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Special Issue "Selected Sensor Related Papers from ICI2017"

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

Deadline for manuscript submissions: closed (30 June 2018).

Special Issue Editors

Prof. Dr. Chien-Hung Liu
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Guest Editor
Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Fax: +886 4 2287 7170
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology
Special Issues and Collections in MDPI journals
Prof. Dr. Chih Jer Lin
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Guest Editor
Graduate Institute of Automation Technology, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan
Interests: numerical simulation; nonlinear control; mechatronics; precision motion control; system identification; sliding-mode control; robotics; evolutionary algorithms
Special Issues and Collections in MDPI journals
Prof. Dr. Cheng-Chi Wang
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Guest Editor
Ph.D. Program, Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
Interests: numerical simulation; chaos; nonlinear control; gas bearing system; advanced manufacturing process
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The aims and scope of the 2017 International Conference on Inventions is to make researchers focus on patent-based research. This Special Issue will also focus on inventions in sensor technology. Sensor-related inventions are a solution to specific technological problems and are an improvement on new processes and new components, which could also achieve a completely unique function or radical breakthrough. However, there is a very thin line between brilliant innovation and absolute failure, as some inventors famously found out, or even face the problem of insufficient funds. Therefore, patent-based research offers a way to provide the ideas and advances of the projects, rapidly, by the successful and practical results of predecessors. We expect the conference to be a platform for successful patent inventors to share their experiences. At the same time, the conference also aims to gather and show high quality papers concerning the discovery of completely unique functions or results, and go further to advance the frontiers of science and extend the standards of excellence established by inventions.

Prof. Dr. Chien-Hung Liu
Prof. Dr. Chih Jer Lin
Prof. Dr. Cheng-Chi Wang
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 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 2000 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

  • Patent based inventions in measurement engineering
  • Optical technology for sensors
  • Sensor related instrument and method
  • Sensor related materials
  • Sensor technology for industry 4.0

Published Papers (5 papers)

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Research

Open AccessArticle
Vehicle Collision Prediction under Reduced Visibility Conditions
Sensors 2018, 18(9), 3026; https://doi.org/10.3390/s18093026 - 10 Sep 2018
Cited by 2
Abstract
Rear-end collisions often cause serious traffic accidents. Conventionally, in intelligent transportation systems (ITS), radar collision warning methods are highly accurate in determining the inter-vehicle distance via detecting the rear-end of a vehicle; however, in poor weather conditions such as fog, rain, or snow, [...] Read more.
Rear-end collisions often cause serious traffic accidents. Conventionally, in intelligent transportation systems (ITS), radar collision warning methods are highly accurate in determining the inter-vehicle distance via detecting the rear-end of a vehicle; however, in poor weather conditions such as fog, rain, or snow, the accuracy is significantly affected. In recent years, the advent of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication systems has introduced new methods for solving the rear-end collision problem. Nevertheless, there is still much left for improvement. For instance, weather conditions have an impact on human-related factors such as response time. To address the issue of collision detection under low visibility conditions, we propose a Visibility-based Collision Warning System (ViCoWS) design that includes four models for prediction horizon estimation, velocity prediction, headway distance prediction, and rear-end collision warning. Based on the history of velocity data, future velocity volumes are predicted. Then, the prediction horizon (number of future time slots to consider) is estimated corresponding to different weather conditions. ViCoWs can respond in real-time to weather conditions with correct collision avoidance warnings. Experiment results show that the mean absolute percentage error of our velocity prediction model is less than 11%. For non-congested traffic under heavy fog (very low visibility of 120 m), ViCoWS warns a driver by as much as 4.5 s prior to a possible future collision. If the fog is medium with a low visibility of 160 m, ViCoWs can give warnings by about 2.1 s prior to a possible future collision. In contrast, the Forward Collision Probability Index (FCPI) method gives warnings by only about 0.6 s before a future collision. For congested traffic under low visibility conditions, ViCoWS can warn a driver by about 1.9 s prior to a possible future collision. In this case, the FCPI method gives 1.2 s for the driver to react before collision. Full article
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
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Open AccessArticle
Fast Visual Tracking Based on Convolutional Networks
Sensors 2018, 18(8), 2405; https://doi.org/10.3390/s18082405 - 24 Jul 2018
Cited by 1
Abstract
Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to [...] Read more.
Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT. Extensive experimental results on land and undersea video sequences show that Fast-CNT outperforms CNT by 2~10 times in terms of computational efficiency. Full article
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
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Open AccessArticle
A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
Sensors 2018, 18(4), 1294; https://doi.org/10.3390/s18041294 - 23 Apr 2018
Cited by 9
Abstract
In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we [...] Read more.
In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision. Full article
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
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Open AccessArticle
Glucose Sensor Using U-Shaped Optical Fiber Probe with Gold Nanoparticles and Glucose Oxidase
Sensors 2018, 18(4), 1217; https://doi.org/10.3390/s18041217 - 16 Apr 2018
Cited by 5
Abstract
In this study, we proposed a U-shaped optical fiber probe fabricated using a flame heating method. The probe was packaged in glass tube to reduce human factors during experimental testing of the probe as a glucose sensor. The U-shaped fiber probe was found [...] Read more.
In this study, we proposed a U-shaped optical fiber probe fabricated using a flame heating method. The probe was packaged in glass tube to reduce human factors during experimental testing of the probe as a glucose sensor. The U-shaped fiber probe was found to have high sensitivity in detecting the very small molecule. When the sensor was dipped in solutions with different refractive indexes, its wavelength or transmission loss changed. We used electrostatic self-assembly to bond gold nanoparticles and glucose oxidase (GOD) onto the sensor’s surface. The results over five cycles of the experiment showed that, as the glucose concentration increased, the refractive index of the sensor decreased and its spectrum wavelength shifted. The best wavelength sensitivity was 2.899 nm/%, and the linearity was 0.9771. The best transmission loss sensitivity was 5.101 dB/%, and the linearity was 0.9734. Therefore, the proposed U-shaped optical fiber probe with gold nanoparticles and GOD has good potential for use as a blood sugar sensor in the future. Full article
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
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Open AccessArticle
Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Self-made Bluetooth Temperature Sensor Module
Sensors 2018, 18(2), 656; https://doi.org/10.3390/s18020656 - 23 Feb 2018
Cited by 2
Abstract
Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the [...] Read more.
Thermal characteristic analysis is essential for machine tool spindles because sudden failures may occur due to unexpected thermal issue. This article presents a lumped-parameter Thermal Network Model (TNM) and its parameter estimation scheme, including hardware and software, in order to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the authors develop a Bluetooth Temperature Sensor Module (BTSM) which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1 + 0.0029|t|) °C, resolution 0.00489 °C, power consumption 7 mW, and size Ø40 mm × 27 mm. For the software, the heat transfer characteristics of the machine tool spindle correlative to rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive TNM of spindles was developed by grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with normalized mean square error of 99.5% agreement, and the present approach is transferable to the other spindles with a similar structure. For realizing the edge computing in smart manufacturing, a reduced-order TNM is constructed by Model Order Reduction (MOR) technique and implemented into the real-time embedded system. Full article
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
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