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Selected Papers from the 13th International Conference on Sensing Technology
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Selected Papers from the 13th International Conference on Sensing Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Materials".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 13554

Special Issue Editors

Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
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
Dr. Krishanthi P. Jayasundera

E-Mail Website
Guest Editor
School of Life Sciences, University Technology Sydney, Building 7 (CB07), 638 Jones Street, Broadway Ultimo, NSW 2007, Australia
Interests: sensors; chemical synthesis; chemistry; organic synthesis; health science; phytochrome; molecular synthesis
Special Issues, Collections and Topics in MDPI journals
Dr. Octavian Postolache

E-Mail Website
Guest Editor
1. Instituto de Telecomunicações, 1049-001 Lisboa, Portugal
2. Escola de Tecnologias e Arquitetura (ISTA), ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
3. DCTI-Departamento de Ciências e Tecnologias da Informação, ISCTE-Instituto Universitário de Lisboa, 1600-077 Lisboa, Portugal
Interests: smart sensors; automated measurement systems; artificial intelligence; biomedical sensors; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 13th International Conference on Sensing Technology (ICST 2019) will be held on December 2 to 4, 2019 at Macquarie University in Sydney, Australia. ICST 2019 is intended to provide a common forum for researchers, scientists, engineers, and practitioners throughout the world to present their latest research findings, developments, and applications in the area of sensing technology. This Special Issue will contain a selection of papers submitted and accepted at ICST 2019.We warmly invite researchers to submit their contributions to this Special Issue. Potential topics include but are not limited to:

1. Vision sensing;
2. Sensors signal processing;
3. Sensors and actuators;
4. Sensors phenomena and modeling;
5. Sensors characterization;
6. Smart sensors and sensor fusion;
7. Electromagnetic sensors;
8. Chemical and gas sensors;
9. Physical sensors;
10. Electronic nose technology;
11. Biological sensors;
12. Electro-optic sensors and systems;
13. Mechanical sensors (inertial, pressure, and tactile);
14. Nano sensors;
15. Acoustic, noise, and vibration sensors;
16. Wireless sensors and WSN;
17. Body area network;
18. Internet of Things (IoT);
19. Security and reliability of WSN and IoT;
20. Optical sensors (radiation sensors, optoelectronic/photonic sensors, and fibers);
21. Lab-on chip;
22. Sensor arrays;
23. Intelligent sensing;
24. Telemetering;
25. Online monitoring;
26. Applications of sensors (automotive, medical, environmental monitoring, earthquake life detection, high speed impact, consumer, alarm and security, military, nautical, aeronautical and space sensor systems, robotics, and automation);
27. Solid state sensors;
28. Sensors for high energy physics application;
29. Particle accelerators and detectors;
30. Internet-based/remote data acquisition;
31. Education using sensors.

Prof. Dr. Subhas Mukhopadhyay
Dr. Krishanthi P. Jayasundera
Dr. Octavian Postolache
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 submissions that pass pre-check are 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 2600 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.

Published Papers (3 papers)

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Research

28 pages, 40670 KiB  
Article
Improving Spatial Resolution of Multispectral Rock Outcrop Images Using RGB Data and Artificial Neural Networks
by Ademir Marques Junior, Eniuce Menezes de Souza, Marianne Müller, Diego Brum, Daniel Capella Zanotta, Rafael Kenji Horota, Lucas Silveira Kupssinskü, Maurício Roberto Veronez, Luiz Gonzaga, Jr. and Caroline Lessio Cazarin
Sensors 2020, 20(12), 3559; https://doi.org/10.3390/s20123559 - 23 Jun 2020
Cited by 3 | Viewed by 2972
Abstract
Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually [...] Read more.
Spectral information provided by multispectral and hyperspectral sensors has a great impact on remote sensing studies, easing the identification of carbonate outcrops that contribute to a better understanding of petroleum reservoirs. Sensors aboard satellites like Landsat series, which have data freely available usually lack the spatial resolution that suborbital sensors have. Many techniques have been developed to improve spatial resolution through data fusion. However, most of them have serious limitations regarding application and scale. Recently Super-Resolution (SR) convolution neural networks have been tested with encouraging results. However, they require large datasets, more time and computational power for training. To overcome these limitations, this work aims to increase the spatial resolution of multispectral bands from the Landsat satellite database using a modified artificial neural network that uses pixel kernels of a single spatial high-resolution RGB image from Google Earth as input. The methodology was validated with a common dataset of indoor images as well as a specific area of Landsat 8. Different downsized scale inputs were used for training where the validation used the ground truth of the original size images, obtaining comparable results to the recent works. With the method validated, we generated high spatial resolution spectral bands based on RGB images from Google Earth on a carbonated outcrop area, which were then properly classified according to the soil spectral responses making use of the advantage of a higher spatial resolution dataset. Full article
(This article belongs to the Special Issue Selected Papers from the 13th International Conference on Sensing Technology)
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18 pages, 5360 KiB  
Article
A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning
by Lucas Silveira Kupssinskü, Tainá Thomassim Guimarães, Eniuce Menezes de Souza, Daniel C. Zanotta, Mauricio Roberto Veronez, Luiz Gonzaga, Jr. and Frederico Fábio Mauad
Sensors 2020, 20(7), 2125; https://doi.org/10.3390/s20072125 - 09 Apr 2020
Cited by 49 | Viewed by 6183
Abstract
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS [...] Read more.
Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8. Full article
(This article belongs to the Special Issue Selected Papers from the 13th International Conference on Sensing Technology)
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16 pages, 10682 KiB  
Article
A Semi-Automatic Coupling Geophone for Tunnel Seismic Detection
by Yao Wang, Nengyi Fu, Zhihong Fu, Xinglin Lu, Xian Liao, Haowen Wang and Shanqiang Qin
Sensors 2019, 19(17), 3734; https://doi.org/10.3390/s19173734 - 29 Aug 2019
Cited by 6 | Viewed by 3447
Abstract
The tunnel seismic method allows for the detection of the geology in front of a tunnel face for the safety of tunnel construction. Conventional geophones have problems such as a narrow spectral width, low sensitivity, and poor coupling with the tunnel wall. To [...] Read more.
The tunnel seismic method allows for the detection of the geology in front of a tunnel face for the safety of tunnel construction. Conventional geophones have problems such as a narrow spectral width, low sensitivity, and poor coupling with the tunnel wall. To tackle issues above, we propose a semi-automatic coupling geophone equipped with a piezoelectric sensor with a spectral range of 10–5000 Hz and a sensitivity of 2.8 V/g. After the geophone was manually pushed into the borehole, it automatically coupled with the tunnel wall under the pressure of the springs within the device. A comparative experiment showed that the data spectrum acquired by the semi-automatic coupling geophone was much higher than that of the conventional geophone equipped with the same piezoelectric sensor. The seismic data were processed in combination with forward modeling. The imaging results also show that the data acquired by the semi-automatic coupling geophone were more in line with the actual geological conditions. In addition, the semi-automatic coupling geophone’s installation requires a lower amount of time and cost. In summary, the semi-automatic coupling geophone is able to efficiently acquire seismic data with high fidelity, which can provide a reference for tunnel construction safety. Full article
(This article belongs to the Special Issue Selected Papers from the 13th International Conference on Sensing Technology)
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