研究者詳細 - 田向　権

論文 - 田向　権

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Prediction of Sensory Information and Generation of Motor Commands for Autonomous Mobile Robots using Reservoir Computing 査読有り

Masafumi Inada, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno

2019 International Symposium on Nonlinear Theory and Its Applications (NOLTA2019) 2019年12月

High-Level Synthesis System to Integrate SoC and ROS 査読有り

Yutaro Ishida, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2019 (APRIS2019) 2019年11月

Construction and verification of person tracking system in autonomous robot for education 査読有り

Ryogo Miyazaki, Hakaru Tamukoh

Asia Pacific Conference on Robot IoT System Development and Platform 2019 (APRIS2019) 2019年11月

A Quick Data Generation Method for Training Object Detection Algorithms in Home Environments 査読有り

Yuma Yoshimoto, Muhammad Farhan Mustafa, Wan Zuha Wan Hasan, Hakaru Tamukoh

2019 International Workshop on Smart Info-Media Systems in Asia (SISA2019) 7 - 10 2019年09月

FPGA-enabled binarized convolutional neural networks toward real-time embedded object recognition system for service robots 査読有り

Yoshimoto Y., Shuto D., Tamukoh H.

2019 4th IEEE International Circuits and Systems Symposium, ICSyS 2019 2019年09月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this presentation, we report the results of applying a binarized Convolutional Neural Network (CNN) and a Field Programmable Gate Array (FPGA) for image-based object recognition. While the demand rises for robots with robust object recognition implemented with Neural Networks, a tradeoff between data processing rate and power consumption persists. Some applications utilise Graphics Processing Units (GPU), which results in high power consumption, thus undesirable for embedded systems, while the others communicate with cloud computers to minimise computational resources at the clients' side, i.e. robots, raising another concern that the robots are unable to perform object recognition without the servers and network connections. To overcome these difficulties, we propose an embedded object recognition system implemented with a binarized CNN and an FPGA. FPGAs consist of a matrix of reconfigurable logic gates allowing parallel computing which befit most image processing algorithms such as the CNN. We train the binarized CNN on one of our datasets that contain images of several kinds of food and beverages. The results of the experiments show that the binarized CNN with an FPGA maintains high accuracy as well as real-time computation, suggesting that the proposed system is suitable for robots to perform their tasks in a real-world environment without needing to communicate with a server.

DOI： 10.1109/ICSyS47076.2019.8982469

Scopus

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Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube 査読有り

Katori Y., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2019-July 2019年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Reservoir computing (RC) is a framework for constructing recurrent neural networks with simple training rule and sparsely and randomly connected nonlinear units. The network (called reservoir) generates complex motion that can be used for many tasks including time series generation and prediction. We construct a reservoir based on the dynamics of the pseudo-billiard system that produce complex motion in a high-dimensional hypercube. In particular, we use the chaotic Boltzmann machine (CBM) whose units exhibit chaotic behavior in the hypercube. The units interact with each other in a time-domain manner through its binary state, and thus an efficient hardware implementation of the system is expected. In order to utilize the CBM as the reservoir, it is necessary to control its chaotic behavior for ensuring the echo state property of RC and establish encoding and decoding for input and output signal. For this purpose, we introduce a reference clock and analyze effects and properties of the reference input. We evaluate the proposed model on the time series generation tasks and show that the model works properly on a broad range of parameter values. Our approach presents a novel mechanism for time-domain information processing and a fundamental technology for a brain like artificial intelligence system.

DOI： 10.1109/IJCNN.2019.8852329

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073235480&origin=inward

A Chaotic Boltzmann Machine Working as a Reservoir and Its Analog VLSI Implementation 査読有り国際誌

Yamaguchi M., Katori Y., Kamimura D., Tamukoh H., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2019-July 2019年07月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

Reservoir computing is attracting great interest because of its high computing ability especially for time-series prediction, despite its simple structure and learning scheme. This paper proposes a reservoir computing hardware model using a chaotic Boltzmann machine (CBM) as the reservoir, which can achieve complex motion in a dynamical system on a high-dimensional hypercube. The CBM uses analog nonlinear dynamics, unlike the stochastic operation of the original Boltzmann machine model. To utilize CBMs as a reservoir, chaotic operation must be suppressed, and the echo state property should be satisfied. We modify the CBM model for simpler analog complementary metal-oxide-semiconductor very-large-scale integration (CMOS VLSI) implementation, and propose its use as a reservoir by adding an external reference clock signal. We then verify its proper operation by numerical simulation. We also refine the CMOS VLSI circuit design based on the proposed modified CBM model to improve power consumption and calculation precision.

