Sensors

Article

24 pages, 4632 KiB

Open AccessEditor’s ChoiceArticle

Design and Intensive Experimental Evaluation of an Enhanced Visible Light Communication System for Automotive Applications

by Sebastian-Andrei Avătămăniței, Alin-Mihai Căilean, Adrian Done, Mihai Dimian, Valentin Popa and Marius Prelipceanu

Sensors 2020, 20(11), 3190; https://doi.org/10.3390/s20113190 - 4 Jun 2020

Cited by 15 | Viewed by 4257

Abstract

As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology [...] Read more.

As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology in vehicular applications. Thus, it provides an extensive presentation concerning the main challenges and issues that are associated to vehicular VLC applications and of some of the existing VLC solutions. Moreover, the article presents the aspects related to the design and intensive experimental evaluation of a new automotive VLC system. The experimental evaluation performed in indoor and outdoor conditions shows that the proposed system can achieve communication distances up to 50 m and bit error ratio (BER) lower than 10⁻⁶, while being exposed to optical and weather perturbations. This article provides important evidence concerning the snowfall effect on middle to long range outdoor VLC, as the proposed VLC system was also evaluated in snowfall conditions. Accordingly, the experimental evaluation showed that snowfall and heavy gust could increase bit error rate by up to 10,000 times. Even so, this article provides encouraging evidence that VLC systems will soon be able to reliably support V2X communications. Full article

(This article belongs to the Special Issue State-of-the-Art Indoor and Outdoor Visible Light Communications (VLC) Applications and Systems)

► Show Figures

Figure 1

19 pages, 3806 KiB

Open AccessEditor’s ChoiceArticle

Strain Transfer in Surface-Bonded Optical Fiber Sensors

by Francesco Falcetelli, Leonardo Rossi, Raffaella Di Sante and Gabriele Bolognini

Sensors 2020, 20(11), 3100; https://doi.org/10.3390/s20113100 - 30 May 2020

Cited by 72 | Viewed by 5957

Abstract

Fiber optic sensors represent one of the most promising technologies for the monitoring of various engineering structures. A major challenge in the field is to analyze and predict the strain transfer to the fiber core reliably. Many authors developed analytical models of a [...] Read more.

Fiber optic sensors represent one of the most promising technologies for the monitoring of various engineering structures. A major challenge in the field is to analyze and predict the strain transfer to the fiber core reliably. Many authors developed analytical models of a coated optical fiber, assuming null strain at the ends of the bonding length. However, this configuration only partially reflects real experimental setups in which the cable structure can be more complex and the strains do not drastically reduce to zero. In this study, a novel strain transfer model for surface-bonded sensing cables with multilayered structure was developed. The analytical model was validated both experimentally and numerically, considering two surface-mounted cable prototypes with three different bonding lengths and five load cases. The results demonstrated the capability of the model to predict the strain profile and, differently from the available strain transfer models, that the strain values at the extremities of the bonded fiber length are not null. Full article

(This article belongs to the Special Issue Fiber Optic Sensing Technology)

► Show Figures

Figure 1

24 pages, 943 KiB

Open AccessEditor’s ChoiceArticle

IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning

by Prince Waqas Khan, Yung-Cheol Byun and Namje Park

Sensors 2020, 20(10), 2990; https://doi.org/10.3390/s20102990 - 25 May 2020

Cited by 221 | Viewed by 16617

Abstract

Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile [...] Read more.

Agriculture and livestock play a vital role in social and economic stability. Food safety and transparency in the food supply chain are a significant concern for many people. Internet of Things (IoT) and blockchain are gaining attention due to their success in versatile applications. They generate a large amount of data that can be optimized and used efficiently by advanced deep learning (ADL) techniques. The importance of such innovations from the viewpoint of supply chain management is significant in different processes such as for broadened visibility, provenance, digitalization, disintermediation, and smart contracts. This article takes the secure IoT–blockchain data of Industry 4.0 in the food sector as a research object. Using ADL techniques, we propose a hybrid model based on recurrent neural networks (RNN). Therefore, we used long short-term memory (LSTM) and gated recurrent units (GRU) as a prediction model and genetic algorithm (GA) optimization jointly to optimize the parameters of the hybrid model. We select the optimal training parameters by GA and finally cascade LSTM with GRU. We evaluated the performance of the proposed system for a different number of users. This paper aims to help supply chain practitioners to take advantage of the state-of-the-art technologies; it will also help the industry to make policies according to the predictions of ADL. Full article

