T
Takuya Sakamoto
Researcher at Kyoto University
Publications - 189
Citations - 2076
Takuya Sakamoto is an academic researcher from Kyoto University. The author has contributed to research in topics: Radar & Radar imaging. The author has an hindex of 22, co-authored 179 publications receiving 1794 citations. Previous affiliations of Takuya Sakamoto include University of Hawaii at Manoa & University of British Columbia.
Papers
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Accurate UWB Radar Three-Dimensional Imaging Algorithm for a Complex Boundary Without Range Point Connections
TL;DR: A novel imaging algorithm without range point connection is proposed to accomplish high-quality and flexible 3-D imaging for various target shapes and several comparative studies of conventional algorithms clarify that the proposed method accomplishes accurate and reliable 3- D imaging even for complex or multiple boundaries.
Journal Article
A Target Shape Estimation Algorithm for Pulse Radar Systems Based on Boundary Scattering Transform
Takuya Sakamoto,Toru Sato +1 more
TL;DR: A non-parametric algorithm is proposed for high-resolution estimation of target shapes using waveform data obtained by scanning an omni-directional antenna for environment measurement.
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Feature-Based Correlation and Topological Similarity for Interbeat Interval Estimation Using Ultrawideband Radar
Takuya Sakamoto,Ryohei Imasaka,Hirofumi Taki,Toru Sato,Mototaka Yoshioka,Kenichi Inoue,Takeshi Fukuda,Hiroyuki Sakai +7 more
TL;DR: The proposed method will be useful in the realization of a remote vital signs monitoring system that enables accurate estimation of HR variability, which has been used in various clinical settings for the treatment of conditions such as diabetes and arterial hypertension.
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A robust and fast imaging algorithm with an envelope of circles for UWB pulse radars
TL;DR: A robust imaging method with an envelope of circles that can realize a level of robust and fast imaging that cannot be achieved by the original SEABED.
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Omnidirectional Motion Classification With Monostatic Radar System Using Micro-Doppler Signatures
TL;DR: This article proposes an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network, and provides a sensible definition of “angle sensitivity.”