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IEEE International Conference on Consumer Electronics (ICCE)

作者： Yuuki Hatanaka Tetsuya Yokotani Takeshi Suehiro Kenichi Nakura Shigenori Tani Seiji Kozaki Atsuko Yokotani Hiroshi Mineno Graduate School of Engineering Kanazawa Institute of Technology Nonoichi Ishikawa Japan Information Technology R&D Center Mitsubishi Electric Corporation Kamakura Kanagawa Japan Communication System Enginnering Center Mitsubishi Electric Corporation Tokyo Japan Graduate School of Science and Technology Shizuoka University Hamamatsu Shizuoka Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In the IoT mature stage, a huge number of devices are deployed in various fields. Particularly, in consumer electronics fields, various purpose devices are deployed, e.g., for energy management, crime prevention, monitoring home electric appliances, etc. In this situation, these devices are associated with servers through heterogenous networks. Conventionally, the architectures for distribution processing have been discussed actively. e.g., Common Object Request Broker Architecture (CORBA). However, these architectures did not consider transfer network types and their resources. This paper proposes the architecture of resource management, considering networking and computing resources to connect massive number of devices and servers. It proposes a framework of the architecture and mechanisms for enabling architecture. Then, it describes a use case based on this architecture, and discusses its implementation.

关键词： Wide area networks Computer architecture Traffic control Resource management Servers Internet of Things Monitoring Consumer electronics Thermal stability Centralized control

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Affinity-Based Power Flow Optimization in Reconfigurable Picogrid

Affinity-Based Power Flow Optimization in Reconfigurable Pic...

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IEEE/SICE International Symposium on system Integration

作者： Ryota Kokubu Kaito Oeda Yuusuke Kawakita Shinji Yokogawa Yoshito Tobe Haruhisa Ichikawa Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Department of Information and Computer Sciences Kanagawa Institute of Technology Atsugi Japan Info-Powered Energy system Research Center The University of Electro-Communications Chofu Japan

In recent years, there has been an increasing demand to adopt renewable energy resources to power grids for environmental issues and energy concerns. A virtual grid system, which is designed to utilize decentralized resources, is proposed as an approach. This system enables users to construct their power grids with virtual grid hubs (VG Hub) which interconnect via commercially available cables according to USB Power Delivery (USB PD). A prototype of VG Hub is capable of synthesizing and distributing DC power for connected devices, and the power flow control is managed by a microcontroller. Our previous study proposed a method called Minimum-Hop Power Path Routing (MHPPR). This method determines the power flow that uses the closest source in candidates which can fulfill the demand when a load connects to the network to minimize power loss due to transmission. However, this could result in unproductive power flow, such as a flow that consumes power stored in batteries prior to surplus power from generators. Therefore, we assumed that there are factors to value a source for a load other than demand and supply capacity, and inferred that the factor derives from the type of device. We defined this factor as the affinity between devices. The objective of this study is to solve the power path optimization problem while taking affinity into account. In this study, we discussed device classification and affinity between types and defined affinity coefficients. We then extended MHPPR by incorporating this affinity coefficient. The results have shown that the proposed method selected the sources following the priority defined by the affinity.

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Semantic Attention-guided Day-to-Night Image Translation Network

Semantic Attention-guided Day-to-Night Image Translation Net...

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International Conference on Intelligent Transportation

作者： Geonkyu Bang Jinho Lee Yuki Endo Toshiaki Nishimori Kenta Nakao Shunsuke Kamijo Emerging Design and Informatics Course Graduate School of Interdisciplinary Information Studies the University of Tokyo Tokyo Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology the University of Tokyo Tokyo Japan Mitsubishi Heavy Industries Machinery Systems Ltd Hyogo Japan Mitsubishi Heavy Industries Ltd Hyogo Japan Institute of Industrial Science the University of Tokyo Tokyo Japan

Perception tasks are critical for an autonomous driving system. In recent years, advancements in deep learning have enabled highly accurate perception. However, conquering perception tasks in poor visibility environments remains challenging. One of the main reasons is that most of the existing datasets are concentrated on visually clear environments. This makes it difficult to train deep learning-based perception models in limited visibility environments. Building a new dataset requires significant time and human resources, and annotating data from adverse visibility environments is even more challenging. To address these problems, many image translation methods have been proposed to translate annotated daytime images into nighttime ones to build a nighttime dataset without annotation. In this paper, we present an unsupervised day-to-night image translation network for generating synthetic data. Our proposed method extracts semantic information from input images. The extracted information is then applied to the image-to-image translation network as spatial attention maps. We conduct experiments to evaluate the proposed method. The experimental results show that our method outperforms the related works both qualitative and quantitative.

