English / Japanese
Hiroki Kuroda, Ph.D.
Assistant Professor,
Dept. of Information and Management Systems Engineering,
Nagaoka University of Technology, Japan
Research Interest
Signal processing, machine learning, inverse problem
theory & method of signal recovery, system identification, regression, classification, detection
application to data science, radar, seismology, communications, acoustics, image
Sparse modeling
sparse representation, sparse estimation, structured sparsity, convexity-preserving nonconvex penalty
Optimization theory
convex optimization, proximal splitting algorithm, online optimization, iterative algorithm
Selected Publications (full list)
H. Kuroda and D. Kitahara, "Block-sparse recovery with optimal block partition," IEEE Transactions on Signal Processing, vol. 70, pp. 1506–1520, 2022. (IEEE SPS Japan Young Author Best Paper Award) official access TechRxiv code (MATLAB)
H. Kuroda, M. Yamagishi, and I. Yamada, "Exploiting sparsity in tight-dimensional spaces for piecewise continuous signal recovery," IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6363–6376, Dec. 2018. official access preprint (©2018 IEEE)
H. Kuroda, D. Kitahara, E. Yoshikawa, H. Kikuchi, and T. Ushio, "Convex Estimation of Sparse-Smooth Power Spectral Densities from Mixtures of Realizations with Application to Weather Radar", IEEE Access, 2023. official access (OA) arXiv
News
A paper on convexity-preserving nonconvex enhancement of minimization induced penalties was presented in EUSIPCO 2024
H. Kuroda,"Sharpening minimization induced penalties," European Signal Processing Conference (EUSIPCO), Lyon, France, pp. 2612–2616, Aug. 2024. TechRxiv.
Received 2023 IEEE Signal Processing Society (SPS) Japan Young Author Best Paper Award for the paper:
H. Kuroda and D. Kitahara, "Block-sparse recovery with optimal block partition," IEEE Transactions on Signal Processing, vol. 70, pp. 1506–1520, 2022. official access TechRxiv code (MATLAB)
A paper on convex optimization-based estimation of sparse and smooth power spectral densties and its application to weather radar is accpeted to IEEE Access.
H. Kuroda, D. Kitahara, E. Yoshikawa, H. Kikuchi, and T. Ushio, "Convex estimation of sparse-smooth power spectral densities from mixtures of realizations with application to weather radar", IEEE Access, vol. 11, pp. 128859-128874, 2023. official access (OA) arXiv
Invited to introduce our paper "H. Kuroda and D. Kitahara, "Block-sparse recovery with optimal block partition," IEEE Transactions on Signal Processing, vol. 70, pp. 1506–1520, 2022." at top conference/journal session of FIT2023.
A paper on power spectral density estimation for weather radar was presented at IEEE ICASSP 2023.
H. Kuroda, D. Kitahara, E. Yoshikawa, H. Kikuchi, and T. Ushio, "Sparsity-smoothness-aware power spectral density estimation with application to phased array weather radar," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rhodes Island, Greek, 5 pages, June 2023. official access
A paper on modal interval regression via spline smoothing is accepted to IEICE Transactions on Fundamentals.
S. Yao, D. Kitahara, H. Kuroda, and A. Hirabayashi, "Modal interval regression based on spline quantile regression," IEICE Transactions on Fundamentals, vol. E106-A, no.2, pp. 106–123, Feb. 2023. official access
A paper on block-sparse recovery is accepted to IEEE Transactions on Signal Processing.
H. Kuroda and D. Kitahara, "Block-sparse recovery with optimal block partition," IEEE Transactions on Signal Processing, vol. 70, pp. 1506–1520, 2022. official access TechRxiv code (MATLAB)
A paper on sparsity-aware beamforming for phased array weather radar is accepted to IEEE Transactions on Geoscience and Remote Sensing.
D. Kitahara, H. Kuroda, A. Hirabayashi, E. Yoshikawa, H. Kikuchi, and T. Ushio, "Nonlinear beamforming based on group-sparsities of periodograms for phased array weather radar," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 19 pages, 2022, Art. no. 4106819. official access TechRxiv
A tutorial on structured sparse estimation was presented at SCI '22.
A paper on graph-structured sparse regularization was presented at IEEE ICASSP 2022.
H. Kuroda and D. Kitahara, "Graph-structured sparse regularization via convex optimization," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, May 2022. official access
A paper on super-resolution using GAN with metric projection is accepted to IEICE Transactions on Fundamentals.
H. Yamamoto, D. Kitahara, H. Kuroda, and A. Hirabayashi, "Image super-resolution via generative adversarial networks using metric projections onto consistent sets for low-resolution inputs," IEICE Transactions on Fundamentals, vol. E105-A, no. 4, pp. 704–718, Apr. 2022.
Contact
1603-1 Kamitomioka-machi, Nagaoka-shi, Niigata, 940-2188 Japan
E-mail: kuroda [at] vos.nagaokaut.ac.jp