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16th Biomedical engineering International Conference, BMEiCON 2024

作者： Nugroho, Totok Yoshimura, Natsue Tejada, Laura Alejandra Martinez Tokyo Institute of Technology Dept. Information and Communications Engineering Tokyo Japan Tokyo Institute of Technology Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798331505431

Stress significantly affects the learning, academic performance, and health of university students. Traditional detection methods often rely on subjective self-reports, that can be stigmatized. In this study, we developed an electroencephalography (EEG)-based system to offer a more objective approach to chronic stress assessment. Fourteen participants (aged 24-35) were evaluated using the Perceived Stress Scale (PSS) and the Trier Inventory for Chronic Stress (TICS), with EEG data collected during resting and cognitive tasks (i.e., the Stroop color word test). The study aimed to identify the most effective brain regions and EEG features for chronic stress detection. Data analysis involved feature extraction, normalization, classification, and regression to build and assess detection models. The results indicated no significant differences among resting state, cognitive task, and perturbation signals in the classification of chronic stress levels. Resting-state signals were more effective, with logistic regression, support vector machine classifier (i.e., SVC), and random forest classifiers achieving accuracies of 93.35%, 93.26%, and 94.69%, respectively. Furthermore, the EEG-based regression models estimated PSS and TICS scores with high accuracy, with adjusted R2 values exceeding 0.90, particularly for PSS. Contributive EEG features for chronic stress detection included gamma in the frontal, central, and parietal regions, as well as beta in the frontal and parietal regions. Limitations related to the sample size and circadian rhythm control were acknowledged, suggesting the need for future research with larger samples and varied recording schedules. © 2024 IEEE.

关键词： Electroencephalography

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo 182-8585Japan Information Technology Center Nagoya UniversityNagoya 464-8601Japan National Institute of Informatics(NII) Tokyo 101-8430Japan Hohhot Minzu College Hohhot 010051China Toyota Motor Corporation Tokyo 112-8701Japan Department of Computer Science and Engineering Shanghai Jiaotong UniversityShanghai 200240China

Remote driving,an emergent technology enabling remote operations of vehicles,presents a significant challenge in transmitting large volumes of image data to a central *** requirement outpaces the capacity of traditional communication *** tackle this,we propose a novel framework using semantic communications,through a region of interest semantic segmentation method,to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise *** solve the knowledge base inconsistencies inherent in semantic communications,we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge *** system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient ***,the implementation of blockchain sharding handles differentiated knowledge bases for various tasks,thus boosting overall blockchain *** results show a great reduction in latency by sharding and an increase in model accuracy,confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Power-Division Integrated Sensing and Communication waveform based on ZC-OFDM 100

Power-Division Integrated Sensing and Communication waveform...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Wang, Mao Yoshii, Kazutoshi Shimamoto, Shigeru Funakoshi, Mizuki Yabuki, Ayumu Funayoshi, Hideto Waseda University Department of Communications and Computer Engineering Tokyo Japan SoftBank Corp. SoftBank Research Institute of Advanced Technology Tokyo Japan

ISBN: (纸本)9798331517786

With the increasing demand for sensing functions in the 6-th generation (6G) wireless communication networks, waveform design has become critical. The waveform must ensure a high quality of service in communication and achieve high precision in sensing;thus, interference between these two functions should be minimized. This paper proposes a power division waveform design for integrated sensing and communication (ISAC) based on orthogonal frequency division modulation (OFDM). This waveform design utilizes the Zadoff-Chu (ZC) sequence as the symbol sequence for sensing. The probability of detection for this method can reach approximately 0.8. The properties of the ZC sequence make it easy to implement in LTE and NR base stations and allow it to be effectively eliminated through successive interference cancellation. Additionally, the potential of this waveform in channel estimation (CE) is also analyzed, demonstrating an accuracy of CE under 3.5 × 10-3 © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Evaluation of Distance Estimation Using Phase Information of OFDM Signal 100

Evaluation of Distance Estimation Using Phase Information of...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Liu, Jingwei Kikuchi, Noriyasu Kubo, Nobuaki Tang, Suhua The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Kanagawa Institute of Technology Department of Mechanical Engineering Kanagawa Japan Tokyo University of Marine Science and Technology Information and Communication Engineering Laboratory Tokyo Japan

