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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; GMR sensor; 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 (2 papers)

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Research

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 1 | 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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Figure 1

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