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Sensors, Volume 21, Issue 14 (July-2 2021) – 324 articles

Cover Story (view full-size image): Air flow measurements provide significant information required for understanding the characteristics of insect movement. This study proposes a four-channel low-noise readout integrated circuit (IC) to measure air flow (air velocity), which can be beneficial to insect biomimetic robot systems. The micro hot-wire anemometer probe converts the heat dissipation by the air flow to the resistance change, and this resistance change is sensed by this IC. The low-noise and low-offset characteristics are achieved by the chopper scheme and automatic offset calibration loop (AOCL). The input-referred noise is 95.4 nV/√Hz at 1 Hz. The performances of the presented air flow sensor system are evaluated using the Vicon position measuring system. View this paper.
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Article
Analysis of Machine Learning Algorithms for Anomaly Detection on Edge Devices
Sensors 2021, 21(14), 4946; https://doi.org/10.3390/s21144946 - 20 Jul 2021
Viewed by 489
Abstract
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the [...] Read more.
The Internet of Things (IoT) consists of small devices or a network of sensors, which permanently generate huge amounts of data. Usually, they have limited resources, either computing power or memory, which means that raw data are transferred to central systems or the cloud for analysis. Lately, the idea of moving intelligence to the IoT is becoming feasible, with machine learning (ML) moved to edge devices. The aim of this study is to provide an experimental analysis of processing a large imbalanced dataset (DS2OS), split into a training dataset (80%) and a test dataset (20%). The training dataset was reduced by randomly selecting a smaller number of samples to create new datasets Di (i = 1, 2, 5, 10, 15, 20, 40, 60, 80%). Afterwards, they were used with several machine learning algorithms to identify the size at which the performance metrics show saturation and classification results stop improving with an F1 score equal to 0.95 or higher, which happened at 20% of the training dataset. Further on, two solutions for the reduction of the number of samples to provide a balanced dataset are given. In the first, datasets DRi consist of all anomalous samples in seven classes and a reduced majority class (‘NL’) with i = 0.1, 0.2, 0.5, 1, 2, 5, 10, 15, 20 percent of randomly selected samples. In the second, datasets DCi are generated from the representative samples determined with clustering from the training dataset. All three dataset reduction methods showed comparable performance results. Further evaluation of training times and memory usage on Raspberry Pi 4 shows a possibility to run ML algorithms with limited sized datasets on edge devices. Full article
(This article belongs to the Special Issue Edge-Based AI for the Internet of Things)
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Article
Water Extraction Method Based on Multi-Texture Feature Fusion of Synthetic Aperture Radar Images
Sensors 2021, 21(14), 4945; https://doi.org/10.3390/s21144945 - 20 Jul 2021
Viewed by 270
Abstract
Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, [...] Read more.
Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds. Full article
(This article belongs to the Section Radar Sensors)
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Article
Classification of the Acoustics of Loose Gravel
Sensors 2021, 21(14), 4944; https://doi.org/10.3390/s21144944 - 20 Jul 2021
Viewed by 308
Abstract
Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. [...] Read more.
Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Towards Lifespan Automation for Caenorhabditis elegans Based on Deep Learning: Analysing Convolutional and Recurrent Neural Networks for Dead or Live Classification
Sensors 2021, 21(14), 4943; https://doi.org/10.3390/s21144943 - 20 Jul 2021
Viewed by 394
Abstract
The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or [...] Read more.
The automation of lifespan assays with C. elegans in standard Petri dishes is a challenging problem because there are several problems hindering detection such as occlusions at the plate edges, dirt accumulation, and worm aggregations. Moreover, determining whether a worm is alive or dead can be complex as they barely move during the last few days of their lives. This paper proposes a method combining traditional computer vision techniques with a live/dead C. elegans classifier based on convolutional and recurrent neural networks from low-resolution image sequences. In addition to proposing a new method to automate lifespan, the use of data augmentation techniques is proposed to train the network in the absence of large numbers of samples. The proposed method achieved small error rates (3.54% ± 1.30% per plate) with respect to the manual curve, demonstrating its feasibility. Full article
(This article belongs to the Section Sensing and Imaging)
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Article
The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods
Sensors 2021, 21(14), 4942; https://doi.org/10.3390/s21144942 - 20 Jul 2021
Viewed by 449
Abstract
Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored [...] Read more.
Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration-based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real-time scour depth measurements. These vibration-based micro-electro-mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater-level changes around the bridge pier are performed using real-time CCTV images by the Mask R-CNN deep learning model. The scour-depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real-time bridge scour-depth evolution. Full article
(This article belongs to the Special Issue Smart Materials for Structural Health Monitoring and Damage Detection)
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Article
MIND: A Multi-Source Data Fusion Scheme for Intrusion Detection in Networks
Sensors 2021, 21(14), 4941; https://doi.org/10.3390/s21144941 - 20 Jul 2021
Viewed by 335
Abstract
In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major [...] Read more.
