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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 49  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MBE, NC
(Joint)
2018-03-13
13:10
Tokyo Kikai-Shinko-Kaikan Bldg. Visual processing system for spatial perception based on motion stereo vision
Shota Kurihara, Hisanao Akima, Susumu Kawakami (Tohoku Univ.), Jordi Madrenas (UPC), Satoshi Moriya, Masafumi Yano, Koji Nakajima, Masao Sakuraba, Shigeo Sato (Tohoku Univ.) NC2017-82
 [more] NC2017-82
pp.85-90
MBE, NC
(Joint)
2017-11-24
13:00
Miyagi Tohoku University Spatial Perception System based on Neural Network Model for Motion Stereo Vision
Shota Kurihara, Hisanao Akima, Susumu Kawakami (Tohoku Univ.), Jordi Madrenas (UPC), Satoshi Moriya, Masafumi Yano, Koji Nakajima, Masao Sakuraba, Shigeo Sato (Tohoku Univ.) NC2017-27
 [more] NC2017-27
pp.1-6
MBE, NC
(Joint)
2015-11-21
13:50
Miyagi Tohoku University Design of an Izhikevich neuron circuit using stochastic logic
Shigeo Sato, Hisanao Akima, Koji Nakajima, Masao Sakuraba (Tohoku Univ.) NC2015-42
 [more] NC2015-42
pp.31-34
NC, IPSJ-BIO, IBISML, IPSJ-MPS
(Joint) [detail]
2015-06-25
13:20
Okinawa Okinawa Institute of Science and Technology An LSI Implementation of a Neural Network Model for Detecting Local Image Motion in the Visual Cortex
Hisanao Akima, Satoshi Moriya (Tohoku Univ.), Susumu Kawakami, Masafumi Yano (Tohoku Univ.), Koji Nakajima, Masao Sakuraba, Shigeo Sato (Tohoku Univ.) NC2015-4
The spatial perception, in which objects motion and positional relation are recognized, is necessary to realize such as ... [more] NC2015-4
pp.57-62
MBE, NC
(Joint)
2014-11-21
11:00
Miyagi Tohoku University A Comparison of Back Propagation Learning between the Inverse-function Delayless Model and a Conventional Model
Yuta Horiuchi (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ) NC2014-26
For the combinatorial optimization problem using the hopfield model, avoidance of the local minimum problem is important... [more] NC2014-26
pp.7-10
MBE, NC
(Joint)
2014-11-21
11:25
Miyagi Tohoku University The Relation between Dispersion of Initial Values and Pre-training of Deep Neural Networks
Seitaro Shinagawa (Tohoku univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku univ.) NC2014-27
 [more] NC2014-27
pp.11-14
MBE, NC
(Joint)
2014-11-22
16:25
Miyagi Tohoku University A simulation study of a neural network model for detecting planar surface by motion stereo vision
Hisanao Akima (Tohoku Univ.), Susumu Kawakami, Koji Nakajima, Masao Sakuraba, Shigeo Sato (Tohoku Univ.) NC2014-43
The spatial perception, in which objects motion and positional relation are recognized, is necessary to realize such as ... [more] NC2014-43
pp.97-100
NLP 2014-06-30
16:00
Miyagi Tohoku Univ. Backpropagation learning using inverse function delay-less model
Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-25
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. ID model has a oscillation capa... [more] NLP2014-25
pp.27-30
NLP 2014-06-30
16:25
Miyagi Tohoku Univ. Study on the hardware of the Bidirectional Associative Memories by using the Inverse Function Delayless model
Chunyu Bao, Takeshi Onomi, Yoshihiro Hayakawa, Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2014-26
In conventional macro models such as the Hopfield model, the problems that are caused by the solution of the network not... [more] NLP2014-26
pp.31-36
NLP 2014-07-01
10:00
Miyagi Tohoku Univ. Learning Restricted Boltzmann Machine with discrete learning parameter
Seitaro Shinagawa (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) NLP2014-27
Recently, the method of Deep Neural Network (DNN) with hierarchical learning has been remarkable for performance to solv... [more] NLP2014-27
pp.37-40
SCE 2014-01-24
13:15
Tokyo Kikaishinkou-kaikan Bldg. Analysis of rf-SQUID ladder circuits with a single flux quantum signal for the transmission direction
Yuya Tsuji, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) SCE2013-51
Although SFQ circuit technique is very predominant in respect of power consumption, the circuit system of a semiconducto... [more] SCE2013-51
pp.97-100
SCE 2014-01-24
13:40
Tokyo Kikaishinkou-kaikan Bldg. Comparison of the final addition circuit in SFQ parallel multiplier with a tree structure partial product adder circuit
Akifumi Yamada, Takeshi Onomi, Koji Nakajima (Tohoku Univ.) SCE2013-52
A single flux quantum (SFQ) circuit is capable of high-speed operation in a few 10 GHz, and it has a big advantage compa... [more] SCE2013-52
pp.101-104
NLP 2014-01-21
13:30
Hokkaido Niseko Park Hotel DTN routing method by using neural networkas.
