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Special Issue "Environmental Sensors and Their Applications"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. Peter W. McCarthy
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Guest Editor
University of South Wales, Pontypridd, UK
Interests: creation and application of techological solutions to solve clinical problems and enhance athletic training and performance
Special Issues and Collections in MDPI journals
Prof. Dr. Zhuofu Liu
Website
Guest Editor
Harbin Univesity of Science and Technology, Harbin, Heilongjiang Province, China
Interests: data analysis; signal measurement and detection; medical information processing
Special Issues and Collections in MDPI journals
Dr. Vincenzo Cascioli
Website
Guest Editor
Murdoch University Chiropractic Clinic, Perth, Western Australia, Australia
Interests: evaluation of sitting comfort and discomfort; signal measurement at the user–seat interface
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The area of environmental monitoring (e.g., in both natural and built environments) plays a role in ensuring optimal functioning across a hugely diverse landscape of activities. Environmental monitoring now also includes the environment closely apposed to the person (e.g., skin). Although comfort is considered to be important in design, all too often, the human body uses its own environmental sensors to compensate for design issues. An area with increasing growth potential is that of development and use of external sensor systems to augment defective human ones.

The decrease in sensor size, apparent reliability, and opportunities for new sensors brought about through technologies such as graphene have fuelled a rapid growth in research across a diverse series of fields from biomedical, agriculture, pharmaceutical to industrial (semiconductor industry and food processing). The changes in size and reliability have also allowed for the creation of combined sensors (e.g., temperature and humidity). This creates a great opportunity for applications that were previously considered impossible. However, an element of caution is still required: As one moves further away from physical measurement of any property, the issues of ensuring reliability and accuracy of calibration become increasingly important.

Never has the need been greater for more in-depth analysis, and from this refinement of these new sensor systems. We consider this an appropriate time to bring together research from across disciplines, explore novel applications, develop internal calibration methodology, and, from this solid basis, develop new applications to address the current issues.

The aim of this Special Issue is to present some of the possibilities that the new generation of sensors offers in terms of environmental monitoring, focusing on the different configurations that can be used and novel applications in any field. Reviews presenting a deep analysis of specific problems, such as calibration and uses in particular topic areas (e.g., clinical/medical), will also be considered.

We welcome original research papers and review articles on environmental sensor technology, their applications, and comparison between types.

Prof. Dr. Peter W. McCarthy
Prof. Dr. Zhuofu Liu
Dr. Vincenzo Cascioli
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

  • Humidity sensor
  • Calibration/reliability
  • Temperature sensor
  • Optical sensor
  • Environmental monitoring
  • Magnetic sensor
  • Nanomaterials
  • High-sensitivity structures, interferometers
  • Rapid response
  • Recovery rates
  • Printed sensors
  • SPR/LMR/LSPR
  • Miniature sensors
  • MEMS
  • RFID
  • Thermal compensation
  • In-field application
  • Embedded/wearable/mobile sensors
  • Wireless sensors
  • Medical/healthcare
  • Food/environmental
  • Profile mapping

Published Papers (5 papers)