(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)

► Show Figures

Figure 1

11 pages, 1358 KiB

Open AccessEditor’s ChoiceArticle

SPR Biosensor Based on Polymer Multi-Mode Optical Waveguide and Nanoparticle Signal Enhancement

by Johanna-Gabriela Walter, Alina Eilers, Lourdes Shanika Malindi Alwis, Bernhard Wilhelm Roth and Kort Bremer

Sensors 2020, 20(10), 2889; https://doi.org/10.3390/s20102889 - 20 May 2020

Cited by 58 | Viewed by 7437

Abstract

We present a surface plasmon resonance (SPR) biosensor that is based on a planar-optical multi-mode (MM) polymer waveguide structure applied for the detection of biomolecules in the lower nano-molar (nM) range. The basic sensor shows a sensitivity of 608.6 nm/RIU when exposed to [...] Read more.

We present a surface plasmon resonance (SPR) biosensor that is based on a planar-optical multi-mode (MM) polymer waveguide structure applied for the detection of biomolecules in the lower nano-molar (nM) range. The basic sensor shows a sensitivity of 608.6 nm/RIU when exposed to refractive index changes with a measurement resolution of 4.3 × 10⁻³ RIU. By combining the SPR sensor with an aptamer-functionalized, gold-nanoparticle (AuNP)-enhanced sandwich assay, the detection of C-reactive protein (CRP) in a buffer solution was achieved with a response of 0.118 nm/nM. Due to the multi-mode polymer waveguide structure and the simple concept, the reported biosensor is well suited for low-cost disposable lab-on-a-chip applications and can be used with rather simple and economic devices. In particular, the sensor offers the potential for fast and multiplexed detection of several biomarkers on a single integrated platform. Full article

(This article belongs to the Collection Photonic Sensors)

► Show Figures

Figure 1

15 pages, 4764 KiB

Open AccessEditor’s ChoiceArticle

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

by Woosuk Kim, Hyunwoong Cho, Jongseok Kim, Byungkwan Kim and Seongwook Lee

Sensors 2020, 20(10), 2897; https://doi.org/10.3390/s20102897 - 20 May 2020

Cited by 60 | Viewed by 8644

Abstract

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two [...] Read more.

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body. Full article

(This article belongs to the Section Electronic Sensors)

► Show Figures

Figure 1

15 pages, 16733 KiB

Open AccessEditor’s ChoiceArticle

Feasibility of a Wearable Reflectometric System for Sensing Skin Hydration

by Raissa Schiavoni, Giuseppina Monti, Emanuele Piuzzi, Luciano Tarricone, Annarita Tedesco, Egidio De Benedetto and Andrea Cataldo

Sensors 2020, 20(10), 2833; https://doi.org/10.3390/s20102833 - 16 May 2020

Cited by 38 | Viewed by 5875

Abstract

One of the major goals of Health 4.0 is to offer personalized care to patients, also through real-time, remote monitoring of their biomedical parameters. In this regard, wearable monitoring systems are crucial to deliver continuous appropriate care. For some biomedical parameters, there are [...] Read more.

One of the major goals of Health 4.0 is to offer personalized care to patients, also through real-time, remote monitoring of their biomedical parameters. In this regard, wearable monitoring systems are crucial to deliver continuous appropriate care. For some biomedical parameters, there are a number of well established systems that offer adequate solutions for real-time, continuous patient monitoring. On the other hand, monitoring skin hydration still remains a challenging task. The continuous monitoring of this physiological parameter is extremely important in several contexts, for example for athletes, sick people, workers in hostile environments or for the elderly. State-of-the-art systems, however, exhibit some limitations, especially related with the possibility of continuous, real-time monitoring. Starting from these considerations, in this work, the feasibility of an innovative time-domain reflectometry (TDR)-based wearable, skin hydration sensing system for real-time, continuous monitoring of skin hydration level was investigated. The applicability of the proposed system was demonstrated, first, through experimental tests on reference substances, then, directly on human skin. The obtained results demonstrate the TDR technique and the proposed system holds unexplored potential for the aforementioned purposes. Full article

(This article belongs to the Special Issue Wearable Devices and Sensors for Innovative Monitoring Systems in the 4.0 Era)

► Show Figures

Figure 1

21 pages, 547 KiB

Open AccessEditor’s ChoiceArticle

Heart Rate Variability and Accelerometry as Classification Tools for Monitoring Perceived Stress Levels—A Pilot Study on Firefighters

by Michał Meina, Ewa Ratajczak, Maria Sadowska, Krzysztof Rykaczewski, Joanna Dreszer, Bibianna Bałaj, Stanisław Biedugnis, Wojciech Węgrzyński and Adam Krasuski

Sensors 2020, 20(10), 2834; https://doi.org/10.3390/s20102834 - 16 May 2020

Cited by 28 | Viewed by 6505

Abstract

Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters [...] Read more.

Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting. Full article

(This article belongs to the Collection Sensors for Gait, Human Movement Analysis, and Health Monitoring)

► Show Figures

Figure 1

15 pages, 3607 KiB

Open AccessEditor’s ChoiceArticle

Hollow-Core Photonic Crystal Fiber Mach–Zehnder Interferometer for Gas Sensing

by Kaveh Nazeri, Farid Ahmed, Vahid Ahsani, Hang-Eun Joe, Colin Bradley, Ehsan Toyserkani and Martin B. G. Jun

Sensors 2020, 20(10), 2807; https://doi.org/10.3390/s20102807 - 15 May 2020

Cited by 30 | Viewed by 5285

Abstract

A novel and compact interferometric refractive index (RI) point sensor is developed using hollow-core photonic crystal fiber (HC-PCF) and experimentally demonstrated for high sensitivity detection and measurement of pure gases. To construct the device, the sensing element fiber (HC-PCF) was placed between two [...] Read more.

A novel and compact interferometric refractive index (RI) point sensor is developed using hollow-core photonic crystal fiber (HC-PCF) and experimentally demonstrated for high sensitivity detection and measurement of pure gases. To construct the device, the sensing element fiber (HC-PCF) was placed between two single-mode fibers with airgaps at each side. Great measurement repeatability was shown in the cyclic test for the detection of various gases. The RI sensitivity of 4629 nm/RIU was demonstrated in the RI range of 1.0000347–1.000436 for the sensor with an HC-PCF length of 3.3 mm. The sensitivity of the proposed Mach–Zehnder interferometer (MZI) sensor increases when the length of the sensing element decreases. It is shown that response and recovery times of the proposed sensor inversely change with the length of HC-PCF. Besides, spatial frequency analysis for a wide range of air-gaps revealed information on the number and power distribution of modes. It is shown that the power is mainly carried by two dominant modes in the proposed structure. The proposed sensors have the potential to improve current technology’s ability to detect and quantify pure gases. Full article

(This article belongs to the Special Issue Fiber Optic Sensors in Chemical and Biological Applications)

► Show Figures

Figure 1

23 pages, 24525 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)

by Pablo Ezquerro, Matteo Del Soldato, Lorenzo Solari, Roberto Tomás, Federico Raspini, Mattia Ceccatelli, José Antonio Fernández-Merodo, Nicola Casagli and Gerardo Herrera

Sensors 2020, 20(10), 2749; https://doi.org/10.3390/s20102749 - 12 May 2020

Cited by 47 | Viewed by 7220

Abstract

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the [...] Read more.

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources. Full article

(This article belongs to the Special Issue Remote Sensing of Geohazards)

► Show Figures

Graphical abstract

15 pages, 7228 KiB

Open AccessEditor’s ChoiceArticle

Examination of Multi-Receiver GPS/EGNOS Positioning with Kalman Filtering and Validation Based on CORS Stations

by Adam Ciećko, Mieczysław Bakuła, Grzegorz Grunwald and Janusz Ćwiklak

Sensors 2020, 20(9), 2732; https://doi.org/10.3390/s20092732 - 11 May 2020

Cited by 24 | Viewed by 5056

Abstract

This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. [...] Read more.

