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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 55676

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Special Issue Editors

Prof. Dr. Peter W. McCarthy

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Guest Editor

Faculty of Life Sciences and Education, University of South Wales, Pontypridd, UK
Interests: humidity sensor
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zhuofu Liu

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Guest Editor

The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin, China
Interests: data analysis; signal measurement and detection; medical information processing
Special Issues, Collections and Topics in MDPI journals

Dr. Vincenzo Cascioli

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Murdoch University Chiropractic Clinic, Perth, WA, Australia
Interests: evaluation of sitting comfort and discomfort; signal measurement at the user–seat interface
Special Issues, Collections and Topics 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

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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

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Published Papers (14 papers)

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Research

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21 pages, 6160 KiB

Open AccessArticle

Design of a Real-Time Salinity Detection System for Water Injection Wells Based on Fuzzy Control

by Bo You, Yuandong Yue, Mingxiao Sun, Jiayu Li and Deli Jia

Sensors 2021, 21(9), 3086; https://doi.org/10.3390/s21093086 - 28 Apr 2021

Cited by 5 | Viewed by 2944

Abstract

Salinity is an important index of water quality in oilfield water injection engineering. To address the need for real-time measurement of salinity in water flooding solutions during oilfield water injection, a salinity measurement system that can withstand a high temperature environment was designed. In terms of the polarization and capacitance effects, the system uses an integrator circuit to collect information and fuzzy control to switch gears to expand the range. Experimental results show that the system can operate stably in a high-temperature environment, with an accuracy of 0.6% and an uncertainty of 0.2% in the measurement range of 1–10 g/L. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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23 pages, 10504 KiB

Open AccessArticle

Performance of the ATMOS41 All-in-One Weather Station for Weather Monitoring

by Olga Dombrowski, Harrie-Jan Hendricks Franssen, Cosimo Brogi and Heye Reemt Bogena

Sensors 2021, 21(3), 741; https://doi.org/10.3390/s21030741 - 22 Jan 2021

Cited by 14 | Viewed by 6178

Abstract

Affordable and accurate weather monitoring systems are essential in low-income and developing countries and, more recently, are needed in small-scale research such as precision agriculture and urban climate studies. A variety of low-cost solutions are available on the market, but the use of non-standard technologies raises concerns for data quality. Research-grade all-in-one weather stations could present a reliable, cost effective solution while being robust and easy to use. This study evaluates the performance of the commercially available ATMOS41 all-in-one weather station. Three stations were deployed next to a high-performance reference station over a three-month period. The ATMOS41 stations showed good performance compared to the reference, and close agreement among the three stations for most standard weather variables. However, measured atmospheric pressure showed uncertainties >0.6 hPa and solar radiation was underestimated by 3%, which could be corrected with a locally obtained linear regression function. Furthermore, precipitation measurements showed considerable variability, with observed differences of ±7.5% compared to the reference gauge, which suggests relatively high susceptibility to wind-induced errors. Overall, the station is well suited for private user applications such as farming, while the use in research should consider the limitations of the station, especially regarding precise precipitation measurements. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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15 pages, 1575 KiB

Open AccessArticle

A Comparison of Various Algorithms for Classification of Food Scents Measured with an Ion Mobility Spectrometry

by Georgy Minaev, Philipp Müller, Katri Salminen, Jussi Rantala, Veikko Surakka and Ari Visa

Sensors 2021, 21(2), 361; https://doi.org/10.3390/s21020361 - 7 Jan 2021

Cited by 1 | Viewed by 2577

Abstract

The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than

3 %

when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least

95 %

99 %

of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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14 pages, 1584 KiB

Open AccessArticle

Detecting Traces of 17α-Ethinylestradiol in Complex Water Matrices

by Paulo M Zagalo, Paulo A Ribeiro and Maria Raposo

Sensors 2020, 20(24), 7324; https://doi.org/10.3390/s20247324 - 20 Dec 2020

Cited by 7 | Viewed by 2996

Abstract

Hormones have a harmful impact on the environment and their detection in water bodies is an urgent matter. In this work, we present and analyze a sensor device able to detect traces of the synthetic hormone 17α-ethinylestradiol (EE2) below 10⁻⁹ M in media of different complexities, namely, ultrapure, mineral and tap waters. This device consists of solid supports with interdigitated electrodes without and with a polyethylenimine (PEI) and poly (sodium 4-styrenesulfonate) (PSS) layer-by-layer film deposited on it. Device response was evaluated through capacitance, loss tangent and electric modulus spectra and the data were analyzed by principal component analysis method. While the three types of spectra were demonstrated to be able to clearly discriminate the different media, loss tangent spectra allow for the detection of EE2 concentration, with a sensitivity of −0.072 ± 0.009 and −0.44 ± 0.03 per decade of concentration, for mineral and tap water, respectively. Detection limits values were found to be lower than the ones present in the literature and presenting values of 8.6 fM (2.6 pg/L) and of 7.5 fM (22.2 pg/L) for tap and mineral waters, respectively. Moreover, the obtained response values follow the same behavior with EE2 concentration in any medium, meaning that loss tangent spectra allow the quantification of EE2 concentration in aqueous complex matrices. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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27 pages, 10924 KiB