ISBN: (纸本)9798331517786

In urban areas, due to the obstruction/reflection of GNSS signals by roadside buildings, position accuracy is greatly degraded, or even outage occurs when no enough satellites are available. To address this problem, vehicles and roadside units have been used as positioning anchors to estimate pedestrian position. Although previous methods exploited channel state information, which is superior to received signal strength for distance estimation, they rely on signal attenuation characteristics, and the accuracy is limited by temporal resolution and multipath propagation. This work focuses on using the phase information of V2X OFDM signals to estimate distance differences more robustly. We investigated the impact of the sampling rate, a crucial parameter, and conducted simulation evaluations using 3D maps and ray-tracing. Additionally, we built a testbed and performed evaluations with real-world OFDM signal data, which demonstrates that phase information can significantly improve the accuracy of distance measurements. © 2024 IEEE.

关键词： Frequency division multiplexing

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An OFDM-ISAC Scheme Based on Zadoff-Chu Sequence for 5G NR Base-Station 22

An OFDM-ISAC Scheme Based on Zadoff-Chu Sequence for 5G NR B...

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22nd IEEE Consumer communications and Networking Conference, CCNC 2025

作者： Wang, Mao Yoshii, Kazutoshi Shimamoto, Shigeru Funakoshi, Mizuki Yabuki, Ayumu Funayoshi, Hideto Waseda University Department of Communications and Computer Engineering Tokyo Japan SoftBank Research Institute of Advanced Technology SoftBank Corp. Tokyo Japan

ISBN: (纸本)9798331508050

This paper proposes an integrated sensing and communication (ISAC) waveform design using the Zadoff-Chu (ZC) sequence based on orthogonal frequency division multiplexing (OFDM). The correlation property of the ZC sequence is utilized to achieve the target range (root mean square error (RMSE) can reach approximately 0.3m) and velocity (RMSE can reach approximately 0.2m/s) detection. This proposal also implements a fixed-angle beam-scanning method to obtain the targets' 3D positions. This scheme is simulated with different bands and numerologies to verify its versatility. © 2025 IEEE.

关键词： Orthogonal frequency division multiplexing

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Discriminative Dimension Selection for Enhancing the Interpretability and Performance of Clustering Output 16

Discriminative Dimension Selection for Enhancing the Interpr...

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16th International Conference on Knowledge and Smart technology, KST 2024

作者： Lian, Hrang Kap Waiyamai, Kitsana Konstantinos, Slavakis Haruechaiyasak, Choochart Kasetsart University Department of Electrical Engineering Bangkok Thailand Kasetsart University Department of Computer Engineering Bangkok Thailand Tokyo Institute of Technology Department of Information and Communications Engineering Tokyo Japan Artificial Intelligence Research Group National Electronics and Computer Technology Center Bangkok Thailand

ISBN: (纸本)9798350370737

Discriminative Dimension Selection (DDS) has emerged as a powerful tool for identifying the most relevant features in high-dimensional datasets, enabling interpretable data analysis and visualization. This paper explores the application of DDS which utilizes overlapping clusters and dimensions to enhance the interpretability and performance of the K-means clustering algorithm. Our approach leverages the post-processing capabilities of K-means to selectively retain informative features and discard redundant or irrelevant ones. This refined feature set not only preserves the clustering performance of K-means but also enhances its interpretability and visualization. We demonstrate the effectiveness of our method using a variety of datasets and compare its performance against traditional K-means and the proposed method Overlap-resolved Clustering (ORC). We also test with multiple cluster validity indices such as the Silhouette Coefficient Score (SS), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). Our results consistently show that ORC produces better clustering results and enhances the interpretability of the datasets. This study highlights the potential of DDS as a valuable tool for improving the interpretability and visualization of high-dimensional data analysis using K-means clustering. © 2024 IEEE.

关键词： Visualization

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Scale-Based Slicer Placement in Elastic Optical Networks

Scale-Based Slicer Placement in Elastic Optical Networks

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2023 Opto-Electronics and communications Conference, OECC 2023

作者： Kitsuwan, Nattapong Pavarangkoon, Praphan The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan King Mongkut's Institute of Technology Ladkrabang School of Information Technology Bangkok Thailand

ISBN: (纸本)9781665462136

This paper proposes a scale-based slicer placement scheme to reduce bandwidth blocking rate (BBR) in elastic optical networks (EONs). In EONs, a slicing-stitching technology is adopted to overcome a spectrum slot allocation rule that the spectrum slots of the same request must be consecutive on the same link. This technology is done by slicers to split a spectrum band into multiple sub-spectrum components. In EONs with limited slicers, determining equally the number of slicers on every node may not result in a low BBR since the traffic volume on each node is not the same. We investigate and determine the number of slicers needed for each node as a scale-based policy. The simulation results show that our scheme achieves a BBR reduction of 91%, compared to the conventional scheme. © 2023 IEEE.

