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Special Issue "Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology"

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

Deadline for manuscript submissions: closed (15 March 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Ruqiang Yan

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Website | E-Mail
Phone: +86-25-8379-4157
Fax: +86-25-8379-4158
Interests: structural health monitoring; wireless sensor networks
Guest Editor
Prof. Dr. Subhas Chandra Mukhopadhyay

Department of Engineering, Macquarie University, NSW 2109, Australia
Website | E-Mail
Phone: +61-2-9850-6510
Fax: +61-2-9850-9128
Interests: smart sensors; sensors modeling; sensor networks; wireless sensor networks; internet of things
Guest Editor
Prof. Gui Yun Tian

School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, UK
Website | E-Mail
Interests: sensor technologies for non-destructive testing and evaluation; structural health monitoring; electromagnetic and optical sensors; sensor network; system design; material identification; networked instrumentation

Special Issue Information

Dear Colleagues,

This Special Issue comprises selected papers from the proceedings of the 10th International Conference on Sensing Technology, held in Nanjing, China, 11–13 November 2016. In this 10th edition of the conference, researchers, scientists, engineers, and practitioners throughout the world were invited to submit papers and present their latest research findings, ideas, developments and applications in the area of sensing technology. Some of the papers, which provided innovative contribution, have been selected and extended for publication in this Special Issue. We hope the readers will find this Special Issue interesting and informative.

Prof. Dr. Ruqiang Yan
Prof. Dr. Subhas Chandra Mukhopadhyay
Prof. Dr. Gui Yun Tian
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 Sensing
  • Sensors and actuators
  • Wireless sensor networks
  • Sensor signal processing
  • Energy Harvesting
  • Internet of Things

Published Papers (6 papers)

