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Probing for Environmental Monitoring

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 13183

Special Issue Editors


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Guest Editor
Department of General Chemistry and Ecology, Kazan National Research Technical University Named after A. N. Tupolev (KNRTU–KAI), 420126 Kazan, Russia
Interests: environmental monitoring; metal detection; healthcare monitoring

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Guest Editor
Department of Radiophotonics and Microwave Technologies, Kazan National Research Technical University Named after A.N. Tupolev-KAI, K. Marx Str. 10, 420111 Kazan, Russia
Interests: fiber optic, sensors; fiber Bragg grating; addressable FBG; microwave photonic interrogation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Processing of sensory data in neural networks and neural network technologies have proven themselves well in solving problems of computational environmental monitoring, where there are many factors, the relationship of which is difficult to describe in the form of a mathematical formal model, but this relationship is clearly reflected in the experimental data. Neural network models make it possible to formalize initially non-obvious and implicit parameter dependencies by implicitly extracting information from experimental data sets. The approach of combining a classical computational model and neural network processing the outputs of such techniques in one model and carrying out a kind of "fine-tuning" of the simulation results seems promising.

Neural networks can be used in various fields of research, such as:

  • Toxic and hazardous metal detection and monitoring;
  • Healthcare environmental monitoring;
  • Urban environmental monitoring;
  • Soil monitoring;
  • Biosensing for environmental monitoring.

Prof. Dr. Yulia Tunakova
Prof. Dr. Oleg G. Morozov
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • environmental monitoring
  • metal detection
  • healthcare monitoring

Published Papers (5 papers)

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Research

15 pages, 5523 KiB  
Article
The Use of Neural Network Modeling Methods to Determine Regional Threshold Values of Hydrochemical Indicators in the Environmental Monitoring System of Waterbodies
by Yulia Tunakova, Svetlana Novikova, Vsevolod Valiev, Evgenia Baibakova and Ksenia Novikova
Sensors 2023, 23(13), 6160; https://doi.org/10.3390/s23136160 - 05 Jul 2023
Viewed by 737
Abstract
The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of [...] Read more.
The regulation of the anthropogenic load on waterbodies is carried out based on water quality standards that are determined using the threshold values of hydrochemical indicators. These applied standards should be defined both geographically and differentially, taking into account the regional specifics of the formation of surface water compositions. However, there is currently no unified approach to defining these regional standards. It is, therefore. appropriate to develop regional water quality standards utilizing modern technologies for the mathematical purpose of methods analysis using both experimental data sources and information system technologies. As suggested by the use of sets of chemical analysis and neural network cluster analysis, both methods of analysis and an expert assessment could identify surface water types as well as define the official regional threshold values of hydrochemical system indicators, to improve the adequacy of assessments and ensure the mathematical justification of developed standards. The process for testing the proposed approach was carried out, using the surface water resource objects in the territory of the Republic of Tatarstan as our example, in addition to using the results of long-term systematic measurements of informative hydrochemical indicators. In the first stage, typing was performed on surface waters using the neural network clustering method. Clustering was performed based on sets of determined hydrochemical parameters in Kohonen’s self-organizing neural network. To assess the uniformity of data, groups in each of the selected clusters were represented by specialists in this subject area’s region. To determine the regional threshold values of hydrochemical indicators, statistical data for the corresponding clusters were calculated, and the ranges of these values were used. The results of testing this proposed approach allowed us to recommend it for identifying surface water types, as well as to define the threshold values of hydrochemical indicators in the territory of any region with different surface water compositions. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
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15 pages, 1912 KiB  
Article
Addressed Combined Fiber-Optic Sensors as Key Element of Multisensor Greenhouse Gas Monitoring Systems
by Oleg Morozov, Yulia Tunakova, Safaa M. R. H. Hussein, Artur Shagidullin, Timur Agliullin, Artem Kuznetsov, Bulat Valeev, Konstantin Lipatnikov, Vladimir Anfinogentov and Airat Sakhabutdinov
Sensors 2022, 22(13), 4827; https://doi.org/10.3390/s22134827 - 26 Jun 2022
Cited by 9 | Viewed by 1810
Abstract
The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg [...] Read more.
The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1–0.5%. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
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17 pages, 6136 KiB  
Article
A Smart Camera Trap for Detection of Endotherms and Ectotherms
by Dean M. Corva, Nathan I. Semianiw, Anne C. Eichholtzer, Scott D. Adams, M. A. Parvez Mahmud, Kendrika Gaur, Angela J. L. Pestell, Don A. Driscoll and Abbas Z. Kouzani
Sensors 2022, 22(11), 4094; https://doi.org/10.3390/s22114094 - 28 May 2022
Cited by 5 | Viewed by 3533
Abstract
Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as [...] Read more.
Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
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12 pages, 13901 KiB  
Article
Scalable Fire and Smoke Segmentation from Aerial Images Using Convolutional Neural Networks and Quad-Tree Search
by Gonçalo Perrolas, Milad Niknejad, Ricardo Ribeiro and Alexandre Bernardino
Sensors 2022, 22(5), 1701; https://doi.org/10.3390/s22051701 - 22 Feb 2022
Cited by 13 | Viewed by 2647
Abstract
Autonomous systems can help firefighting operations by detecting and locating the fire spot from surveillance images and videos. Similar to many other areas of computer vision, Convolutional Neural Networks (CNNs) have achieved state-of-the-art results for fire and smoke detection and segmentation. In practice, [...] Read more.
Autonomous systems can help firefighting operations by detecting and locating the fire spot from surveillance images and videos. Similar to many other areas of computer vision, Convolutional Neural Networks (CNNs) have achieved state-of-the-art results for fire and smoke detection and segmentation. In practice, input images to a CNN are usually downsized to fit into the network to avoid computational complexities and restricted memory problems. Although in many applications downsizing is not an issue, in the early phases of fire ignitions downsizing may eliminate the fire regions since the incident regions are small. In this paper, we propose a novel method to segment fire and smoke regions in high resolution images based on a multi-resolution iterative quad-tree search algorithm , which manages the application of classification and segmentation CNNs to focus the attention on informative parts of the image. The proposed method is more computationally efficient compared to processing the whole high resolution input, and contains parameters that can be tuned based on the needed scale precision. The results show that the proposed method is capable of detecting and segmenting fire and smoke with higher accuracy and is useful for segmenting small regions of incident in high resolution aerial images in a computationally efficient way. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
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19 pages, 17323 KiB  
Article
Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices
by Hoa Thi Pham, Joseph Awange, Michael Kuhn, Binh Van Nguyen and Luyen K. Bui
Sensors 2022, 22(3), 719; https://doi.org/10.3390/s22030719 - 18 Jan 2022
Cited by 19 | Viewed by 3764
Abstract
Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed [...] Read more.
Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability. Full article
(This article belongs to the Special Issue Probing for Environmental Monitoring)
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