In recent years, there is an exponential explosion of data generation, collection, and processing in computer networks. With this expansion of data, network attacks have also become a congenital problem in complex networks. The resource utilization, complexity, and false alarm rates are major challenges in current Network Intrusion Detection Systems (NIDS). The data fusion technique is an emerging technology that merges data from multiple sources to form more certain, precise, informative, and accurate data. Moreover, most of the earlier intrusion detection models suffer from overfitting problems and lack optimal detection of intrusions. In this paper, we propose a multi-source data fusion scheme for intrusion detection in networks (MIND) , where data fusion is performed by the horizontal emergence of two datasets. For this purpose, the Hadoop MapReduce tool such as, Hive is used. In addition, a machine learning ensemble classifier is used for the fused dataset with fewer parameters. Finally, the proposed model is evaluated with a 10-fold-cross validation technique. The experiments show that the average accuracy, detection rate, false positive rate, true positive rate, and F-measure are 99.80%, 99.80%, 0.29%, 99.85%, and 99.82% respectively. Moreover, the results indicate that the proposed model is significantly effective in intrusion detection compared to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Optical Network Automation)
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Article
Wearable Robotic Gait Training in Persons with Multiple Sclerosis: A Satisfaction Study
Sensors 2021, 21(14), 4940; https://doi.org/10.3390/s21144940 - 20 Jul 2021
Viewed by 306
Abstract
Wearable exoskeletons have showed improvements in levels of disability and quality of life in people with neurological disorders. However, it is important to understand users’ perspectives. The aim of this study was to explore the patients’ and physiotherapists’ satisfaction from gait training with [...] Read more.
Wearable exoskeletons have showed improvements in levels of disability and quality of life in people with neurological disorders. However, it is important to understand users’ perspectives. The aim of this study was to explore the patients’ and physiotherapists’ satisfaction from gait training with the EKSO GT® exoskeleton in people with multiple sclerosis (MS). A cross-sectional study with 54 participants was conducted. Clinical data and self-administered scales data were registered from all patients who performed sessions with EKSO GT®. To evaluate patients’ satisfaction the Quebec User Evaluation with Assistive Technology and Client Satisfaction Questionnaire were used. A high level of satisfaction was reported for patients and for physiotherapists. A moderate correlation was found between the number of sessions and the patients’ satisfaction score (rho = 0.532; p < 0.001), and an excellent correlation between the physiotherapists’ time of experience in neurology rehabilitation and the satisfaction with the possibility of combining the device with other gait trainings approaches (rho = 0.723; p = 0.003). This study demonstrates a good degree of satisfaction for people with MS (31.3 ± 5.70 out of 40) and physiotherapists (38.50 ± 3.67 out of 45 points) with the EKSO GT®. Effectiveness, safety and impact on the patients’ gait were the most highly rated characteristics of EKSO GT®. Features such as comfort or weight of the device should be improved from the patients’ perspectives. Full article
(This article belongs to the Special Issue Rehabilitation Robots and Sensors)
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Article
Towards a Machine Learning Based Situational Awareness Framework for Cybersecurity: An SDN Implementation
Sensors 2021, 21(14), 4939; https://doi.org/10.3390/s21144939 - 20 Jul 2021
Viewed by 443
Abstract
The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems’ vulnerabilities and alleviating the impact of these threats, [...] Read more.
The ever-increasing number of internet-connected devices, along with the continuous evolution of cyber-attacks, in terms of volume and ingenuity, has led to a widened cyber-threat landscape, rendering infrastructures prone to malicious attacks. Towards addressing systems’ vulnerabilities and alleviating the impact of these threats, this paper presents a machine learning based situational awareness framework that detects existing and newly introduced network-enabled entities, utilizing the real-time awareness feature provided by the SDN paradigm, assesses them against known vulnerabilities, and assigns them to a connectivity-appropriate network slice. The assessed entities are continuously monitored by an ML-based IDS, which is trained with an enhanced dataset. Our endeavor aims to demonstrate that a neural network, trained with heterogeneous data stemming from the operational environment (common vulnerability enumeration IDs that correlate attacks with existing vulnerabilities), can achieve more accurate prediction rates than a conventional one, thus addressing some aspects of the situational awareness paradigm. The proposed framework was evaluated within a real-life environment and the results revealed an increase of more than 4% in the overall prediction accuracy. Full article
(This article belongs to the Special Issue Cyber Situational Awareness in Computer Networks)
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Communication
An Electrochemical Ti3C2Tx Aptasensor for Sensitive and Label-Free Detection of Marine Biological Toxins
Sensors 2021, 21(14), 4938; https://doi.org/10.3390/s21144938 - 20 Jul 2021
Viewed by 279
Abstract
Saxitoxin (STX) belongs to the family of marine biological toxins, which are major contaminants in seafood. The reference methods for STX detection are mouse bioassay and chromatographic analysis, which are time-consuming, high costs, and requirement of sophisticated operation. Therefore, the development of alternative [...] Read more.
