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Special Issue "Innovative Sensor Technology for Intelligent System and Computing"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 September 2018)

Special Issue Editors

Guest Editor
Dr. Hwa-Young (Michael) Jeong

Humanitas College, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, 130-701, Korea
Website | E-Mail
Interests: Sensor network; Ubiquitous sensor network (USN); Intelligent software and system; Software and system model for USN; Application and control for manufacturing system (semiconductor); Factory automation
Guest Editor
Prof. Dr. Luigi Fortuna

DIEEI University of Catania Italy
Website | E-Mail
Phone: 39 095 7382307
Interests: nonlinear electronic circuits; automatic control; system theory
Guest Editor
Dr. Hye-Jung Jung

Department of Information and Communication, Pyeong Taek University, 111 Yongyi-Dong, Pyeongtaek-si, Gyeonggi-Do 17869, Korea
E-Mail
Interests: Sensor data analysis; Data processing from sensor network; Data security from sensors; U-city management system; Communication and control devices; Mobile RFID system

Special Issue Information

Dear Colleagues,

Generally, a sensor is a device for detecting and signaling a changing condition. There are two ways of using sensor devices, wire and wireless. Additionally, nowadays, our environment that is in industry, social, human activity, and all kinds of computing materials, need to change and be applied to various fields and types for a more efficient and convenient life. Furthermore, after experiencing prior cycles, such as mainframe computing, personal computing, and network computing, the computer industry and academia have entered a new cycle of technological innovation and growth, which we are calling 'Intelligent Computing'. Intelligent Computing, often termed intelligent analytics, is all about utilizing the incredible processing power of today’s computing environments to optimize decisions for various fields in real time and convert the ever-growing flood of data into both meaningful information and actionable intelligence. Intelligent Computing usually makes use of collaborative work using software more effectively. In addition, computing devices have become smaller, more mobile, and smarter. In this contents, many devices can be connected by various type of sensors and its technology develops into smart and future oriented home environment technology in human being centric service. Wearable device with sensor, wireless sensor network, ad hoc network with sensors and multi and bio sensors are good examples. In order to achieve the objectives of Intelligent Computing, advanced technologies are required and, thus, grow quickly.

This Special Issue calls for high-quality, up-to-date research related innovative sensor technologies for intelligent system and computing. In particular, the Special Issue is going to showcase the most recent achievements and developments in the sensor technology for intelligent computing. All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this Special Issue.

Dr. Hwa-Young (Michael) Jeong
Prof. Luigi Fortuna
Dr. Hye-Jung Jung
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 papers will be 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 monthly 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 1800 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.

Keywords

  • Intelligent Devices and Sensor
  • Wireless Sensor Network for Intelligent Computing
  • Cognitive Sensor Technology for Intelligent Computing
  • Cloud Computing for Intelligent Mobile Devices with Sensor Network
  • High performance wireless/mobile computing and service
  • Real-World Sensing and Interaction
  • Smart Vehicles with Intelligent Sensor Technology
  • Artificial Intelligence for Intelligent Computing with Sensor Technology
  • Big Data Processing and Data Mining with Sensor Technology
  • Intelligent Sensor Technology based Entertainment and Games
  • Smart Power Electronics
  • Intelligent Control and Robot Systems with Sensor Technology
  • Autonomous Systems with Innovative Sensor Technology
  • Cognitive systems with Intelligent Sensor Technology
  • Decision Support System from Sensing Data
  • Human-Computer Interaction with Innovative Sensor Technology
  • Information retrieval and extraction with Sensor Technology
  • Machine Learning from Sensing Data

Published Papers (7 papers)

