Chen, Yin

写真a

Affiliation

Graduate School of Media and Governance (Shonan Fujisawa)

Position

Project Associate Professor (Non-tenured)

Related Websites
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Profile 【 Display / hide 】

  • Yin Chen received the B.S. and M.S. degrees from the School of Computer Science and Technology, Xidian University, Xi’an, China, in 2008 and 2011, respectively, and the Ph.D. degree from the School of Systems Information Science, Future University Hakodate, Japan, in 2014. From April to October 2014, he was a Postdoctoral Researcher with Future University Hakodate. He is currently serving as a Senior Research Assistant Professor with the Graduate School of Media and Governance, Keio University, Fujisawa, Japan. His research interests are in the wide area of wireless communication and networks and their applications in the IoT and smart cities. He is a member of IEEE, ACM and IPSJ.

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Career 【 Display / hide 】

  • 2014.11
    -
    2018.10

    Keio University, Graduate School of Media and Governance, Research Assistant Professor

  • 2018.11
    -
    Present

    Keio University, Graduate School of Media and Governance, Senior Research Assistant Professor

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Academic Background 【 Display / hide 】

  • 2004.08
    -
    2008.07

    Xidian University, School of Computer Science and Technology, Computer Science

    China, University, Graduated

  • 2008.08
    -
    2011.03

    Xidian University, Graduate School of Computer Science and Technology, Computer Science

    China, Graduate School, Completed, Master's course

  • 2011.04
    -
    2014.03

    FUTURE UNIVERSITY-HAKODATE, Graduate School of Systems Information Science, Systems Information Science

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • Master, Xidian University, Dissertation, 2011.03

  • PhD (System Information Science）, FUTURE UNIVERSITY-HAKODATE, Dissertation, 2014.03

    Exact throughput capacity studies for mobile ad hoc networks

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Licenses and Qualifications 【 Display / hide 】

  • TOIEC 925点 , 2014

  • 日本語能力試験1級, 2019.07

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Research Areas 【 Display / hide 】

  • Informatics / Information network

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Research Keywords 【 Display / hide 】

  • Urban Sensing

  • Stochastic Modelling

  • Wireless Networks

  • Wireless Sensor Networks

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Papers 【 Display / hide 】

  • Time–frequency fusion for enhancement of deep learning-based physical layer identification

    Zeng S., Chen Y., Li X., Zhu J., Shen Y., Shiratori N.

    Ad Hoc Networks (Ad Hoc Networks)  142 2023.04

    ISSN  15708705

    　View Summary

    Existing studies on deep learning-based physical layer identification have mainly exploited raw in-phase/ quadrature (IQ) samples or power spectral density (PSD) samples as inputs independently. The raw IQ and PSD samples represent the information in the time and frequency domains, respectively. It has been observed from the results of existing studies that identification using raw IQ samples outperforms that using PSD in low signal-to-noise ratio (SNR) regimes, and that identification using PSD outperforms that using raw IQ in high SNR regimes. In this paper, we propose to use the fusion of raw IQ and PSD samples to enhance deep learning-based physical layer identification. In particular, we design three general fusion frameworks, i.e., input, feature, and decision fusions, and integrate them with three typical deep neural network architectures of fully connected neural network, convolutional neural network, and recurrent neural network to form fusion identification schemes. We conduct experiments using 50 off-the-shelf Wi-Fi devices to validate the concerned fusion schemes and investigate their performance gains in identification and model training. Our experimental results verify that the proposed fusion identification schemes can achieve comparable or superior identification performances to the state-of-the-art schemes in the entire SNR regime. Moreover, for the considered fusion schemes, we further investigate the impacts of fusion strategies, deep-learning networks, and SNR conditions on the identification performance and training time.

  • Fractal Dimension of DSSS Frame Preamble: Radiometric Feature for Wireless Device Identification

    Li X., Chen Y., Zhu J., Zeng S., Shen Y., Jiang X., Zhang D.