Saxitoxin (STX) belongs to the family of marine biological toxins, which are major contaminants in seafood. The reference methods for STX detection are mouse bioassay and chromatographic analysis, which are time-consuming, high costs, and requirement of sophisticated operation. Therefore, the development of alternative methods for STX analysis is urgent. Electrochemical analysis is a fast, low-cost, and sensitive method for biomolecules analysis. Thus, in this study, an electrolyte-insulator-semiconductor (EIS) sensor based on aptamer-modified two-dimensional layered Ti3C2Tx nanosheets was developed for STX detection. The high surface area and rich functional groups of MXene benefited the modification of aptamer, which had specific interactions with STX. Capacitance-voltage (C-V) and constant-capacitance (ConCap) measurement results indicated that the aptasensor was able to detect STX with high sensitivity and good specificity. The detection range was 1.0 nM to 200 nM and detection limit was as low as 0.03 nM. Moreover, the aptasensor was found to have a good selectivity and two-week stability. The mussel tissue extraction test suggested the potential application of this biosensor in detecting STX in real samples. This method provides a convenient approach for low-cost, rapid, and label-free detection of marine biological toxins. Full article
(This article belongs to the Special Issue State-of-the-Art Biosensors Technology in China 2020–2021)
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Article
Regulatory, Legal, and Market Aspects of Smart Wearables for Cardiac Monitoring
Sensors 2021, 21(14), 4937; https://doi.org/10.3390/s21144937 - 20 Jul 2021
Viewed by 501
Abstract
In the area of cardiac monitoring, the use of digitally driven technologies is on the rise. While the development of medical products is advancing rapidly, allowing for new use-cases in cardiac monitoring and other areas, regulatory and legal requirements that govern market access [...] Read more.
In the area of cardiac monitoring, the use of digitally driven technologies is on the rise. While the development of medical products is advancing rapidly, allowing for new use-cases in cardiac monitoring and other areas, regulatory and legal requirements that govern market access are often evolving slowly, sometimes creating market barriers. This article gives a brief overview of the existing clinical studies regarding the use of smart wearables in cardiac monitoring and provides insight into the main regulatory and legal aspects that need to be considered when such products are intended to be used in a health care setting. Based on this brief overview, the article elaborates on the specific requirements in the main areas of authorization/certification and reimbursement/compensation, as well as data protection and data security. Three case studies are presented as examples of specific market access procedures: the USA, Germany, and Belgium. This article concludes that, despite the differences in specific requirements, market access pathways in most countries are characterized by a number of similarities, which should be considered early on in product development. The article also elaborates on how regulatory and legal requirements are currently being adapted for digitally driven wearables and proposes an ongoing evolution of these requirements to facilitate market access for beneficial medical technology in the future. Full article
(This article belongs to the Special Issue Smart Wearables for Cardiac Monitoring)
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Article
Fast Beam Training Technique for Millimeter-Wave Cellular Systems with an Intelligent Reflective Surface
Sensors 2021, 21(14), 4936; https://doi.org/10.3390/s21144936 - 20 Jul 2021
Viewed by 421
Abstract
The concept of an intelligent reflecting surface (IRS) has recently emerged as a promising solution for improving the coverage and energy/spectral efficiency of future wireless communication systems. However, as the number of reflecting elements in an IRS increase, the beam training protocol in [...] Read more.
The concept of an intelligent reflecting surface (IRS) has recently emerged as a promising solution for improving the coverage and energy/spectral efficiency of future wireless communication systems. However, as the number of reflecting elements in an IRS increase, the beam training protocol in IRS-assisted millimeter-wave (mmWave) cellular systems requires a large beam training time because it needs to find the best beam pairs for the link between the base station (BS) and the IRS, as well as the link between the IRS and the mobile station (MS). In this paper, a fast beam training technique for IRS-assisted mmWave cellular systems with a uniform rectangular array is proposed for detecting the best beam pairs of BS-IRS and IRS-MS links simultaneously. Two different types of beam training signals (BTSs) are proposed to distinguish simultaneously transmitted beams from the BSs in multi-cell multi-beam environments: the Zadoff–Chu sequence based BTS (ZC-BTS) and m-sequence based BTS (m-BTS). The correlation properties of ZC-BTSs and m-BTSs are analyzed in multi-cell multi-beam environments. In addition, the effect of symbol time offset on the ZC-BTS and m-BTS is analyzed. Finally, simulation results reveal that the proposed technique can significantly reduce the beam training time for IRS-assisted mmWave cellular systems. Full article
(This article belongs to the Section Communications)
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Article
Secure and Efficient High Throughput Medium Access Control for Vehicular Ad-Hoc Network
Sensors 2021, 21(14), 4935; https://doi.org/10.3390/s21144935 - 20 Jul 2021
Viewed by 362
Abstract
The evolution of the internet has led to the growth of smart application requirements on the go in the vehicular ad hoc network (VANET). VANET enables vehicles to communicate smartly among themselves wirelessly. Increasing usage of wireless technology induces many security vulnerabilities. Therefore, effective security and authentication mechanism is needed to prevent an intruder. However, authentication may breach user privacy such as location or identity. Cryptography-based approach aids in preserving the privacy of the user. However, the existing security models incur communication and key management overhead since they are designed considering a third-party server. To overcome the research issue, this work presents an efficient security model namely secure performance enriched channel allocation (SPECA) by using commutative RSA. This work further presents the commutative property of the proposed security scheme. Experiments conducted to evaluate the performance of the proposed SPECA over state-of-the-art models show significant improvement. The outcome shows that SPECA minimizes collision and maximizes system throughput considering different radio propagation environments. Full article
(This article belongs to the Special Issue Advanced Vehicular Ad Hoc Networks)
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Article
Screen Printing Carbon Nanotubes Textiles Antennas for Smart Wearables
Sensors 2021, 21(14), 4934; https://doi.org/10.3390/s21144934 - 20 Jul 2021
Viewed by 362
Abstract
Electronic textiles have become a dynamic research field in recent decades, attracting attention to smart wearables to develop and integrate electronic devices onto clothing. Combining traditional screen-printing techniques with novel nanocarbon-based inks offers seamless integration of flexible and conformal antenna patterns onto fabric [...] Read more.