关键词： Fiber optic networks

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Enhancing the Sense of Unity with Performers in VR Space by Mutual Presentation of Electroencephalogram Information 11

Enhancing the Sense of Unity with Performers in VR Space by ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Ide, Kota Horie, Ryota Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665492324

Performances in VR space, such as VR music concerts, are less likely to convey audience's reactions than in real space, making it difficult to feel the sense of unity. In this paper, we introduce a system that enhances the sense of unity among performers and audience in VR space by generating visual effects based on the beta-alpha ratio signals obtained from a single-channel electroencephalograph and sharing them with the others. We evaluated the significance of the system by conducting juggling-shows in VR space using the system. The results showed the potential to enhance the sense of unity among the performers and the audience. © 2022 IEEE.

关键词： Electroencephalography

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Boosting Offline Optimizers with Surrogate Sensitivity 41

Boosting Offline Optimizers with Surrogate Sensitivity

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41st International Conference on Machine Learning, ICML 2024

作者： Dao, Manh Cuong Le Nguyen, Phi Truong, Thao Nguyen Hoang, Trong Nghia School of Information and Communications Technology Hanoi University of Science and Technology Hanoi Viet Nam National Institute of Advanced Industrial Science and Technology Tokyo Japan School of Electrical Engineering and Computer Science Washington State University Washington United States

Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in silico maximization of a surrogate of the black-box function. Although such a surrogate can be learned from offline data, its prediction might not be reliable outside the offline data regime, which happens when the surrogate has narrow prediction margin and is (therefore) sensitive to small perturbations of its parameterization. This raises the following questions: (1) how to regulate the sensitivity of a surrogate model;and (2) whether conditioning an offline optimizer with such less sensitive surrogate will lead to better optimization performance. To address these questions, we develop an optimizable sensitivity measurement for the surrogate model, which then inspires a sensitivity-informed regularizer that is applicable to a wide range of offline optimizers. This development is both orthogonal and synergistic to prior research on offline optimization, which is demonstrated in our extensive experiment benchmark. Copyright 2024 by the author(s)

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Detection of Chronic Stress Based on Electroencephalography Responses Induced by the Stroop Color Word Test

Detection of Chronic Stress Based on Electroencephalography ...

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Biomedical engineering International Conference (BMEiCON)

作者： Totok Nugroho Natsue Yoshimura Laura Alejandra Martinez Tejada Dept. Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan Dept. Computer Science Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

Stress significantly affects the learning, academic performance, and health of university students. Traditional detection methods often rely on subjective self-reports, that can be stigmatized. In this study, we developed an electroencephalography (EEG)-based system to offer a more objective approach to chronic stress assessment. Fourteen participants (aged 24–35) were evaluated using the Perceived Stress Scale (PSS) and the Trier Inventory for Chronic Stress (TICS), with EEG data collected during resting and cognitive tasks (i.e., the Stroop color word test). The study aimed to identify the most effective brain regions and EEG features for chronic stress detection. Data analysis involved feature extraction, normalization, classification, and regression to build and assess detection models. The results indicated no significant differences among resting state, cognitive task, and perturbation signals in the classification of chronic stress levels. Resting-state signals were more effective, with logistic regression, support vector machine classifier (i.e., SVC), and random forest classifiers achieving accuracies of 93.35%, 93.26%, and 94.69%, respectively. Furthermore, the EEG-based regression models estimated PSS and TICS scores with high accuracy, with adjusted R2 values exceeding 0.90, particularly for PSS. Contributive EEG features for chronic stress detection included gamma in the frontal, central, and parietal regions, as well as beta in the frontal and parietal regions. Limitations related to the sample size and circadian rhythm control were acknowledged, suggesting the need for future research with larger samples and varied recording schedules.

关键词： Logistic regression Accuracy Image color analysis Static VAr compensators Feature extraction Brain modeling Electroencephalography Recording Circadian rhythm Stress

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