Open AccessArticle

Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture

by Sergio Trilles, Pablo Juan, Carlos Díaz-Avalos, Sara Ribeiro and Marco Painho

Sensors 2020, 20(22), 6597; https://doi.org/10.3390/s20226597 - 18 Nov 2020

Cited by 1 | Viewed by 2212

Abstract

Temperature, humidity and precipitation have a strong influence on the generation of diseases in different crops, especially in vine. In recent years, advances in different disciplines have enabled the deployment of sensor nodes on agricultural plots. These sensors are characterised by a low cost and so the reliability of the data obtained from them can be compromised, as they are built from low-confidence components. In this research, two studies were carried out to determine the reliability of the data obtained by different SEnviro nodes installed in vineyards. Two networks of meteorological stations were used to carry out these studies, one official and the other professional. The first study was based on calculating the homogenisation of the data, which was performed using the Climatol tool. The second study proposed a similarity analysis using cross-correlation. The results showed that the low-cost node can be used to monitor climatic conditions in an agricultural area in the central zone of the province of Castelló (Spain) and to obtain reliable observations for use in previously published fungal disease models. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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17 pages, 7227 KiB

Open AccessArticle

Research on Temperature Variation during Coal and Gas Outbursts: Implications for Outburst Prediction in Coal Mines

by Chaolin Zhang, Enyuan Wang, Jiang Xu and Shoujian Peng

Sensors 2020, 20(19), 5526; https://doi.org/10.3390/s20195526 - 27 Sep 2020

Cited by 18 | Viewed by 2715

Abstract

Coal and gas outbursts are among the most severe disasters threatening the safety of coal mines around the world. They are dynamic phenomena characterized by large quantities of coal and gas ejected from working faces within a short time. Numerous researchers have conducted studies on outburst prediction, and a variety of indices have been developed to this end. However, these indices are usually empirical or based on local experience, and the accurate prediction of outbursts is not feasible due to the complicated mechanisms of outbursts. This study conducts outburst experiments using large-scale multifunctional equipment developed in the laboratory to develop a more robust outburst prediction method. In this study, the coal temperature during the outburst process was monitored using temperature sensors. The results show that the coal temperature decreased rapidly as the outburst progressed. Meanwhile, the coal temperature in locations far from the outburst mouth increased. The coal broken in the stress concentration state is the main factor causing the abnormal temperature rise. The discovery of these phenomena lays a theoretical foundation and provides an experimental basis for an effective outburst prediction method. An outburst prediction method based on monitoring temperature was proposed, and has a simpler and faster operation process and is not easily disturbed by coal mining activities. What is more, the critical values of coal temperature rises or temperature gradients can be flexibly adjusted according to the actual situations of different coal mines to predict outbursts more effectively and accurately. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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24 pages, 9851 KiB

Open AccessArticle

Analysis of Environmental and Typhoon Effects on Modal Frequencies of a Power Transmission Tower

by Ting-Yu Hsu, Cheng-Chin Chien, Shen-Yuan Shiao, Chun-Chung Chen and Kuo-Chun Chang

Sensors 2020, 20(18), 5169; https://doi.org/10.3390/s20185169 - 10 Sep 2020

Cited by 3 | Viewed by 2344

Abstract

The structural health monitoring of power transmission towers (PTTs) has drawn increasing attention from researchers in recent years; however, no long-term monitoring of the dynamic parameters of PTTs has previously been reported in the literature. This study performed the long-term monitoring of an instrumented PTT. An automated subspace identification technique was used to extract the dynamic parameters of the PTT from ambient vibration measurements taken over approximately ten months in 2017. Ten target modal frequencies were selected to explore the effects of environmental factors, such as temperature and wind speed, as well as the root-mean-square (RMS) acceleration response of the PTT. Variations in the modal frequencies of approximately 2% to 8% were observed during the study period. In general, among the environmental factors, the temperature was found to be the primary cause of decreases in the modal frequencies, except in the case of some of the higher modes. Typhoon Nesat, which affected the PTT on July 29th, 2017, seems to have decreased the modal frequencies of the PTT, especially for the higher modes. This reduction in the modal frequencies seems to have lasted for approximately two and a half months, after which they recovered to their normal state, probably due to a seasonal cool down in temperature. The reduction percentages in the modal frequencies due to Typhoon Nesat were quantified as approximately −0.89% to −1.34% for the higher modes, but only −0.07% to −0.46% for the remaining lower modes. Although the unusual reductions in the modal frequencies are reported in this study, the reason for this phenomenon is not clear yet. Further studies would be required in the future in order to find the cause. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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19 pages, 5581 KiB

