Faculty Profiles - EDAHIRO Masato

Papers - EDAHIRO Masato

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Special Section on Discrete Mathematics and Its Applications

CHAO Jinhui, EDAHIRO Masato, FUJITO Toshihiro, HAGIHARA Kenichi, HANDA Keiichi, HIRAISHI Kunihiko, ITO Hiro, MAMBO Masahiro, MATSUI Tomomi, MIYANO Eiji, OKAMOTO Eiji, OSAWA Shingo, OKABE Yasuo, IMAI Keiko

IEICE transactions on fundamentals of electronics, communications and computer sciences Vol. 84 ( 5 ) page： 1093 2001.5

Parallel Metaheuristic Algorithms: for Task Scheduling Problems Reviewed

Mao Sirui, M. Edahiro

International Journal of Computer & Technology Vol. 25 page： 1-13 2025.3

Quality Evaluation for 3D Point Cloud Maps Using Entropy Focused on Road Surfaces Reviewed

Vol. 91 ( 3 ) page： 390 - 396 2025.3

LLVM Instruction Latency Measurement for Software-Hardware Interface for Multi-many-core Reviewed

H. Mikami, K. Torigoe, M. Inokawa, and M. Edahiro

COOL CHIPS 25 2022.4

Mapping Method Usable with Clustered Many-core Platforms for Simulink Model Reviewed

Yutaro Kobayashi, Kentaro Honda, Sasuga Kojima, Hiroshi Fujimoto, Masato Edahiro, and Takuya Azumi

Vol. 63 ( 2 ) 2022.2

Robustness Evaluation of Vehicle Localization in 3D Map Using Convergence of Scan Matching

Kitsukawa Yuki, Minami Tatsuya, Yamazaki Yudai, Meguro Junich, Takeuchi Eijiro, Ninomiya Yoshiki, Kato Shinpei, Edahiro Masato

International Journal of Automotive Engineering Vol. 13 ( 4 ) page： 206 - 213 2022

Language：English Publishing type：Research paper (scientific journal) Publisher：Society of Automotive Engineers of Japan, INC

ABSTRACT: Ego-vehicle localization is a critical technology in autonomous driving systems, and one of the widely used methods for localization is scan matching between a 3D map and real-time LiDAR scan. This method is known to fail due to factors such as an incorrect initial position and orientation for scan matching. In this paper, we propose a simulator-based localization evaluation framework to verify the robustness of localization. By using a simulator, localization can be evaluated without driving a real vehicle, and can be evaluated by creating disturbances such as traffic jams. Our framework also allows to evaluate the robustness of localization by using multiple particles with random errors of the initial position and orientation for scan matching to simulate dead reckoning errors caused by multiple factors such as road surface conditions and tire diameter. In the evaluation experiments, we confirmed that the robustness of localization can be evaluated by applying this method to factors such as sensor setup, disturbances in the traffic environment, and the amount of 3D features in the environment.

DOI： 10.20485/jsaeijae.13.4_206

CiNii Research

A Study on the Implementation of Vector Control System Using the Model Base Parallelization Tool on Multi-Core Processor

Jinsoo Kim, Seiya Kato, Shinya Honda, Masato Edahiro, Shinji Doki

23rd International Conference on Electrical Machines and Systems, ICEMS 2020 page： 1951 - 1956 2020.11

Language：English Publishing type：Research paper (international conference proceedings) Publisher：Institute of Electrical and Electronics Engineers Inc.

The scope of embedded systems used by multi-core processors is widely applied to various products that require control, such as home appliances, industrial devices, transportation devices, and communication terminals. Model-Base Parallelization technology was proposed towards efficient parallelization to implement a control system in the introduction of multi-core processors. By applying MBP, it is possible to parallelization even details that people are not aware of, but the control system with numerous causal relationships still does not sufficiently utilize multi-core. Therefore, a method of inserting a time-delay is being studied as a method for improving the parallelization of multi-cores.In this paper, We describe the insertion location that improves parallelization and does not affect the control performance by inserting time-delay to implement the vector control system of PMSM on multi-core efficiently.

DOI： 10.23919/ICEMS50442.2020.9290965

Scopus

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering Reviewed

Patricia Ortal and Masato Edahiro

IEEE Access Vol. 8 page： 213401 - 213415 2020.9

Similarity Measure for Product Attribute Estimation Reviewed

Patricia Ortal and Masato Edahiro

IEEE Access Vol. 8 page： 179073 - 179082 2020.9

FPGA混在のHM-SoCに対するモデルベース並列化設計開発環境の検討 Reviewed

山本椋太, 小川真彩高, 生沼正博, 近藤真己, 本田晋也, 枝廣正人

DAシンポジウム論文集 page： 81 - 88 2020.9

モデルベース並列化ツールを用いたマルチコアシステム開発フローの提案 Reviewed

生沼正博, 山本椋太, 竹内成樹, 権藤正樹, 本田晋也, 近藤真己, 枝廣正人

DAシンポジウム論文集 page： 73 - 80 2020.9

Fast Euclidean Cluster Extraction Using GPUs Reviewed

Anh Nguyen, Abraham Monrroy Cano, Masato Edahiro, and Shinpei Kato

Journal of Robotics and Mechatronics Vol. 32 ( 3 ) page： 548 - 560 2020.6

Fast Euclidean Cluster Extraction Using GPUs

Nguyen Anh, Cano Abraham Monrroy, Edahiro Masato, Kato Shinpei

Journal of Robotics and Mechatronics Vol. 32 ( 3 ) page： 548 - 560 2020.6

Language：English Publishing type：Research paper (scientific journal) Publisher：Fuji Technology Press Ltd.