Electronic textiles have become a dynamic research field in recent decades, attracting attention to smart wearables to develop and integrate electronic devices onto clothing. Combining traditional screen-printing techniques with novel nanocarbon-based inks offers seamless integration of flexible and conformal antenna patterns onto fabric substrates with a minimum weight penalty and haptic disruption. In this study, two different fabric-based antenna designs called PICA and LOOP were fabricated through a scalable screen-printing process by tuning the conductive ink formulations accompanied by cellulose nanocrystals. The printing process was controlled and monitored by revealing the relationship between the textiles’ nature and conducting nano-ink. The fabric prototypes were tested in dynamic environments mimicking complex real-life situations, such as being in proximity to a human body, and being affected by wrinkling, bending, and fabric care such as washing or ironing. Both computational and experimental on-and-off-body antenna gain results acknowledged the potential of tunable material systems complimenting traditional printing techniques for smart sensing technology as a plausible pathway for future wearables. Full article
(This article belongs to the Special Issue Textile Sensors Based on Printed Electronics Technology)
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Article
F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes
Sensors 2021, 21(14), 4933; https://doi.org/10.3390/s21144933 - 20 Jul 2021
Viewed by 312
Abstract
With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. [...] Read more.
With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches. Full article
(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)
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Article
Towards Smart Home Automation Using IoT-Enabled Edge-Computing Paradigm
Sensors 2021, 21(14), 4932; https://doi.org/10.3390/s21144932 - 20 Jul 2021
Viewed by 301
Abstract
Smart home applications are ubiquitous and have gained popularity due to the overwhelming use of Internet of Things (IoT)-based technology. The revolution in technologies has made homes more convenient, efficient, and even more secure. The need for advancement in smart home technology is [...] Read more.
Smart home applications are ubiquitous and have gained popularity due to the overwhelming use of Internet of Things (IoT)-based technology. The revolution in technologies has made homes more convenient, efficient, and even more secure. The need for advancement in smart home technology is necessary due to the scarcity of intelligent home applications that cater to several aspects of the home simultaneously, i.e., automation, security, safety, and reducing energy consumption using less bandwidth, computation, and cost. Our research work provides a solution to these problems by deploying a smart home automation system with the applications mentioned above over a resource-constrained Raspberry Pi (RPI) device. The RPI is used as a central controlling unit, which provides a cost-effective platform for interconnecting a variety of devices and various sensors in a home via the Internet. We propose a cost-effective integrated system for smart home based on IoT and Edge-Computing paradigm. The proposed system provides remote and automatic control to home appliances, ensuring security and safety. Additionally, the proposed solution uses the edge-computing paradigm to store sensitive data in a local cloud to preserve the customer’s privacy. Moreover, visual and scalar sensor-generated data are processed and held over edge device (RPI) to reduce bandwidth, computation, and storage cost. In the comparison with state-of-the-art solutions, the proposed system is 5% faster in detecting motion, and 5 ms and 4 ms in switching relay on and off, respectively. It is also 6% more efficient than the existing solutions with respect to energy consumption. Full article
(This article belongs to the Section Internet of Things)
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Article
Lung Auscultation Using the Smartphone—Feasibility Study in Real-World Clinical Practice
Sensors 2021, 21(14), 4931; https://doi.org/10.3390/s21144931 - 20 Jul 2021
Viewed by 273
Abstract
Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone built-in microphone in real-world clinical practice. We recruited 134 patients (median[interquartile range] 16[11–22.25]y; [...] Read more.
