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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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28 pages, 4342 KiB  
Review
Review of Current Guided Wave Ultrasonic Testing (GWUT) Limitations and Future Directions
by Samuel Chukwuemeka Olisa, Muhammad A. Khan and Andrew Starr
Sensors 2021, 21(3), 811; https://doi.org/10.3390/s21030811 - 26 Jan 2021
Cited by 66 | Viewed by 9949
Abstract
Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave [...] Read more.
Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave ultrasonic testing (GWUT). This method is cost-effective and possesses an enormous capability for long-range inspection of corroded structures, detection of sundries of crack and other metallic damage structures at low frequency and energy attenuation. However, the parametric features of the GWUT are affected by structural and environmental operating conditions and result in masking damage signal. Most studies focused on identifying individual damage under varying conditions while combined damage phenomena can coexist in structure and hasten its deterioration. Hence, it is an impending task to study the effect of combined damage on a structure under varying conditions and correlate it with GWUT parametric features. In this respect, this work reviewed the literature on UGWs, damage inspection, severity, temperature influence on the guided wave and parametric characteristics of the inspecting wave. The review is limited to the piezoelectric transduction unit. It was keenly observed that no significant work had been done to correlate the parametric feature of GWUT with combined damage effect under varying conditions. It is therefore proposed to investigate this impending task. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)
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22 pages, 6191 KiB  
Review
Fiber-Optic Localized Surface Plasmon Resonance Sensors Based on Nanomaterials
by Seunghun Lee, Hyerin Song, Heesang Ahn, Seungchul Kim, Jong-ryul Choi and Kyujung Kim
Sensors 2021, 21(3), 819; https://doi.org/10.3390/s21030819 - 26 Jan 2021
Cited by 50 | Viewed by 7326
Abstract
Applying fiber-optics on surface plasmon resonance (SPR) sensors is aimed at practical usability over conventional SPR sensors. Recently, field localization techniques using nanostructures or nanoparticles have been investigated on optical fibers for further sensitivity enhancement and significant target selectivity. In this review article, [...] Read more.
Applying fiber-optics on surface plasmon resonance (SPR) sensors is aimed at practical usability over conventional SPR sensors. Recently, field localization techniques using nanostructures or nanoparticles have been investigated on optical fibers for further sensitivity enhancement and significant target selectivity. In this review article, we explored varied recent research approaches of fiber-optics based localized surface plasmon resonance (LSPR) sensors. The article contains interesting experimental results using fiber-optic LSPR sensors for three different application categories: (1) chemical reactions measurements, (2) physical properties measurements, and (3) biological events monitoring. In addition, novel techniques which can create synergy combined with fiber-optic LSPR sensors were introduced. The review article suggests fiber-optic LSPR sensors have lots of potential for measurements of varied targets with high sensitivity. Moreover, the previous results show that the sensitivity enhancements which can be applied with creative varied plasmonic nanomaterials make it possible to detect minute changes including quick chemical reactions and tiny molecular activities. Full article
(This article belongs to the Special Issue Plasmonic Sensing Techniques with Nanomaterials)
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19 pages, 4762 KiB  
Article
Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis
by Andrea Gaiardo, David Novel, Elia Scattolo, Michele Crivellari, Antonino Picciotto, Francesco Ficorella, Erica Iacob, Alessio Bucciarelli, Luisa Petti, Paolo Lugli and Alvise Bagolini
Sensors 2021, 21(3), 783; https://doi.org/10.3390/s21030783 - 25 Jan 2021
Cited by 23 | Viewed by 3715
Abstract
The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process [...] Read more.
The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications. Full article
(This article belongs to the Special Issue Advanced Micro and Nano Technologies for Gas Sensing)
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20 pages, 8467 KiB  
Article
Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands
by Kamil Bechta, Jan M. Kelner, Cezary Ziółkowski and Leszek Nowosielski
Sensors 2021, 21(3), 793; https://doi.org/10.3390/s21030793 - 25 Jan 2021
Cited by 21 | Viewed by 4367
Abstract
This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams [...] Read more.
This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams of a 5G-based station (gNodeB). In the developed methodology, a multi-ellipsoidal propagation model (MPM) provides a mapping of the multipath propagation phenomenon and considers the directivity of antenna beams. To demonstrate the designation procedure of interference level we use simulation tests. For exemplary scenarios in downlink and uplink, we showed changes in a signal-to-interference ratio versus a separation angle between the serving (useful) and interfering beams and the distance between the gNodeB and user equipment. This evaluation is the basis for determining the minimum separation angle for which an acceptable interference level is ensured. The analysis was carried out for the lower millimeter-wave band, which is planned to use in 5G micro-cells base stations. Full article
(This article belongs to the Special Issue Advanced Physical-Layer Technologies for beyond 5G Wireless Communication Networks)
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18 pages, 5887 KiB  
Review
Genetically Encoded Biosensors Based on Fluorescent Proteins
by Hyunbin Kim, Jeongmin Ju, Hae Nim Lee, Hyeyeon Chun and Jihye Seong
Sensors 2021, 21(3), 795; https://doi.org/10.3390/s21030795 - 25 Jan 2021
Cited by 28 | Viewed by 9797
Abstract
Genetically encoded biosensors based on fluorescent proteins (FPs) allow for the real-time monitoring of molecular dynamics in space and time, which are crucial for the proper functioning and regulation of complex cellular processes. Depending on the types of molecular events to be monitored, [...] Read more.
Genetically encoded biosensors based on fluorescent proteins (FPs) allow for the real-time monitoring of molecular dynamics in space and time, which are crucial for the proper functioning and regulation of complex cellular processes. Depending on the types of molecular events to be monitored, different sensing strategies need to be applied for the best design of FP-based biosensors. Here, we review genetically encoded biosensors based on FPs with various sensing strategies, for example, translocation, fluorescence resonance energy transfer (FRET), reconstitution of split FP, pH sensitivity, maturation speed, and so on. We introduce general principles of each sensing strategy and discuss critical factors to be considered if available, then provide representative examples of these FP-based biosensors. These will help in designing the best sensing strategy for the successful development of new genetically encoded biosensors based on FPs. Full article
(This article belongs to the Special Issue DNA-Based Sensors for Single-Molecule Biology)
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24 pages, 1263 KiB  
Review
Wearable Devices for Ergonomics: A Systematic Literature Review
by Elena Stefana, Filippo Marciano, Diana Rossi, Paola Cocca and Giuseppe Tomasoni
Sensors 2021, 21(3), 777; https://doi.org/10.3390/s21030777 - 24 Jan 2021
Cited by 78 | Viewed by 10733
Abstract
Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation [...] Read more.
Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation in ergonomics. Therefore, we conducted a systematic review to identify wearable devices proposed in the scientific literature for ergonomic purposes and analyse how they can support the improvement of ergonomic conditions. Twenty-eight papers were retrieved and analysed thanks to eleven comparison dimensions related to ergonomic factors, purposes, and criteria, populations, application and validation. The majority of the available devices are sensor systems composed of different types and numbers of sensors located in diverse body parts. These solutions also represent the technology most frequently employed for monitoring and reducing the risk of awkward postures. In addition, smartwatches, body-mounted smartphones, insole pressure systems, and vibrotactile feedback interfaces have been developed for evaluating and/or controlling physical loads or postures. The main results and the defined framework of analysis provide an overview of the state of the art of smart wearables in ergonomics, support the selection of the most suitable ones in industrial and non-industrial settings, and suggest future research directions. Full article
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)
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25 pages, 25970 KiB  
Article
An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing
by Zhen Li, Changgen Peng, Weijie Tan and Liangrong Li
Sensors 2021, 21(3), 758; https://doi.org/10.3390/s21030758 - 23 Jan 2021
Cited by 31 | Viewed by 3605
Abstract
With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the [...] Read more.
With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the cloud applications. Facing the demands, an efficient and secure plaintext-related chaotic image encryption scheme is proposed based on compressive sensing for achieving the compression and encryption simultaneously. In the proposed scheme, the internal keys for controlling the whole process of compression and encryption is first generated by plain image and initial key. Subsequently, discrete wavelets transform is used in order to convert the plain image to the coefficient matrix. After that, the permutation processing, which is controlled by the two-dimensional Sine improved Logistic iterative chaotic map (2D-SLIM), was done on the coefficient matrix in order to make the matrix energy dispersive. Furthermore, a plaintext related compressive sensing has been done utilizing a measurement matrix generated by 2D-SLIM. In order to make the cipher image lower correlation and distribute uniform, measurement results quantified the 0∼255 and the permutation and diffusion operation is done under the controlling by two-dimensional Logistic-Sine-coupling map (2D-LSCM). Finally, some common compression and security performance analysis methods are used to test our scheme. The test and comparison results shown in our proposed scheme have both excellent security and compression performance when compared with other recent works, thus ensuring the digital image application in the network. Full article
(This article belongs to the Special Issue Machine Learning in Sensors and Imaging)
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23 pages, 7037 KiB  
Article
Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning
by Canh Nguyen, Vasit Sagan, Matthew Maimaitiyiming, Maitiniyazi Maimaitijiang, Sourav Bhadra and Misha T. Kwasniewski
Sensors 2021, 21(3), 742; https://doi.org/10.3390/s21030742 - 22 Jan 2021
Cited by 105 | Viewed by 13267
Abstract
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at [...] Read more.
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples. Full article
(This article belongs to the Section Sensing and Imaging)
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41 pages, 2176 KiB  
Review
Design Strategies for Electrochemical Aptasensors for Cancer Diagnostic Devices
by Kamila Malecka, Edyta Mikuła and Elena E. Ferapontova
Sensors 2021, 21(3), 736; https://doi.org/10.3390/s21030736 - 22 Jan 2021
Cited by 37 | Viewed by 6244
Abstract
Improved outcomes for many types of cancer achieved during recent years is due, among other factors, to the earlier detection of tumours and the greater availability of screening tests. With this, non-invasive, fast and accurate diagnostic devices for cancer diagnosis strongly improve the [...] Read more.
Improved outcomes for many types of cancer achieved during recent years is due, among other factors, to the earlier detection of tumours and the greater availability of screening tests. With this, non-invasive, fast and accurate diagnostic devices for cancer diagnosis strongly improve the quality of healthcare by delivering screening results in the most cost-effective and safe way. Biosensors for cancer diagnostics exploiting aptamers offer several important advantages over traditional antibodies-based assays, such as the in-vitro aptamer production, their inexpensive and easy chemical synthesis and modification, and excellent thermal stability. On the other hand, electrochemical biosensing approaches allow sensitive, accurate and inexpensive way of sensing, due to the rapid detection with lower costs, smaller equipment size and lower power requirements. This review presents an up-to-date assessment of the recent design strategies and analytical performance of the electrochemical aptamer-based biosensors for cancer diagnosis and their future perspectives in cancer diagnostics. Full article
(This article belongs to the Special Issue Electrochemical Aptamer-Based Biosensors)
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24 pages, 10566 KiB  
Review
A Review on Humidity, Temperature and Strain Printed Sensors—Current Trends and Future Perspectives
by Dimitris Barmpakos and Grigoris Kaltsas
Sensors 2021, 21(3), 739; https://doi.org/10.3390/s21030739 - 22 Jan 2021
Cited by 61 | Viewed by 7421
Abstract
Printing technologies have been attracting increasing interest in the manufacture of electronic devices and sensors. They offer a unique set of advantages such as additive material deposition and low to no material waste, digitally-controlled design and printing, elimination of multiple steps for device [...] Read more.
Printing technologies have been attracting increasing interest in the manufacture of electronic devices and sensors. They offer a unique set of advantages such as additive material deposition and low to no material waste, digitally-controlled design and printing, elimination of multiple steps for device manufacturing, wide material compatibility and large scale production to name but a few. Some of the most popular and interesting sensors are relative humidity, temperature and strain sensors. In that regard, this review analyzes the utilization and involvement of printing technologies for full or partial sensor manufacturing; production methods, material selection, sensing mechanisms and performance comparison are presented for each category, while grouping of sensor sub-categories is performed in all applicable cases. A key aim of this review is to provide a reference for sensor designers regarding all the aforementioned parameters, by highlighting strengths and weaknesses for different approaches in printed humidity, temperature and strain sensor manufacturing with printing technologies. Full article
(This article belongs to the Special Issue 2D/3D Printed Sensors and Electronics)
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14 pages, 1597 KiB  
Article
IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks
by Andrej Zgank
Sensors 2021, 21(3), 676; https://doi.org/10.3390/s21030676 - 20 Jan 2021
Cited by 38 | Viewed by 5109
Abstract
Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural [...] Read more.
Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system. Full article
(This article belongs to the Special Issue AI for IoT)
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25 pages, 11129 KiB  
Article
Flexural Damage Diagnosis in Reinforced Concrete Beams Using a Wireless Admittance Monitoring System—Tests and Finite Element Analysis
by Constantin E. Chalioris, Violetta K. Kytinou, Maristella E. Voutetaki and Chris G. Karayannis
Sensors 2021, 21(3), 679; https://doi.org/10.3390/s21030679 - 20 Jan 2021
Cited by 60 | Viewed by 5704
Abstract
The utilization and effectiveness of a custom-made, portable and low-cost structural health monitoring (SHM) system that implements the PZT-based electro-mechanical admittance (EMA) methodology for the detection and evaluation of the damage of flexural reinforced concrete (RC) beams is presented. Tests of large-scale beams [...] Read more.
The utilization and effectiveness of a custom-made, portable and low-cost structural health monitoring (SHM) system that implements the PZT-based electro-mechanical admittance (EMA) methodology for the detection and evaluation of the damage of flexural reinforced concrete (RC) beams is presented. Tests of large-scale beams under monotonic and cyclic reversal-imposed deformations have been carried out using an integrated wireless impedance/admittance monitoring system (WiAMS) that employs the voltage measurements of PZT transducers. Small-sized PZT patches that have been epoxy-bonded on the steel bars surface and on the external concrete face of the beams are utilized to diagnose damages caused by steel yielding and concrete cracking. Excitations and simultaneous measurements of the voltage signal responses of the PZT transducers have been carried out at different levels of the applied load during the tests using the developed SHM devices, which are remotely controlled by a terminal emulator. Each PZT output voltage versus frequency response is transferred wireless and in real-time. Statistical index values are calculated based on the signals of the PZT transducers to represent the differences between their baseline response at the healthy state of the beam and their response at each loading/damage level. Finite Element Modeling (FEM) simulation of the tested beams has also been performed to acquire numerical results concerning the internal cracks, the steel strains and the energy dissipation and instability parameters. FEM analyses are used to verify the experimental results and to support the visual observations for a more precise damage evaluation. Findings of this study indicate that the proposed SHM system with the implementation of two different PZT transducer settings can be effectively utilized for the assessment of structural damage caused by concrete cracking and steel yielding in flexural beams under monotonic and cyclic loading. Full article
(This article belongs to the Special Issue Damage Detection of Structures Based on Piezoelectric Sensors)
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19 pages, 6261 KiB  
Article
Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver
by Martina De Landro, Ignacio Espíritu García-Molina, Manuel Barberio, Eric Felli, Vincent Agnus, Margherita Pizzicannella, Michele Diana, Emanuele Zappa and Paola Saccomandi
Sensors 2021, 21(2), 643; https://doi.org/10.3390/s21020643 - 18 Jan 2021
Cited by 22 | Viewed by 3370
Abstract
This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used [...] Read more.
This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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19 pages, 779 KiB  
Article
Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning
by Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa, Gabriella Olmo and Fernanda Irrera
Sensors 2021, 21(2), 614; https://doi.org/10.3390/s21020614 - 17 Jan 2021
Cited by 71 | Viewed by 7259
Abstract
Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published [...] Read more.
Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness. Full article
(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)
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15 pages, 1300 KiB  
Article
Using Wearable Sensor Technology to Measure Motion Complexity in Infants at High Familial Risk for Autism Spectrum Disorder
by Rujuta B. Wilson, Sitaram Vangala, David Elashoff, Tabitha Safari and Beth A. Smith
Sensors 2021, 21(2), 616; https://doi.org/10.3390/s21020616 - 17 Jan 2021
Cited by 32 | Viewed by 4260
Abstract
Background: Motor dysfunction has been reported as one of the first signs of atypical development in infants at high familial risk for autism spectrum disorder (ASD) (HR infants). However, studies have shown inconsistent results regarding the nature of motor dysfunction and whether it [...] Read more.
Background: Motor dysfunction has been reported as one of the first signs of atypical development in infants at high familial risk for autism spectrum disorder (ASD) (HR infants). However, studies have shown inconsistent results regarding the nature of motor dysfunction and whether it can be predictive of later ASD diagnosis. This is likely because current standardized motor assessments may not identify subtle and specific motor impairments that precede clinically observable motor dysfunction. Quantitative measures of motor development may address these limitations by providing objective evaluation of subtle motor differences in infancy. Methods: We used Opal wearable sensors to longitudinally evaluate full day motor activity in HR infants, and develop a measure of motion complexity. We focus on complexity of motion because optimal motion complexity is crucial to normal motor development and less complex behaviors might represent repetitive motor behaviors, a core diagnostic symptom of ASD. As proof of concept, the relationship of the motion complexity measure to developmental outcomes was examined in a small set of HR infants. Results: HR infants with a later diagnosis of ASD show lower motion complexity compared to those that do not. There is a stronger correlation between motion complexity and ASD outcome compared to outcomes of cognitive ability and adaptive skills. Conclusions: Objective measures of motor development are needed to identify characteristics of atypical infant motor function that are sensitive and specific markers of later ASD risk. Motion complexity could be used to track early infant motor development and to discriminate HR infants that go on to develop ASD. Full article
(This article belongs to the Section Wearables)
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25 pages, 7804 KiB  
Article
Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model
by Guiyun Liu, Baihao Peng and Xiaojing Zhong
Sensors 2021, 21(2), 594; https://doi.org/10.3390/s21020594 - 15 Jan 2021
Cited by 18 | Viewed by 2802
Abstract
Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need [...] Read more.
Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment. Full article
(This article belongs to the Special Issue Security and Privacy in the Internet of Things (IoT))
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18 pages, 13311 KiB  
Article
Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet
by Yong Zhu, Guangpeng Li, Rui Wang, Shengnan Tang, Hong Su and Kai Cao
Sensors 2021, 21(2), 549; https://doi.org/10.3390/s21020549 - 14 Jan 2021
Cited by 41 | Viewed by 4072
Abstract
Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods [...] Read more.
Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust. Full article
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis Technologies and Systems: Part Ⅰ )
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27 pages, 29319 KiB  
Article
Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments
by Juan Bravo-Arrabal, J. J. Fernandez-Lozano, Javier Serón, Jose Antonio Gomez-Ruiz and Alfonso García-Cerezo
Sensors 2021, 21(2), 567; https://doi.org/10.3390/s21020567 - 14 Jan 2021
Cited by 27 | Viewed by 4551
Abstract
The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, [...] Read more.
The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment. Full article
(This article belongs to the Special Issue LoRa Sensor Network)
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13 pages, 4830 KiB  
Article
A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field
by Le Wang, Lirong Xiang, Lie Tang and Huanyu Jiang
Sensors 2021, 21(2), 507; https://doi.org/10.3390/s21020507 - 13 Jan 2021
Cited by 36 | Viewed by 3992
Abstract
Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be [...] Read more.
Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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16 pages, 3459 KiB  
Article
Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters
by Mahmoud Elsisi, Karar Mahmoud, Matti Lehtonen and Mohamed M. F. Darwish
Sensors 2021, 21(2), 487; https://doi.org/10.3390/s21020487 - 12 Jan 2021
Cited by 79 | Viewed by 7250
Abstract
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and [...] Read more.
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 2017 KiB  
Article
Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration
by John-Lewis Zinia Zaukuu, Zoltan Gillay and Zoltan Kovacs
Sensors 2021, 21(2), 481; https://doi.org/10.3390/s21020481 - 12 Jan 2021
Cited by 22 | Viewed by 3583
Abstract
The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty [...] Read more.
The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty application in the meat industry. This study aimed to determine the optimal dilution level of meat extract for e-tongue evaluations and also to develop three standardized meat extraction methods. For practicality, the developed methods were applied to detect low levels of meat adulteration using beef and pork mixtures and turkey and chicken mixtures as case studies. Dilution factor of 1% w/v of liquid meat extract was determined to be the optimum for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w chicken in turkey and pork in beef with linear discriminant analysis accuracies (LDA) of 78.13% (recognition) and 64.73% (validation). Even higher LDA accuracies of 89.62% (recognition) and 68.77% (validation) were achieved for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w of pork in beef. Partial least square models could predict both sets of meat mixtures with good accuracies. Extraction by cooking was the best method for discriminating meat mixtures and can be applied for meat quality evaluations with the e-tongue. Full article
(This article belongs to the Special Issue Breakthroughs and Challenges in Food Quality Assessment Using Advanced Rapid Techniques: Electronic Tongue, Electronic Nose, and Near-Infrared Spectroscopy)
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22 pages, 10507 KiB  
Article
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
by Hammam Alshazly, Christoph Linse, Erhardt Barth and Thomas Martinetz
Sensors 2021, 21(2), 455; https://doi.org/10.3390/s21020455 - 11 Jan 2021
Cited by 151 | Viewed by 8690
Abstract
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored [...] Read more.
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 7201 KiB  
Article
Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor
by Xiaobai Meng, Mingyang Lu, Wuliang Yin, Abdeldjalil Bennecer and Katherine J. Kirk
Sensors 2021, 21(2), 419; https://doi.org/10.3390/s21020419 - 9 Jan 2021
Cited by 22 | Viewed by 4000
Abstract
Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on [...] Read more.
Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm. Full article
(This article belongs to the Section Electronic Sensors)
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10 pages, 2116 KiB  
Communication
Soft Wireless Bioelectronics and Differential Electrodermal Activity for Home Sleep Monitoring
by Hojoong Kim, Shinjae Kwon, Young-Tae Kwon and Woon-Hong Yeo
Sensors 2021, 21(2), 354; https://doi.org/10.3390/s21020354 - 7 Jan 2021
Cited by 24 | Viewed by 5243
Abstract
Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using [...] Read more.
Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using polysomnography is not available for a home sleep evaluation, while it requires multiple sensors, tabletop electronics, and sleep specialists. More importantly, the mandatory sleep in a designated lab facility disrupts a subject’s regular sleep pattern, which does not capture one’s everyday sleep behaviors. Recent studies report that galvanic skin response (GSR) measured on the skin can be one indicator to evaluate the sleep quality daily at home. However, the available GSR detection devices require rigid sensors wrapped on fingers along with separate electronic components for data acquisition, which can interrupt the normal sleep conditions. Here, we report a new class of materials, sensors, electronics, and packaging technologies to develop a wireless, soft electronic system that can measure GSR on the wrist. The single device platform that avoids wires, rigid sensors, and straps offers the maximum comfort to wear on the skin and minimize disruption of a subject’s sleep. A nanomaterial GSR sensor, printed on a soft elastomeric membrane, can have intimate contact with the skin to reduce motion artifact during sleep. A multi-layered flexible circuit mounted on top of the sensor provides a wireless, continuous, real-time recording of GSR to classify sleep stages, validated by the direct comparison with the standard method that measures other physiological signals. Collectively, the soft bioelectronic system shows great potential to be working as a portable, at-home sensor system for assessing sleep quality before a hospital visit. Full article
(This article belongs to the Section Biomedical Sensors)
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29 pages, 6522 KiB  
Review
Review on Conductive Polymer/CNTs Nanocomposites Based Flexible and Stretchable Strain and Pressure Sensors
by Olfa Kanoun, Ayda Bouhamed, Rajarajan Ramalingame, Jose Roberto Bautista-Quijano, Dhivakar Rajendran and Ammar Al-Hamry
Sensors 2021, 21(2), 341; https://doi.org/10.3390/s21020341 - 6 Jan 2021
Cited by 148 | Viewed by 13524
Abstract
In the last decade, significant developments of flexible and stretchable force sensors have been witnessed in order to satisfy the demand of several applications in robotic, prosthetics, wearables and structural health monitoring bringing decisive advantages due to their manifold customizability, easy integration and [...] Read more.
In the last decade, significant developments of flexible and stretchable force sensors have been witnessed in order to satisfy the demand of several applications in robotic, prosthetics, wearables and structural health monitoring bringing decisive advantages due to their manifold customizability, easy integration and outstanding performance in terms of sensor properties and low-cost realization. In this paper, we review current advances in this field with a special focus on polymer/carbon nanotubes (CNTs) based sensors. Based on the electrical properties of polymer/CNTs nanocomposite, we explain underlying principles for pressure and strain sensors. We highlight the influence of the manufacturing processes on the achieved sensing properties and the manifold possibilities to realize sensors using different shapes, dimensions and measurement procedures. After an intensive review of the realized sensor performances in terms of sensitivity, stretchability, stability and durability, we describe perspectives and provide novel trends for future developments in this intriguing field. Full article
(This article belongs to the Section Physical Sensors)
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18 pages, 1657 KiB  
Article
An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning
by Kim Bjerge, Jakob Bonde Nielsen, Martin Videbæk Sepstrup, Flemming Helsing-Nielsen and Toke Thomas Høye
Sensors 2021, 21(2), 343; https://doi.org/10.3390/s21020343 - 6 Jan 2021
Cited by 59 | Viewed by 16037
Abstract
Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper [...] Read more.
Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths. Full article
(This article belongs to the Section Intelligent Sensors)
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17 pages, 2595 KiB  
Article
Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors
by Emilio Guirado, Javier Blanco-Sacristán, Emilio Rodríguez-Caballero, Siham Tabik, Domingo Alcaraz-Segura, Jaime Martínez-Valderrama and Javier Cabello
Sensors 2021, 21(1), 320; https://doi.org/10.3390/s21010320 - 5 Jan 2021
Cited by 35 | Viewed by 8042
Abstract
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. [...] Read more.
Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands. Full article
(This article belongs to the Special Issue Deep Learning Methods for Remote Sensing)
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17 pages, 5378 KiB  
Article
Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application
by Dimitrios Panagiotidis, Azadeh Abdollahnejad and Martin Slavík
Sensors 2021, 21(1), 301; https://doi.org/10.3390/s21010301 - 5 Jan 2021
Cited by 13 | Viewed by 3219
Abstract
Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point [...] Read more.
Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry. Full article
(This article belongs to the Section Remote Sensors)
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31 pages, 3872 KiB  
Review
Sensors for Structural Health Monitoring of Agricultural Structures
by Chrysanthos Maraveas and Thomas Bartzanas
Sensors 2021, 21(1), 314; https://doi.org/10.3390/s21010314 - 5 Jan 2021
Cited by 27 | Viewed by 7614
Abstract
The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors [...] Read more.
The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors have been employed for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete. The cost–benefits of each type of sensor and utility in a farm environment are explored in the review. Current literature suggests that the functionality of sensors has improved with progress in technology. Notable improvements made with the progress in technology include better accuracy of the measurements, reduction of signal-to-noise ratio, and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in smart IoT-based agriculture. Key challenges include inconsistent installation of sensors in farm structures, technical constraints, and lack of support infrastructure, awareness, and preference for traditional inspection methods. Full article
(This article belongs to the Special Issue Smart Sensors for Automation in Agriculture 4.0)
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15 pages, 6953 KiB  
Letter
Design of Flexible Pressure Sensor Based on Conical Microstructure PDMS-Bilayer Graphene
by Lixia Cheng, Renxin Wang, Xiaojian Hao and Guochang Liu
Sensors 2021, 21(1), 289; https://doi.org/10.3390/s21010289 - 4 Jan 2021
Cited by 32 | Viewed by 4714
Abstract
As a new material, graphene shows excellent properties in mechanics, electricity, optics, and so on, which makes it widely concerned by people. At present, it is difficult for graphene pressure sensor to meet both high sensitivity and large pressure detection range at the [...] Read more.
As a new material, graphene shows excellent properties in mechanics, electricity, optics, and so on, which makes it widely concerned by people. At present, it is difficult for graphene pressure sensor to meet both high sensitivity and large pressure detection range at the same time. Therefore, it is highly desirable to produce flexible pressure sensors with sufficient sensitivity in a wide working range and with simple process. Herein, a relatively high flexible pressure sensor based on piezoresistivity is presented by combining the conical microstructure polydimethylsiloxane (PDMS) with bilayer graphene together. The piezoresistive material (bilayer graphene) attached to the flexible substrate can convert the local deformation caused by the vertical force into the change of resistance. Results show that the pressure sensor based on conical microstructure PDMS-bilayer graphene can operate at a pressure range of 20 kPa while maintaining a sensitivity of 0.122 ± 0.002 kPa−1 (0–5 kPa) and 0.077 ± 0.002 kPa−1 (5–20 kPa), respectively. The response time of the sensor is about 70 ms. In addition to the high sensitivity of the pressure sensor, it also has excellent reproducibility at different pressure and temperature. The pressure sensor based on conical microstructure PDMS-bilayer graphene can sense the motion of joint well when the index finger is bent, which makes it possible to be applied in electronic skin, flexible electronic devices, and other fields. Full article
(This article belongs to the Section Sensor Materials)
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23 pages, 16142 KiB  
Article
Deepint.net: A Rapid Deployment Platform for Smart Territories
by Juan M. Corchado, Pablo Chamoso, Guillermo Hernández, Agustín San Roman Gutierrez, Alberto Rivas Camacho, Alfonso González-Briones, Francisco Pinto-Santos, Enrique Goyenechea, David Garcia-Retuerta, María Alonso-Miguel, Beatriz Bellido Hernandez, Diego Valdeolmillos Villaverde, Manuel Sanchez-Verdejo, Pablo Plaza-Martínez, Manuel López-Pérez, Sergio Manzano-García, Ricardo S. Alonso, Roberto Casado-Vara, Javier Prieto Tejedor, Fernando de la Prieta, Sara Rodríguez-González, Javier Parra-Domínguez, Mohd Saberi Mohamad, Saber Trabelsi, Enrique Díaz-Plaza, Jose Alberto Garcia-Coria, Tan Yigitcanlar, Paulo Novais and Sigeru Omatuadd Show full author list remove Hide full author list
Sensors 2021, 21(1), 236; https://doi.org/10.3390/s21010236 - 1 Jan 2021
Cited by 44 | Viewed by 5057
Abstract
This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use [...] Read more.
This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques. Full article
(This article belongs to the Special Issue Computational Intelligence and Intelligent Contents (CIIC))
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13 pages, 1239 KiB  
Article
A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
by Duy Tang Hoang, Xuan Toa Tran, Mien Van and Hee Jun Kang
Sensors 2021, 21(1), 244; https://doi.org/10.3390/s21010244 - 1 Jan 2021
Cited by 34 | Viewed by 4491
Abstract
This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a [...] Read more.
This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data. Full article
(This article belongs to the Collection Sensors for Fault Diagnosis, Fault Tolerance and Resilient Control)
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18 pages, 2057 KiB  
Article
Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy
by Jelena Muncan, Kyoko Tei and Roumiana Tsenkova
Sensors 2021, 21(1), 177; https://doi.org/10.3390/s21010177 - 29 Dec 2020
Cited by 36 | Viewed by 9202
Abstract
Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production [...] Read more.
Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830–2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954–1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process. Full article
(This article belongs to the Special Issue Breakthroughs and Challenges in Food Quality Assessment Using Advanced Rapid Techniques: Electronic Tongue, Electronic Nose, and Near-Infrared Spectroscopy)
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21 pages, 436 KiB  
Article
An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian
by Marco Pota, Mirko Ventura, Rosario Catelli and Massimo Esposito
Sensors 2021, 21(1), 133; https://doi.org/10.3390/s21010133 - 28 Dec 2020
Cited by 90 | Viewed by 10684
Abstract
Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better [...] Read more.
Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages. Full article
(This article belongs to the Special Issue Selected Papers from the 11-th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2019) and the 22nd International Conference on Network-Based Information Systems (NBiS-2019))
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19 pages, 2229 KiB  
Article
A Novel Epidemic Model for Wireless Rechargeable Sensor Network Security
by Guiyun Liu, Baihao Peng and Xiaojing Zhong
Sensors 2021, 21(1), 123; https://doi.org/10.3390/s21010123 - 27 Dec 2020
Cited by 21 | Viewed by 3142
Abstract
With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging [...] Read more.
With the development of wireless rechargeable sensor networks (WRSNs ), security issues of WRSNs have attracted more attention from scholars around the world. In this paper, a novel epidemic model, SILS(Susceptible, Infected, Low-energy, Susceptible), considering the removal, charging and reinfection process of WRSNs is proposed. Subsequently, the local and global stabilities of disease-free and epidemic equilibrium points are analyzed and simulated after obtaining the basic reproductive number R0. Detailedly, the simulations further reveal the unique characteristics of SILS when it tends to being stable, and the relationship between the charging rate and R0. Furthermore, the attack-defense game between malware and WRSNs is constructed and the optimal strategies of both players are obtained. Consequently, in the case of R0<1 and R0>1, the validity of the optimal strategies is verified by comparing with the non-optimal control group in the evolution of sensor nodes and accumulated cost. Full article
(This article belongs to the Special Issue Wireless Communication in Internet of Things)
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23 pages, 3763 KiB  
Review
Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Review in Terms of Central Performances and Outliers
by Andrea Ponzoni
Sensors 2021, 21(1), 29; https://doi.org/10.3390/s21010029 - 23 Dec 2020
Cited by 19 | Viewed by 3543
Abstract
SnO2 is one of the most studied materials in gas sensing and is often used as a benchmark for other metal oxide-based gas sensors. To optimize its structural and functional features, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and [...] Read more.
SnO2 is one of the most studied materials in gas sensing and is often used as a benchmark for other metal oxide-based gas sensors. To optimize its structural and functional features, the fine tuning of the morphology in nanoparticles, nanowires, nanosheets and their eventual hierarchical organization has become an active field of research. In this paper, the different SnO2 morphologies reported in literature in the last five years are systematically compared in terms of response amplitude through a statistical approach. To have a dataset as homogeneous as possible, which is necessary for a reliable comparison, the analysis is carried out on sensors based on pure SnO2, focusing on ethanol detection in a dry air background as case study. Concerning the central performances of each morphology, results indicate that none clearly outperform the others, while a few individual materials emerge as remarkable outliers with respect to the whole dataset. The observed central performances and outliers may represent a suitable reference for future research activities in the field. Full article
(This article belongs to the Special Issue Gas Sensing Materials)
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31 pages, 5086 KiB  
Article
Ultra Stable Molecular Sensors by Submicron Referencing and Why They Should Be Interrogated by Optical Diffraction—Part II. Experimental Demonstration
by Andreas Frutiger, Karl Gatterdam, Yves Blickenstorfer, Andreas Michael Reichmuth, Christof Fattinger and János Vörös
Sensors 2021, 21(1), 9; https://doi.org/10.3390/s21010009 - 22 Dec 2020
Cited by 7 | Viewed by 5415
Abstract
Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental [...] Read more.
Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental noise sources. In this paper, we demonstrate that sensors that apply the spatial affinity lock-in principle (part I) and perform readout by diffraction overcome the drawbacks of established refractometric biosensors. We show this with a direct comparison of the cover refractive index jump sensitivity as well as the surface mass resolution of an unstabilized diffractometric biosensor with a state-of-the-art Biacore 8k. A combined refractometric diffractometric biosensor demonstrates that a refractometric sensor requires a much higher measurement precision than the diffractometric to achieve the same resolution. In a conceptual and quantitative discussion, we elucidate the physical reasons behind and define the figure of merit of diffractometric biosensors. Because low-precision unstabilized diffractometric devices achieve the same resolution as bulky stabilized refractometric sensors, we believe that label-free optical sensors might soon move beyond the drug discovery lab as miniaturized, mass-produced environmental/medical sensors. In fact, combined with the right surface chemistry and recognition element, they might even bring the senses of smell/taste to our smart devices. Full article
(This article belongs to the Special Issue Advanced Biophotonic Sensors)
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32 pages, 796 KiB  
Review
A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies
by Santosh Subedi and Jae-Young Pyun
Sensors 2020, 20(24), 7230; https://doi.org/10.3390/s20247230 - 17 Dec 2020
Cited by 74 | Viewed by 8529
Abstract
In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location [...] Read more.
In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development. Full article
(This article belongs to the Special Issue Indoor Positioning and Navigation)
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19 pages, 6036 KiB  
Article
Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network
by Dengji Zhou, Guizhou Wang, Guojin He, Tengfei Long, Ranyu Yin, Zhaoming Zhang, Sibao Chen and Bin Luo
Sensors 2020, 20(24), 7241; https://doi.org/10.3390/s20247241 - 17 Dec 2020
Cited by 31 | Viewed by 3515
Abstract
Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is [...] Read more.
Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building. The network is an end-to-end approach. In the training stage, the input is the remote sensing image and corresponding label, and the output is probability map (the probability that each pixel is or is not building). In the prediction stage, the input is the remote sensing image, and the output is the extraction result of the building. The complexity of the network structure was reduced so that it is easy to implement. The proposed PISANet was tested on two datasets. The result shows that the overall accuracy reached 94.50 and 96.15%, the intersection-over-union reached 77.45 and 87.97%, and F1 index reached 87.27 and 93.55%, respectively. In experiments on different datasets, PISANet obtained high overall accuracy, low error rate and improved integrity of individual buildings. Full article
(This article belongs to the Special Issue Mobile Multi-Sensors in Positioning, Navigation, and Mapping Applications)
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15 pages, 2356 KiB  
Article
A Double-Channel Hybrid Deep Neural Network Based on CNN and BiLSTM for Remaining Useful Life Prediction
by Chengying Zhao, Xianzhen Huang, Yuxiong Li and Muhammad Yousaf Iqbal
Sensors 2020, 20(24), 7109; https://doi.org/10.3390/s20247109 - 11 Dec 2020
Cited by 71 | Viewed by 5137
Abstract
In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted [...] Read more.
