電気学会論文誌C(電子・情報・システム部門誌)
Online ISSN : 1348-8155
Print ISSN : 0385-4221
ISSN-L : 0385-4221
140 巻, 9 号
選択された号の論文の11件中1~11を表示しています
論文
<情報通信工学>
  • W.A. Shanaka P. Abeysiriwardhana, Janaka L. Wijekoon, Hiroaki Nishi
    2020 年 140 巻 9 号 p. 1030-1039
    発行日: 2020/09/01
    公開日: 2020/09/01
    ジャーナル 認証あり

    A smart community utilizes information technology to interconnect and manage community infrastructures. Smart community networks should support a large number of Internet of Things (IoT) devices in community infrastructures to provide services such as smart grids and health monitoring systems. In comparison to cloud-based solutions, smart community services can be deployed in the edge computing area to reduce service latency and to encapsulate private and local information. Furthermore, smart community services can leverage network virtualization technologies to support IoT network services at the edge. A service-oriented container-based solution that processes data streams from IoT sensors using conventional hardware will improve the compatibility and latency of these virtualized network services at the edge. To this end, a software-based edge computing node, namely, the smart community edge (SCE), was proposed to develop a platform for smart community services. SCE supports data-tapping applications, especially for IoT devices, and has a stream processing feature with a comparatively shorter processing delay. This tapping and processing function was named multi-service authorized stream content analysis. SCE captures network stream data and enables service applications using shared memory buffers for a shorter processing delay. SCE supports services as Docker containers to provide remote deployment, service compatibility, and service isolation. SCE allows IoT services to run at the edge through conventional hardware devices, thus, reducing the service latency for delay-sensitive services, which approximately require to sustain latency less than 10 ms. The proposed SCE achieves 10 Gbps bandwidth with a 16 core server when compared to the f-stack library with a 5 Gbps bandwidth. SCE deployment on conventional hardware devices shows its capability of operating at 1-10 Gbps line rates to support up to eight services at 500 Mbps data bandwidth per service, while keeping the overall latency below 1 ms. Therefore, SCE provides a platform for delay-sensitive IoT services at the network edge.

<生体医工学・福祉工学>
<システム・計測・制御>
<知能,ロボティクス>
  • 中込 広幸, 布施 嘉裕, 永田 靖貴, 宮本 博永, 横塚 将志, 神村 明哉, 渡辺 寛望, 丹沢 勉, 小谷 信司
    2020 年 140 巻 9 号 p. 1082-1090
    発行日: 2020/09/01
    公開日: 2020/09/01
    ジャーナル 認証あり

    In this paper, we propose the method for efficient real-time estimation of the odometry using a rotating 2D laser scanner and a scan-matching method. Our scan-matching method uses the odometry model, constituted 9-DoF parameters of positions, postures and linear velocities. And all parameters are estimated by minimizing the cost-function obtained distances between point clouds to surfels. In the experiment, we evaluated the accuracy of our method when the robot passes on a rough terrain space and a foresty work road. As result, The maximum position error is 0.19 m of 30 m distance traveled, compared with the RTK-GNSS output in a rough terrain space. And more highly accurately than a conventional method in a foresty work road.

<音声画像処理・認識>
  • David Pich, Katsuya Nakahira
    2020 年 140 巻 9 号 p. 1091-1095
    発行日: 2020/09/01
    公開日: 2020/09/01
    ジャーナル 認証あり

    A spectacular diversity of fishes under a crystal clear seawater in Okinawa attracts numerous scuba divers, snorkelers around the world. With the advancement in computer vision and deep learning, object detection is much more reliable than ever and find its application almost in every industry, and also in marine leisure activity. Being able to detect and recognize all underwater objects provides both an educational and amazing experience to divers and snorkelers to explore the underworld. However, it requires a system that could work in real-time with high accuracy. This is a challenge that all deep learning-based object detection algorithm is facing since there is a trade-off between time and accuracy. YOLOv3 is one of the fastest object detection algorithms that can work in real-time. We use this to train and test on our custom dataset. We collected the underwater fish image and built our dataset that contains 3548 images. We provide a comparative analysis of the training and evaluation of three different datasets. With data augmentation, our model can achieve up to 92% of mAP, and we also show what role that negative data impact the performance of the model.

<情報システム,エレクトロニック・コマース>
  • 松本 慎平, 大下 昌紀, 買田 康介
    2020 年 140 巻 9 号 p. 1096-1109
    発行日: 2020/09/01
    公開日: 2020/09/01
    ジャーナル 認証あり

    In order to improve programming education in C language, it is important to develop an environment that is easy for beginners to learn. At the same time, a function that can support the instructor is also essential, such as monitoring learners and collecting learning logs. In this paper, we develop a programming learning support system for beginners of C language to achieve the three objectives: (1) providing a function for beginners to make them concentrate on essential learning intended by the instructor, (2) providing a function to collect the log data on the learning process for realizing the analysis required by the instructor, and (3) providing a function to realize presentation and collection of learning tasks commonly performed in C language education, and to support these evaluations and counting. This paper explains the concept and function of the proposed system in detail, and to evaluate the usefulness of the proposed system from the viewpoints of both the learners and its instructors through the practical use and the experiments.

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