Conventional lung auscultation is essential in the management of respiratory diseases. However, detecting adventitious sounds outside medical facilities remains challenging. We assessed the feasibility of lung auscultation using the smartphone built-in microphone in real-world clinical practice. We recruited 134 patients (median[interquartile range] 16[11–22.25]y; 54% male; 31% cystic fibrosis, 29% other respiratory diseases, 28% asthma; 12% no respiratory diseases) at the Pediatrics and Pulmonology departments of a tertiary hospital. First, clinicians performed conventional auscultation with analog stethoscopes at 4 locations (trachea, right anterior chest, right and left lung bases), and documented any adventitious sounds. Then, smartphone auscultation was recorded twice in the same four locations. The recordings (n = 1060) were classified by two annotators. Seventy-three percent of recordings had quality (obtained in 92% of the participants), with the quality proportion being higher at the trachea (82%) and in the children’s group (75%). Adventitious sounds were present in only 35% of the participants and 14% of the recordings, which may have contributed to the fair agreement between conventional and smartphone auscultation (85%; k = 0.35(95% CI 0.26–0.44)). Our results show that smartphone auscultation was feasible, but further investigation is required to improve its agreement with conventional auscultation. Full article
(This article belongs to the Special Issue Smartphone Sensors for Health Monitoring and Diagnosis)
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Article
Drone-Based Gamma Radiation Dose Distribution Survey with a Discrete Measurement Point Procedure
Sensors 2021, 21(14), 4930; https://doi.org/10.3390/s21144930 - 20 Jul 2021
Viewed by 284
Abstract
A dose distribution map can be created using geographic information system (GIS) methods from sensor data that do not provide image information in a classical way. The results of discrete radiation measurements can be properly represented in a uniform raster above the surface. [...] Read more.
A dose distribution map can be created using geographic information system (GIS) methods from sensor data that do not provide image information in a classical way. The results of discrete radiation measurements can be properly represented in a uniform raster above the surface. If the radiation measured at each site does not show a jump-like change, a dose distribution map can be prepared by interpolating the measured values. The coordinates of the measuring points can be used to calibrate the map. The calibrated and georeferenced map is suitable for locating hidden or lost radiation sources or for mapping active debris scattered during a possible reactor accident. The advantage of the developed method is the measurement can be performed with a small multicopter, cost-effectively, even without human intervention. The flight time of small multicopters is very limited, so it is especially important to increase the efficiency of the measurement. During the experiments, a practical comparison of several methods was made with regard to the measurement procedure. Similarly, based on the measurement experience, the detector system was further developed and tested in three main steps. A system was developed with a detector system with a total weight of 500 g, including a battery capable of operating the detector for at least 120 min. The device is capable of detecting an average of 30 events/min at of 0.01 μSv/h background radiation. Experiments have shown that the system is able to significantly detect a source with an activity of 300 μSv/h by scanning above 10 m ground level. Full article
(This article belongs to the Section Vehicular Sensing)
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Article
Crack Detection in Images of Masonry Using CNNs
Sensors 2021, 21(14), 4929; https://doi.org/10.3390/s21144929 - 20 Jul 2021
Viewed by 281
Abstract
While there is a significant body of research on crack detection by computer vision methods in concrete and asphalt, less attention has been given to masonry. We train a convolutional neural network (CNN) on images of brick walls built in a laboratory environment [...] Read more.
While there is a significant body of research on crack detection by computer vision methods in concrete and asphalt, less attention has been given to masonry. We train a convolutional neural network (CNN) on images of brick walls built in a laboratory environment and test its ability to detect cracks in images of brick-and-mortar structures both in the laboratory and on real-world images taken from the internet. We also compare the performance of the CNN to a variety of simpler classifiers operating on handcrafted features. We find that the CNN performed better on the domain adaptation from laboratory to real-world images than these simple models. However, we also find that performance is significantly better in performing the reverse domain adaptation task, where the simple classifiers are trained on real-world images and tested on the laboratory images. This work demonstrates the ability to detect cracks in images of masonry using a variety of machine learning methods and provides guidance for improving the reliability of such models when performing domain adaptation for crack detection in masonry. Full article
(This article belongs to the Special Issue Vision Sensors and Systems in Structural Health Monitoring)
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Article
A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors
Sensors 2021, 21(14), 4928; https://doi.org/10.3390/s21144928 - 20 Jul 2021
Viewed by 323
Abstract
Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given [...] Read more.
Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors. Full article
(This article belongs to the Special Issue Computer Aided Diagnosis Sensors)
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Article
Deep-Learning-Based Multimodal Emotion Classification for Music Videos
Sensors 2021, 21(14), 4927; https://doi.org/10.3390/s21144927 - 20 Jul 2021
Viewed by 323
Abstract
Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents [...] Read more.
Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents an affective computing system that relies on music, video, and facial expression cues, making it useful for emotional analysis. We applied the audio–video information exchange and boosting methods to regularize the training process and reduced the computational costs by using a separable convolution strategy. In sum, our empirical findings are as follows: (1) Multimodal representations efficiently capture all acoustic and visual emotional clues included in each music video, (2) the computational cost of each neural network is significantly reduced by factorizing the standard 2D/3D convolution into separate channels and spatiotemporal interactions, and (3) information-sharing methods incorporated into multimodal representations are helpful in guiding individual information flow and boosting overall performance. We tested our findings across several unimodal and multimodal networks against various evaluation metrics and visual analyzers. Our best classifier attained 74% accuracy, an f1-score of 0.73, and an area under the curve score of 0.926. Full article
(This article belongs to the Special Issue Sensor Based Multi-Modal Emotion Recognition)
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Article
Learning Carbohydrate Digestion and Insulin Absorption Curves Using Blood Glucose Level Prediction and Deep Learning Models
Sensors 2021, 21(14), 4926; https://doi.org/10.3390/s21144926 - 20 Jul 2021
Viewed by 256
Abstract
Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels [...] Read more.
Type 1 diabetes is a chronic disease caused by the inability of the pancreas to produce insulin. Patients suffering type 1 diabetes depend on the appropriate estimation of the units of insulin they have to use in order to keep blood glucose levels in range (considering the calories taken and the physical exercise carried out). In recent years, machine learning models have been developed in order to help type 1 diabetes patients with their blood glucose control. These models tend to receive the insulin units used and the carbohydrate taken as inputs and generate optimal estimations for future blood glucose levels over a prediction horizon. The body glucose kinetics is a complex user-dependent process, and learning patient-specific blood glucose patterns from insulin units and carbohydrate content is a difficult task even for deep learning-based models. This paper proposes a novel mechanism to increase the accuracy of blood glucose predictions from deep learning models based on the estimation of carbohydrate digestion and insulin absorption curves for a particular patient. This manuscript proposes a method to estimate absorption curves by using a simplified model with two parameters which are fitted to each patient by using a genetic algorithm. Using simulated data, the results show the ability of the proposed model to estimate absorption curves with mean absolute errors below 0.1 for normalized fast insulin curves having a maximum value of 1 unit. Full article
(This article belongs to the Section Biomedical Sensors)
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Article
Design of Flow Velocity and Direction Monitoring Sensor Based on Fiber Bragg Grating
Sensors 2021, 21(14), 4925; https://doi.org/10.3390/s21144925 - 20 Jul 2021
Viewed by 295
Abstract
The real-time monitoring of the flow environment parameters, such as flow velocity and direction, helps to accurately analyze the effect of water scour and provide technical support for the maintenance of pier and abutment foundations in water. Based on the principle of the [...] Read more.
The real-time monitoring of the flow environment parameters, such as flow velocity and direction, helps to accurately analyze the effect of water scour and provide technical support for the maintenance of pier and abutment foundations in water. Based on the principle of the Fiber Brag Grating sensor, a sensor for monitoring the flow velocity and direction in real-time is designed in this paper. Meanwhile, the theoretical calculation formulas of flow velocity and direction are derived. The structural performance of the sensor is simulated and analyzed by finite element analysis. The performance requirements of different parts of the sensor are clarified. After a sample of the sensor is manufactured, calibration experiments are conducted to verify the function and test the accuracy of the sensor, and the experimental error is analyzed. The experimental results indicate that the sensor designed in this paper achieves a high accuracy for the flow with a flow velocity of 0.05–5 m/s and the flow velocity monitoring error is kept within 7%, while the flow direction monitoring error is kept within 2°. The sensor can meet the actual monitoring requirements of the structures in water and provide reliable data sources for water scour analysis. Full article
(This article belongs to the Section Optical Sensors)
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Article
Evaluation of Shear Horizontal Surface Acoustic Wave Biosensors Using “Layer Parameter” Obtained from Sensor Responses during Immunoreaction
Sensors 2021, 21(14), 4924; https://doi.org/10.3390/s21144924 - 20 Jul 2021
Viewed by 260
Abstract
Shear horizontal surface acoustic wave (SH-SAW) biosensors measure the reaction of capture antibodies immobilized on the sensing surface to capture test molecules (antigens) by using the change in SH-SAW propagation characteristics. SH-SAW displacement exists not only on the SH-SAW propagating surface, but also [...] Read more.