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Research

Open AccessArticle Design and Implementation of a Hypothermic Machine Perfusion Device for Clinical Preservation of Isolated Organs
Sensors 2017, 17(6), 1256; doi:10.3390/s17061256
Received: 28 February 2017 / Revised: 17 May 2017 / Accepted: 18 May 2017 / Published: 1 June 2017
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Abstract
The imbalance between limited organ supply and huge potential need has hindered the development of organ-graft techniques. In this paper a low-cost hypothermic machine perfusion (HMP) device is designed and implemented to maintain suitable preservation surroundings and extend the survival life of isolated
[...] Read more.
The imbalance between limited organ supply and huge potential need has hindered the development of organ-graft techniques. In this paper a low-cost hypothermic machine perfusion (HMP) device is designed and implemented to maintain suitable preservation surroundings and extend the survival life of isolated organs. Four necessary elements (the machine perfusion, the physiological parameter monitoring, the thermostatic control and the oxygenation apparatus) involved in this HMP device are introduced. Especially during the thermostatic control process, a modified Bayes estimation, which introduces the concept of improvement factor, is realized to recognize and reduce the possible measurement errors resulting from sensor faults and noise interference. Also, a fuzzy-PID controller contributes to improve the accuracy and reduces the computational load using the DSP. Our experiments indicate that the reliability of the instrument meets the design requirements, thus being appealing for potential clinical preservation applications. Full article
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Open AccessArticle Development of a Flow Injection Based High Frequency Dual Channel Quartz Crystal Microbalance
Sensors 2017, 17(5), 1136; doi:10.3390/s17051136
Received: 15 March 2017 / Revised: 4 May 2017 / Accepted: 10 May 2017 / Published: 16 May 2017
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Abstract
When the quartz crystal microbalance (QCM) is used in liquid for adsorption or desorption monitoring based bio- or chemical sensing applications, the frequency shift is not only determined by the surface mass change, but also by the change of liquid characteristics, such as
[...] Read more.
When the quartz crystal microbalance (QCM) is used in liquid for adsorption or desorption monitoring based bio- or chemical sensing applications, the frequency shift is not only determined by the surface mass change, but also by the change of liquid characteristics, such as density and viscosity, which are greatly affected by the liquid environmental temperature. A monolithic dual-channel QCM is designed and fabricated by arranging two QCM resonators on one single chip for cancelling the fluctuation induced by environmental factors. In actual applications, one QCM works as a specific sensor by modifying with functional membranes and the other acts as a reference, only measuring the liquid property. The dual-channel QCM is designed with an inverted-mesa structure, aiming to realize a high frequency miniaturized chip and suppress the frequency interference between the neighbored QCM resonators. The key problem of dual-channel QCMs is the interference between two channels, which is influenced by the distance of adjacent resonators. The diameter of the reference electrode has been designed into several values in order to find the optimal parameter. Experimental results demonstrated that the two QCMs could vibrate individually and the output frequency stability and drift can be greatly improved with the aid of the reference QCM. Full article
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Open AccessArticle Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion
Sensors 2017, 17(5), 1037; doi:10.3390/s17051037
Received: 19 February 2017 / Revised: 28 April 2017 / Accepted: 2 May 2017 / Published: 5 May 2017
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Abstract
In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage
[...] Read more.
In mobile augmented/virtual reality (AR/VR), real-time 6-Degree of Freedom (DoF) motion tracking is essential for the registration between virtual scenes and the real world. However, due to the limited computational capacity of mobile terminals today, the latency between consecutive arriving poses would damage the user experience in mobile AR/VR. Thus, a visual-inertial based real-time motion tracking for mobile AR/VR is proposed in this paper. By means of high frequency and passive outputs from the inertial sensor, the real-time performance of arriving poses for mobile AR/VR is achieved. In addition, to alleviate the jitter phenomenon during the visual-inertial fusion, an adaptive filter framework is established to cope with different motion situations automatically, enabling the real-time 6-DoF motion tracking by balancing the jitter and latency. Besides, the robustness of the traditional visual-only based motion tracking is enhanced, giving rise to a better mobile AR/VR performance when motion blur is encountered. Finally, experiments are carried out to demonstrate the proposed method, and the results show that this work is capable of providing a smooth and robust 6-DoF motion tracking for mobile AR/VR in real-time. Full article
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Open AccessArticle Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method
Sensors 2017, 17(4), 814; doi:10.3390/s17040814
Received: 23 January 2017 / Revised: 24 March 2017 / Accepted: 29 March 2017 / Published: 10 April 2017
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Abstract
Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed
[...] Read more.
Laser stripe center extraction is a key step for the profile measurement of line structured light sensors (LSLS). To accurately obtain the center coordinates at sub-pixel level, an improved gray-gravity method (IGGM) was proposed. Firstly, the center points of the stripe were computed using the gray-gravity method (GGM) for all columns of the image. By fitting these points using the moving least squares algorithm, the tangential vector, the normal vector and the radius of curvature can be robustly obtained. One rectangular region could be defined around each of the center points. Its two sides that are parallel to the tangential vector could alter their lengths according to the radius of the curvature. After that, the coordinate for each center point was recalculated within the rectangular region and in the direction of the normal vector. The center uncertainty was also analyzed based on the Monte Carlo method. The obtained experimental results indicate that the IGGM is suitable for both the smooth stripes and the ones with sharp corners. The high accuracy center points can be obtained at a relatively low computation cost. The measured results of the stairs and the screw surface further demonstrate the effectiveness of the method. Full article
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Open AccessArticle Comparison of Two Types of Overoxidized PEDOT Films and Their Application in Sensor Fabrication
Sensors 2017, 17(3), 628; doi:10.3390/s17030628
Received: 18 January 2017 / Revised: 16 March 2017 / Accepted: 17 March 2017 / Published: 19 March 2017
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Abstract
Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results
[...] Read more.
Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results in the film overoxidized for 45 s at 1.35 V presenting a strong adsorption. The other one-step overoxidation film prepared by direct CV ranging from −0.6 V to 1.35 V was polymerized for comparison. Scanning electron microscope (SEM) analysis and Fourier transform infrared (FTIR) spectroscopy were used for monitoring morphological changes and the evolution of functional groups. Both of them indicate increased abundant oxygen functional groups and roughness, yet the products exhibit dendritic morphology and piles of spherical protrusions, respectively. Moreover, double-step overoxidized film showed better electrochemical performance toward lead ion sensing. These characterizations highlight some novel properties that may be beneficial for specific sensing applications. Full article
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Open AccessArticle Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Sensors 2017, 17(2), 273; doi:10.3390/s17020273
Received: 24 November 2016 / Revised: 12 January 2017 / Accepted: 24 January 2017 / Published: 30 January 2017
Cited by 2 | PDF Full-text (856 KB) | HTML Full-text | XML Full-text
Abstract
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic
[...] Read more.
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. Full article
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