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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 71  /  [Next]  
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
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
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
 Results 1 - 20 of 71  /  [Next]  
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