Textbok reading seminar 2026
Channel Model (CM)
- 電気電子工学文献詳読I（M1)・II（M2）, 外国語論文解説・討論 (D)
- Time: 5th, Wed
- Lecturers (D/M2/M1)
- Articles (2026)
| Date | Topic | Charge |
|---|---|---|
| 5/13 | S. Waqar, M. Muaaz, S. Sigg and M. Pätzold, “A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis,” in IEEE Sensors Journal, vol. 24, no. 10, pp. 16614-16628, 15 May15, 2024 | Wang |
| 5/20 | – N. Vaara, et al., “Differentiable Ray Tracing for THz Radio Channel Characterization with Point Clouds,” EuCAP 2026. – Ben Chen, et al., “Terahertz Signal Coverage Enhancement in Hall Scenarios Based on Single-Hop and Dual-Hop Reconfigurable Intelligent Surfaces,” EuCAP 2026. | Mashima |
| 5/22 (Fri, 4th) | – Karan Ahuja, Yue Jiang, Mayank Goel, and Chris Harrison. 2021. Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 292, 1–10. DOI:https://doi.org/10.1145/3411764.3445138 – Y. Zhou, M. López-Benítez, L. Yu and Y. Yue, “Text2Doppler: Generating Radar Micro–Doppler Signatures for Human Activity Recognition via Textual Descriptions,” in IEEE Sensors Letters, vol. 8, no. 10, pp. 1-4, Oct. 2024, Art no. 3503504, doi: 10.1109/LSENS.2024.3457169 | Nishi |
| 5/29 (Fri. 4th) | X. Xu et al., “Swin-Loc: Transformer-Based CSI Fingerprinting Indoor Localization With MIMO ISAC System,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 8, pp. 11664-11679, Aug. 2024, doi: 10.1109/TVT.2024.3381433 | Koularp |
| 6/3 | T. Kürner, A. F. Molisch and M. Shafi, “Review of Wireless Propagation Research – Past, Current and Future Developments,” in IEEE Transactions on Antennas and Propagation, doi: 10.1109/TAP.2026.3675326 | Zheng |
| 6/10 | Sabery, S.M.; Bystrov, A.; Navarro-Cía, M.; Gardner, P.; Gashinova, M. Study of Low Terahertz Radar Signal Backscattering for Surface Identification. Sensors 2021, 21, 2954. https://doi.org/10.3390/s21092954 | Sato |
| 7/1 | J. M. Merlo, S. Wagner, J. B. Lancaster and J. A. Nanzer, “Real-Time High-Accuracy Digital Wireless Time, Frequency, and Phase Calibration for Coherent Distributed Antenna Arrays,” in IEEE Transactions on Microwave Theory and Techniques, vol. 74, no. 2, pp. 1962-1980, Feb. 2026, doi: 10.1109/TMTT.2025.3639461 | Masuda |
| 7/8 | J. M. Eckhardt, V. Petrov, D. Moltchanov, Y. Koucheryavy and T. Kürner, “Channel Measurements and Modeling for Low-Terahertz Band Vehicular Communications,” in IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1590-1603, June 2021 | Asano |
| 7/15 | G. Jing et al., “A Multimodal Agentic AI Framework for Environmental Reconstruction and Semantic Channel Modeling,” in IEEE Transactions on Cognitive Communications and Networking, vol. 12, pp. 7893-7908, 2026, doi: 10.1109/TCCN.2026.3689820 | Shen |
| 7/22 | – Toding, A., Khandaker, M.R. & Rong, Y. Joint source and relay optimization for parallel MIMO relay networks. EURASIP J. Adv. Signal Process. 2012, 174 (2012). – Toding, A., Khandaker, M.R. & Rong, Y. Joint source and relay design for MIMO multi-relay systems using projected gradient approach. J Wireless Com Network 2014, 151 (2014). | Hirai |
| 7/29 | I. Atzeni et al., “Sub-THz Communications: Perspective and Results From the Hexa-X-II Project,” in IEEE Open Journal of the Communications Society, vol. 6, pp. 7495-7540, 2025. (Sections I~III) | Yomoda |
Machine Learning (ML)
- 電気電子工学文献詳読I（M1)・II（M2）, 外国語論文解説・討論 (D)
- Textbook (Articles)
- Time: 16:30-18:00, Fri
- Lecturers (D/M2/M1)
- Schedule
| Date | Topic | Charge |
|---|---|---|
| 5/15 | Chapter 1, Chapter 2 (ADALINE) | Masuda |
| 5/22 | Chapter 3 (Logistic Regression, SVM, DT, KNN) 1 | Asano |
| 5/29 | Chapter 3 (Logistic Regression, SVM, DT, KNN) 2 | Shen |
| 6/5 | Chapter 4 (Preprocessing) | Hirai |
| 6/12 | Chapter 5 (Reduction) | Yomoda |
| 6/26 | Chapter 6 (Tuning) | Wang |
| 7/3 | Chapter 7 (Ensemble) | Mashima |
| 7/10 | Chapter 10 (Regression) | Nishi |
| 7/17 | Chapter 11 (Clustering) | Koularp |
| 7/31 (4th) | Chapter 12 (Neural Network) 1 | Zheng |
| 7/31 | Chapter 12 (Neural Network) 2 | Sato |
Python 環境構築
既にgitとかpythonをインストール済みの人がやると面倒なことになるかもしれないので気をつけてください．
パワーシェルを立ち上げてPSVersionを確認する．
PS> $PSVersionTable
PSVersionが5.0以上だったらScoopをインストールする．（PSVersionが5.0未満だったらパワーシェルの最新版をインストールしてから行うこと．）
PS> Set-ExecutionPolicy RemoteSigned -scope CurrentUser
PS> iex (new-object net.webclient).downloadstring('https://get.scoop.sh')
成功したら，gitのインストール
PS> scoop install git
次に，Pythonのインストール（Anacondaを使用）
PS> scoop bucket add extras
PS> scoop install anaconda3
最後に任意のディレクトリに教科書のソースコードをクローン
PS> mkdir Work # 任意のディレクトリ作成（不必要なら無視）
PS> cd Work # 任意のディレクトリに移動
PS> git clone https://github.com/rasbt/python-machine-learning-book.git # クローン
エラーとか出たら聞いてください．環境構築まだの人は↑を参考に各自やっておいてください．
・pythonがつかえる
・ソースコードをダウンロード（クローン）済み
になっている人はOKです．あと教科書（日本語版（第1版））は，「\\133.35.167.74\kimlab\users\lab\勉強会資料\ML\教科書」に置いたので各自見てください．
一応，自分で使う目的以外には使用しないでください．
