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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IPSJ-CVIM | 2024-05-16 10:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
An acceleration method by image selection for wide-area 3D scene Takayuki Kurozumi, Shota Yamada, Seiichi Konya, Shinji Fukatsu (NTT) |
(To be available after the conference date) [more] | |
| MIKA (3rd) |
2023-10-10 15:35 |
Okinawa | Okinawa Jichikaikan (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Investigation of Ultra-Fast Beam Selection Optimization Method Based on Ising Model Naganuma Shunta, Fujita Tsukumo, Arai Maki (Tokyo Univ. of Science), Li Aohan (The Univ. of Electro-Communications), Hasegawa Mikio (Tokyo Univ. of Science) |
The Ising model is a mathematical model that represents the spin interactions of microscopic components of a substance. ... [more] | |
| MI | 2023-09-08 10:30 |
Osaka | (Primary: On-site, Secondary: Online) |
Adaptive Selection of Acquisition Parameter for Multi-Parameter Mapping in MRI Huijie Zhang (Tokyo Tech), Shigehide Kuhara (KU), Jun'ichi Takeuchi (QU), Yutaka Jitsumatsu (Tokyo Tech)  MI2023-15 |
We have proposed an adaptive and iterative method to determine the acquisition parameters for a multi-parameter mapping... [more] |
MI2023-15 pp.3-6 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-15 13:15 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Analysis of Backoff Time in LPWA Mesh Networking Using Opportunistic Routing Yuta Kobayashi (SIT), Eri Hosonuma (UT), Taku Yamazaki, Takumi Miyoshi, Yoshihiro Niitsu, Thomas Silverston (SIT) NS2022-56 |
For collecting multipoint sensor information, we have been developing a wireless mesh network that combines LPWA (low po... [more] | NS2022-56 pp.143-146 |
| SR | 2021-05-21 14:00 |
Online | Online | [Technology Exhibit]
Radio monitoring node and Wi-Fi/LPWA device to optimally configure wireless networks Satoru Shimizu, Takuya Kurihara, Kenta Suzuki, Kazuto Yano, Yoshinori Suzuki (ATR), Hiroaki Sudo, Kazuhiro Kosaka, Akihiko Odani, Noriyuki Gejoh, Takeshi Yasunaga (Panasonic) SR2021-15 |
In recent years, the use of wireless in various devices has had many effects on the radio wave environment. We had been ... [more] | SR2021-15 pp.99-104 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Lattice Model Selection of Gaussian Markov Random Field Hirosato Ito, Hirotaka Sakamoto, Shun Katakami, Masato Okada (Univ. Tokyo) IBISML2018-69 |
Recently, many observed images have been obtained in various natural scientific fields. It is very important issue to e... [more] | IBISML2018-69 pp.191-196 |
| ICSS | 2016-11-25 14:15 |
Kanagawa | Institute of Information Security | Study of behavior each malware function from the API calls Nobuki Suetsugu, Masaki Hashimoto (IISEC) ICSS2016-40 |
In recent years, there are various studies to malicious behavior detection. In particular, for the detection, much techn... [more] | ICSS2016-40 pp.9-14 |
| NC, MBE | 2015-03-17 13:50 |
Tokyo | Tamagawa University | Optimization of LASSO Learning using WAIC and Its Application to City Data Analysis Dai Miyazaki, Sumio Watanabe (Tokyo Tech) MBE2014-175 NC2014-126 |
LASSO(Least Absolute Shrinkage and Selection Operator) is a method adding a penalty term consisting of absolute values o... [more] | MBE2014-175 NC2014-126 pp.331-336 |
| EMM | 2015-03-12 16:00 |
Okinawa | Relations of Compression Ratio and the Most Suitable Parameter of Additive Watermarking under the IHC Criteria Hironobu Tozuka (Osaka Univ.), Maki Yoshida (NICT), Toru Fujiwara (Osaka Univ.) EMM2014-91 |
In this paper, we show relations of the compression ratio and the optimum parameter, which maximizes the performance of ... [more] | EMM2014-91 pp.85-90 |
|
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Optimization Method of LASSO Hyperparameter using WAIC Dai Miyazaki, Sumio Watanabe (Tokyo Tech) IBISML2014-63 |
LASSO (Least Absolute Shrinkage and Selection Operator) was proposed as a regression method using a penalty term made of... [more] | IBISML2014-63 pp.213-218 |
