| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 09:20 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
A Study of Few-shot NeRF by Pseudo-Feature Vectors Evaluation for Unknown Viewpoints Daiju Kanaoka (Kyutech/RIKEN), Motoharu Sonogashira (RIKEN), Hakaru Tamukoh (Kyutech/Neumorph Center), Yasutomo Kawanishi (RIKEN)  PRMU2022-101 IBISML2022-108 |
Neural Radiance Fields (NeRF) is a powerful method for novel view synthesis. However, NeRF requires a large number of ... [more] |
PRMU2022-101 IBISML2022-108 pp.220-225 |
| MBE, NC | 2022-12-03 14:20 |
Osaka | Osaka Electro-Communication University | Action Generation for Home Service Robots by Hippocampus Model with Memory Accumulation and Recall Functions Akinobu Mizutani, Ichiro Kawashima, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Osamu Nomura, Takashi Morie (Kyutech) MBE2022-37 NC2022-59 |
This paper proposes a brain-inspired artificial intelligence model inspired by brain functions related to the representa... [more] | MBE2022-37 NC2022-59 pp.68-73 |
| SIS, IPSJ-AVM | 2022-06-09 13:00 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
Hardware Implementation of Annealing Machine using Chaotic Boltzmann Machine Kanta Yoshioka (Kyutech), Ichiro Kawashima, Hakaru Tamukoh (Kyutech/Neumorph Center) SIS2022-1 |
With the end of Moore's law, annealing quantum computers that can solve various combinatorial optimization problems are ... [more] | SIS2022-1 pp.1-6 |
| SIS, IPSJ-AVM | 2022-06-10 11:40 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
An embedded-oriented sound classification system using reservoir computing Yuichiro Tanaka, Issei Uchino (Kyutech), Kazunobu Ohkuri (Sony), Hakaru Tamukoh (Kyutech) SIS2022-9 |
Although deep neural networks (DNNs) have achieved state-of-the-art results in sound classification tasks in recent year... [more] | SIS2022-9 pp.41-44 |
| SIS, IPSJ-AVM | 2022-06-10 13:00 |
Fukuoka | KIT(Wakamatsu Campus) (Primary: On-site, Secondary: Online) |
[Tutorial Lecture]
How to build a High-Precision and Efficient Robot Vision: Dataset Generation and Hardware Implementation for Deep Learning Hakaru Tamukoh (Kyutech) SIS2022-10 |
This tutorial lecture explains a construction method for high-precision and efficient robot vision that includes a semi-... [more] | SIS2022-10 pp.45-48 |
| SIS | 2021-12-03 11:20 |
Online | Online | Open Set Recognition Focusing on Multidimensional Feature Space Daiju Kanaoka (Kyutech), Yuichiro Tanaka, Hakaru Tamukoh (Kyutech/Kyushu Institute of Technology/Research Center for Neuro) SIS2021-23 |
In general, image recognition assumes that all classes used in testing are known. Therefore, when an unknown class is in... [more] | SIS2021-23 pp.11-14 |
| SIS, ITE-BCT | 2021-10-08 11:10 |
Online | Online | A Hardware-Oriented Algorithm for Person Recognition Processing using GMM-MRCoHOG features Ryogo Takemoto, Yuya Nagamine, Kazuki Yoshihiro (Kyutech), Masatoshi Shibata, Hideo Yamada (AISIN), Shuichi Enokida, Hakaru Tamukoh (Kyutech) SIS2021-19 |
In this research, we focus on Gaussian Mixture Model-MultiResolution Co-occurrence Histograms of Oriented Gradients (GMM... [more] | SIS2021-19 pp.48-53 |
| SIS | 2021-03-04 14:10 |
Online | Online | Hardware Implementation of Object Recognition Neural Network using Depth Images Yuma Yoshimoto (Kyutech/JSPS Research Fellow), Hakaru Tamukoh (Kyutech/Research Center for Neuromorphic AI Hardware) SIS2020-47 |
In this study, we propose an object recognition neural network using depth images, implemented on an FPGA for service ro... [more] | SIS2020-47 pp.67-70 |
| SIS | 2020-12-01 15:15 |
Online | Online | A Proposal of Convolutional Neural Networks detecting and removing noise for Random-Valued Impulse Noise Denoising Yukiya Fukuda (Kytutech), Ryosuke Kubota (NITUC), Hakaru Tamukoh (Kyutech) SIS2020-34 |
When digital images are transmitted, Random-Valued Impulse Noise (RVIN) may cause image degradation. RVIN is known as no... [more] | SIS2020-34 pp.35-40 |
| SIS, ITE-BCT | 2020-10-01 14:00 |