This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. Next, the Kalman filter was employed to give the final solution. It was proven that EGNOS positioning allows to obtain an accuracy in the range of about 0.5–1.5 m. The proposed solution involving the use of three mobile receivers and Kalman filtering allowed to reduce the 3D error to a level below 0.3 m. Such an accuracy was achieved using only GPS L1 code observations and EGNOS corrections. Additionally, a reliable monitoring of quality of GPS/EGNOS positioning in the test area based on CORS stations was presented. Full article

(This article belongs to the Special Issue GNSS Sensors in Aerial Navigation)

► Show Figures

Figure 1

10 pages, 1353 KiB

Open AccessEditor’s ChoiceArticle

Multi-Addressed Fiber Bragg Structures for Microwave-Photonic Sensor Systems

by Oleg Morozov, Airat Sakhabutdinov, Vladimir Anfinogentov, Rinat Misbakhov, Artem Kuznetsov and Timur Agliullin

Sensors 2020, 20(9), 2693; https://doi.org/10.3390/s20092693 - 9 May 2020

Cited by 41 | Viewed by 4372

Abstract

The new theory and technique of Multi-Addressed Fiber Bragg Structure (MAFBS) usage in Microwave Photonics Sensor Systems (MPSS) is presented. This theory is the logical evolution of the theory of Addressed Fiber Bragg Structure (AFBS) usage as sensors in MPSS. The mathematical model [...] Read more.

The new theory and technique of Multi-Addressed Fiber Bragg Structure (MAFBS) usage in Microwave Photonics Sensor Systems (MPSS) is presented. This theory is the logical evolution of the theory of Addressed Fiber Bragg Structure (AFBS) usage as sensors in MPSS. The mathematical model of additive response from a single MAFBS is presented. The MAFBS is a special type of Fiber Bragg Gratings (FBG), the reflection spectrum of which has three (or more) narrow notches. The frequencies of narrow notches are located in the infrared range of electromagnetic spectrum, while differences between them are located in the microwave frequency range. All cross-differences between optical frequencies of single MAFBS are called the address frequencies set. When the additive optical response from a single MAFBS, passed through an optic filter with an oblique amplitude–frequency characteristic, is received on a photodetector, the complex electrical signal, which consists of all cross-frequency beatings of all optical frequencies, which are included in this optical signal, is taken at its output. This complex electrical signal at the photodetector’s output contains enough information to determine the central frequency shift of the MAFBS. The method of address frequencies analysis with the microwave-photonic measuring conversion method, which allows us to define the central frequency shift of a single MAFBS, is discussed in the work. Full article

(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors and Systems)

► Show Figures

Figure 1

25 pages, 1481 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning on Mainstream Microcontrollers

by Fouad Sakr, Francesco Bellotti, Riccardo Berta and Alessandro De Gloria

Sensors 2020, 20(9), 2638; https://doi.org/10.3390/s20092638 - 5 May 2020

Cited by 84 | Viewed by 10914

Abstract

This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support [...] Read more.

This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a linear kernel, k-Nearest Neighbors (K-NN), and Decision Tree (DT)), and exploits STM X-Cube-AI to implement Artificial Neural Networks (ANNs) on STM32 Nucleo boards. We investigated the performance of these algorithms on six embedded boards and six datasets (four classifications and two regression). Our analysis—which aims to plug a gap in the literature—shows that the target platforms allow us to achieve the same performance score as a desktop machine, with a similar time latency. ANN performs better than the other algorithms in most cases, with no difference among the target devices. We observed that increasing the depth of an NN improves performance, up to a saturation level. k-NN performs similarly to ANN and, in one case, even better, but requires all the training sets to be kept in the inference phase, posing a significant memory demand, which can be afforded only by high-end edge devices. DT performance has a larger variance across datasets. In general, several factors impact performance in different ways across datasets. This highlights the importance of a framework like ELM, which is able to train and compare different algorithms. To support the developer community, ELM is released on an open-source basis. Full article

(This article belongs to the Special Issue Applications in Electronics Pervading Industry, Environment and Society – Sensing Systems and Pervasive Intelligence)

► Show Figures

Figure 1

8 pages, 1900 KiB

Open AccessEditor’s ChoiceArticle

Design Rule of Mach-Zehnder Interferometer Sensors for Ultra-High Sensitivity

by Yiwei Xie, Ming Zhang and Daoxin Dai

Sensors 2020, 20(9), 2640; https://doi.org/10.3390/s20092640 - 5 May 2020

Cited by 36 | Viewed by 6818

Abstract

A design rule for a Mach-Zehnder interferometer (MZI) sensor is presented, allowing tunable sensitivity by appropriately choosing the MZI arm lengths according to the formula given in this paper. The present MZI sensor designed by this method can achieve an ultra-high sensitivity, which [...] Read more.