In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models. Full article
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
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72 pages, 22070 KiB  
Review
InAsSb-Based Infrared Photodetectors: Thirty Years Later On
by Antoni Rogalski, Piotr Martyniuk, Malgorzata Kopytko, Pawel Madejczyk and Sanjay Krishna
Sensors 2020, 20(24), 7047; https://doi.org/10.3390/s20247047 - 9 Dec 2020
Cited by 53 | Viewed by 8164
Abstract
In 1989, one author of this paper (A.R.) published the very first review paper on InAsSb infrared detectors. During the last thirty years, many scientific breakthroughs and technological advances for InAsSb-based photodetectors have been made. Progress in advanced epitaxial methods contributed considerably to [...] Read more.
In 1989, one author of this paper (A.R.) published the very first review paper on InAsSb infrared detectors. During the last thirty years, many scientific breakthroughs and technological advances for InAsSb-based photodetectors have been made. Progress in advanced epitaxial methods contributed considerably to the InAsSb improvement. Current efforts are directed towards the photodetector’s cut-off wavelength extension beyond lattice-available and lattice-strained binary substrates. It is suspected that further improvement of metamorphic buffers for epitaxial layers will lead to lower-cost InAsSb-based focal plane arrays on large-area alternative substrates like GaAs and silicon. Most photodetector reports in the last decade are devoted to the heterostructure and barrier architectures operating in high operating temperature conditions. In the paper, at first InAsSb growth methods are briefly described. Next, the fundamental material properties are reviewed, stressing electrical and optical aspects limiting the photodetector performance. The last part of the paper highlights new ideas in design of InAsSb-based bulk and superlattice infrared detectors and focal plane arrays. Their performance is compared with the state-of-the-art infrared detector technologies. Full article
(This article belongs to the Section Optical Sensors)
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37 pages, 22464 KiB  
Review
Optical Fiber Sensors by Direct Laser Processing: A Review
by David Pallarés-Aldeiturriaga, Pablo Roldán-Varona, Luis Rodríguez-Cobo and José Miguel López-Higuera
Sensors 2020, 20(23), 6971; https://doi.org/10.3390/s20236971 - 6 Dec 2020
Cited by 24 | Viewed by 6385
Abstract
The consolidation of laser micro/nano processing technologies has led to a continuous increase in the complexity of optical fiber sensors. This new avenue offers novel possibilities for advanced sensing in a wide set of application sectors and, especially in the industrial and medical [...] Read more.
The consolidation of laser micro/nano processing technologies has led to a continuous increase in the complexity of optical fiber sensors. This new avenue offers novel possibilities for advanced sensing in a wide set of application sectors and, especially in the industrial and medical fields. In this review, the most important transducing structures carried out by laser processing in optical fiber are shown. The work covers different types of fiber Bragg gratings with an emphasis in the direct-write technique and their most interesting inscription configurations. Along with gratings, cladding waveguide structures in optical fibers have reached notable importance in the development of new optical fiber transducers. That is why a detailed study is made of the different laser inscription configurations that can be adopted, as well as their current applications. Microcavities manufactured in optical fibers can be used as both optical transducer and hybrid structure to reach advanced soft-matter optical sensing approaches based on optofluidic concepts. These in-fiber cavities manufactured by femtosecond laser irradiation followed by chemical etching are promising tools for biophotonic devices. Finally, the enhanced Rayleigh backscattering fibers by femtosecond laser dots inscription are also discussed, as a consequence of the new sensing possibilities they enable. Full article
(This article belongs to the Collection Optical Fiber Sensors)
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20 pages, 17834 KiB  
Article
Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach
by Francesca Manni, Fons van der Sommen, Himar Fabelo, Svitlana Zinger, Caifeng Shan, Erik Edström, Adrian Elmi-Terander, Samuel Ortega, Gustavo Marrero Callicó and Peter H. N. de With
Sensors 2020, 20(23), 6955; https://doi.org/10.3390/s20236955 - 5 Dec 2020
Cited by 37 | Viewed by 5370
Abstract
The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal [...] Read more.
The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D–2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D–2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications. Full article
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
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22 pages, 2261 KiB  
Review
The Application of Prussian Blue Nanoparticles in Tumor Diagnosis and Treatment
by Xiaoran Gao, Qiaowen Wang, Cui Cheng, Shujin Lin, Ting Lin, Chun Liu and Xiao Han
Sensors 2020, 20(23), 6905; https://doi.org/10.3390/s20236905 - 3 Dec 2020
Cited by 23 | Viewed by 5233
Abstract
Prussian blue nanoparticles (PBNPs) have attracted increasing research interest in immunosensors, bioimaging, drug delivery, and application as therapeutic agents due to their large internal pore volume, tunable size, easy synthesis and surface modification, good thermal stability, and favorable biocompatibility. This review first outlines [...] Read more.
Prussian blue nanoparticles (PBNPs) have attracted increasing research interest in immunosensors, bioimaging, drug delivery, and application as therapeutic agents due to their large internal pore volume, tunable size, easy synthesis and surface modification, good thermal stability, and favorable biocompatibility. This review first outlines the effect of tumor markers using PBNPs-based immunosensors which have a sandwich-type architecture and competitive-type structure. Metal ion doped PBNPs which were used as T1-weight magnetic resonance and photoacoustic imaging agents to improve image quality and surface modified PBNPs which were used as drug carriers to decrease side effects via passive or active targeting to tumor sites are also summarized. Moreover, the PBNPs with high photothermal efficiency and excellent catalase-like activity were promising for photothermal therapy and O2 self-supplied photodynamic therapy of tumors. Hence, PBNPs-based multimodal imaging-guided combinational tumor therapies (such as chemo, photothermal, and photodynamic therapies) were finally reviewed. This review aims to inspire broad interest in the rational design and application of PBNPs for detecting and treating tumors in clinical research. Full article
(This article belongs to the Special Issue Multifunctional Materials Sensors for Theranostic Nanomedicine)
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14 pages, 3619 KiB  
Article
Real-Time Plant Leaf Counting Using Deep Object Detection Networks
by Michael Buzzy, Vaishnavi Thesma, Mohammadreza Davoodi and Javad Mohammadpour Velni
Sensors 2020, 20(23), 6896; https://doi.org/10.3390/s20236896 - 3 Dec 2020
Cited by 54 | Viewed by 6528
Abstract
The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved. While these networks have made considerable advances in plant phenotyping, the results are processed too slowly [...] Read more.
