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All Technical Committee Conferences (Searched in: Recent 10 Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| EST, MW, EMCJ, IEE-EMC [detail] | 2024-10-17 09:00 |
Akita | Akita Art Village Onsen Yupopo (Primary: On-site, Secondary: Online) |
Integrating Circuits and Graph Neural Networks for Circuit Constants Optimization Yusuke Yamakaji (Mitsubishi Electric/Univ. of Electro-Communications), Hayaru Shouno (The University of Electro-Communications), Kunihiko Fukushima (Fuzzy Logic Systems Inst.)  EMCJ2024-36 MW2024-89 EST2024-60 |
Circuit design requires empirical rules based on trial and error through prototyping and simulation because each compone... [more] | EMCJ2024-36 MW2024-89 EST2024-60 pp.1-6 |
| MBE, NC (Joint) |
2022-03-02 16:10 |
Online | Online | XMCD-CT Reconstruction Using Compressed Sensing Tsukito Takizawa, Hayaru Shouno (The Univ. of Electro-Communications), Masaichiro Mizumaki (JASRI), Motohiro Suzuki (Kwansei Gakuin Univ.) NC2021-58 |
Observation of the magnetic domain structure is important for understanding the magnetic properties of materials includi... [more] | NC2021-58 pp.62-67 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online | Online | Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] | NC2021-44 pp.59-64 |
| NC, MBE (Joint) |
2021-03-03 13:25 |
Online | Online | Visualization of CNNs using Preferred Stimulus in Receptive Fields Genta Kobayashi, Hayaru Shouno (UEC) NC2020-47 |
Convolutional neural networks have shown high performance at image processing task, and they are interpreted by variou... [more] |
NC2020-47 pp.25-30 |
| NC, MBE (Joint) |
2021-03-03 13:50 |
Online | Online | Analysis of deep convolutional neural network texture representation using Portilla-Simoncelli statistics Yusuke Hamano, Hayaru Shouno (UEC) NC2020-48 |
Recently, DCNN has achieved significant success in the field of computer vision. It is suggested that the DCNN, which ar... [more] | NC2020-48 pp.31-36 |
| SIP | 2020-08-28 10:30 |
Online | Online | Improvement Convergence Rate of the Sign Algorithm by Natural Gradient Method Taiyo Mineo, Hayaru Shouno (UEC) SIP2020-34 |
In lossless audio compression, it is essential to predictive residuals to be sparse, since we apply entropy codings to r... [more] | SIP2020-34 pp.19-24 |
| NLP, NC (Joint) |
2020-01-24 11:10 |
Okinawa | Miyakojima Marine Terminal | Proposal of Compression Method for Planetary Surface Image using Sparse Coding Yoshifumi Uesaka, Hayaru Shouno (UEC) NC2019-65 |
In recent years, the demand for space development has been increasing. We treat an efficient image transmitting system f... [more] | NC2019-65 pp.33-38 |
| NLP, NC (Joint) |
2020-01-25 13:30 |
Okinawa | Miyakojima Marine Terminal | Estimation of the high-risk area of potential crime using sparse estimation and consideration of crime occurrence mechanism Sho Ichigozaki (NPA/UEC), Takahiro Kawashima, Hayaru Shouno (UEC) NC2019-71 |
[more] | NC2019-71 pp.69-74 |
| NC, MBE | 2019-12-06 14:40 |
Aichi | Toyohashi Tech | Implementation of an FPGA-based energy-efficient MCMC method for 2D Lenz-Ising model Patrick Tchicali, Hayaru Shouno (UEC) MBE2019-54 NC2019-45 |
MCMC methods are arguably one of the most useful sampling methods. MCMC while being very useful and practical remains a ... [more] | MBE2019-54 NC2019-45 pp.55-60 |
| MBE, NC | 2019-10-11 15:00 |
Miyagi | Analysis of diffuse lung disease shadows using Bolasso feature selection method Akihiro Endo (UEC), Kenji Nagata (NIMS), Shoji Kido (Osaka Univ.), Hayaru Shouno (UEC) MBE2019-33 NC2019-24 |
