Ohtsuki, Tomoaki

写真a

Affiliation

Faculty of Science and Technology, Department of Information and Computer Science ( Yagami )

Position

Professor

Related Websites

External Links
Scopus Paper Info  
Paper Count: 839 Citation Count: 10601 h-index: 46

Click to view the Scopus page. The data was downloaded from Scopus API in July 19, 2026, via http://api.elsevier.com and http://www.scopus.com .

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Career 【 Display / hide 】

  • 2000.04
    -
    2001.03

    大学訪問講師(理工学部)

  • 2001.04
    -
    2002.03

    大学訪問講師(理工学部)

  • 2002.04
    -
    2003.03

    大学訪問講師(理工学部)

  • 2003.04
    -
    2004.03

    大学専任講師(有期・NTTドコモ「次世代ブロードバンド移動通信研究プロジェクト」)(非常勤)(大学院理工学研究科)

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Academic Background 【 Display / hide 】

  • 1990.03

    Keio University, Faculty of Science and Engineering, 電気工学科

    University, Graduated

  • 1992.03

    Keio University, Graduate School, Division of Science and Engineering, 電気工学専攻

    Graduate School, Completed, Master's course

  • 1994.09

    Keio University, Graduate School, Division of Science and Engineering, 電気工学専攻

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 工学博士, Keio University, Coursework, 1994.09

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Books 【 Display / hide 】

  • ひと見守りテクノロジー

    OHTSUKI TOMOAKI, ,エヌ・ティー・エス, 2017.09

  • Radar for Indoor Monitoring: Detection, Classification, and Assessment

    OHTSUKI TOMOAKI, CRC Press, 2017.09

  • シミュレーション辞典

    OHTSUKI TOMOAKI, 日本シミュレーション学会編，コロナ社, 2012

  • 無線分散ネットワーク

    OHTSUKI TOMOAKI, コロナ社, 2011.03

    Scope: 224

  • 映像情報メディア工学大事典

    OHTSUKI TOMOAKI, オーム社, 2010.06

    Scope: 1760

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Papers 【 Display / hide 】

  • SAR image change detection based on saliency region guidance and SIFT keypoint extraction

    Wang L., Sun B., Zhao C., Mazhar S., Ohtsuki T., Takis Mathiopoulos P., Adachi F.

    Pattern Recognition 172 2026.04

    ISSN  00313203

    　View Summary

    Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT's stable description of local features with ViT's modeling capability for global features, improving the model's accuracy and robustness.

  • Depthwise-Attentive Hierarchical Cross-Modal Knowledge Distillation Network for Rail Surface Defect Detection

    Zhou X., Guan X., Peng Y., Zhang Z., Zhou X., Chen H., Ohtsuki T., Han Z.

    IEEE Internet of Things Journal 13 ( 6 ) 10915 - 10928 2026.03

    　View Summary

    Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on red-green-blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current RGB and depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher-student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancer (CAFE) module to capture contextual information, along with a depthwise feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multilevel distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 to 13.97 MParams, and the computational cost decreases from 19.79 to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

  • MuECNet: A Lightweight Multiuser Enhanced Convolutional Architecture for Robust CSI-Based Human Activity Recognition in Real-World IoT Environments

    Miao F., Takyu O., Shan L., Yin Y., Zhao O., Ohtsuki T., Gui G.

    IEEE Internet of Things Journal 13 ( 6 ) 12209 - 12219 2026.03

    　View Summary

    Wi-Fi channel state information (CSI)-based human activity recognition (HAR) leverages rich channel propagation characteristics to enable nonintrusive, privacy-preserving, and device-free sensing. In multiuser wireless environments, however, HAR faces significant challenges, including multipath interference, signal overlap, label ambiguity, cross-domain channel variability, and constraints imposed by real-time deployment on resource-limited edge devices. This article presents multiuser enhanced convolutional network (MuECNet), a lightweight and modular deep learning framework designed to operate under realistic multiuser, multiactivity CSI sensing conditions. MuECNet integrates three key components: 1) an enhanced convolutional encoding module (ECEM) for fine-grained temporal-spectral feature extraction that preserves channel propagation signatures; 2) a branch-wise feature normalization (BFN) module for user-specific channel representation learning; and 3) an adaptive decision module for multilabel activity inference. To improve robustness under diverse and dynamic channel conditions, we introduce MixUp-based data augmentation to emulate activity overlap and reduce label ambiguity. Evaluations on the WiMANS dataset show that MuECNet achieves 64.46% (2.4 GHz) and 64.15% (5 GHz) accuracy with only 1.19-M parameters, 1.74-G FLOPs, and 0.28-s inference latency, outperforming baseline models such as ABLSTM and THAT while reducing model size by up to 75%. Ablation studies confirm the contribution of each module, with accuracy drops of up to 6.41% when removed. These results demonstrate that MuECNet provides a robust and communication-efficient solution for integrating CSI-based sensing into future wireless networks and Internet of Things (IoT) systems.

  • Synthetic aperture radar image change detection based on multi-scale deep adaptive convolution and spatial-frequency dual-domain feature extraction

    Wang L., Jiahui E., Zhao C., Gao W., Mathiopoulos P., Ohtsuki T.