    IEEE Transactions on Mobile Computing (IEEE Transactions on Mobile Computing)   2023

    ISSN  15361233

    　View Summary

    This paper demonstrates that the <italic>fractal dimension of frame preamble</italic> serves as a new radiometric feature that can be used together with other known radiometric features to enhance the identification accuracy in wireless device identification. We first propose a fractal dimension estimation scheme for direct-sequence spread spectrum (DSSS) frame preamble, then provide theoretical analysis to reveal how the fractal dimension is primarily determined by the device hardware imperfections, and thus prove that the fractal dimension serves as an intrinsic radiometric feature. We further show simulation results to verify our theoretical modeling of the fractal dimension and also numerically evaluate the effects of device hardware imperfections and wireless channels on the fractal dimension. Finally, by jointly applying the fractal dimension and the five features reported in the literature, we conduct extensive experiments to demonstrate that the fractal dimension can lead to a further improvement of the state-of-the-art result in the radiometric feature-based device identification.

  • 3D Spatial Sensing and Analysis Model of Closed Space CO2 Concentration

    Goto D., Tsuge A., Huang W., Kawasaki T., Chen Y., Okoshi T., Nakazawa J.

    2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023 (2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023)     408 - 411 2023

    　View Summary

    We propose a 3-dimensional(3D) CO2 concentration analyzing system to monitor the ventilation of the closed space. this system employs a 3D scan to reduce the labor associated with creating a 3D space model. This research uses multiple CO2 sensors and considers the model to map point CO2 concentration data to the spatial CO2 distribution. We experimented with evaluating the accuracy of the model in the actual bus space.

  • Bus Crowdedness Sensing System Based on Carbon Dioxide Concentration

    Huang W., Tsuge A., Chen Y., Okoshi T., Nakazawa J.

    SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems)     806 - 807 2022.11

    　View Summary

    Crowdedness sensing of buses is playing an important role in the disease control of COVID-19 and bus resource scheduling. This research analyzes the relationship between carbon dioxide concentration, bus environment and the number of passengers by linear regression. Our prototype system collects the data of bus environment and carbon dioxide concentration to estimate the number of passengers in real time. By collecting the sensing data from a shuttle bus of university campus, we experimentally evaluate the feasibility and sensing performance of the crowdedness estimation model.

  • A Bus Crowdedness Sensing System Using Deep-Learning Based Object Detection

    Huang W., Tsuge A., Chen Y., Okoshi T., Nakazawa J.

    IEICE Transactions on Information and Systems (IEICE Transactions on Information and Systems)  E105D ( 10 ) 1712 - 1720 2022.10

    ISSN  09168532

    　View Summary

    Crowdedness of buses is playing an increasingly important role in the disease control of COVID-19. The lack of a practical approach to sensing the crowdedness of buses is a major problem. This paper proposes a bus crowdedness sensing system which exploits deep learningbased object detection to count the numbers of passengers getting on and off a bus and thus estimate the crowdedness of buses in real time. In our prototype system, we combine YOLOv5s object detection model with Kalman Filter object tracking algorithm to implement a sensing algorithm running on a Jetson nano-based vehicular device mounted on a bus. By using the driving recorder video data taken from real bus, we experimentally evaluate the performance of the proposed sensing system to verify that our proposed system system improves counting accuracy and achieves real-time processing at the Jetson Nano platform.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

  • 地域を網羅する IoT と情報の力による街のスマート化

    陳　寅, 中澤　仁

    電気学会誌 141 ( 1 ) 11 - 14 2021.01

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work,  ISSN  1340-5551

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

  • Study on an intelligent sensing system for fine-grained data of urban garbage discharge

    2021.04
    -
    2024.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Early-Career Scientists , Research grant, Principal investigator

    　View Summary

    ゴミ収集動画を用い、物体検出及び追跡技術による、収集されたゴミ袋の数を自動的に計数する知的なセンシングシステムを開発し、藤沢市のゴミ清掃車に装着し実証実験を行う。細粒度的な都市ゴミのセンシングシステムが世界中の最初の試みとして、本研究は、a）提案されたシステムで精度と処理速度をに評価し、b）収集されたデータの応用性を調査し、c）新しい車両エッジ中心のコンピューティングパラダイムを検証する。

