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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIS | 2019-12-12 14:35 |
Okayama | Okayama University of Science | A Dimensionality Reduction Method with Random Sampling for Privacy-Preserving Machine Learning Ayana Kawamura, Kenta Iida, Hitoshi Kiya (Tokyo Metro. Univ.)  SIS2019-26 |
In this paper, we propose a dimensionality reduction method with random sampling for privacy-preserving machine learning... [more] | SIS2019-26 pp.17-21 |
| SIS | 2019-12-12 16:55 |
Okayama | Okayama University of Science | [Fellow Memorial Lecture]
Adaptive signal processing and image processing, linear or non-linear? Mitsuji Muneyasu (Kansai Univ.) SIS2019-30 |
It is well known that an image is nonstationary, the processing according to its local property should be required. Ther... [more] | SIS2019-30 p.37 |
| ISEC, SITE, LOIS | 2019-11-02 15:00 |
Osaka | Osaka Univ. | On Robustness of Machine-Learning-Based Malware Detection Wanjia Zheng (U. Tsukuba), Kazumasa Omote (U. Tsukuba/NICT) ISEC2019-83 SITE2019-77 LOIS2019-42 |
As the 2020 Tokyo Olympics are approaching, the possibility of being targeted by attackers has further increased in Japa... [more] | ISEC2019-83 SITE2019-77 LOIS2019-42 pp.133-140 |
| EA, ASJ-H | 2019-08-09 11:00 |
Miyagi | Tohoku Univ. | Study on Noise Suppression by Differential-type Microphone Array and Machine Learning of 2D Spectrum Koichiro Shiozawa, Kenji Ozawa (Univ. Yamanashi), Tomohiko Ise (AlpsAlpine) EA2019-31 |
This project aims to achieve noise suppression by processing of the output of a microphone array with an artificial neur... [more] | EA2019-31 pp.53-58 |
| MW, EST, OPE, MWP, EMT, IEE-EMT, THz [detail] | 2019-07-18 13:40 |
Hokkaido | Hakodate City Central Library | Design and Evaluation of 3-Dimensional Printed Reflector Fresnel Lens Antenna Based on Acrylonitrile Butadiene Styrene Plastics for W-Band Millimeter-Wave Radar Applications Shunichi Futatsumori, Nobuhiro Sakamoto, Tomio Soga (ENRI) EMT2019-13 MW2019-26 OPE2019-17 EST2019-15 MWP2019-13 |
This report discusses the design and evaluation of 3-dimensional (3D) printed reflector Fresnel lens antenna based on Ac... [more] | EMT2019-13 MW2019-26 OPE2019-17 EST2019-15 MWP2019-13 pp.41-45 |
| NC, IBISML, IPSJ-MPS, IPSJ-BIO [detail] | 2019-06-18 10:25 |
Okinawa | Okinawa Institute of Science and Technology | Spatial-Temporal decomposition to resting and task MEG using DMD Fumiya Nakai (NAIST), Okito Yamashita (ATR) NC2019-13 IBISML2019-11 |
Magneto/electroencephalography (M/EEG) observe neural activities with high spatial-temporal resolution without invasive ... [more] | NC2019-13 IBISML2019-11 pp.51-56(NC), pp.73-78(IBISML) |
| RCS, SR, SRW (Joint) |
2019-03-06 10:30 |
Kanagawa | YRP | A Study on Layered Belief Propagation for Large MIMO Detection based on Statistical Beams Takumi Takahashi (Osaka Univ.), Antti Tolli (Univ. of Oulu), Shinsuke Ibi, Seiichi Sampei (Osaka Univ.) RCS2018-285 |
This paper proposes a novel layered belief propagation (BP) detector with a concatenated structure of two different BP l... [more] | RCS2018-285 pp.19-24 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2018-09-21 13:30 |
Fukuoka | Modification of Bayesian Optimization for Efficient Calibration of Simulation Model Daiki Kiribuchi, Takeichiro Nishikawa (Toshiba), Satoru Yokota, Ryota Narasaki, Soh Koike (Toshiba Memory) PRMU2018-63 IBISML2018-40 |
(To be available after the conference date) [more] | PRMU2018-63 IBISML2018-40 pp.195-200 |
|
| EST | 2018-09-07 10:20 |
Okinawa | Kumejima-machi, Okinawa | Numerical Analysis of 3-Dimensional Printed Reflector-Lens Antenna Based on Acrylonitrile Butadiene Styrene Plastics for W-Band Millimeter-Wave Radar Applications Shunichi Futatsumori, Nobuhiro Sakamoto (ENRI) EST2018-54 |
This report discusses the numerical analysis of 3-dimensional (3D) printed reflector-lens antenna based on Acrylonitrile... [more] | EST2018-54 pp.63-67 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 10:25 |