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Research

Open AccessArticle Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving
Sensors 2018, 18(10), 3392; https://doi.org/10.3390/s18103392
Received: 30 July 2018 / Revised: 2 October 2018 / Accepted: 3 October 2018 / Published: 10 October 2018
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Abstract
This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console
[...] Read more.
This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console during driving, as well as the time taken for the driver to access the center console. Three infrared sensors on the center console are used to detect the movement of the driver’s hand. These sensors are installed in three locations: the air conditioner or heater (temperature control) button, wind direction control button, and wind intensity control button. A driver’s danger level is estimated to be based on a linear regression analysis of the distance and time of movement between the driver’s hand and the center console, as measured in the proposed scenarios. In the experimental results of the proposed scenarios, the root mean square error of driver H using distance and time of movement between the driver’s hand and the center console is 0.0043, which indicates the best estimation of a driver’s danger level. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data
Sensors 2018, 18(9), 3172; https://doi.org/10.3390/s18093172
Received: 30 July 2018 / Revised: 16 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
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Abstract
In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion
[...] Read more.
In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion area in which there are some risks of ship collision. They want to control the traffic of the caution area at a proper level to lessen risk. Inertial ship movement makes swift changes in direction and speed difficult. It is hence important to predict future traffic of the caution area earlier on so as to get enough time for control actions on ship movements. In the harbor area, VTS stations collect a large volume of Automatic Identification Service (AIS) sensor data, which contain information about ship movement and ship attributes. This paper proposes a new deep neural network model called Ship Traffic Extraction Network (STENet) to predict the medium-term traffic and long-term traffic of the caution area. The STENet model is trained with AIS sensor data. The STENet model is organized into a hierarchical architecture in which the outputs of the movement and contextual feature extraction modules are concatenated and fed into a prediction module. The movement module extracts the features of overall ship movements with a convolutional neural network. The contextual modules consist of five separated fully-connected neural networks, each of which receives an associated attribute. The separation of feature extraction modules at the front phase helps extract the effective features by preventing unrelated attributes from crosstalking. To evaluate the performance of the proposed model, the developed model is applied to a real AIS sensor dataset, which has been collected over two years at a Korean port called Yeosu. In the experiments, four methods have been compared including two new methods: STENet and VGGNet-based models. For the real AIS sensor dataset, the proposed model has shown 50.65% relative performance improvement on average for the medium-term predictions and 57.65% improvement on average for the long-term predictions over the benchmark method, i.e., the SVR-based method. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle Complex Event Processing for Sensor Stream Data
Sensors 2018, 18(9), 3084; https://doi.org/10.3390/s18093084
Received: 12 August 2018 / Revised: 7 September 2018 / Accepted: 11 September 2018 / Published: 13 September 2018
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Abstract
As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by
[...] Read more.
As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging
Sensors 2018, 18(9), 3050; https://doi.org/10.3390/s18093050
Received: 14 July 2018 / Revised: 3 September 2018 / Accepted: 9 September 2018 / Published: 12 September 2018
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Abstract
In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and
[...] Read more.
In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and a protective coating that is too thin. The fingerprint recognition sensor can also be placed under a glass screen to meet the needs of the full screen design of the mobile phone. In this paper, the electric field distribution around the fingerprint is analyzed. The electrostatic imaging sensor design is carried out based on the electrostatic detection principle and MEMS (micro-electro-mechanical system) technology. The MEMS electrostatic imaging array, analog, and digital signal processing circuit structure are designed. Simulation and testing are carried out as well. According to the simulation and prototype test device test results, it is confirmed that our proposed electrostatic imaging-based fingerprint sensing technology can increase fingerprint recognition distance by 46% compared to the existing capacitive fingerprint sensing technology. A distance of more than 439 μm is reached. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol
Sensors 2018, 18(9), 2960; https://doi.org/10.3390/s18092960
Received: 7 August 2018 / Revised: 2 September 2018 / Accepted: 3 September 2018 / Published: 5 September 2018
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Abstract
This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep
[...] Read more.
This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep interval to efficiently calculate forest fire occurrence risk according to the environment. Performance was verified experimentally, and the proposed ADX-MAC protocol improved throughput by 19% and was 24% more energy efficient compared to the X-MAC protocol. The duty-cycle was shortened as forest fire probability increased, ensuring forest fires were detected at faster cycle rate. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control
Sensors 2018, 18(8), 2539; https://doi.org/10.3390/s18082539
Received: 4 June 2018 / Revised: 12 July 2018 / Accepted: 26 July 2018 / Published: 3 August 2018
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Abstract
Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve
[...] Read more.
Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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Open AccessArticle Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics
Sensors 2018, 18(6), 1677; https://doi.org/10.3390/s18061677
Received: 10 April 2018 / Revised: 17 May 2018 / Accepted: 17 May 2018 / Published: 23 May 2018
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Abstract
Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less
[...] Read more.
Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less than one millisecond, low cost, and does not require a camera or wearable devices. A formula is first created to sense array signals with five spherical electrodes. Next, a solving algorithm for the real-time measured hand position is introduced and solving equations for three-dimensional coordinates of hand position are obtained. A non-contact real-time hand position sensing system was established to perform verification experiments, and the principle error of the algorithm and the systematic noise were also analyzed. The results show that this novel technology can determine the dynamic parameters of hand movements with good robustness to meet the requirements of complicated HCI. Full article
(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)
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