Shear horizontal surface acoustic wave (SH-SAW) biosensors measure the reaction of capture antibodies immobilized on the sensing surface to capture test molecules (antigens) by using the change in SH-SAW propagation characteristics. SH-SAW displacement exists not only on the SH-SAW propagating surface, but also partially penetrates the specimen liquid to a certain depth, which is determined by the liquid properties of the specimen and the operating frequency of the SH-SAW. This phenomenon is called viscosity penetration. In previous studies, the effect of viscosity penetration was not considered in the measurement of SH-SAW biosensors, and the mass or viscosity change caused by the specific binding of capture antibodies to the target antigen was mainly used for the measurement. However, by considering the effect of viscosity penetration, it was found that the antigen–antibody reaction could be measured and the detection characteristics of the biosensor could be improved. Therefore, this study aims to evaluate the detection properties of SH-SAW biosensors in the surface height direction by investigating the relationship between molecular dimensions and SH-SAW propagation characteristics, which are pseudo-changed by varying the diameter of gold nanoparticles. For the evaluation, we introduced a layer parameter defined by the ratio of the SH-SAW amplitude change to the SH-SAW velocity change caused by the antigen–antibody reaction. We found a correlation between the layer parameter and pseudo-varied molecular dimensions. The results suggest that SH-SAW does not only measure the mass and viscosity but can also measure the size of the molecule to be detected. This shows that SH-SAW biosensors can be used for advanced functionality. Full article
(This article belongs to the Section Biosensors)
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Article
Design of Double-Layer Electrically Extremely Small-Size Displacement Sensor
Sensors 2021, 21(14), 4923; https://doi.org/10.3390/s21144923 - 20 Jul 2021
Viewed by 231
Abstract
In this paper, a displacement sensor with an electrically extremely small size and high sensitivity is proposed based on an elaborately designed metamaterial element, i.e., coupled split-ring resonators (SRRs). The sensor consists of a feeding structure with a rectangular opening loop and a [...] Read more.
In this paper, a displacement sensor with an electrically extremely small size and high sensitivity is proposed based on an elaborately designed metamaterial element, i.e., coupled split-ring resonators (SRRs). The sensor consists of a feeding structure with a rectangular opening loop and a sensing structure with double-layer coupled SRRs. The movable double-layer structures can be used to measure the relative displacement. The size of microwave displacement sensors can be significantly reduced due to the compact feeding and sensing structures. By adjusting the position of the split gap within the resonator, the detection directions of the displacement sensing can be further expanded accordingly (along with the x- or y-axis) without increasing its physical size. Compared with previous works, the extremely compact size of 0.05λ0 × 0.05λ0 (λ0 denotes the free-space wavelength), a high sensitivity, and a high quality factor (Q-factor) can be achieved by the proposed sensor. From the perspective of the advantages above, the proposed sensor holds promise for being applied in many high-precision industrial measurement scenarios. Full article
(This article belongs to the Special Issue Metamaterial-Based Microwave Sensors)
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Article
A Cu(II)-MOF Based on a Propargyl Carbamate-Functionalized Isophthalate Ligand as Nitrite Electrochemical Sensor
Sensors 2021, 21(14), 4922; https://doi.org/10.3390/s21144922 - 20 Jul 2021
Viewed by 265
Abstract
This paper investigates the electrochemical properties of a new Cu(II)-based metal-organic framework (MOF). Noted as Cu-YBDC, it is built upon a linker containing the propargyl carbamate functionality and immobilized on a glassy carbon electrode by drop-casting (GC/Cu-YBDC). Afterward, GC/Cu-YBDC was treated with HAuCl [...] Read more.
This paper investigates the electrochemical properties of a new Cu(II)-based metal-organic framework (MOF). Noted as Cu-YBDC, it is built upon a linker containing the propargyl carbamate functionality and immobilized on a glassy carbon electrode by drop-casting (GC/Cu-YBDC). Afterward, GC/Cu-YBDC was treated with HAuCl4 and the direct electro-deposition of Au nanoparticles was carried at 0.05 V for 600 s (GC/Au/Cu-YBDC). The performance of both electrodes towards nitrite oxidation was tested and it was found that GC/Au/Cu-YBDC exhibited a better electrocatalytic behavior toward the oxidation of nitrite than GC/Cu-YBDC with enhanced catalytic currents and a reduced nitrite overpotential from 1.20 to 0.90 V. Additionally GC/Au/Cu-YBDC showed a low limit of detection (5.0 μM), an ultrafast response time (<2 s), and a wide linear range of up to 8 mM in neutral pH. Full article
(This article belongs to the Special Issue New Generation of Electrochemical Sensors)
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Communication
Experimental Demonstration of 3 × 3 MIMO LED-to-LED Communication Using RGB Colors
Sensors 2021, 21(14), 4921; https://doi.org/10.3390/s21144921 - 20 Jul 2021
Viewed by 270
Abstract
Optical wireless communication (OWC) is one of the promising candidates for beyond fifth-generation communication (B5G). Depending on the type of transmitters, receivers, and information carriers applied in the system, OWC can be categorized into visible light communication, light fidelity, free-space optical communication, optical [...] Read more.