| IBISML | 2014-11-18 15:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Unsupervised Dimension Reduction via Least-Squares Quadratic Mutual Information Janya Sainui (Tokyo Inst. of Tech.), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-69 |
The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic p... [more] | IBISML2014-69 pp.259-262 |
| SP | 2014-02-28 10:30 |
Tokushima | The University of Tokushima | Evaluation of reverberant speech recognition by selecting suitable acoustic model with acoustic parameters Takahiro Fukumori, Masato Nakayama, Takanobu Nishiura, Yoichi Yamashita (Ritsumeikan Univ.) SP2013-108 |
The reverberant speech degrades the speech recognition performance in the field of distant-talking speech. As one of app... [more] | SP2013-108 pp.7-12 |
| MW (Workshop) |
2013-12-02 - 2013-12-04 |
Overseas | King Mongkut's University of Technology North Bangkok, Thailand | Millimeter Wave MU-MIMO System with PAE Array Yuyuan Chang, Noriaki Nagashima, Keisuke Hirota, Gia Khanh Tran, Kiyomichi Araki (Tokyo Tech.) |
For the spread of multimedia applications, high speed wireless communication techniques are necessary to transmit a larg... [more] | |
| ASN, IPSJ-UBI | 2013-05-16 16:40 |
Kumamoto | Kumamoto University | [Poster Presentation]
A Study on Congestion Control Parameter Selection in Wireless Direct Sensor Data Collection System. Koichiro Okimura, Kenichi Mase, Keisuke Nakano (Niigata Univ.) ASN2013-31 |
This paper deals with a wireless periodical sensor data collection system, which accommodates a number of sensor nodes d... [more] | ASN2013-31 pp.155-158 |
| IBISML | 2012-03-12 11:50 |
Tokyo | The Institute of Statistical Mathematics | Hyperparameter Selection of Infinite Gaussian Mixture Model via Widely Applicable Information Criterion Takushi Miki, Masahiro Kohjima, Sumio Watanabe (titech) IBISML2011-91 |
Recently, nonparametric Bayesian method is applied to wide range of research fields such as natural language processing,... [more] | IBISML2011-91 pp.29-33 |
| IBISML | 2011-11-10 15:45 |
Nara | Nara Womens Univ. | Image segmentation and restoration by variational Bayesian method and MCMC Kenta Kayano (Kansai Univ.), Kenji Nagata, Masato Okada (Univ. of Tokyo), Seiji Miyoshi (Kansai Univ.) IBISML2011-68 |
In this paper, we derive a deterministic algorithm that restores and segments an image by using variational Bayesian met... [more] | IBISML2011-68 pp.175-180 |
| CS, SIP, CAS | 2011-03-03 13:30 |
Okinawa | Ohhamanobumoto memorial hall (Ishigaki)( | Model Selection with Low Computational Costs in Kernel Ridge Regression Toru Takei, Akira Tanaka, Masaaki Miyakoshi (Hokkaido Univ.) CAS2010-133 SIP2010-149 CS2010-103 |
In kernel ridge regression with a given class of parameterized kernels, it is necessary to select a kernel parameter and... [more] | CAS2010-133 SIP2010-149 CS2010-103 pp.185-190 |
| NC, NLP | 2011-01-25 15:00 |
Hokkaido | Hokakido Univ. | A Proposal of Evolutionary Particle Swarm Optimizer with Inertia Weight Hong Zhang (K.I.T.) NLP2010-151 NC2010-115 |
In this paper, we propose an evolutionary particle swarm optimizer with inertia weight (EPSOIW) for obtaining the PSOIW ... [more] | NLP2010-151 NC2010-115 pp.153-158 |
| IBISML | 2010-11-04 15:00 |
Tokyo | IIS, Univ. of Tokyo | [Poster Presentation]
Image Segmentation by Region-Based Latent Variables and Belief Propagation Ryota Hasegawa, Seiji Miyoshi (Kansai Univ.), Masato Okada (Univ. of Tokyo) IBISML2010-71 |
To represent edges in image processing based on Bayesian inference, it is very effective to introduce latent variables. ... [more] | IBISML2010-71 pp.91-97 |
| NC | 2009-01-20 14:40 |
Hokkaido | Hokkaido Univ. | Which model can properly describe dynamics and smoothness of firing rate? Ken Takiyama (The Univ. of Tokyo), Kentaro Katahira, Masato Okada (The Univ. of Tokyo/RIKEN) NC2008-98 |
We construct the algorithm using belief propagation(BP), which algorithm simultaneously estimates firing rate and calcu... [more] |
NC2008-98 pp.89-94 |
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