The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved. While these networks have made considerable advances in plant phenotyping, the results are processed too slowly to allow for real-time decision-making. Therefore, being able to perform plant phenotyping computations in real-time has become a critical part of precision agriculture and agricultural informatics. In this work, we utilize state-of-the-art object detection networks to accurately detect, count, and localize plant leaves in real-time. Our work includes the creation of an annotated dataset of Arabidopsis plants captured using Cannon Rebel XS camera. These images and annotations have been complied and made publicly available. This dataset is then fed into a Tiny-YOLOv3 network for training. The Tiny-YOLOv3 network is then able to converge and accurately perform real-time localization and counting of the leaves. We also create a simple robotics platform based on an Android phone and iRobot create2 to demonstrate the real-time capabilities of the network in the greenhouse. Additionally, a performance comparison is conducted between Tiny-YOLOv3 and Faster R-CNN. Unlike Tiny-YOLOv3, which is a single network that does localization and identification in a single pass, the Faster R-CNN network requires two steps to do localization and identification. While with Tiny-YOLOv3, inference time, F1 Score, and false positive rate (FPR) are improved compared to Faster R-CNN, other measures such as difference in count (DiC) and AP are worsened. Specifically, for our implementation of Tiny-YOLOv3, the inference time is under 0.01 s, the F1 Score is over 0.94, and the FPR is around 24%. Last, transfer learning using Tiny-YOLOv3 to detect larger leaves on a model trained only on smaller leaves is implemented. The main contributions of the paper are in creating dataset (shared with the research community), as well as the trained Tiny-YOLOv3 network for leaf localization and counting. Full article
(This article belongs to the Section Sensors and Robotics)
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15 pages, 665 KiB  
Article
Design of a Scalable and Fast YOLO for Edge-Computing Devices
by Byung-Gil Han, Joon-Goo Lee, Kil-Taek Lim and Doo-Hyun Choi
Sensors 2020, 20(23), 6779; https://doi.org/10.3390/s20236779 - 27 Nov 2020
Cited by 30 | Viewed by 7737
Abstract
With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional [...] Read more.
With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model. Full article
(This article belongs to the Section Sensing and Imaging)
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22 pages, 1035 KiB  
Review
Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0
by Tahera Kalsoom, Naeem Ramzan, Shehzad Ahmed and Masood Ur-Rehman
Sensors 2020, 20(23), 6783; https://doi.org/10.3390/s20236783 - 27 Nov 2020
Cited by 138 | Viewed by 22343
Abstract
The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and [...] Read more.
The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories. Full article
(This article belongs to the Special Issue Next-Generation Internet of Things (IoT): Opportunities, Challenges, and Solutions)
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17 pages, 1483 KiB  
Article
Is Standardization Necessary for Sharing of a Large Mid-Infrared Soil Spectral Library?
by Shree R. S. Dangal and Jonathan Sanderman
Sensors 2020, 20(23), 6729; https://doi.org/10.3390/s20236729 - 25 Nov 2020
Cited by 25 | Viewed by 4398
Abstract
Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library [...] Read more.
Recent developments in diffuse reflectance soil spectroscopy have increasingly focused on building and using large soil spectral libraries with the purpose of supporting many activities relevant to monitoring, mapping and managing soil resources. A potential limitation of using a mid-infrared (MIR) spectral library developed by another laboratory is the need to account for inherent differences in the signal strength at each wavelength associated with different instrumental and environmental conditions. Here we apply predictive models built using the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC-KSSL) MIR spectral library (n = 56,155) to samples sets of European and US origin scanned on a secondary spectrometer to assess the need for calibration transfer using a piecewise direct standardization (PDS) approach in transforming spectra before predicting carbon cycle relevant soil properties (bulk density, CaCO3, organic carbon, clay and pH). The European soil samples were from the land use/cover area frame statistical survey (LUCAS) database available through the European Soil Data Center (ESDAC), while the US soil samples were from the National Ecological Observatory Network (NEON). Additionally, the performance of the predictive models on PDS transfer spectra was tested against the direct calibration models built using samples scanned on the secondary spectrometer. On independent test sets of European and US origin, PDS improved predictions for most but not all soil properties with memory based learning (MBL) models generally outperforming partial least squares regression and Cubist models. Our study suggests that while good-to-excellent results can be obtained without calibration transfer, for most of the cases presented in this study, PDS was necessary for unbiased predictions. The MBL models also outperformed the direct calibration models for most of the soil properties. For laboratories building new spectroscopy capacity utilizing existing spectral libraries, it appears necessary to develop calibration transfer using PDS or other calibration transfer techniques to obtain the least biased and most precise predictions of different soil properties. Full article
(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)
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25 pages, 1252 KiB  
Article
A Modular Experimentation Methodology for 5G Deployments: The 5GENESIS Approach
by Almudena Díaz Zayas, Giuseppe Caso, Özgü Alay, Pedro Merino, Anna Brunstrom, Dimitris Tsolkas and Harilaos Koumaras
Sensors 2020, 20(22), 6652; https://doi.org/10.3390/s20226652 - 20 Nov 2020
Cited by 18 | Viewed by 4781
Abstract
The high heterogeneity of 5G use cases requires the extension of the traditional per-component testing procedures provided by certification organizations, in order to devise and incorporate methodologies that cover the testing requirements from vertical applications and services. In this paper, we introduce an [...] Read more.
The high heterogeneity of 5G use cases requires the extension of the traditional per-component testing procedures provided by certification organizations, in order to devise and incorporate methodologies that cover the testing requirements from vertical applications and services. In this paper, we introduce an experimentation methodology that is defined in the context of the 5GENESIS project, which aims at enabling both the testing of network components and validation of E2E KPIs. The most important contributions of this methodology are its modularity and flexibility, as well as the open-source software that was developed for its application, which enable lightweight adoption of the methodology in any 5G testbed. We also demonstrate how the methodology can be used, by executing and analyzing different experiments in a 5G Non-Standalone (NSA) deployment at the University of Malaga. The key findings of the paper are an initial 5G performance assessment and KPI analysis and the detection of under-performance issues at the application level. Those findings highlight the need for reliable testing and validation procedures towards a fair benchmarking of generic 5G services and applications. Full article
(This article belongs to the Special Issue Experimentation in 5G and beyond Networks: State of the Art and the Way Forward)
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20 pages, 6081 KiB  
Article
An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump
by Shengnan Tang, Shouqi Yuan, Yong Zhu and Guangpeng Li
Sensors 2020, 20(22), 6576; https://doi.org/10.3390/s20226576 - 18 Nov 2020
Cited by 25 | Viewed by 3706
Abstract
A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the [...] Read more.
A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump. Full article
(This article belongs to the Special Issue Deep Learning, Artificial Neural Networks and Sensors for Fault Diagnosis)
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