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Open AccessArticle
A Simple, Reliable, and Inexpensive Solution for Contact Color Measurement in Small Plant Samples
Sensors 2020, 20(8), 2348; https://doi.org/10.3390/s20082348 - 20 Apr 2020
Abstract
Correct color measurement by contact-type color measuring devices requires that the sample surface fully covers the head of the device, so their use on small samples remains a challenge. Here, we propose to use cardboard adaptors on the two aperture masks (3 and [...] Read more.
Correct color measurement by contact-type color measuring devices requires that the sample surface fully covers the head of the device, so their use on small samples remains a challenge. Here, we propose to use cardboard adaptors on the two aperture masks (3 and 8 mm diameter measuring area) of a broadly used portable spectrophotometer. Adaptors in black and white to reduce the measuring area by 50% and 70% were applied in this study. Representatives of the family Campanulaceae have been used to test the methodology, given the occurrence of small leaves. Our results show that, following colorimetric criteria, the only setting providing indistinguishable colors according to the perception of the human eye is the use of a 50%-reducing adaptor on the 3-mm aperture. In addition, statistical analysis suggests the use of the white adaptor. Our contribution offers a sound measurement technique to gather ecological information from the color of leaves, petals, and other small samples. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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Open AccessArticle
A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet
Sensors 2020, 20(4), 1151; https://doi.org/10.3390/s20041151 - 19 Feb 2020
Abstract
LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve [...] Read more.
LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classification. The capsule network represents the features by vectors, which can account for the direction of the features and the relative position between the features. Therefore, more detailed feature information can be extracted. ResNet protects the integrity of information by passing input information to the output directly, which can solve the problem of network degradation caused by information loss in the traditional CNN propagation process to a certain extent. Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. The experimental results show that ResCapNet proposed in this article `improve the performance of LiDAR classification. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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Open AccessArticle
A Visual and Persuasive Energy Conservation System Based on BIM and IoT Technology
Sensors 2020, 20(1), 139; https://doi.org/10.3390/s20010139 - 24 Dec 2019
Cited by 2
Abstract
Comfort level in the human body is an index that is always difficult to evaluate in a general and objective manner. Therefore, building owners and managers have been known to adjust environmental physical parameters such as temperature, humidity, and air quality based on [...] Read more.
Comfort level in the human body is an index that is always difficult to evaluate in a general and objective manner. Therefore, building owners and managers have been known to adjust environmental physical parameters such as temperature, humidity, and air quality based on people’s subjective sensations to yield satisfactory feelings of comfort. Furthermore, electricity consumption could be reduced by minimizing unnecessary use of heating and cooling equipment based on precise knowledge of comfort levels in interior spaces. To achieve the aforementioned objectives, this study undertook the following four tasks: first, providing visualization and smart suggestion functions to assist building managers and users in analyzing and developing plans based on the demands of space usage and electrical equipment; second, using Internet of Things technology to minimize the difference between real situations and those simulated in building information modeling (BIM); third, accurately evaluating interior environment comfort levels and improving equipment operating efficiency based on quantized comfort levels; and fourth, establishing a persuasive workflow for building energy saving systems. Through developing this system, COZyBIM will help to enhance the satisfactions of comfort level in interior space and operate energy consuming equipment efficiently, to reach the target of energy saving. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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Open AccessArticle
A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification
Sensors 2019, 19(22), 4927; https://doi.org/10.3390/s19224927 - 12 Nov 2019
Cited by 1
Abstract
Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. [...] Read more.
Light detection and ranging (LiDAR) is a frequently used technique of data acquisition and it is widely used in diverse practical applications. In recent years, deep convolutional neural networks (CNNs) have shown their effectiveness for LiDAR-derived rasterized digital surface models (LiDAR-DSM) data classification. However, many excellent CNNs have too many parameters due to depth and complexity. Meanwhile, traditional CNNs have spatial redundancy because different convolution kernels scan and store information independently. SqueezeNet replaces a part of 3 × 3 convolution kernels in CNNs with 1 × 1 convolution kernels, decomposes the original one convolution layer into two layers, and encapsulates them into a Fire module. This structure can reduce the parameters of network. Octave Convolution (OctConv) pools some feature maps firstly and stores them separately from the feature maps with the original size. It can reduce spatial redundancy by sharing information between the two groups. In this article, in order to improve the accuracy and efficiency of the network simultaneously, Fire modules of SqueezeNet are used to replace the traditional convolution layers in OctConv to form a new dual neural architecture: OctSqueezeNet. Our experiments, conducted using two well-known LiDAR datasets and several classical state-of-the-art classification methods, revealed that our proposed classification approach based on OctSqueezeNet is able to provide competitive advantages in terms of both classification accuracy and computational amount. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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Open AccessLetter
An Integrated Gold-Film Temperature Sensor for In Situ Temperature Measurement of a High-Precision MEMS Accelerometer
Sensors 2020, 20(13), 3652; https://doi.org/10.3390/s20133652 - 29 Jun 2020
Abstract
Temperature sensors are one of the most important types of sensors, and are employed in many applications, including consumer electronics, automobiles and environmental monitoring. Due to the need to simultaneously measure temperature and other physical quantities, it is often desirable to integrate temperature [...] Read more.
Temperature sensors are one of the most important types of sensors, and are employed in many applications, including consumer electronics, automobiles and environmental monitoring. Due to the need to simultaneously measure temperature and other physical quantities, it is often desirable to integrate temperature sensors with other physical sensors, including accelerometers. In this study, we introduce an integrated gold-film resistor-type temperature sensor for in situ temperature measurement of a high-precision MEMS accelerometer. Gold was chosen as the material of the temperature sensor, for both its great resistance to oxidation and its better compatibility with our in-house capacitive accelerometer micro-fabrication process. The proposed temperature sensor was first calibrated and then evaluated. Experimental results showed the temperature measurement accuracy to be 0.08 °C; the discrepancies among the sensors were within 0.02 °C; the repeatability within seven days was 0.03 °C; the noise floor was 1 mK/√[email protected] Hz and 100 μK/√[email protected] Hz. The integration test with a MEMS accelerometer showed that by subtracting the temperature effect, the bias stability within 46 h for the accelerometer could be improved from 2.15 μg to 640 ng. This demonstrates the capability of measuring temperature in situ with the potential to eliminate the temperature effects of the MEMS accelerometer through system-level compensation. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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