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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, MSS | 2025-03-13 16:45 |
Okinawa | Miyakojima City Central Community Center (Okinawa) | Investigation of the effect of Photoplethysmogram time series length and sampling frequency on dynamical properties extraction by recurrence quantification analysis Sora Okazaki, Nina Sviridova (TCU) |
[more] | |
| NLP, CAS | 2024-10-17 11:20 |
Tottori | Information Center, Tottori University (Tottori) | Estimation of the Optimal Ratio of Image R and B Channels in Imaging Photoplethysmogram Ayane Mine, Nina Sviridova (Tokyo City Univ.)  CAS2024-32 NLP2024-62 |
[more] | CAS2024-32 NLP2024-62 pp.27-32 |
| NLP | 2024-09-05 10:30 |
Gifu | Takayama City Library (Gifu) | Dependence of green light photoplethysmogram dynamical properties extraction on the time series length. Sora Okazaki, Nina Sviridova (TCU) NLP2024-41 |
Photoplethysmogram (PPG) is pulse wave data obtained by projecting infrared, red, or green light to capture the volume c... [more] | NLP2024-41 pp.1-5 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 10:30 |
Tokushima | Naruto University of Education (Tokushima) | Estimation of Determinism for Imaging Photoplethysmogram Ayane Mine, Nina Sviridova (TCU) NLP2023-86 MICT2023-41 MBE2023-32 |
Photoplethysmography is a method of acquiring biological information by irradiating light onto the fingertip and measuri... [more] | NLP2023-86 MICT2023-41 MBE2023-32 pp.16-20 |
| NLP, MSS | 2023-03-16 10:20 |
Nagasaki | (Nagasaki, Online) (Primary: On-site, Secondary: Online) |
Calculation of Lyapunov Exponent for Imaging Photoplethysmogram Hirofumi Nakayama, Nina Sviridova, Tohru Ikeguchi (TUS) MSS2022-81 NLP2022-126 |
Photoplethysmogram is a biological signal of the cardiovascular system that measures changes in the amount of light refl... [more] | MSS2022-81 NLP2022-126 pp.95-99 |
| NC, NLP | 2023-01-28 10:40 |
Hokkaido | Future University Hakodate (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Study on minimal diagonal line length effect on recurrence quantification analysis. Nina Sviridova, Tohru Ikeguchi (TUS) NLP2022-83 NC2022-67 |
The recurrence plot visualizes the complex multidimensional system’s dynamics as a two-dimensional binary image. Applied... [more] | NLP2022-83 NC2022-67 pp.11-15 |
| NLP | 2022-11-25 15:15 |
Shiga | (Shiga, Online) (Primary: On-site, Secondary: Online) |
Towards Defining Minimal Time Series Length for Normalized Recurrence Quantification Analysis Nina Sviridova, Tohru Ikeguchi (TUS) NLP2022-78 |
Estimating the minimal required time series length is an important problem in many applied studies. In our previous stud... [more] | NLP2022-78 pp.97-102 |
| CAS, NLP | 2022-10-20 16:10 |
Niigata | (Niigata, Online) (Primary: On-site, Secondary: Online) |
Learning Method for Echo State Networks Constructed by Chaotic Neuron Models by Innate Training Yudai Ebato, Sou Nobukawa, Yusuke Sakemi (CIT), Takashi Kanamaru (kougakuin univ), Nina Sviridova (Tokyo Univ. of Science), Kazuyuki Aihara (UTokyo) CAS2022-26 NLP2022-46 |
Echo State Network (ESN) is a machine learning method that consists of an input layer, a layer of recurrent neural netwo... [more] | CAS2022-26 NLP2022-46 pp.35-40 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-22 16:25 |
Online | Online (Online) | Reconstructing dynamical system from marked point processes and its application to real world data Kazuya Sawada, Nina Sviridova (TUS), Yutaka Shimada (Saitama Univ.), Tour Ikeguchi (TUS) NLP2021-116 MICT2021-91 MBE2021-77 |
In this report, we applied the method of reconstructing dynamical system for the marked point process to the human pho... [more] |
NLP2021-116 MICT2021-91 MBE2021-77 pp.209-212 |
| NC, NLP (Joint) |
2021-01-21 10:05 |
Online | Online (Online) | Extraction of Property for Nonlinear Time Series by Changing Density of Recurrence Plots Shiki Kanamaru, Nina Sviridova (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2020-41 |
A recurrence plot is one of the most effective nonlinear time series analysis methods for qualitatively understanding a ... [more] | NLP2020-41 pp.7-12 |
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