Open AccessArticle

A Simple, Reliable, and Inexpensive Solution for Contact Color Measurement in Small Plant Samples

by Patricia Sanmartín, Michela Gambino, Elsa Fuentes and Miguel Serrano

Sensors 2020, 20(8), 2348; https://doi.org/10.3390/s20082348 - 20 Apr 2020

Cited by 7 | Viewed by 3995

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 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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20 pages, 6295 KiB

Open AccessArticle

A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet

by Aili Wang, Minhui Wang, Haibin Wu, Kaiyuan Jiang and Yuji Iwahori

Sensors 2020, 20(4), 1151; https://doi.org/10.3390/s20041151 - 19 Feb 2020

Cited by 28 | Viewed by 4635

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 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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13 pages, 6328 KiB

Open AccessArticle

A Visual and Persuasive Energy Conservation System Based on BIM and IoT Technology

by I-Chen Wu and Chi-Chang Liu

Sensors 2020, 20(1), 139; https://doi.org/10.3390/s20010139 - 24 Dec 2019

Cited by 27 | Viewed by 5348

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 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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15 pages, 6914 KiB

Open AccessArticle

A Dual Neural Architecture Combined SqueezeNet with OctConv for LiDAR Data Classification

by Aili Wang, Minhui Wang, Kaiyuan Jiang, Mengqing Cao and Yuji Iwahori

Sensors 2019, 19(22), 4927; https://doi.org/10.3390/s19224927 - 12 Nov 2019

Cited by 17 | Viewed by 4181

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. 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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Review

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30 pages, 1955 KiB

Open AccessReview

Review of Measuring Microenvironmental Changes at the Body–Seat Interface and the Relationship between Object Measurement and Subjective Evaluation

by Zhuofu Liu, Vincenzo Cascioli and Peter W. McCarthy

Sensors 2020, 20(23), 6715; https://doi.org/10.3390/s20236715 - 24 Nov 2020

Cited by 8 | Viewed by 3171

Abstract

Being seated has increasingly pervaded both working and leisure lifestyles, with development of more comfortable seating surfaces dependent on feedback from subjective questionnaires and design aesthetics. As a consequence, research has become focused on how to objectively resolve factors that might underpin comfort and discomfort. This review summarizes objective methods of measuring the microenvironmental changes at the body–seat interface and examines the relationship between objective measurement and subjective sensation. From the perspective of physical parameters, pressure detection accounted for nearly two thirds (37/54) of the publications, followed by microclimatic information (temperature and relative humidity: 18/54): it is to be noted that one article included both microclimate and pressure measurements and was placed into both categories. In fact, accumulated temperature and relative humidity at the body–seat interface have similarly negative effects on prolonged sitting to that of unrelieved pressure. Another interesting finding was the correlation between objective measurement and subjective evaluation; however, the validity of this may be called into question because of the differences in experiment design between studies. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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23 pages, 3330 KiB

Open AccessReview

A Review of Measurement Calibration and Interpretation for Seepage Monitoring by Optical Fiber Distributed Temperature Sensors

by Yaser Ghafoori, Andrej Vidmar, Jaromír Říha and Andrej Kryžanowski

Sensors 2020, 20(19), 5696; https://doi.org/10.3390/s20195696 - 6 Oct 2020

Cited by 29 | Viewed by 5449

Abstract

Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage. Thus, continuous temperature measurements can allow detection of seepage flows. With the recent advances in optical fiber temperature sensor technology, accurate and fast temperature measurements, with relatively high spatial resolution, have been made possible using optical fiber distributed temperature sensors (DTSs). As with any sensor system, to obtain a precise temperature, the DTS measurements need to be calibrated. DTS systems automatically calibrate the measurements using an internal thermometer and reference section. Additionally, manual calibration techniques have been developed which are discussed in this paper. The temperature data do not provide any direct information about the seepage, and this requires further processing and analysis. Several methods have been developed to interpret the temperature data for the localization of the seepage and in some cases to estimate the seepage quantity. An efficient DTS application in seepage monitoring strongly depends on the following factors: installation approach, calibration technique, along with temperature data interpretation and post-processing. This paper reviews the different techniques for calibration of DTS measurements as well as the methods of interpretation of the temperature data. Full article

(This article belongs to the Special Issue Environmental Sensors and Their Applications)

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13 pages, 5564 KiB

Open AccessLetter

An Integrated Gold-Film Temperature Sensor for In Situ Temperature Measurement of a High-Precision MEMS Accelerometer

by Xiaoxiao Song, Huafeng Liu, Yanyan Fang, Chun Zhao, Ziqiang Qu, Qiu Wang and Liang-Cheng Tu

Sensors 2020, 20(13), 3652; https://doi.org/10.3390/s20133652 - 29 Jun 2020

Cited by 24 | Viewed by 5510

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 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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