DOI： 10.1109/IJCNN.2019.8852325

Scopus

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A Hardware Implementation of Craik-O’Brien Effect-Based Contrast Improvement for Dichromats 査読有り

Tomohiro Ono, Ryosuke Kubota, Noriaki Suetake, Hakaru Tamukoh

2019 Joint International Workshop on Advanced Image Technology and International Forum on Medical Imaging in Asia (IWAIT-IFMIA2019) 2019年01月

Live demonstration: Hardware implementation of brain-inspired amygdala model 査読有り

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE This live demonstration presents a brain-inspired amygdala model. Amygdala is an area of the brain that is associated with fear conditioning, which is a type of classical conditioning. The model can learn preferences through human-robot interactions by application of classical conditioning to the model. Additionally, to develop a high speed and low power system, we design a hardware of the amygdala model, and implemented the hardware into field programmable gate array.

DOI： 10.1109/ISCAS.2019.8702213

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066814574&origin=inward

Live demonstration: A VLSI implementation of time-domain analog weighted-sum calculation model for intelligent processing on robots 査読有り

Yamaguchi M., Iwamoto G., Abe Y., Tanaka Y., Ishida Y., Tamukoh H., Morie T.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE This live demonstration presents a VLSI chip based on “Time-domain Analog Computing with Transient states (TACT)” approach for intelligent processing on robots. This TACT chip, fabricated using 250-nm CMOS technology, implements a time-domain analog weighted-sum calculation model with very high energy efficiency. We integrate the TACT chip into a robot via Robot Operating System (ROS) interfaces. A human tracking robot demonstration is performed by the TACT chip with energy efficiency of 300 TOPS/W.

DOI： 10.1109/ISCAS.2019.8702222

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066803614&origin=inward

Hardware implementation of brain-inspired amygdala model 査読有り

Tanaka Y., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2019-May 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 IEEE Deep neural networks (DNNs) have achieved state-of-the-art results in several computing tasks. However, the performance of these DNNs is reliant on the availability of large amounts of training data, which is not always present. We approached this problem by developing a brain-inspired amygdala model to achieve computer learning based on limited training data. The amygdala is an area of the brain associated with classical fear conditioning. The proposed amygdala model is composed of a single layer of deep self-organizing map network (deep SOM network) and a fully-connected neural network (FCNN), which imitates the function and structure of an amygdala. We applied the proposed amygdala model to a robot waiter task in a restaurant. The experimental results show that the model learned a customer's preferences after only a few human robot interactions. To develop the digital hardware of the amygdala model, we designed hardware for the deep SOM network and the FCNN and implemented them in an XCZU9EG field programmable gate array (FPGA). Our FPGA implementation of a deep SOM network with 272 neurons and an FCNN with three output neurons outperformed a software implementation on an Intel Core i5-3470 CPU by over 600 times.

DOI： 10.1109/ISCAS.2019.8702430

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066804926&origin=inward

A hardware implementation of Craik-O'Brien effect-based contrast improvement for dichromats 査読有り

Ono T., Kubota R., Suetake N., Tamukoh H.

Proceedings of SPIE - The International Society for Optical Engineering 11050 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2019 SPIE. In this study, we designed a digital hardware circuit for a field-programmable gate array (FPGA) to provide an effective contrast improvement algorithm for dichromats. The proposed method employs the Craik-O 'Brien (C-O) effect. The C-O effect is an optical illusion effect in which subjective contrast is created from contour information. In the proposed method, the contrast modification is only conducted around the contours of objects to apply the C-O effect for dichromats. To extract the contour information of objects, a T-model filter which only requires a one-line buffer is introduced. The proposed method can realize the C-O effect without using dividers and multipliers. Therefore, it is relatively simple to implement in the FPGA. Through experiments with software and logic simulation, the effectiveness and validity of the proposed method were evaluated.

DOI： 10.1117/12.2521580

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063911033&origin=inward

A color quantization method preserving infrequent salient colors and its implementation on mobile devices 査読有り

Fukuda Y., Misawa H., Tamukoh H., Kubota R., Cha B., Aso T.

Proceedings of SPIE - The International Society for Optical Engineering 11049 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© COPYRIGHT SPIE. In this paper, we propose a new color quantization method that can preserve infrequent salient colors of an original image. The infrequent salient colors mean that they are not dominant globally, but are dominant locally and are important to keep the impression of the original image. In the proposed method, color quantization is realized by k-means clustering and an input dataset for the clustering are adaptively and repeatedly modified based on local quantization errors to preserve the infrequent salient colors. The proposed method is implemented as an Android application to verify the feasibility of the use of the proposed method on mobile devices.

DOI： 10.1117/12.2521607

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063916118&origin=inward

Modified hierarchical k-nearest neighbor method with application to land-cover classification 査読有り

Hayashi T., Tamukoh H., Kubota R.

Proceedings of SPIE - The International Society for Optical Engineering 11049 2019年01月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© COPYRIGHT SPIE. In this paper, we propose a land-cover classification method based on a modified hierarchical k-nearest neighbor (MHkNN) algorithm to achieve a high classification accuracy. The proposed method introduces a reliability measure for each training sample, which is defined as confidence in the sample belonging to each of the considered classes. The method performs the majority voting considering not only the number of the training samples, but also their reliabilities. The classification performance of the proposed method is compared to that of the conventional land-cover classification methods. The effectiveness of the proposed method is verified by applying it to real remote sensing images.