Optical wireless communication (OWC) is one of the promising candidates for beyond fifth-generation communication (B5G). Depending on the type of transmitters, receivers, and information carriers applied in the system, OWC can be categorized into visible light communication, light fidelity, free-space optical communication, optical camera communication, etc. In addition to these OWC subcategories, this paper proposes light-emitting diode (LED)-to-LED communication as another subcategory of OWC technique. Furthermore, we show an experimental demonstration of the multiple-input multiple-output (MIMO) LED-to-LED communication system using red, green, and blue colored LEDs. We believe that LED-to-LED communication is an effective solution to resolve the communication burden arising from massive connectivity in B5G internet of things. Along with the measurement results of the transmitter LED, receiver LED, and the channel properties, it is shown that the MIMO LED-to-LED system is able to successfully recover the transmitted signal with low inter-channel interferences due to the receiver LED’s unique characteristics. Finally, the bit error rate (BER) performance of the MIMO LED-to-LED system is shown in comparison with the BER performance of the single-input single-output (SISO) LED-to-LED system. We successfully implemented the 3 × 3 MIMO LED-to-LED communication system using RGB colors at a data rate of 30.62 kbps over a 10 cm transmission distance along with direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) modulation and zero-forcing (ZF) equalizer. Full article
(This article belongs to the Section Communications)
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Editorial
Antennas and Propagation: A Sensor Approach
Sensors 2021, 21(14), 4920; https://doi.org/10.3390/s21144920 - 20 Jul 2021
Viewed by 272
Abstract
Antennas are essentially transducers, as they convert electromagnetic fields into signals and vice versa [...] Full article
(This article belongs to the Special Issue Antennas and Propagation)
Article
Joining of Electrodes to Ultra-Thin Metallic Layers on Ceramic Substrates in Cryogenic Sensors
Sensors 2021, 21(14), 4919; https://doi.org/10.3390/s21144919 - 20 Jul 2021
Viewed by 253
Abstract
Microjoining technologies are crucial for producing reliable electrical connections in modern microelectronic and optoelectronic devices, as well as for the assembly of electronic circuits, sensors, and batteries. However, the production of miniature sensors presents particular difficulties, due to their non-standard designs, unique functionality [...] Read more.
Microjoining technologies are crucial for producing reliable electrical connections in modern microelectronic and optoelectronic devices, as well as for the assembly of electronic circuits, sensors, and batteries. However, the production of miniature sensors presents particular difficulties, due to their non-standard designs, unique functionality and applications in various environments. One of the main challenges relates to the fact that common methods such as reflow soldering or wave soldering cannot be applied to making joints to the materials used for the sensing layers (oxides, polymers, graphene, metallic layers) or to the thin metallic layers that act as contact pads. This problem applies especially to sensors designed to work at cryogenic temperatures. In this paper, we demonstrate a new method for the dynamic soldering of outer leads in the form of metallic strips made from thin metallic layers on ceramic substrates. These leads can be used as contact pads in sensors working in a wide temperature range. The joints produced using our method show excellent electrical, thermal, and mechanical properties in the temperature range of 15–300 K. Full article
(This article belongs to the Section Sensor Materials)
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Article
Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation
Sensors 2021, 21(14), 4918; https://doi.org/10.3390/s21144918 - 19 Jul 2021
Viewed by 346
Abstract
Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte [...] Read more.
Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson’s r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson’s r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland–Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation. Full article
(This article belongs to the Section Biomedical Sensors)
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Article
Recording the Presence of Peanibacillus larvae larvae Colonies on MYPGP Substrates Using a Multi-Sensor Array Based on Solid-State Gas Sensors
Sensors 2021, 21(14), 4917; https://doi.org/10.3390/s21144917 - 19 Jul 2021
Viewed by 261
Abstract
American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, [...] Read more.
American foulbrood is a dangerous disease of bee broods found worldwide, caused by the Paenibacillus larvae larvae L. bacterium. In an experiment, the possibility of detecting colonies of this bacterium on MYPGP substrates (which contains yeast extract, Mueller-Hinton broth, glucose, K2HPO4, sodium pyruvate, and agar) was tested using a prototype of a multi-sensor recorder of the MCA-8 sensor signal with a matrix of six semiconductors: TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from Figaro. Two twin prototypes of the MCA-8 measurement device, M1 and M2, were used in the study. Each prototype was attached to two laboratory test chambers: a wooden one and a polystyrene one. For the experiment, the strain used was P. l. larvae ATCC 9545, ERIC I. On MYPGP medium, often used for laboratory diagnosis of American foulbrood, this bacterium produces small, transparent, smooth, and shiny colonies. Gas samples from over culture media of one- and two-day-old foulbrood P. l. larvae (with no colonies visible to the naked eye) and from over culture media older than 2 days (with visible bacterial colonies) were examined. In addition, the air from empty chambers was tested. The measurement time was 20 min, including a 10-min testing exposure phase and a 10-min sensor regeneration phase. The results were analyzed in two variants: without baseline correction and with baseline correction. We tested 14 classifiers and found that a prototype of a multi-sensor recorder of the MCA-8 sensor signal was capable of detecting colonies of P. l. larvae on MYPGP substrate with a 97% efficiency and could distinguish between MYPGP substrates with 1–2 days of culture, and substrates with older cultures. The efficacy of copies of the prototypes M1 and M2 was shown to differ slightly. The weighted method with Canberra metrics (Canberra.811) and kNN with Canberra and Manhattan metrics (Canberra. 1nn and manhattan.1nn) proved to be the most effective classifiers. Full article
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