Okinawa | Okinawa Institute of Science and Technology | Feature scaling in spectral classification of high dimensional data Momo Matsuda, Keiichi Morikuni, Akira Imakura, Tetsuya Sakurai (Univ. Tsukuba) IBISML2018-2 |
We consider the classification problem for high dimensional data. Using prior knowledge on the labels of partial samples... [more] | IBISML2018-2 pp.9-14 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2018-06-13 13:25 |
Okinawa | Okinawa Institute of Science and Technology | A supervised dimensionality reduction method using linear combinations of multiple eigenvectors Akira Imakura, Momo Matsuda, Tetsuya Sakurai (Univ. Tsukuba) IBISML2018-6 |
Dimensionality reduction methods that reduce the dimension of original data to a low-dimensional subspace such as LPP an... [more] | IBISML2018-6 pp.39-45 |
| RCS, SR, SRW (Joint) |
2018-03-01 10:00 |
Kanagawa | YRP | CSI Overhead Reduction for Massive MIMO using Multi-Dimensional Scaling Extended in Time-Domain and AR Model Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.) RCS2017-349 |
Massive MIMO (multiple-input multiple-output) is one of the technologies that has been focused in 5G (5th generation mob... [more] | RCS2017-349 pp.185-190 |
| MBE, NC (Joint) |
2017-11-24 16:50 |
Miyagi | Tohoku University | NC2017-32 |
Continuous latent variable model is a category of dimension reduction methods, which estimates low dimensional latent va... [more] | NC2017-32 pp.29-34 |
| AP, RCS (Joint) |
2017-11-08 16:20 |
Fukuoka | Fukuoka University | Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.), Wenjie Jiang, Yasushi Takatori (NTT) RCS2017-221 |
Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). However, th... [more] | RCS2017-221 pp.93-98 |
| PRMU, BioX | 2017-03-20 10:50 |
Aichi | Structure Estimation of Topological Manifolds and Manifold Learning Hajime Tasaki (Chuo Univ.), Reiner Lenz (Linkoping Univ.), Jinhui Chao (Chuo Univ.) BioX2016-35 PRMU2016-198 |
Manifold learning algorithms try to find the low dimensional representation of high dimensional data for the visualizati... [more] | BioX2016-35 PRMU2016-198 pp.11-15 |
|
| MBE, NC (Joint) |
2017-03-13 13:35 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis Aiga Suzuki, Hayaru Shouno (UEC) NC2016-83 |
Modeling of natural textures in an important task for microscopic structure of natural images. Portilla and Simon- cell... [more] |
NC2016-83 pp.115-120 |
| IE, ITS, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] | 2017-02-20 16:00 |
Hokkaido | Hokkaido Univ. | A note on estimating human emotion evoked by visual stimuli using fNIRS signals Kento Sugata, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents a method that estimates human emotion evoked by visual stimuli using functional near-infrared spectr... [more] | |
| NLP | 2016-12-12 15:25 |
Aichi | Chukyo Univ. | Visualization and Classification by ElasticSOM Yuto Take, Pitoyo Hartono (Chukyo Univ.) NLP2016-89 |
Due to its simplicity, Self-Organizing Maps(SOM) are often utilized to visualize high dimensional data. While SOM is abl... [more] | NLP2016-89 pp.27-32 |
| SIS | 2016-12-08 14:10 |
Hiroshima | Hiroshima City Univ. | A Proposal of Two-Dimensional Histogram Specification Method Based on Dimension Reduction Yoshiaki Ueda (Yamaguchi Univ.), Takanori Koga (NIT, Tokuyama College), Noriaki Suetake, Eiji Uchino (Yamaguchi Univ.) SIS2016-33 |
Histogram-based image transformation methods, such as histogram equalization and color distribution transfer, are widely... [more] | SIS2016-33 pp.31-34 |
| MI, MICT | 2016-09-16 15:10 |
Tokyo | Koganei Campus, Tokyo University of Agriculture and Technology | Object Oriented Data Analysis for Volumetric Medical Data using Multiway Principal Component Analysis Hayato Itoh, Atsushi Imiya (Chiba Univ.), Tomoya Sakai (Nagasaki Univ.) MICT2016-43 MI2016-57 |
For object oriented data analysis of volumetric medical data, we introduce principal component analysis for the third or... [more] | MICT2016-43 MI2016-57 pp.41-46 |
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