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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI, IPSJ-ITS, IPSJ-MBL, IPSJ-DPS | 2024-05-17 09:50 |
Okinawa | Hierarchical ArUco Marker Array for Coarse-to-Fine Phase Identification Leo Miyashita, Satoshi Tabata, Masatoshi Ishikawa (TUS) |
(To be available after the conference date) [more] | ||
| MI | 2024-03-04 15:58 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Representations obtained by self-supervised learning of hierarchical ViT to discriminate between benign and malignant breast tumors Akiomi Ishikawa (NIT), Kouji Arihiro (HIrosima Univ), Hidekata Hontani (NIT)  MI2023-88 |
In this paper, I report a method to apply the representation of pathological microscopic images obtained by self-supervi... [more] | MI2023-88 pp.184-185 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 10:00 |
Tokushima | Naruto University of Education | Hierarchical lossless compression of high dynamic range images using predictors based on cellular neural networks Seiya Kushi, Kazuki Nakashima, Hideharu Toda (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2023-85 MICT2023-40 MBE2023-31 |
We have been developing a scalable lossless coding method using cellular neural networks (CNN) as predictors. This metho... [more] | NLP2023-85 MICT2023-40 MBE2023-31 pp.12-15 |
| SIP, IT, RCS | 2024-01-18 11:45 |
Miyagi | (Primary: On-site, Secondary: Online) |
A Study on Massive MIMO Channel Estimation Based on Sparse Bayesian Learning Using Hierarchical Model Kengo Furuta, Takumi Takahashi, Kenta Ito (Osaka Univ.), Shinsuke Ibi (Doshisha Uni.) IT2023-34 SIP2023-67 RCS2023-209 |
Massive multi-input multi-output (MIMO) channels are known to have pseudo-sparsity in the angular (beam) domain, and it ... [more] | IT2023-34 SIP2023-67 RCS2023-209 pp.25-30 |
| NS | 2023-10-04 11:35 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
NS2023-70 |
In an all-photonics network (APN), all terminal endpoints are connected with full optical mesh paths. Because optical fi... [more] | NS2023-70 pp.14-19 |
| NLP, MSS | 2023-03-17 14:30 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Global Convergence Analysis of Distributed HALS Algorithm for Nonnegative Matrix Factorization Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-104 NLP2022-149 |
As a fast computational method for Nonnegative Matrix Factorization (NMF), the Hierarchical Alternating Least Squares ... [more] |
MSS2022-104 NLP2022-149 pp.198-203 |
| NLP, MSS | 2023-03-17 14:50 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Reformulation of Optimization Problem in Randomized NMF and Proposal of A Novel Iterative Update Algorithm Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2022-105 NLP2022-150 |
As an approach to efficiently perform large-scale Nonnegative Matrix Factorization (NMF), a randomized NMF was recently ... [more] | MSS2022-105 NLP2022-150 pp.204-209 |
| NLP, MSS | 2023-03-17 16:25 |
Nagasaki | (Primary: On-site, Secondary: Online) |
Investigation on improving diversity of options in option-critic reinforcement learning Aya Nakagawa, Hidehiro Nakano (Tokyo City Univ.) MSS2022-109 NLP2022-154 |
Recently, reinforcement learning has been attracting attention in various fields such as automatic control and game AI. ... [more] | MSS2022-109 NLP2022-154 pp.225-230 |
| IN, NS (Joint) |
2023-03-02 16:50 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
Hierarchical Piece Distribution Method for Cooperative Groups Formed within Wireless LANs in BitTorrent Mikiya Senso, Akihiro Fujimoto (Wakayama Univ.), Hideki Tode (Osaka Metropolitan Univ.) NS2022-210 |
(To be available after the conference date) [more] | NS2022-210 pp.245-250 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 10:45 |
Hokkaido | Hokkaido Univ. | A Note on Multi-label Image Classification in Animation Illustration Considering Hierarchical Relationships of Attributes Ziwen Lan, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a multi-label classification of animated illustrations considering the hierarchical relationships of... [more] | |
| NLP | 2022-11-24 14:50 |