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Deep Learning Algorithm for Room Temperature Detection Using Bioelectric Potential of Plant Data

SSRN

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SSRN 2024年

作者： Tahyudin, Imam Rozaq, Hasri Akbar Awal Nambo, Hidetaka Department of Information System Faculty of Computer Science Amikom Purwokerto University Jl. Letjendpol Soemarto Banyumas Purwanegara 53127 Indonesia Graduate School of Informatics Faculty of Computer Science Gazi Üniversitesi Ankara Yenimahalle Turkey Graduate School of NST Division of Electrical Engineering and Computer Science Kanazawa University Kakumamachi Ishikawa Kanazawa920-1192 Japan

Plants are living things that have many secrets in them. One that is very interesting is how plants can communicate with the surrounding environment through the activities of the plants themselves. Then, from this communication activity, plants produce electricity, which we can use to study further the behavior of these plants, commonly known as bioelectric potential. This study has aim to analyze the ability of bioelectric potential of plant to response a room temperature in different condition. This research is analyzed using a deep learning algorithm, namely Long-Short Term Memory (LSTM). The data was recording using a modified amplifier and the output is signal data. The signal obtained is an analog signal which is then converted into a digital signal. The data that has been obtained is then transformed first before being analyzed using the Wavelet Transform method. The algorithm will be evaluated between the baseline and hyperparameter tuning models. The result showed that hyperparameter tuning has the smallest RMSE. Therefore, it is better than baseline model. © 2024, The Authors. All rights reserved.

关键词： Wavelet transforms

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Proposed integrated control mechanisms between computing and network resources by online monitoring

Proposed integrated control mechanisms between computing and...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Hatanaka, Yuuki Yokotani, Tetsuya Suehiro, Takeshi Nakura, Kenichi Tani, Shigenori Kozaki, Seiji Yokotani, Atsuko Mineno, Hiroshi Graduate School of Engineering Kanazawa Institute of Technology Ishikawa Nonoichi Japan College of Engineering Kanazawa Institute of Technology Ishikawa Nonoichi Japan Information Technology R&D Center Mitsubishi Electric Corporation Kanagawa Kamakura Japan Communication System Enginnering Center Mitsubishi Electric Corporation Chiyoda Tokyo Japan Graduate School of Science and Technology Shizuoka University Shizuoka Hamamatsu Japan

ISBN: (纸本)9798331521165

关键词： Resource allocation

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105-GHz Electric Field Visualization by Electro-Optic Imaging system Using Polarization Image Sensor

105-GHz Electric Field Visualization by Electro-Optic Imagin...

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European Conference on Microwave

作者： Kiyotaka Sasagawa Ryoma Okada Maya Mizuno Hironari Takehara Makito Haruta Hiroyuki Tashiro Jun Ohta Division of Materials Science Graduate School of Science and Technology Nara Institute of Science and Technology Japan Radio Research Institute National Institute of Information and Communications Technology Japan Department of Opto-Electronic System Engineering Chitose Institute of Science and Technology Japan Faculty of Medical Sciences Kyushu University Japan

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

In this paper, we present a system designed for visualization of high-frequency electric field distributions at 105 GHz. Our approach employs an electro-optic crystal plate as the electric field probe, and the changes in the birefringence distribution induced by the electric field are measured through light. The detection system utilizes an optical system for high-sensitivity polarization imaging, which integrates a polarization image sensor with a uniform polarizer. This system converts the high-frequency electric field signal from the device-under-test into a frequency that the polarization image sensor can detect, employing the optical heterodyne method. The light source is generated by an optical modulator and a semiconductor optical amplifier, enabling the measurement of the 105-GHz electric field distribution with the image sensor. This system paves the way for advancements in next-generation wireless communications through precise high-frequency electric field measurements.

