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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
AP, SANE, SAT (Joint) |
2021-07-28 12:40 |
Online |
Online |
Microwave Blood Pressure Estimation Using Machine Learning Kota Sasaki, Naoki Honma, Kentaro Murata, Morio Iwai, Koichiro Kobayashi (Iwate Univ.), Atsushi Sato (EQUOS RESEARCH) AP2021-27 SANE2021-17 |
This report proposes and experimentally assesses a non-contact blood pressure estimation method using a microwave and KN... [more] |
AP2021-27 SANE2021-17 pp.19-24(AP), pp.12-17(SANE) |
ISEC |
2021-05-19 15:00 |
Online |
Online |
[Invited Talk]
Efficiency and Accuracy Improvements of Secure Floating-Point Addition over Secret Sharing (from IWSEC 2020) Kota Sasaki (The Univ. of Tokyo), Koji Nuida (Kyushu Univ.) ISEC2021-8 |
In secure multiparty computation (MPC), floating-point numbers should be handled in many potential applications, but the... [more] |
ISEC2021-8 p.33 |
NC, MBE (Joint) |
2020-03-05 16:35 |
Tokyo |
University of Electro Communications (Cancelled but technical report was issued) |
Sparse STC estimation of suppressive elements for neurons in primary visual cortex Reiji Tanaka (Osaka Univ.), Kota Sasaki (Osaka Univ./NICT), Hirotaka Sakamoto, Yoshihiro Nagano (Tokyo Univ.), Yonghao Yue (Aoyama Gakuin Univ.), Masato Okada (Tokyo Univ./RIKEN), Izumi Ohzawa (Osaka Univ./NICT) NC2019-102 |
To improve a functional model for visual neurons, we have been developing a novel technique (sparse STC) where a spike-t... [more] |
NC2019-102 pp.155-159 |
NC, NLP (Joint) |
2017-01-26 16:00 |
Fukuoka |
Kitakyushu Foundation for the Advanement of Ind. Sci. and Tech. |
Fast Receptive field Inference with Sparse Fourirer Representation by using LASSO Takeshi Tanida, Hirotaka Sakamoto, Yasuhiko Igarashi, Takeshi Ideriha, Satoru Tokuda (Univ. of Tokyo), Kota Sasaki, Izumi Ohzawa (Osaka Univ.), Masato Okada (Univ. of Tokyo/RIKEN) NC2016-52 |
We propose fast receptive eld(RF) inference. The RF describes how a neuron sums up its inputs across
space and time. T... [more] |
NC2016-52 pp.25-30 |
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