Shiga | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Hierarchical recurrence plot analysis for music waveform and MIDI as marked point process Miwa Fukino (Panasonic Holdings) NLP2022-64 |
A method for using recurrence plots (RPs) for music analysis is introduced. When analyzing music waveform data, one song... [more] | NLP2022-64 p.35 |
| CAS, NLP | 2022-10-20 14:55 |
Niigata | (Primary: On-site, Secondary: Online) |
Hierarchical Lossless Coding with Arithmetic Coders for Each CNN Predictor Kazuki Nakashima, Ryo Nakazawa, Hideharu Toda, Hisashi Aomori (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Ichiro Matsuda, Susumu Itoh (TUS) CAS2022-23 NLP2022-43 |
We have been developing a scalable lossless coding method using the cellular neural networks (CNN) as predictors. This ... [more] |
CAS2022-23 NLP2022-43 pp.20-24 |
| MVE | 2022-09-09 11:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
An Image Recognition Model of Danger Objects for Diverse Clients using Federated Learning Yasuhiro Nitta, Ryo Yonetani, Maki Sugimoto, Hideo Saito (Keio Univ.) MVE2022-15 |
A disabled person can have cognition of danger objects during walking, which might not coincide with a non-disabled pers... [more] | MVE2022-15 pp.26-31 |
| IN | 2022-01-18 11:35 |
Online | Online | Evaluation on Prediction Method for Missing Probability of Sensor Value based on Hierarchical Structure of Missing Value Norifumi Hirata, Osamu Maeshima, Kiyohito Yoshihara (KDDI Research) IN2021-25 |
Collecting sensor data via networks is important for IoT (Internet of Things) services.However, sensors sometimes have m... [more] | IN2021-25 pp.7-12 |
| SRW, SeMI, CNR (Joint) |
2021-11-26 09:50 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
Terminal Location Estimation Using Delay Profile by Machine Learning with Hierarchical Clustering Jun Hirata, Keichi Mizutani, Hiroshi Harada (Kyoto Univ.) SRW2021-36 |
A location estimation method using machine learning with hierarchical clustering of delay profiles has been proposed as ... [more] | SRW2021-36 pp.37-42 |
| CCS | 2021-11-19 11:10 |
Osaka | Osaka Univ. (Primary: On-site, Secondary: Online) |
Toward Human Cognition-inspired High-Level Decision Making For Hierarchical Reinforcement Learning Agents Rousslan Fernand Julien Dossa (Kobe Univ.), Takashi Matsubara (Osaka Univ.) CCS2021-28 |
Hierarchical reinforcement learning (HRL) methods aim to leverage the concept of temporal abstraction to efficiently sol... [more] | CCS2021-28 pp.61-66 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-28 13:50 |
Online | Online | Simplification of Average Consensus Algorithm in Distributed HALS Algorithm for NMF Keiju Hayashi, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-3 IBISML2021-3 |
Nonnegative Matrix Factorization (NMF) is the process of approximating a given nonnegative matrix by the product of two ... [more] | NC2021-3 IBISML2021-3 pp.15-22 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2021-06-28 14:15 |
Online | Online | Modification of Optimization Problem in Randomized NMF and Design of Optimization Method based on HALS Algorithm Takao Masuda, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NC2021-4 IBISML2021-4 |
Nonnegative matrix factorization (NMF) is the process of decomposing a given nonnegative matrix into two nonnegative fac... [more] | NC2021-4 IBISML2021-4 pp.23-30 |
| IN, NS (Joint) |
2021-03-05 09:10 |
Online | Online | Failure Node Detection Method for Wireless Sensor Network with Hierarchical Topology Shuichi Kawaguchi, Kohta Ohshima (TUMSAT) NS2020-139 |
In this paper, we propose a failure node detection method for short-range multi-hop communication with a hierarchical to... [more] | NS2020-139 pp.97-102 |
| NC, MBE (Joint) |
2020-03-06 14:55 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
Efficient cluster mapping for conditions of weather based on combination of self-organizing map and hierarchical clustering Kazuki Osawa, Keiji Kamei (NIT), Masumi Ishikawa (KIT) NC2019-113 |
Recently, applications of Deep Learning(AI) for solving social problems have been frequently proposed. However, there ar... [more] | NC2019-113 pp.213-218 |
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