A design rule for a Mach-Zehnder interferometer (MZI) sensor is presented, allowing tunable sensitivity by appropriately choosing the MZI arm lengths according to the formula given in this paper. The present MZI sensor designed by this method can achieve an ultra-high sensitivity, which is much higher than any other traditional MZI sensors. An example is given with silicon-on-insulator (SOI) nanowires and the device sensitivity is as high as 10⁶ nm/refractive-index -unit (or even higher), by choosing the MZI arms appropriately. This makes it possible for one to realize a low-cost optical sensing system with a detection limit as high as 10⁻⁶ refractive-index-unit, even when a cheap optical spectrum analyzer with low-resolution (e.g., 1 nm) is used for the wavelength-shift measurement. Full article

(This article belongs to the Section Optical Sensors)

► Show Figures

Figure 1

19 pages, 3275 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent

by Krzysztof M. Markowicz and Michał T. Chiliński

Sensors 2020, 20(9), 2617; https://doi.org/10.3390/s20092617 - 4 May 2020

Cited by 22 | Viewed by 5313

Abstract

The aerosol scattering coefficient and Ångström exponent (AE) are important parameters in the understanding of aerosol optical properties and aerosol direct effect. These parameters are usually measured by a nephelometer network which is under-represented geographically; however, a rapid growth of air-pollution monitoring, using [...] Read more.

The aerosol scattering coefficient and Ångström exponent (AE) are important parameters in the understanding of aerosol optical properties and aerosol direct effect. These parameters are usually measured by a nephelometer network which is under-represented geographically; however, a rapid growth of air-pollution monitoring, using low-cost particle sensors, may extend observation networks. This paper presents the results of co-located measurements of aerosol optical properties, such as the aerosol scattering coefficient and the scattering AE, using low-cost sensors and using a scientific-grade polar Aurora 4000 nephelometer. A high Pearson correlation coefficient (0.94–0.96) between the low-cost particulate matter (PM) mass concentration and the aerosol scattering coefficient was found. For the PM₁₀ mass concentration, the aerosol scattering coefficient relation is linear for the Dfrobot SEN0177 sensor and non-linear for the Alphasense OPC-N2 device. After regression analyses, both low-cost instruments provided the aerosol scattering coefficient with a similar mean square error difference (RMSE) of about 20 Mm⁻¹, which corresponds to about 27% of the mean aerosol scattering coefficient. The relative uncertainty is independent of the pollution level. In addition, the ratio of aerosol number concentration between different bins showed a significant statistical (95% of confidence level) correlation with the scattering AE. For the SEN0177, the ratio of the particle number in bin 1 (radius of 0.15–0.25 µm) to bin 4 (radius of 1.25–2.5 µm) was a linear function of the scattering AE, with a Pearson correlation coefficient of 0.74. In the case of OPC-N2, the best correlation (r = 0.66) was found for the ratio between bin 1 (radius of 0.19–0.27 µm) and bin 2 (radius of 0.27–0.39 µm). Comparisons of an estimated scattering AE from a low-cost sensor with Aurora 4000 are given with the RMSE of 0.23–0.24, which corresponds to 16–19%. In addition, a three-year (2016–2019) observation by SEN0177 indicates that this sensor can be used to determine an annual cycle as well as a short-term variability. Full article

(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)

► Show Figures

Figure 1

12 pages, 1419 KiB

Open AccessEditor’s ChoiceArticle

Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds

by Antonio Celesti, Armando Ruggeri, Maria Fazio, Antonino Galletta, Massimo Villari and Agata Romano

Sensors 2020, 20(9), 2590; https://doi.org/10.3390/s20092590 - 2 May 2020

Cited by 102 | Viewed by 9194

Abstract

In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. [...] Read more.

In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform. Full article

(This article belongs to the Special Issue Blockchain in the Internet of Things: Opportunities, Challenges and Solutions)

► Show Figures

Figure 1

16 pages, 2643 KiB

Open AccessEditor’s ChoiceArticle

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

by Vittorio Mazzia, Lorenzo Comba, Aleem Khaliq, Marcello Chiaberge and Paolo Gay

Sensors 2020, 20(9), 2530; https://doi.org/10.3390/s20092530 - 29 Apr 2020

Cited by 112 | Viewed by 10448

Abstract

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, [...] Read more.

