MASAAKI NAGAHARA

Last Updated :2025/07/07

Affiliations, Positions
Graduate School of Advanced Science and Engineering, Professor
E-mail
nagamhiroshima-u.ac.jp

Basic Information

Academic Degrees

  • Kyoto University
  • Kyoto University

Educational Activity

Course in Charge

  1. 2025, Liberal Arts Education Program1, 1Term, Linear AlgebraI
  2. 2025, Liberal Arts Education Program1, 3Term, Linear AlgebraII
  3. 2025, Undergraduate Education, 1Term, Sparse Estimation
  4. 2025, Undergraduate Education, Intensive, Long-term Fieldwork I
  5. 2025, Undergraduate Education, 1Term, Intelligence Science Seminar I
  6. 2025, Undergraduate Education, 2Term, Intelligence Science Seminar II
  7. 2025, Undergraduate Education, Second Semester, Graduation Thesis
  8. 2025, Graduate Education (Master's Program) , 1Term, Special Exercises on Informatics and Data Science A
  9. 2025, Graduate Education (Master's Program) , 2Term, Special Exercises on Informatics and Data Science A
  10. 2025, Graduate Education (Master's Program) , 3Term, Special Exercises on Informatics and Data Science B
  11. 2025, Graduate Education (Master's Program) , 4Term, Special Exercises on Informatics and Data Science B
  12. 2025, Graduate Education (Master's Program) , Year, Special Study on Informatics and Data Science
  13. 2025, Graduate Education (Master's Program) , 3Term, Control of multi-agent systems

