K
Kiyoshi Nishikawa
Researcher at Tokyo Metropolitan University
Publications - 151
Citations - 1045
Kiyoshi Nishikawa is an academic researcher from Tokyo Metropolitan University. The author has contributed to research in topics: Adaptive filter & Kernel adaptive filter. The author has an hindex of 16, co-authored 150 publications receiving 993 citations. Previous affiliations of Kiyoshi Nishikawa include Kanazawa University & Sumitomo Electric Industries.
Papers
More filters
Journal ArticleDOI
A development of symmetric extension method for subband image coding
TL;DR: The development of a technique for subband image coding called the symmetric extension method which utilizes the nature of a symmetrically extended image to achieve high quality coding is described.
Journal ArticleDOI
A formulation and numerical approach to molecular systems by the Green function method without the Born–Oppenheimer approximation
TL;DR: In this article, a new numerical scheme for the non-Born-Oppenheimer density functional calculation based upon the Green function techniques within the GW approximation for evaluating molecular properties in the full quantum mechanical treatment was proposed.
Proceedings ArticleDOI
Wideband beamforming using fan filter
TL;DR: In this paper, the authors proposed a beamforming method using FIR (finite impulse response) fan filters for wideband beamforming using a linear array of sensors, which is based on two-dimensional filtering for the outputs of the sensors.
Journal ArticleDOI
Molecular collective dynamics in solid para-hydrogen and ortho-deuterium: The Parrinello–Rahman-type path integral centroid molecular dynamics approach
TL;DR: In this paper, the single-particle and collective dynamics of hydrogen/deuterium molecules in solid hcp para-hydrogen (p-H2) and o-D2 has been investigated by using the path integral centroid molecular dynamics (CMD) simulations at zero-pressure and 5.0 K, respectively.
Journal ArticleDOI
Pipelined LMS adaptive filter using a new look-ahead transformation
TL;DR: The proposed algorithm shows the possibility of having LMS algorithms perform pipelined processing without degrading the convergence characteristics and an architecture based on the proposed algorithm is considered.