Online | Online | An FPGA Implementation of Human Recognition using MRCoHOG Features Yuya Nagamine, Kazuki Yoshihiro (Kyutech), Masatoshi Shibata, Hideo Yamada (EQUOS RESEARCH), Shuichi Enokida, Hakaru Tamukoh (Kyutech) SIS2020-16 |
In this research, we design a hardware of human recognition using Multiresolution Co-occurrence Histograms of Oriented G... [more] | SIS2020-16 pp.35-39 |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2020-06-03 13:40 |
Online | Online | A Color Image Quantization Method Taking Account of Chromatic Visual Impression Yukiya Fukuda (Kyutech), Ryosuke Kubota (NIT,UC), Hakaru Tamukoh (Kyutech) SIS2020-3 |
[more] | SIS2020-3 pp.13-18 |
| SIS | 2020-03-05 15:30 |
Saitama | Saitama Hall (Cancelled but technical report was issued) |
Deep Neural Networks for Object Detection and Classification on Domestic Service Robots Yutaro Ishida, Hakaru Tamukoh (Kyutech) SIS2019-45 |
We propose a semi-automatic data set generation method, and a system integration method of robot operating system (ROS) ... [more] | SIS2019-45 pp.45-50 |
| CAS, MSS, IPSJ-AL [detail] | 2019-11-28 17:10 |
Fukuoka | [Invited Talk]
Circuits and Systems for Brain-Inspired Artificial Intelligence on Home Service Robots Hakaru Tamukoh (Kyutech) CAS2019-54 MSS2019-33 |
[more] | CAS2019-54 MSS2019-33 p.71 |
|
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 11:20 |
Nagasaki | Fukue Culture Center | A random number generation method for hardware implemented neural networks Sansei Hori, Hakaru Tamukoh (Kyushu Inst. of Tech.) SIS2019-1 |
This study proposes a hardware oriented random number generation method to implement a stochastically neural networks su... [more] | SIS2019-1 pp.1-4 |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 13:15 |
Nagasaki | Fukue Culture Center | Autoencoders having surplus neurons in a hidden layer Akihiro Suzuki, Hakaru Tamukoh (KYUTECH) SIS2019-3 |
Unknown data is not compatible with a supervised training. This study employ autoencoders (AEs) to detect unknwon data. ... [more] | SIS2019-3 pp.11-16 |
| SIS | 2019-03-06 13:00 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | Evaluation of an FPGA Implementation of MRCoHOG Feature using High-Level Synthesis Yuya Nagamine, Kazuki Yoshihiro, Hakaru Tamukoh (Kyutech) SIS2018-37 |
In this report, we evaluate a Field Programmable Gate Array (FPGA) implementation of Multiresolution Co-occurrence Histo... [more] | SIS2018-37 pp.1-4 |
| SIS | 2019-03-06 14:30 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | Complementary Color Reconstruction by Autoencoders Akihiro Suzuki, Hakaru Tamukoh (Kyutech) SIS2018-41 |
This study proposes a novel training method for autoencoders (AEs) that gives the AEs complementary color images as targ... [more] | SIS2018-41 pp.23-28 |
| SIS | 2019-03-07 12:15 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | A Color Quantization Method Preserving Infrequent Salient Colors for Image Enlargement Yukiya Fukuda, Hideaki Misawa (NIT, UC), Hakaru Tamukoh (KIT), Ryosuke Kubota (NIT, UC), Byungki Cha, Takashi Aso (KIIT) SIS2018-51 |
In this report, we propose an image enlargement algorithm with a color quantization considering infrequent salient color... [more] | SIS2018-51 pp.75-80 |
| NLP, NC (Joint) |
2019-01-23 15:20 |
Hokkaido | The Centennial Hall, Hokkaido Univ. | An integrated circuit model of hippocampus and entorhinal cortex for home service robots Ryo Shimodome, Masashi Kawauchi, Kensuke Takada, Katsumi Tateno, Hakaru Tamukoh, Takashi Morie (Kyutech) NC2018-39 |
To construct brain-like memory systems, which are desired in the current artificial intelligence, with dedicated hardwar... [more] | NC2018-39 pp.5-10 |
| SIS | 2018-12-07 10:30 |
Yamaguchi | Hagi Civic Center | Hardware Oriented Object Recognition Neural Network using Depth Image Yuma Yoshimoto, Hakaru Tamukoh (KIT) SIS2018-32 |
In recent years, deep learning using Convolutional Neural Network (CNN) has attracted attention as a powerful method for... [more] | SIS2018-32 pp.55-60 |
[Return to Top Page]