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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IPSJ-CVIM, IPSJ-DCC, IPSJ-CGVI | 2023-11-16 16:50 |
Tottori | (Primary: On-site, Secondary: Online) |
Flood forecasting in Matsue City using statistical methods Kazuki Yamamoto, Hitoshi Sakano, Hiroshi Yajima (Shimane Univ.)  PRMU2023-23 |
In this study, we applied a statistical modeling approach to flood forecasting in rivers within Matsue City, examining a... [more] | PRMU2023-23 pp.43-46 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Study on Scheduled Sampling for Neural Transducer-based ASR Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura (NTT) EA2022-100 SIP2022-144 SP2022-64 |
In this paper, we propose scheduled sampling approaches suited for the recurrent neural network-transducer (RNNT) that i... [more] | EA2022-100 SIP2022-144 SP2022-64 pp.147-152 |
| IT, EMM | 2022-05-17 13:25 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
A Note on Time-Varying Two-Dimensional Autoregressive Models and the Bayes Codes Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2022-2 EMM2022-2 |
This paper proposes a two-dimensional autoregressive model with time-varying parameters as a stochastic model for explai... [more] | IT2022-2 EMM2022-2 pp.7-12 |
| PRMU | 2021-12-16 15:15 |
Online | Online | Multivariate time series forecasting accuracy improvement method based on LSTNet Hayato Sano, Jun Rokui (Univ of Shizuoka) PRMU2021-37 |
Multivariate time series forecasting is a field to predict future values by analyzing the past of multiple time series d... [more] | PRMU2021-37 pp.71-76 |
| SIP, IT, RCS | 2021-01-22 15:15 |
Online | Online | An Image Generative Model with Various Auto-regressive Coefficients Depending on Neighboring Pixels and the Bayes Code for It Masahiro Takano, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-108 SIP2020-86 RCS2020-199 |
In this papar, we propose an expanded model of an autoregressive stochastic generative model for images. This model cont... [more] | IT2020-108 SIP2020-86 RCS2020-199 pp.253-258 |
| NLC, IPSJ-NL, SP, IPSJ-SLP [detail] | 2020-12-02 09:40 |
Online | Online | Fast End-to-End Speech Recognition with CTC and Mask Predict Yosuke Higuchi (Waseda Univ.), Hirofumi Inaguma (Kyoto Univ.), Shinji Watanabe (JHU), Tetsuji Ogawa, Tetsunori Kobayashi (Waseda Univ.) NLC2020-13 SP2020-16 |
We present a fast non-autoregressive (NAR) end-to-end automatic speech recognition (E2E-ASR) framework, which generates ... [more] | NLC2020-13 SP2020-16 pp.1-6 |
| IT, EMM | 2020-05-28 15:25 |
Online | Online | An Autoregressive Image Generative Model and the Bayes Code for It Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-4 EMM2020-4 |
In this paper, we propose an autoregressive stochastic generative model for images. This model should be one of the mos... [more] |
IT2020-4 EMM2020-4 pp.19-24 |
| MBE, NC | 2019-10-11 16:45 |
Miyagi | Biometric Authentication Using Multivariate Autoregressive Coefficients for Photic Driving Response Akiyama Shohei (Yamagata Univ.), Takamasa Shimada (Tokyo Denki Univ.), Tadaniri Fukami (Yamagata Univ.) MBE2019-37 NC2019-28 |
In recent years, various biometrics such as fingerprints and veins have become widespread. However, there is a problem t... [more] | MBE2019-37 NC2019-28 pp.41-44 |
|
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 09:35 |
Osaka | I-Site Nanba（Osaka） | A study on Auto-Regressive modeling of Duty Cycle Kohei Okawa, Hiroki Iwata, Kenta Umebayashi (Tokyo Univ. of Agriculture and Tech.), Janne Lehtomäki (Univ. of Oulu), Miguel López-Benítez (Univ. of Liver), Satya Joshi (Univ. of Oulu) SR2019-29 |
In dynamic spectrum sharing, it is useful to exploit statistical information on spectrum usage. In this paper, we inve... [more] |
SR2019-29 pp.59-64 |
| RCS, SIP, IT | 2019-02-01 10:50 |
Osaka | Osaka University | A Study on Categorization of Busy/Idle History for Autoregressive Based Busy/Idle Duration Prediction over Real Environmental Channel Yusuke Tanaka, yafei HOU, Satoshi Denno (Okayama Univ.), Yoshinori Suzuki (ATR) IT2018-56 SIP2018-86 RCS2018-263 |
Predicting the channel spectrum (busy or idle) is one of important but challenging topic for a cognitive radio (CR) syst... [more] | IT2018-56 SIP2018-86 RCS2018-263 pp.121-126 |
| ICM, LOIS | 2019-01-24 14:00 |