    Expert Systems with Applications 301 2026.03

    ISSN  09574174

    　View Summary

    Synthetic Aperture Radar (SAR) change detection is the process of identifying image changes by comparing SAR images of the same area captured at different time intervals. Considering the interference of speckle noise and the lack of spatial adaptability, this paper proposes a spatial-frequency domain feature fusion cooperative detection framework. This method extracts local structural features in the spatial domain while incorporating high-frequency components from the frequency domain to enhance edge feature representation and improve the discrimination of change regions. Additionally, a multi-region dynamic selection mechanism is designed to adaptively capture multi-scale contextual information through cascaded convolutional layers, overcoming the limitations of fixed receptive fields in complex dynamic scenarios. Furthermore, by integrating an attention mechanism with the Swish-Gated Linear Unit (SWiGLU), the network’s feature representation capability is optimized, effectively enhancing the precision of subtle change detection and significantly reducing the impact of speckle noise. This further improves the stability and reliability of SAR change detection. Through performance evaluation on five datasets, we compare the proposed method with state-of-the-art SAR change detection techniques. The results demonstrate that our method exhibits significant advantages in terms of accuracy and kappa coefficient.

  • Clock Asynchronous Traffic Signal Timing for Multi-Intersections Based on a Joint Traffic Prediction and Control Method

    Wang T., Yang G., Wang L., Mathiopoulos P.T., Ohtsuki T., Ouyang M.

    IEEE Transactions on Vehicular Technology 75 ( 1 ) 1111 - 1125 2026.01

    ISSN  00189545

    　View Summary

    One major challenge in improving traffic efficiency for multi-intersection signal timing is the synchronous iteration across all intersections. To deal with this problem, in this paper, a novel clock asynchronous traffic signal timing method for multi-intersections based on a joint traffic prediction and control method is proposed and its performance is analyzed. To improve traffic signal timing efficiency, each intersection is viewed as an agent which by following this joint approach designs, in a flexible way, actions, states and rewards. Using traffic prediction and control data, each intersection first computes a value which corresponds to a change in the vehicle position in its lane under control and then adds up this value to other values obtained from all the lanes under control to produce the reward. Traditional methods cannot effectively coordinate multi-intersections with different signal timing time. This has led to the introduction of a novel clock consistency mechanism the operation of which is implemented through a Multi-Agent Coordinated Reinforcement Learning (MACRL) algorithm. For any traffic intersection, the mechanism can estimate, within the duration of the previous action, the impact of adjacent intersections. This is accomplished by considering the previous intersections' reward information and quantify it as its joint reward. The proposed MACRL algorithm optimizes multi-intersections traffic by jointly considering these rewards. Using real traffic data, extensive simulations show that the proposed method outperforms existing schemes under the same conditions in traffic efficiency. The method also delivers more accurate traffic signal timing, reducing congestion and improving overall efficiency.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Reviews, Commentaries, etc. 【 Display / hide 】

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Presentations 【 Display / hide 】

  • Correlation analysis of objective features and online meeting quality

    R. Oba

    HCI International 2025 Conference (Gothenburg) , 

    2025.06

  • Beamforming and Trajectory Planning Method under Fixed-Footprint Conditions for Multi-HAPS Systems

    T. Mori

    [International presentation]  ICC2025 (Montreal) , 

    2025.06

  • A Novel Physical Spoofing Technique Using Radio Frequency Fingerprint Emulation and Model Fitting

    Z. Yao

    ICC2025 (Montreal) , 

    2025.06

  • A Novel MIMO FMCW Radar-based Approach for Heart Rate Estimation Using Positional Feature Selection

    S. Nakatani

    ICC2025 (Montreal) , 

    2025.06

  • Stress Detection Through Eye Tracking Incorporating Custom Lasso Stress Index

    X. Meng

    ICC2025 (Montreal) , 

    2025.06

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • Intelligent wireless communication system based on Transformer

    2024.04
    -
    2028.03

    基盤研究(B), Principal investigator

  • 条件付き相互情報量規範適応量子化に基づく信号処理設計と深層学習を用いた無線通信

    2019.04
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    2023.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Principal investigator

  • User Centric Wireless Communications by Traffic Prediction based on Preference Analysis and Integrated Environment Recognition

    2015.04
    -
    2018.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Principal investigator

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Intellectual Property Rights, etc. 【 Display / hide 】

  • イベント検出装置

    Date issued： 特許第4576515号  2010.09

    Patent, Joint

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Awards 【 Display / hide 】

  • 2020年度矢上賞（起業支援）

    2021.04, 慶應義塾大学 理工学部同窓会（同窓会研究教育奨励基金）

    Type of Award： Keio commendation etc.

  • Best paper Award

    A. Li, X. Guan, Z. Yang, and T. Ohtsuki,, 2014.08, 9th International Conference on Communications and Networking in China 2014 (CHINACOM '14), Coalition Graph Game for Multi-hop Routing Path Selection in Cooperative Cognitive Radio Networks

    Type of Award： International academic award (Japan or overseas),  Country： China

  • 電子情報通信学会通信ソサイエティ活動功労賞

    OHTSUKI TOMOAKI, 2013.09, 電子情報通信学会

    Type of Award： Award from Japanese society, conference, symposium, etc.

    　View Description

    電子情報通信学会ユビキタス・センサネットワーク研究会幹事としての貢献に対して

  • ETRI Journal’s 2012 Best Reviewer Award

    2013.02, Electronics and Telecommunications Research Institute（ETRI）

  • 電気通信普及財団第27回テレコムシステム技術賞

    2012.04, 公益財団法人 電気通信普及財団

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Courses Taught 【 Display / hide 】

  • INTERNATIONAL INITIATIVE IN JAPAN 1

    2026

  • GRADUATE RESEARCH ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS 1

    2026

  • INTRODUCTION TO INFORMATION AND COMPUTER SCIENCE

    2026

  • INTERNATIONAL INITIATIVE IN JAPAN 4

    2026

  • GRADUATE RESEARCH ON SCIENCE FOR OPEN AND ENVIRONMENTAL SYSTEMS 2

    2026

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