Research Activities

Academic Papers

  1. Localized Data-Driven Distributed Controller Design for Positive Stabilization, IEEE CONTROL SYSTEMS LETTERS, 9, 456-461, 2025
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  2. LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based Approaches for Analyzing Household Energy Data: Towards Electricity Self-Sufficient Houses, ENERGIES, 17(17), 202409
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  3. Design of Sparse Control With Minimax Concave Penalty, IEEE CONTROL SYSTEMS LETTERS, 8, 544-549, 202405
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  4. Introduction to Compressed Sensing with Python, IEICE TRANSACTIONS ON COMMUNICATIONS, E107B(1), 126-138, 202401
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  5. ★, A survey on compressed sensing approach to systems and control, MATHEMATICS OF CONTROL SIGNALS AND SYSTEMS, 36(1), 1-20, 202403
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  6. Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu, ENERGIES, 16(16), 202308
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  7. H-infinity design of periodically nonuniform interpolation and decimation for non-band-limited signals, SICE Journal of Control, Measurement, and System Integration, 4(5), 341-348, 2011
  8. Digital Cancelation of Self-Interference for Single-Frequency Full-Duplex Relay Stations via Sampled-Data Control, SICE Journal of Control, Measurement, and System Integration, 8(5), 321-327, 2015
  9. Characterization of maximum hands-off control, Systems & Control Letters, 94, 31-36, 2016
  10. Discrete-time hands-off control by sparse optimization, EURASIP Journal on Advances in Signal Processing, 2016
  11. Robust AC Voltage Regulation of Microgrids in Islanded Mode with Sinusoidal Internal Model, SICE Journal of Control, Measurement, and System Integration, 10(2), 62-69, 2017
  12. Multiuser Detection based on MAP Estimation with Sum-of-Absolute-Values Relaxation, IEEE Transactions on Signal Processing, 65(21), 5621-5634, 2017
  13. Distributed Proximal Minimization Algorithm for Constrained Convex Optimization over Strongly Connected Networks, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E102-A(02), 2019
  14. Self-Interference Suppression based on Sampled-Data H-infinity Control for Baseband Signal Subspaces, SICE Journal of Control, Measurement, and System Integration, 12(5), 182-189, 2019
  15. Multihop TDMA-based wireless networked control systems robust against bursty packet losses: a two-path approach, IEICE Transactions on Communications, E103-B(3), 200-210, 2020
  16. CLOT norm minimization for continuous hands-off control, Automatica, 113, 108679, 2020
  17. Bayesian LPV-FIR Identification of Wheelchair Dynamics and Its Application to Feedforward Control, SICE Journal of Control, Measurement, and System Integration, 13(4), 208-213, 2020
  18. Maximum hands-off control with time-space sparsity, IEEE Control Systems Letters, 5(4), 1213-1218, 2021
  19. Majority determination in binary-valued communication networks, IEEE Transactions on Control of Network Systems, 8(2), 838-846, 2021
  20. Control, intervention, and behavioral economics over human social networks against COVID-19, Advanced Robotics, 35(11), 733-739, 2021
  21. Iterative greedy LMI for sparse control, IEEE Control Systems Letters, 6, 986-991, 2021
  22. Constrained smoothing splines by optimal control, IEEE Control Systems Letters, 6, 1298-1303, 2021
  23. Sparse optimal control problems with intermediate constraints: Necessary conditions, Optimal Control, Applications and Methods, 43, 369-385, 2022
  24. Resource-aware time-optimal control with multiple sparsity measures, Automatica, 135, 2022
  25. Fast hands-off control using ADMM real-time iterations, IEEE Transactions on Automatic Control, 67(10), 5416-5423, 2022
  26. Control of a quadrotor group based on maximum hands-off distributed control, International Journal of Mechatronics and Automation, 8(4), 2021
  27. Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation, Asian Journal of Control, 2022
  28. Distributed sparse optimization for source localization over diffusion fields with cooperative spatiotemporal sensing, Advanced Robotics, 2022
  29. A new perspective on cooperative control of multi-agent systems through different types of graph Laplacians, Advanced Robotics, 2022
  30. Platooning control of drones with real-time deep learning object detection, Advanced Robotics, 2022
  31. Projection onto the set of rank-constrained structured matrices for reduced-order controller design, 2022
  32. ★, Sparse control for continuous-time systems, International Journal of Robust and Nonlinear Control, 33, 6-22, 202301
  33. Hypertracking and hyperrejection: Control of signals beyond the Nyquist frequency, IEEE Transactions on Automatic Control, 2022
  34. Risk-aware maximum hands-off control using worst-case conditional value-at-risk, IEEE Transactions on Automatic Control, 20230109
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  35. Min-Max Design of Error Feedback Quantizers Without Overloading, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 65(4), 1395-1405, 2018
  36. Discrete-Valued Model Predictive Control Using Sum-of-Absolute-Values Optimization, ASIAN JOURNAL OF CONTROL, 20(1), 196-206, 2018
  37. Mean Squared Error Analysis of Quantizers With Error Feedback, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 65(22), 5970-5981, 2017
  38. Time-optimal hands-off control for linear time-invariant systems, AUTOMATICA, 99, 54-58, 2019
  39. Discrete Signal Reconstruction by Sum of Absolute Values, IEEE SIGNAL PROCESSING LETTERS, 22(10), 1575-1579, 2015
  40. L-1 Control Theoretic Smoothing Splines, IEEE SIGNAL PROCESSING LETTERS, 21(11), 1394-1397, 2014
  41. Value function in maximum hands-off control for linear systems, AUTOMATICA, 64, 190-195, 2016
  42. Maximum Hands-Off Control: A Paradigm of Control Effort Minimization, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 61(3), 735-747, 2016
  43. Digital repetitive controller design via sampled-data delayed signal reconstruction, AUTOMATICA, 65, 203-209, 2016
  44. Symbol Detection for Faster-Than-Nyquist Signaling by Sum-of-Absolute- Values Optimization, IEEE SIGNAL PROCESSING LETTERS, 23(12), 1853-1857, 2016
  45. Discrete-Valued Control of Linear Time-Invariant Systems by Sum-of-Absolute-Values Optimization, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 62(6), 2750-2763, 2017
  46. Maximum Hands-Off Distributed Control for Consensus of Multiagent Systems with Sampled-Data State Observation, IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 6(2), 852-862, 2019
  47. H-infinity-Optimal Fractional Delay Filters, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 61(18), 4473-4480, 2013
  48. Sparse Packetized Predictive Control for Networked Control Over Erasure Channels, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 59(7), 1899-1905, 2014
  49. H-infinity optimal approximation for causal spline interpolation, SIGNAL PROCESSING, 91(2), 176-184, 2011
  50. Link Quality Classifier with Compressed Sensing Based on l(1)-l(2) Optimization, IEEE COMMUNICATIONS LETTERS, 15(10), 1117-1119, 2011
  51. Signal Reconstruction via H-infinity Sampled-Data Control Theory-Beyond the Shannon Paradigm, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 60(2), 613-625, 2012
  52. A BRIEF OVERVIEW OF SIGNAL RECONSTRUCTION VIA SAMPLED-DATA H-infinity OPTIMIZATION, APPLIED AND COMPUTATIONAL MATHEMATICS, 11(1), 3-18, 2012
  53. Compressive Sampling for Remote Control Systems, IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, E95A(4), 713-722, 2012
  54. Frequency Domain Min-Max Optimization of Noise-Shaping Delta-Sigma Modulators, IEEE TRANSACTIONS ON SIGNAL PROCESSING, 60(6), 2828-2839, 2012
  55. Monotone Smoothing Splines using General Linear Systems, ASIAN JOURNAL OF CONTROL, 15(2), 461-468, 2013
  56. A User's Guide to Compressed Sensing for Communications Systems, IEICE TRANSACTIONS ON COMMUNICATIONS, E96B(3), 685-712, 2013
  57. Optimizing FIR approximation for discrete-time IIR filters, IEEE SIGNAL PROCESSING LETTERS, 10(9), 273-276, 2003
  58. Optimal wavelet expansion via sampled-data control theory, IEEE SIGNAL PROCESSING LETTERS, 11(2), 79-82, 2004