Kagoshima | Analysis of behaviors of audience in presentations (Third report) Eiji Watanabe (Konan Univ.), Takashi Ozeki (Fukuyama Univ.), Takeshi Kohama (Kindai Univ.) ICM2018-39 LOIS2018-45 |
In presentations using slides, lecturers have to estimate the interests of the audience based on the behaviors of the au... [more] | ICM2018-39 LOIS2018-45 pp.21-26 |
|
| SRW | 2018-08-20 11:20 |
Okayama | Okayama Univ. | A Study of Autoregressive Model and Autoregressive Integrated Model Based Channel Idle/ Busy Status Duration Prediction for Real Environment WLAN Channel Naoya Hokimoto, Yafei Hou, Satoshi Denno (Okayama Univ.) SRW2018-13 |
Recently, due to the increase of huge number of wireless devices such as smartphones or sensors, mobile wireless traffic... [more] | SRW2018-13 pp.25-30 |
| EA, ASJ-H, ASJ-AA | 2018-07-25 13:40 |
Hokkaido | Hokkaido Univ. | Interference-free power spectral representations of periodic sounds and their application to VOCODERs Hideki Kawahara (Wakayama Univ.), Masanori Morise (Univ. Yamanashi), Kanru Hua (Univ. Illinois) EA2018-23 |
We propose a method to calculate the spectral envelope of voiced sounds for VOCODER applications. In our previous techni... [more] | EA2018-23 pp.135-140 |
| RCS, SR, SRW (Joint) |
2018-02-28 13:45 |
Kanagawa | YRP | A Study on Time Series Modeling of Duty Cycle for Smart Spectrum Access Daiki Cho, Kenta Umebayashi (TUAT), Shusuke Narieda (NIT, Akashi College), Miguel Lopez Bentez (UoL) SR2017-112 |
For an efficient spectrum sharing by primary user (PU) and secondary user (SU), SU needs to understand the spectrum usag... [more] | SR2017-112 pp.1-7 |
| AP, RCS (Joint) |
2017-11-08 14:55 |
Fukuoka | Fukuoka University | [Invited Lecture]
Channel Prediction in LOS Environment by AR Model Using Estimation of LOS Propagation Parameters Naoto Setoguchi, Hiroaki Nakabayashi, Keizo Cho (Chiba Inst.Tech.) AP2017-117 RCS2017-214 |
In MIMO technology, feedback delay of channel state information is regarded as a cause of deterioration of transmission ... [more] | AP2017-117 RCS2017-214 pp.49-54(AP), pp.53-58(RCS) |
| SP, SIP, EA | 2017-03-02 09:00 |
Okinawa | Okinawa Industry Support Center | [Poster Presentation]
An adaptive ARMA fitting model for conventional room transfer function a comparison study Chibana Kengo, Bruno Senzio Savino Barzel (Ryukyu Univ) EA2016-128 SIP2016-183 SP2016-123 |
In this research, a series of simulations consisting of different echo paths and signals were implemented. For ARMA fitt... [more] | EA2016-128 SIP2016-183 SP2016-123 pp.261-265 |
| AP (2nd) |
2017-01-26 - 2017-01-27 |
Overseas | Malaysia-Japan International Institute of Technology (MJIIT) | Prediction Accuracy Using Theoretical Autocorrelation Coefficient of Fading Channel in Line-of-Sight Environment Naoto Setoguchi, Hiroaki Nakabayashi, Keizo Cho (Chiba Inst.Tech.) |
Abstract In this report, we investigate prediction accuracy of fading channels in line-of-sight (LOS) environment when ... [more] | |
| EA, SP, SIP | 2016-03-29 09:00 |
Oita | Beppu International Convention Center B-ConPlaza | [Poster Presentation]
Majorisation-minimization based composite autoregressive system optimization with a glottal source model prior Lauri Juvela (Aalto Univ.), Hirokazu Kameoka (Tokyo Univ.), Junichi Yamagishi (NII) EA2015-115 SIP2015-164 SP2015-143 |
The Composite Autoregressive System solves the speech source-filter decomposition problem in a robust manner and can be ... [more] | EA2015-115 SIP2015-164 SP2015-143 pp.273-278 |
| RCS, CCS, SR, SRW (Joint) |
2016-03-04 09:25 |
Tokyo | Tokyo Institute of Technology | Interference Alignment for Time-Varying Channel with Low Complexity Channel Prediction based on Auto Regressive Model Masayoshi Ozawa, Tomoaki Ohtsuki (Keio Univ.), Wenjie Jiang, Yasushi Takatori, Tadao Nakagawa (NTT) RCS2015-381 |
Interference alignment (IA) is a interference suppression technique with a few number of antennas by aligning interferen... [more] | RCS2015-381 pp.279-284 |
| RCS | 2015-06-25 13:40 |
Hokkaido | Hokkaido Univ. | Interference Alignment for Time-varying Channel with Channel and Weight Predictions based on Auto Regressive Model Masayoshi Ozawa, Tomoaki Ohtsuki (Keio Univ.), Wenjie Jiang, Yasushi Takatori (NTT) RCS2015-72 |
In interference alignment (IA), interference signals are aligned in a certain signal subspace of each receiver and elimi... [more] | RCS2015-72 pp.155-160 |
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