关键词： Optical filters Semiconductor optical amplifiers Image sensors Optical polarization Optical mixing Optical resonators Optical imaging Optical sensors Optical modulation Electric fields

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Modeling of Integrated Fluid Dynamics of Cerebral Circulation and Cerebrospinal Fluid Flow

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 764-766页

作者： Yamada, Shigeki Oshima, Marie Yuhn, Changyoung Ito, Hirotaka Watanabe, Yoshiyuki Maeda, Shusaku Takeish, Naoki Otani, Tomohito Wada, Shigeo Nozaki, Kazuhiko Department of Neurosurgery Shiga University of Medical Science Japan Interfaculty Initiative in Information Studies Institute of Industrial Science The University of Tokyo Japan Medical System Research & Development Center FUJIFILM Corporation Japan Department of Radiology Shiga University of Medical Science Japan Department of Mechanical Science and Bioengineering Graduate School of Engineering Science Osaka University Japan

Objectives: In the fluid dynamics of cerebral circulation and cerebrospinal fluid (CSF) motion, the computational simulation model has not been established. We conducted the multi-scale simulation in the cerebral circulation model. Therefore, we would like to integrate these two different fluid dynamics models. Methods & Results: Using the 3 tesla MRI and 3D workstation, the flow volumes of blood and CSF were measured in the 20 healthy volunteers. In addition, the 3D structures and movements of the brain, intracranial CSF spaces and major arteries were reconstructed for computational fluid dynamics. Conclusions: The CSF movements synchronized with a heartbeat were driven by the pulsation of large intracranial arteries and brain. In the current concepts of the cerebral circulation and fluid exchange of CSF and interstitial fluid, i.e., glymphatic system, the simulation model of the brain fluid dynamic is extremely complicated. There are many black boxes in this field. © 2021, Japan Soc. of Med. Electronics and Biol. engineering. All rights reserved.

关键词： Computational fluid dynamics

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DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Distillation for Object Detection 39

DCSF-KD: Dynamic Channel-wise Spatial Feature Knowledge Dist...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Lin, Yang Guo, Hang Wang, Jinbao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

Knowledge distillation (KD) has recently gained great success in the field of object detection. By transferring the knowledge of the spatial or channel domain from the teacher model to the student model, it allows for a more compact representation with minimal performance loss. Despite this progress, existing KD methods typically treat knowledge from spatial or channel domains independently, ignoring the exploitation of the mutual relationship between these domains. In this work, we first explore the connection between spatial and channel domains and find there exists a strong correlation between them, i.e. the salient channels tend to contain significant object regions in the spatial domain. Motivated by this observation, we propose DCSF-KD, a novel Dynamic Channel-wise Spatial Feature Knowledge Distillation framework for object detection by fully exploiting both spatial and channel knowledge. Specifically, we introduce channel-wise spatial feature distillation and global channel attention distillation, using information from both domains to improve the accuracy of the student network. Experiments demonstrate that our DCSF-KD outperforms existing detection methods on both homogeneous and heterogeneous teacher-student network pairs. For example, when using the MaskRCNN-Swin detector as the teacher, and based on RetinaNet and FCOS with ResNet-50 on MS COCO, our DCSF-KD can achieve 41.9% and 44.1% mAP, respectively. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Premilitary Performance Evaluation of Model-mediated Telerehabilitation system with Task Stiffness Estimation

Premilitary Performance Evaluation of Model-mediated Telereh...

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IEEE International Conference on Mechatronics and Automation

作者： Ziyi Yang Shuxiang Guo Graduate School of Engineering Kagawa University Hayashi-cho Takamatsu Japan The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen Guangdong China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology Haidian District Beijing China

For the telerehabilitation robotic system, the stable and transparent remote therapist-patient interaction under the internet communication latency remains an open challenge. In this paper, we proposed a model-mediated telerehabilitation system to avoid the delayed force feedback on the therapist side. Moreover, a variable stiffness robot is employed in the proposed telerehabilitation system to improve the patient-robot interaction on the slave side. On the slave side, a performance-based variable stiffness interactive assistance control law was utilized and the task stiffness estimation method is designed for master-side interaction model updating. On the master side, the master subsystem was implemented by a haptic device with visual and force feedback. The overall telerehabilitation was realized by the TCP/IP internet communication method. From the premilitary experimental results, the therapist subject could conduct the therapy with the aid of visual and force feedback, and the patient subject could complete the training task with their active efforts.

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