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

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

Deadline for manuscript submissions: closed (15 March 2017) | Viewed by 58263

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Prof. Dr. Ruqiang Yan

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Guest Editor

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: structural health monitoring; wireless sensor networks

Prof. Dr. Subhas Mukhopadhyay

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Guest Editor

School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart sensors; sensing technology; WSN; IoT; ICT; smart grid; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gui Yun Tian

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Guest Editor

School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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 Issues, Collections and Topics in MDPI journals

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

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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

5632 KiB

Open AccessArticle

Design and Implementation of a Hypothermic Machine Perfusion Device for Clinical Preservation of Isolated Organs

by Fei Shen and Ruqiang Yan

Sensors 2017, 17(6), 1256; https://doi.org/10.3390/s17061256 - 01 Jun 2017

Cited by 3 | Viewed by 5469

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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2317 KiB

Open AccessArticle

Development of a Flow Injection Based High Frequency Dual Channel Quartz Crystal Microbalance

by Jinxing Liang, Jing Zhang, Wenxiang Zhou and Toshitsugu Ueda

Sensors 2017, 17(5), 1136; https://doi.org/10.3390/s17051136 - 16 May 2017

Cited by 5 | Viewed by 5334

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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5660 KiB

Open AccessArticle

Real-Time Motion Tracking for Mobile Augmented/Virtual Reality Using Adaptive Visual-Inertial Fusion

by Wei Fang, Lianyu Zheng, Huanjun Deng and Hongbo Zhang

Sensors 2017, 17(5), 1037; https://doi.org/10.3390/s17051037 - 05 May 2017

Cited by 46 | Viewed by 9729

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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6501 KiB

Open AccessArticle

Sub-Pixel Extraction of Laser Stripe Center Using an Improved Gray-Gravity Method ^†

by Yuehua Li, Jingbo Zhou, Fengshan Huang and Lijian Liu

Sensors 2017, 17(4), 814; https://doi.org/10.3390/s17040814 - 10 Apr 2017

Cited by 95 | Viewed by 9157

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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3066 KiB

Open AccessArticle

Comparison of Two Types of Overoxidized PEDOT Films and Their Application in Sensor Fabrication

by Yun Hui, Chao Bian, Jinfen Wang, Jianhua Tong and Shanhong Xia

Sensors 2017, 17(3), 628; https://doi.org/10.3390/s17030628 - 19 Mar 2017

Cited by 20 | Viewed by 5693

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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856 KiB

Open AccessArticle

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

by Rui Zhao, Ruqiang Yan, Jinjiang Wang and Kezhi Mao

Sensors 2017, 17(2), 273; https://doi.org/10.3390/s17020273 - 30 Jan 2017

Cited by 584 | Viewed by 21758

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 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

(This article belongs to the Special Issue Intelligent Sensing and Information Mining—Selected Papers from the 10th International Conference on Sensing Technology)

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