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All Technical Committee Conferences (All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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-3DIT [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 |
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| SIS, IPSJ-AVM, ITE-3DIT [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-3DIT [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 |
| SIS, ITE-BCT | 2018-10-26 11:30 |
Kyoto | Kyoto University Clock Tower Centennial Hall | A Hardware Implementation of Craik-O'Brien Effect-Based Contrast Improvement for Dichromats Tomohiro Ono (Kyutech), Ryosuke Kubota (NITUC), Noriaki Suetake (YU), Hakaru Tamukoh (Kyutech) SIS2018-20 |
In this paper, we design a digital hardware circuit to realize the contrast improvement algorithm for dichromats. The pr... [more] | SIS2018-20 pp.95-100 |
| SIS, IPSJ-AVM, ITE-3DIT [detail] | 2018-06-07 14:20 |
Hokkaido | Jozankei View Hotel | Estimation of heart rate variability parameters using pulse waved measured by smartphone cameras Yuichiro Tanaka, Akihiro Suzuki (Kyutech), Hirohisa Isogai (Kyushu Sangyo University), Masaaki Iwasaki (Bratech), Hakaru Tamukoh (Kyutech) SIS2018-4 |
Heart rate variability (HRV) parameters are used for analysing activations of autonomic nervous. Generally, the HRV para... [more] | SIS2018-4 pp.35-38 |
| RECONF | 2018-05-25 14:30 |
Tokyo | GATE CITY OHSAKI | [Invited Lecture]
Intelligent processing on robots expected to achieve speed-up and low-power consumption Hakaru Tamukoh, Takeshi Nishida, Yutaro Ishida (Kyutech) RECONF2018-16 |
(To be available after the conference date) [more] | RECONF2018-16 pp.75-80 |
| ICD | 2018-04-19 13:00 |
Tokyo | [Invited Talk]
VLSI implementation of chaotic Boltzmann machine for deep learning hardware Takashi Morie, Masatoshi Yamaguchi, Ichiro Kawashima, Hakaru Tamukoh (Kyushu Inst. of Tech.) ICD2018-4 |
The Boltzmann machine (BM) model has been proposed as an optimization-problem solver as well as a learning machine using... [more] | ICD2018-4 p.13 |
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| SIS | 2018-03-08 15:25 |
Aichi | Meijo Univ. Tempaku Campus | DNN:-MPC: A Hardware oriented Deep Neural Networks for Model Predictive Control Kentaro Honda, Naoki Iwaya (Kyutech), Teppei Hirotsu, Toshiaki Nakamura, Tatuya Horiguchi (HITACHI), Hakaru Tamukoh (Kyutech) SIS2017-60 |
Model Predictive Control (MPC) is one of the control systems, where it uses "predictive model" to control objects. Howev... [more] | SIS2017-60 pp.17-22 |
| SIS | 2017-12-14 13:50 |
Tottori | Tottori Prefectural Center for Lifelong Learning | Object Recognition System using Deep Learning with Depth Image for Home Service Robots Yuma Yoshimoto, Hakaru Tamukoh (Kyutech) SIS2017-55 |
In an aging society with fewer children, home service robots are expected to be realized. In order to bring a realizati... [more] |
SIS2017-55 pp.123-128 |
| SIS, IPSJ-AVM | 2017-10-12 15:20 |
Nara | Todaiji Culture Center | An autoencoder reversing abnormal inputs Akihiro Suzuki, Hakaru Tamukoh (Kyushu Inst. of Tech.) SIS2017-26 |
Usages of autoencoders (AEs) are not only a dimension reducer, but a generative model using reconstruction. AEs are used... [more] | SIS2017-26 pp.29-34 |
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