Publications such as books

  1. 2025/04/07, Compressed Sensing Approach to Systems and Control, Compressed sensing, also known as sparse representation or sparse modeling, has experienced substantial growth in research fields such as signal processing, machine learning, and statistics. In recent years, this powerful tool has been successfully applied to the design of control systems. This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control. This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control. This book is intended for graduate students and researchers who already have a foundational understanding of basic calculus and linear algebra. Its primary objective is to equip readers with the practical skills to apply compressed sensing techniques to a range of engineering problems, with a particular emphasis on systems and control. It presents a comprehensive collection of efficient algorithms for addressing the problems discussed in the text. Moreover, the book includes accompanying Python programs, which enable readers to actively experiment with these algorithms first-hand. By engaging with these practical examples, readers will develop a deeper understanding of compressed sensing techniques and their applications to systems and control., Now Publishers, 2025, 04, Scholarly Book, Single work, English, Masaaki Nagahara, 978-1638285045, 274
    Amazon URL
  2. 2024/08/13, Control of Multi-agent Systems: Theory and Simulations with Python, This textbook teaches control theory for multi-agent systems. Readers will learn the basics of linear algebra and graph theory, which are then developed to describe and solve multi-agent control problems. The authors address important and fundamental problems including: • consensus control; • coverage control; • formation control; • distributed optimization; and • the viral spreading phenomenon. Students' understanding of the core theory for multi-agent control is enhanced through worked examples and programs in the popular Python language. End-of-chapter exercises are provided to help assess learning progress. Instructors who adopt the book for their courses can download a solutions manual and the figures in the book for lecture slides. Additionally, the Python programs are available for download and can be used for experiments by students in advanced undergraduate or graduate courses based on this text. The broad spectrum of applications relevant to this material includes the Internet of Things, cyber-physical systems, robot swarms, communications networks, smart grids, and truck platooning. Additionally, in the spheres of social science and public health, it applies to opinion dynamics and the spreading of viruses in social networks. Students interested in learning about such applications, or in pursuing further research in multi-agent systems from a theoretical perspective, will find much to gain from Control of Multi-agent Systems. Instructors wishing to teach the subject will also find it beneficial., Springer Nature, 2024, 08, Scholarly Book, Joint work, English, Masaaki Nagahara , Shun-Ichi Azuma , Hyo-Sung Ahn, 978-3-031-52980-1, 228
    Amazon URL