT-Lab Electrotechnics and Telecommunications Department ISMAI and INESC TEC Instituto Politécnico de Viana do Castelo ISMAI Porto10587 Portugal International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Economic and Statistics Guangzhou Higher Education Mega Center Guangzhou University Guangzhou510006 China Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method i

关键词： Quality of service

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

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University 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. © 2025, CC BY.

关键词： Mobile edge computing

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

Energy Efficient Transmission Parameters Selection Method Us...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Airiyoshi, Ryotai Hasegawa, Mikio Ohtsuki, Tomoaki Li, Aohan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Tokyo University of Science Department of Electrical Engineering Tokyo Japan Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798350368369

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency. © 2025 IEEE.

关键词： Reinforcement learning

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An Evaluation of the Presentation of Acoustic Cues for Shorelining Techniques

An Evaluation of the Presentation of Acoustic Cues for Shore...

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2023 Immersive and 3D Audio: from Architecture to Automotive, I3DA 2023

作者： Nguyen, Minh Zhu, Howe Sun, Huiyuan Nguyen, Vincent Deverell, Lil Singh, Avinash Jin, Craig Lin, Chin-Teng University of Technology Sydney Computer Science Sydney Australia The University of Sydney Electrical and Information Engineering Sydney Australia University of Technology Sydney Graduate School of Health Sydney Australia Aria Research Pty Ltd Orientation and Mobility Sydney Australia

ISBN: (纸本)9798350311044

The study explores various scenarios for providing shorelining information via spatial-audio auditory sensory augmentation with a view to assisting people who are blind or have low-vision. Shorelining is a strategy often associated with the use of the "white cane"and generally refers to path-following approaches that rely on contours of structures within a built environment. The aim is to support clear, logical paths of travel. In one approach, auditory shorelining cues are provided via a sequence of two auditory earcons rendered spatially along the shoreline and at the participant's left or right side. In another approach, the auditory cues are rendered spatially as "gate posts"in between which the participant should walk. The rendering of the auditory earcons follows the participant's path of travel so that at any given time, there is only ever a sequence of two auditory earcons that provides local shorelining information. The auditory earcons are rendered via two methods: (i) using loudspeakers appropriately placed along the path of travel and (ii) using binaural rendering of virtual speakers via smart glasses. We compared the performance of spatial-audio sensory augmentation with non-spatialized spoken language instruction. Participants' performance was measured in terms of task accuracy, time, and walking behavior. The cognitive workload was measured using a self-reporting questionnaire. Results indicate that sensory augmentation provided via spatial-audio earcons can provide faster and smoother navigational assistance and guidance compared with spoken verbal instructions. The results also suggest that current binaural rendering using simple augmented-reality tool sets is not as robust as real sound in providing navigational guidance. Lastly, the "gate post"shorelining approach seems more effective than the left and right shorelining technique. © 2023 IEEE.

关键词： Navigation

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100 GHz Thin-Film LNOI/Si Optical Modulator Fabricated by Room Temperature Wafer Bonding

100 GHz Thin-Film LNOI/Si Optical Modulator Fabricated by Ro...

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IEEE International Workshop on Low Temperature Bonding for 3D Integration (LTB-3D)

作者： Seigo Murakami Yoshiki Katoda Yuya Yamaguchi Takahide Sakamoto Ryo Takigawa Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan National Institute of Information and Communications Technology (NICT) Tokyo Japan Department of Electrical Engineering and Computer Science Tokyo Metropolitan University Tokyo Japan

This paper demonstrated 100 GHz thin-film LNOI/Si optical modulator fabricated by room temperature wafer bonding with activated Si atomic layer.

ISBN: (数字)9798331519919

ISBN: (纸本)9798331519926

This paper demonstrated 100 GHz thin-film LNOI/Si optical modulator fabricated by room temperature wafer bonding with activated Si atomic layer.

关键词： Wafer bonding Atom optics Optical modulators Conferences Atomic layer deposition Silicon Bonding Three-dimensional integrated circuits

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Machine learning-based evaluation of cancer pain from functional brain images

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological engineering 2021年第PROC期Annual59卷 780-782页

作者： Ogawa, Mayuko Ben, Hui Ono, Yumie Lin, Wenying Electrical Engineering Program Graduate School Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan National Taiwan University Hospital Cancer Center Taiwan

Pain management is one of the important treatments of cancer patients. This study aims to develop a classifier of chronic cancer pain patients from their brain metabolic activity measured by FDG-PET. We compared FDG-PET brain images of 74 painful and 29 painless cancer patients to clarify the brain activity specific to chronic pain of cancer. Using the detected brain activity pattern, we further developed a classifier that determines the presence or absence of chronic pain from PET brain images by machine learning methods. The painful cancer patients showed significantly increased activity in the amygdala, hippocampus, and decreased activity in the cingulate gyrus and precuneus (p © 2021, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Diseases

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Frequency Bandwidth Reduction of High-Frequency Intra-Body Propagation Characteristics Used for Attacker's Touch Detection in Fingerprint Authentication

Frequency Bandwidth Reduction of High-Frequency Intra-Body P...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Shunichi Tomura Koki Yamada Takahiro Yoshida Department of Electrical Engineering Graduate School of Engineering Tokyo University of Science Tokyo Japan Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering Faculty of Engineering Tokyo University of Science Tokyo Japan

In previous studies, we have proposed the attacker’s touch detection method in fingerprint authentication using high-frequency intra-body propagation characteristics to detect attacks in that an attacker holds the user's finger/wrist and presses the user's finger to a fingerprint scanner. However, the previous method used a wide range of frequency bands from 300 kHz to 10 MHz, and this caused problems in the circuit scale, cost, and size of the device in development for practical use. Therefore, we searched effective frequency bands and applied K-nearest neighbor with data augmentation in this study. As results, we achieved a 99.1 % touch detection rate with a single frequency and a 99.9 % touch detection rate with a combination of two frequencies. We achieved a significant reduction in the frequency bandwidth of the propagation characteristics.

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

Energy Efficient Transmission Parameters Selection Method Us...

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IEEE Conference on Wireless Communications and Networking

作者： Ryotai Airiyoshi Mikio Hasegawa Tomoaki Ohtsuki Aohan Li Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Department of Electrical Engineering Tokyo University of Science Tokyo Japan Department of Information and Computer Science Keio University Yokohama Japan

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

关键词： Performance evaluation Power demand LoRa Reinforcement learning Energy efficiency Internet of Things

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