sensors-logo

Journal Browser

Journal Browser

Selected Papers from the Eighth International Conference on ICT Convergence (ICTC 2017)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 February 2018) | Viewed by 14654

Special Issue Editors

Computer Engineering Department, Hongik University 94 Wausan-ro, Mapo-gu, Seoul 04066, Korea
Interests: sensor networks; mobile computing; network coding; machine learning
Special Issues, Collections and Topics in MDPI journals
Advanced Wireless and Communication Research Center (AWCC), The University of Electro-Communications, Tokyo 182-8585, Japan
Interests: wireless ad-hoc network; cognitive radio; wireless sensing technology; wireless network protocol; mobile network communications; ITS and software radio
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will include selected papers from the eighth International Conference on ICT Convergence (ICTC 2017), to be held on Jeju Island, Korea, 18–20 October, 2017. The conference is organized by KICS (The Korean Institutes of Communications and Information Sciences) with the technical co-sponsorship of the IEEE Communications Society and the IEICE-CS. There have been a number of trials to apply information and communication technology (ICT) to other industrial sectors, such as green convergence, smart screen and appliances, next generation broadcasting and media, mobile convergence networks, and other ICT convergence applications and services, all under the name of “ICT convergence.” ICTC 2017 will be a unique global premier event for researchers, industry professionals, and academics, which aims at interacting with, and disseminating information on, the latest developments in the emerging industrial convergence centered around information and communication technologies. The authors of selected papers from ICTC 2017 within the scope of this journal will be invited to submit extended and enhanced versions of their papers to this Special Issue. These extended papers must contain considerable amounts of new material, and will be subject to a new round of reviews before being published in the Special Issue.

Prof. Joon-Sang Park
Prof. Takeo Fujii
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 5687 KiB  
Article
Optical and Acoustic Sensor-Based 3D Ball Motion Estimation for Ball Sport Simulators †
by Sang-Woo Seo, Myunggyu Kim and Yejin Kim
Sensors 2018, 18(5), 1323; https://doi.org/10.3390/s18051323 - 25 Apr 2018
Cited by 5 | Viewed by 5552
Abstract
Estimation of the motion of ball-shaped objects is essential for the operation of ball sport simulators. In this paper, we propose an estimation system for 3D ball motion, including speed and angle of projection, by using acoustic vector and infrared (IR) scanning sensors. [...] Read more.
Estimation of the motion of ball-shaped objects is essential for the operation of ball sport simulators. In this paper, we propose an estimation system for 3D ball motion, including speed and angle of projection, by using acoustic vector and infrared (IR) scanning sensors. Our system is comprised of three steps to estimate a ball motion: sound-based ball firing detection, sound source localization, and IR scanning for motion analysis. First, an impulsive sound classification based on the mel-frequency cepstrum and feed-forward neural network is introduced to detect the ball launch sound. An impulsive sound source localization using a 2D microelectromechanical system (MEMS) microphones and delay-and-sum beamforming is presented to estimate the firing position. The time and position of a ball in 3D space is determined from a high-speed infrared scanning method. Our experimental results demonstrate that the estimation of ball motion based on sound allows a wider activity area than similar camera-based methods. Thus, it can be practically applied to various simulations in sports such as soccer and baseball. Full article
Show Figures

Figure 1

17 pages, 1309 KiB  
Article
Power-Efficient Beacon Recognition Method Based on Periodic Wake-Up for Industrial Wireless Devices
by Soonyong Song, Donghun Lee, Ingook Jang, Jinchul Choi and Youngsung Son
Sensors 2018, 18(4), 1237; https://doi.org/10.3390/s18041237 - 17 Apr 2018
Cited by 2 | Viewed by 3304
Abstract
Energy harvester-integrated wireless devices are attractive for generating semi-permanent power from wasted energy in industrial environments. The energy-harvesting wireless devices may have difficulty in their communication with access points due to insufficient power supply for beacon recognition during network initialization. In this manuscript, [...] Read more.
Energy harvester-integrated wireless devices are attractive for generating semi-permanent power from wasted energy in industrial environments. The energy-harvesting wireless devices may have difficulty in their communication with access points due to insufficient power supply for beacon recognition during network initialization. In this manuscript, we propose a novel method of beacon recognition based on wake-up control to reduce instantaneous power consumption in the initialization procedure. The proposed method applies a moving window for the periodic wake-up of the wireless devices. For unsynchronized wireless devices, beacons are always located in the same positions within each beacon interval even though the starting offsets are unknown. Using these characteristics, the moving window checks the existence of the beacon associated withspecified resources in a beacon interval, checks again for neighboring resources at the next beacon interval, and so on. This method can reduce instantaneous power and generates a surplus of charging time. Thus, the proposed method alleviates the problems of power insufficiency in the network initialization. The feasibility of the proposed method is evaluated using computer simulations of power shortage in various energy-harvesting conditions. Full article
Show Figures

Figure 1

11 pages, 4251 KiB  
Article
On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor
by Woosuk Kim and Myunggyu Kim
Sensors 2018, 18(3), 913; https://doi.org/10.3390/s18030913 - 19 Mar 2018
Cited by 13 | Viewed by 5186
Abstract
In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically [...] Read more.
In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis. Full article
Show Figures

Figure 1

Back to TopTop