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers. Full article

(This article belongs to the Special Issue Metrology for Agriculture and Forestry 2019)

► Show Figures

Figure 1

11 pages, 3501 KiB

Open AccessEditor’s ChoiceArticle

Sensitivity Improvement of a Surface Plasmon Resonance Sensor Based on Two-Dimensional Materials Hybrid Structure in Visible Region: A Theoretical Study

by Zhining Lin, Shujing Chen and Chengyou Lin

Sensors 2020, 20(9), 2445; https://doi.org/10.3390/s20092445 - 25 Apr 2020

Cited by 61 | Viewed by 4578

Abstract

In this paper, we propose a surface plasmon resonance (SPR) sensor based on two-dimensional (2D) materials (graphene, MoS₂, WS₂ and WSe₂) hybrid structure, and theoretically investigate its sensitivity improvement in the visible region. The thickness of metal (Au, [...] Read more.

In this paper, we propose a surface plasmon resonance (SPR) sensor based on two-dimensional (2D) materials (graphene, MoS₂, WS₂ and WSe₂) hybrid structure, and theoretically investigate its sensitivity improvement in the visible region. The thickness of metal (Au, Ag or Cu) and the layer number of each 2D material are optimized using genetic algorithms to obtain the highest sensitivity for a specific wavelength of incident light. Then, the sensitivities of proposed SPR sensors with different metal films at various wavelengths are compared. An Ag-based SPR sensor exhibits a higher sensitivity than an Au- or Cu-based one at most wavelengths in the visible region. In addition, the sensitivity of the proposed SPR sensor varies obviously with the wavelength of incident light, and shows a maximum value of 159, 194 or 155°/RIU for Au, Ag or Cu, respectively. It is demonstrated that the sensitivity of the SPR sensor based on 2D materials’ hybrid structure can be further improved by optimizing the wavelength of incident light. Full article

(This article belongs to the Section Optical Sensors)

► Show Figures

Figure 1

26 pages, 18576 KiB

Open AccessEditor’s ChoiceArticle

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

by Sergio Trilles, Alberto González-Pérez and Joaquín Huerta

Sensors 2020, 20(8), 2418; https://doi.org/10.3390/s20082418 - 24 Apr 2020

Cited by 76 | Viewed by 10667

Abstract

Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety [...] Read more.

Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform. Full article

(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)

► Show Figures

Figure 1

15 pages, 2571 KiB

Open AccessEditor’s ChoiceArticle

Effect of the Elastomer Matrix on Thermoplastic Elastomer-Based Strain Sensor Fiber Composites

by Antonia Georgopoulou, Claudia Kummerlöwe and Frank Clemens

Sensors 2020, 20(8), 2399; https://doi.org/10.3390/s20082399 - 23 Apr 2020

Cited by 24 | Viewed by 4321

Abstract

In this study, a thermoplastic elastomer sensor fiber was embedded in an elastomer matrix. The effect of the matrix material on the sensor properties and the piezoresistive behavior of the single fiber-matrix composite system was investigated. For all composites, cycling test (dynamic test) [...] Read more.

In this study, a thermoplastic elastomer sensor fiber was embedded in an elastomer matrix. The effect of the matrix material on the sensor properties and the piezoresistive behavior of the single fiber-matrix composite system was investigated. For all composites, cycling test (dynamic test) and the relaxation behavior at different strains (quasi-static test) were investigated. In all cases, dynamic properties and quasi-static significantly changed after embedding, compared to the pure fiber. The composite with the silicone elastomer PDMS (Polydimethylsiloxane) as matrix material exhibited deviation from linear response of the resistivity at low strains and proved an unsuitable choice compared to natural rubber. The addition of a spring construct in the embedded sensor fiber natural rubber composite improved the linearity at low strains but increased the mechanical and electrical hysteresis of the soft matter sensor composite. Using pre-vulcanized natural rubber improved linearity at low strains and reduced significantly the stress and relative resistance relaxation as well as the resistance hysteresis, especially if the resistance remained low. In both cases of the pre-vulcanized rubber and the spring structure, the piezoresistive behavior was improved, and at the same time, the stiffness of the system was increased indicating that using a stiffer matrix can be a strategy for improving the sensor properties. Full article

(This article belongs to the Section Sensor Materials)

► Show Figures

Figure 1

19 pages, 12592 KiB

Open AccessEditor’s ChoiceArticle

Ultra-Wideband Diversity MIMO Antenna System for Future Mobile Handsets

by Naser Ojaroudi Parchin, Haleh Jahanbakhsh Basherlou, Yasir I. A. Al-Yasir, Ahmed M. Abdulkhaleq and Raed A. Abd-Alhameed

Sensors 2020, 20(8), 2371; https://doi.org/10.3390/s20082371 - 22 Apr 2020

Cited by 47 | Viewed by 5661

Abstract

A new ultra-wideband (UWB) multiple-input/multiple-output (MIMO) antenna system is proposed for future smartphones. The structure of the design comprises four identical pairs of compact microstrip-fed slot antennas with polarization diversity function that are placed symmetrically at different edge corners of the smartphone mainboard. [...] Read more.