  • Modeling, Design and Implementation of Heterogeneous Opportunistic Urban Sensor Network using Garbage-collecting Trucks as Communication Backbones

    2017.04
    -
    2019.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, CHEN Yin, Grant-in-Aid for Young Scientists (B), Research grant, Principal investigator

    　View Summary

    To implement the vehicular urban sensing technology, we have investigated: (1) Network modeling for the transmission opportunity of mobile network, (2) experiment system using sensors installed on garbage trucks, and (3) applications, like Omimamori service and sensing of garbage disposal, based on the data collected from the experiment system. It is expected that the developed technologies will be applied to fulfill the version of a super smart society.

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Intellectual Property Rights, etc. 【 Display / hide 】

  • 通信装置及びプログラム

    Date applied： 特願2021-087819  2021.05 

    Date issued： 特願2021-087819  2021.05

    Patent, Joint

  • 探索装置、探索方法および 探索プログラム

    Date applied： 特願2021-076602  2021.04 

    Date issued： 特願2021-076602  2021.04

    Patent, Joint

  • 画像処理装置、画像処理方法、および、画像処理プログラム

    Date applied： 特願2020-189851  2020.11 

    Date issued： 特願2020-189851  2020.11

    Patent, Joint

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Awards 【 Display / hide 】

  • WSN-IoT AWARD 2019 最優秀賞

    2019.05, YRP研究開発推進協会 WSN協議会

    Type of Award： Award from publisher, newspaper, foundation, etc.

  • 情報処理学会MBL研究会 第88回研究会 優秀論文

    米澤拓郎，伊藤友隆，陳寅，中澤仁, 2018.08, 情報処理学会MBL研究会, SOXFire: XMPPに基づく都市センサ情報流通基盤

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

  • デジタルプラクティス論文賞

    中澤仁 陳寅 米澤拓郎 大越匡 徳田英幸, 2018.02, 情報処理学会

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

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Courses Taught 【 Display / hide 】

  • FUNDAMENTALS OF INFORMATION TECHNOLOGY 2

    2020

  • FUNDAMENTALS OF INFORMATION TECHNOLOGY 1

    2020

  • FUNDAMENTALS OF INFORMATION TECHNOLOGY 2

    2019

  • FUNDAMENTALS OF INFORMATION TECHNOLOGY 1

    2019

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Courses Previously Taught 【 Display / hide 】

  • FUNDAMENTALS OF INFORMATION TECHNOLOGY

    Keio University

    2018.04
    -
    2019.03
    , 

    Full academic year, Lecture, Lecturer outside of Keio, 26people

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Educational Activities and Special Notes 【 Display / hide 】

  • ミニプロジェクト実装を活用したプログラミング学習促進の取組

    2018.04
    -
    2021

    , Device of Educational Contents

    　View Details

    大学一年生を対象とする情報基礎の授業において、ミニプロジェクト実装を活用し、学生のプログラミング学習を促進する取り組みを実践した。

  • 社会連携型の研究会教育の実践

    2014.11
    -
    Present

    , Device of Educational Contents

    　View Details

    研究室に履修した学生を積極的に自治体との共同研究に取り込んで、社会問題の解決を目指す研究をさせることで、学生の研究意欲、コミュニケーション力及び社会貢献意識を同時に教育実践を行なった。

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Memberships in Academic Societies 【 Display / hide 】

  • Association for Computing Machinery, 

    2018.10
    -
    Present

  • IPSJ, 

    2018.04
    -
    Present

  • IEEE Computer Society, 

    2014.11
    -
    Present
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Committee Experiences 【 Display / hide 】

  • 2020

    Local chair, ACM SenSys2020 Organizing Committee

  • 2020

    Local chair, ACM BuildSys2020 Organizing Committee

  • 2018

    Registration chair, IEEE RTCSA2018 Organizing Committee

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