<p>Clustering is the task of dividing an input dataset into groups of objects based on their similarity. This process is frequently required in many applications. However, it is computationally expensive when running on traditional CPUs due to the large number of connections and objects the system needs to inspect. In this paper, we investigate the use of NVIDIA graphics processing units and their programming platform CUDA in the acceleration of the Euclidean clustering (EC) process in autonomous driving systems. We propose GPU-accelerated algorithms for the EC problem on point cloud datasets, optimization strategies, and discuss implementation issues of each method. Our experiments show that our solution outperforms the CPU algorithm with speedup rates up to 87X on real-world datasets.</p>

DOI： 10.20965/jrm.2020.p0548

CiNii Research

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles Reviewed

A. C. Monrroy, E. Takeuchi, M. Edahiro and S. Kato

IEICE Transastions on Fundamentals Vol. E103-A ( 1 ) page： 252-264 2020.1

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles Reviewed

A. C. Monrroy, E. Takeuchi, M. Edahiro, S. Kato

IEICE Transastions on Fundamentals Vol. E103-A ( 1 ) page： 252-264 2020.1

Model-based Parallelization for Simulink Models on Multicore CPUs and GPUs Reviewed

Z. Zhong, M. Edahiro

International Journal of Computer & Technology Vol. 20 page： 1-13 2020.1

Mapping Method of MATLAB/Simulink Model for Embedded Many-Core Platform

Kentaro Honda, Sasuga Kojima, Hiroshi Fujimoto, Masato Edahiro, Takuya Azumi

Proceedings - 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2020 page： 182 - 186 2020

Language：English Publishing type：Research paper (international conference proceedings) Publisher：Institute of Electrical and Electronics Engineers Inc.

Multi-/many-core processors are being increasingly used to reduce power consumption and improve performance. In addition, the use of Model-Based Development for embedded systems has been increasing. Relative to these trends, Model-Based Parallelizer (MBP) has an essential role in parallelizing applications (i.e., Simulink blocks) at the model level. MBP maps Simulink blocks to cores using various types of information such as block characteristics, a C code, and the multi-/many-core hardware implementation. However, MBP does not consider many-core hardware with cluster structures. This paper proposes an algorithm that decides on core allocations by considering cluster structures. The proposed algorithm combines two other algorithms: one algorithm uses the core allocation of MBP and path analysis at the cluster-level and considers the influence of communication contention to decide on cluster allocations, and the other algorithm uses the results of MBP and remaps cluster allocations. The proposed algorithm produces better results than its component algorithms could separately. Evaluations demonstrate that the proposed algorithm obtained the better results than the existing method in terms of execution time on random and real models.

DOI： 10.1109/PDP50117.2020.00034

Web of Science

Scopus

Patricia Ortal, Masato Edahiro

IEEE Access Vol. 8 page： 179073 - 179082 2020

Language：English Publishing type：Research paper (scientific journal) Publisher：Institute of Electrical and Electronics Engineers Inc.

Representing products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits
from improving existing products or developing new ones, to improving the quality of site search and offering better item recommendations to users. In this paper, we propose a method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception. In the proposed method, first, a base product set with known attribute values is built based on consumers’ perceptions. Then, new product attribute vectors are estimated using product similarity. The proposed method also incorporates a new similarity measure that is based on purchase behavior and which is suitable for estimating product attribute vector distances. Because it takes into account the magnitude of the individual components of the vectors under comparison, the proposed method is free from the limitations of conventional similarity measures. The results of experiments conducted using real-world data indicate that the proposed method has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE).

DOI： 10.1109/ACCESS.2020.3027023

Web of Science

Scopus

Switching Hybrid Method Based on User Similarity and Global Statistics for Collaborative Filtering

Patricia Ortal, Masato Edahiro

IEEE Access Vol. 8 page： 213401 - 213415 2020

Language：English Publishing type：Research paper (scientific journal) Publisher：Institute of Electrical and Electronics Engineers Inc.

Collaborative filtering (CF) is a technique used in recommender systems to provide meaningful suggestions based on known feedback obtained from like-minded users. The measure of similarity plays a critical role in the performance of neighborhood-based CF methods. However, conventional similarity measures suffer from limitations because they only consider the direction of the rating vectors. We propose a novel similarity measure that considers the semantic nuances of the ratings
in particular, it weights the contributions of ratings in proportion to the users' degree of indifference towards the items. Additionally, to address the sparsity problem that affects the performance of CF techniques, we propose a switching hybrid method that predicts user ratings based on either our custom similarity measure or through user and item biases. We evaluated the proposed method on six different datasets and compared it with other CF methods. The results show that the proposed recommender consistently outperforms those using conventional similarity measures when the sparsity of the dataset is high.

DOI： 10.1109/ACCESS.2020.3040432

Web of Science

Scopus

Research on highly parallel embedded control system design and implementation method Invited

Masato Edahiro, Masaki Gondo

Impact Vol. 2019 ( 10 ) page： 44-46 2019.12

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Papers - EDAHIRO Masato