DOI： 10.1117/12.2521356

Scopus

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高位合成によるORB-SLAMのFPGA実装と評価査読有り

岩渕甲誠, 松本茂樹, 松尾幸治, 石田裕太郎, 廣瀬尚三, 長瀬雅之, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2019 ( 0 ) 1A1-F06 2019年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>In recent years, automated driving and AGVs are actively researched and developed. The important technique is Simultaneous Localization and Mapping (SLAM) which creates maps and self-location estimation at simultaneously. There are several kinds of SLAM depending on the sensor and algorithm used, ORB-SLAM uses only monocular camera. Since it is cheaper than other sensors, it is easy to board it into AGV etc. However, the calculation amount is large. Although high performance PCs is required, it is not able to board it into AGV etc., so we focused on FPGA. The FPGA development environment is also actively developed, and the development with high-level synthesis has become realistic. Therefore, in this research, in order to improve FPGA development efficiency, we implemented ORB-SLAM from open source software to FPGA.</p>

DOI： 10.1299/jsmermd.2019.1a1-f06

CiNii Article

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1A1-F06/_pdf

High-Speed Synchronization of Pulse-Coupled Phase Oscillators on Multi-FPGA 査読有り

Pramanta D., Tamukoh H.

Communications in Computer and Information Science 1143 CCIS 318 - 329 2019年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This study proposes High-Speed Synchronization of Pulse-Coupled Phase Oscillators on multiple field-programmable-gate-array (FPGA). Winfree model is used for oscillators communication based and two FPGAs are connected by a gigabit transceiver (GTX). In order to verify the effect of communication delay and in-phase synchronization phenomenon between FPGAs, we implement various number of oscillators on the multi-FPGA platform. Four-oscillator network achieved first spike synchronization over two FPGAs within 12.47 (formula presented)s and datastream bitrate up tp 3.2 Gbps. We have successfully expanded the network from the previous study and verified that the 10 (formula presented) 10 pulse-coupled phase oscillators synchronized over two FPGAs via high-speed serial communication with a 0.1 (formula presented)s delay and the network reached a steady synchronization state after the spike-count reached 100 with a maximum frequency 298.014 MHz.

DOI： 10.1007/978-3-030-36802-9_34

Scopus

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社会実装を目指したホームサービスロボットの研究開発査読有り

小野智寛, 堀三晟, 石田裕太郎, 田中悠一朗, 吉元裕真, 阿部佑志, 武藤冬樹, 椛島康平, 福宿将士, 坂田拓馬, 吉井拓巳, 上村大地, 金丸和樹, 中村健太郎, 西村雄太, 森江隆, 田向権

ロボティクス・メカトロニクス講演会講演概要集 ( 一般社団法人日本機械学会 ) 2019 ( 0 ) 1P2-I02 2019年01月

担当区分：最終著者記述言語：日本語掲載種別：研究論文（学術雑誌）

<p>Home service robots have begun attracting attention due to decreasing birthrate and increasing aging population. We,“Hibikino-Musashi@Home”team, develop home service robots, and participate international competitions; RoboCup@Home and World Robot Challenge (WRC), which are established to realize that robots will work and live with people in domestic environments. In this paper, we introduce our robots and technologies, especially an object recognition and manipulation system. We use You Only Look Once (YOLO) for object recognition. To train YOLO, a big dataset which is difficult to prepare in a short term is required. For this, we develop an automatic annotation system for YOLO which can generate 1.7 million training data, and we can complete training of YOLO in a day. To grasp objects, estimation of object orientations is required. Our system can detect the orientations by processing point clouds of objects. By using the system, we obtained about 30% of scores in Tidy Up Task in the WRC.</p>

DOI： 10.1299/jsmermd.2019.1p2-i02

CiNii Article

CiNii Research

その他リンク： https://www.jstage.jst.go.jp/article/jsmermd/2019/0/2019_1P2-I02/_pdf

決定表に基づく自動運転用判断システムの構築と評価査読有り

宮﨑椋瑚, 三井悠也, 石田裕太郎, 渡辺政彦, 宇井健一, 横田剛典, 市瀬龍太朗, 我妻広明, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 35 ( 0 ) 722 - 727 2019年01月

ハードウェア指向 GMM-MRCoHOG 特徴量を用いた人物検出査読有り

長嶺佑哉, 吉弘憲大, 柴田雅聡, 山田英夫, 榎田修一, 田向権

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 35 ( 0 ) 715 - 719 2019年01月

Alternative Dropout for Hardware Implementation in Recurrent Neural Networks, 査読有り

Yoeng Jye Yeoh, Hakaru Tamukoh

Proc. of Int. Workshop on Smart Info-Media Systems in Asia (SISA2018) 101 - 104 2018年12月

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