Diffuse lung disease is an intractable disease and abnormal shadows appear on lung X-ray CT images. Since various patte... [more] |
MBE2019-33 NC2019-24 pp.23-27 |
|
| IT, ISEC, WBS | 2019-03-07 16:55 |
Tokyo | University of Electro-Communications | [Invited Talk]
Deepning and evolution of Deep learning technology Hayaru Shouno (UEC) IT2018-88 ISEC2018-94 WBS2018-89 |
[more] | IT2018-88 ISEC2018-94 WBS2018-89 p.83 |
| NC, MBE (Joint) |
2019-03-06 15:50 |
Tokyo | University of Electro Communications | PET Image Reconstruction by use of Dictionary Learning Naohiro OKumura, Hayaru Shouno (UEC) NC2018-85 |
Nowadays, Positron Emission Tomography (PET) scan is focused in the field of pathological diagnosis.In order to obtain a... [more] | NC2018-85 pp.221-226 |
| NC, MBE (Joint) |
2019-03-06 16:15 |
Tokyo | University of Electro Communications | Examination of Super Resolution and Noise Removal for MicroCT Image Miku Mashimo, Hayaru Shouno (UEC) NC2018-86 |
The purpose of this research is to increase the resolution of MicroCT (Computed Tomography) images. The MicroCT image i... [more] |
NC2018-86 pp.227-232 |
| NC, MBE (Joint) |
2019-03-06 16:40 |
Tokyo | University of Electro Communications | Study on data augmentation using Fourier transform for texture image classification Daigo Nitta, Hayaru Shouno (UEC) NC2018-87 |
In the field of medical imaging such like computed tomography analysis, it is difficult to prepare a sufficient amount o... [more] | NC2018-87 pp.233-238 |
| NLP, NC (Joint) |
2019-01-23 15:40 |
Hokkaido | The Centennial Hall, Hokkaido Univ. | Measuring the Convolution Neural Network similarities trained with different dataset using SVCCA Toya Teramoto, Hayaru Shouno (UEC) NC2018-40 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classif... [more] | NC2018-40 pp.11-16 |
| MBE, NC (Joint) |
2018-03-13 11:15 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Application of U-Net to spine image extraction in CT image Mikoto Kamata, Masayuki Kikuchi (Tokyo Univ.of Tech.), Hayaru Shouno (Univ. of Electro-Communications.), Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (Fuzzy Logic Systems Inst.) NC2017-81 |
In this study, we aimed at automatic extraction of spinal parts in CT images using deep learning as a foothold for autom... [more] | NC2017-81 pp.81-84 |
| 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 |
| PRMU, IPSJ-CVIM, IBISML [detail] | 2016-09-05 16:15 |
Toyama | The Validity of Network In Network as a Visual System
-- From the Point of View of the Orientation Selectivity Map -- Satoshi Suzuki, Hayaru Shouno (UEC) PRMU2016-68 IBISML2016-23 |
In recent years, Deep Convolutional Neural Network (DCNN) has shown excellent performance in image recognition field. DC... [more] | PRMU2016-68 IBISML2016-23 pp.113-120 |
|
| SIP | 2016-08-25 15:30 |
Chiba | Chiba Institute of Technology, Tsudanuma Campus | [Invited Talk]
Applications of Deep learning for image diagnosis Hayaru Shouno (UEC) SIP2016-76 |
The ``deep learning'' is the 3rd generation neural network technology, which is exhibiting its characteristics in the bi... [more] | SIP2016-76 pp.23-24 |
| MBE, NC (Joint) |
2016-03-23 10:50 |
Tokyo | Tamagawa University | A Study of Sparse Feature Learning in the Learning Process of Deep Convolutional Neural Network Yoshihiro Kusano, Hayaru Shouno (UEC) NC2015-81 |
A study to make an image and a video high resolution with the improvement of the display and print technology is conduct... [more] | NC2015-81 pp.65-70 |
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