Daisuke Sasaki (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2013-136
A Disruption tolerant Network (DTN) is studied as a communicating technique for the time when a network infrastructure w... [more] NLP2013-136
pp.41-44
NLP 2014-01-21
13:50
Hokkaido Niseko Park Hotel Solving Optimization Problems Using DS-net and IDL model
Yuto Watanabe (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2013-137
The Inverse function DelayLess (IDL) model has been proposed as one of novel neural models. Since the IDL model can set ... [more] NLP2013-137
pp.45-50
NLP 2014-01-21
15:40
Hokkaido Niseko Park Hotel Neural Network learning using Inverse Function Delayless Model
Yuta Horiuchi (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2013-142
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has an ability of ... [more] NLP2013-142
pp.73-76
NLP 2014-01-22
10:20
Hokkaido Niseko Park Hotel Studies on the hardware of neural associative memory with a broad basin
Jiang Jing (Tohoku Univ), Yoshihiro Hayakawa (Sendai NT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2013-147
Abstract Because associative operation of the Hopfield neural network model are trapped in spurious memory, the associat... [more] NLP2013-147
pp.99-102
NLP 2013-10-29
13:30
Kagawa Sanport Hall Takamatsu Discussion about the effectiveness of a DS-net and an active neuron model
Yoshihiro Hayakawa, Hikaru Okuda (Sendai NCT.), Yuto Watanabe, Koji Nakajima (Tohoku Univ.) NLP2013-100
The active neuron model, which means an active region in output space,
is an effective tool to avoid local minimum pro... [more]
NLP2013-100
pp.159-164
NLP 2012-12-18
09:45
Fukui Fukui City Communication Plaza Inverse Function Delayless (IDL) Model
Yuto Watanabe (Tohoku Univ.), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2012-98
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has the negative r... [more] NLP2012-98
pp.57-60
NLP 2012-12-18
10:10
Fukui Fukui City Communication Plaza Hardware implementation of the discrete Inverse-function Delayed network with Higher order synaptic Connections
Kosuke Matsui (Tohoku Univ.), Yoshihiro Hayakawa (Sendai N.C.T.), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2012-99
The HC-ID network has been proposed as a network model that avoids local minima when it is applied to combinatorial opti... [more] NLP2012-99
pp.61-64
NLP 2012-12-18
10:35
Fukui Fukui City Communication Plaza Optimization of Scheduling in Disruption-Tolerant Networks by Neural Network
Daisuke Sasaki (Tohoku Univ), Yoshihiro Hayakawa (SNCT), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2012-100
A Disruption tolerant Network (DTN) is studied as a communicating technique when a network infrastructure was destroyed ... [more] NLP2012-100
pp.65-68
 Results 1 - 20 of 49  /  [Next]  
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