A new ultra-wideband (UWB) multiple-input/multiple-output (MIMO) antenna system is proposed for future smartphones. The structure of the design comprises four identical pairs of compact microstrip-fed slot antennas with polarization diversity function that are placed symmetrically at different edge corners of the smartphone mainboard. Each antenna pair consists of an open-ended circular-ring slot radiator fed by two independently semi-arc-shaped microstrip-feeding lines exhibiting the polarization diversity characteristic. Therefore, in total, the proposed smartphone antenna design contains four horizontally-polarized and four vertically-polarized elements. The characteristics of the single-element dual-polarized UWB antenna and the proposed UWB-MIMO smartphone antenna are examined while using both experimental and simulated results. An impedance bandwidth of 2.5–10.2 GHz with 121% fractional bandwidth (FBW) is achieved for each element. However, for S₁₁ ≤ −6 dB, this value is more than 130% (2.2–11 GHz). The proposed UWB-MIMO smartphone antenna system offers good isolation, dual-polarized function, full radiation coverage, and sufficient efficiency. Besides, the calculated diversity performances of the design in terms of the envelope correlation coefficient (ECC) and total active reflection coefficient (TARC) are very low over the entire operating band. Full article

(This article belongs to the Special Issue Antenna Design for 5G and Beyond)

► Show Figures

Figure 1

13 pages, 3030 KiB

Open AccessEditor’s ChoiceArticle

Development of an Aptamer Based Luminescent Optical Fiber Sensor for the Continuous Monitoring of Hg²⁺ in Aqueous Media

by Nerea De Acha, César Elosúa and Francisco J. Arregui

Sensors 2020, 20(8), 2372; https://doi.org/10.3390/s20082372 - 22 Apr 2020

Cited by 22 | Viewed by 4046

Abstract

A fluorescent optical fiber sensor for the detection of mercury (Hg²⁺) ions in aqueous solutions is presented in this work. The sensor was based on a fluorophore-labeled thymine (T)-rich oligodeoxyribonucleotide (ON) sequence that was directly immobilized onto the tip of a [...] Read more.

A fluorescent optical fiber sensor for the detection of mercury (Hg²⁺) ions in aqueous solutions is presented in this work. The sensor was based on a fluorophore-labeled thymine (T)-rich oligodeoxyribonucleotide (ON) sequence that was directly immobilized onto the tip of a tapered optical fiber. In the presence of mercury ions, the formation of T–Hg²⁺-T mismatches quenches the fluorescence emission by the labeled fluorophore, which enables the measurement of Hg²⁺ ions in aqueous solutions. Thus, in contrast to commonly designed sensors, neither a fluorescence quencher nor a complementary ON sequence is required. The sensor presented a response time of 24.8 seconds toward 5 × 10⁻¹² M Hg²⁺. It also showed both good reversibility (higher than the 95.8%) and selectivity: the I₀/I variation was 10 times higher for Hg²⁺ ions than for Mn²⁺ ions. Other contaminants examined (Co²⁺, Ag⁺, Cd²⁺, Ni²⁺, Ca²⁺, Pb²⁺, Mn²⁺, Zn²⁺, Fe³⁺, and Cu²⁺) presented an even lower interference. The limit of detection of the sensor was 4.73 × 10⁻¹³ M Hg²⁺ in buffer solution and 9.03 × 10⁻¹³ M Hg²⁺ in ultrapure water, and was also able to detect 5 × 10⁻¹² M Hg²⁺ in tap water. Full article

(This article belongs to the Special Issue Calibration of Chemical Sensors Based on Photoluminescence)

► Show Figures

Figure 1

18 pages, 3566 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid

by Daniele Croce, Fabrizio Giuliano, Ilenia Tinnirello and Laura Giarré

Sensors 2020, 20(8), 2249; https://doi.org/10.3390/s20082249 - 16 Apr 2020

Cited by 9 | Viewed by 3598

Abstract

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power [...] Read more.

In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings. Full article

(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)

► Show Figures

Figure 1

17 pages, 6799 KiB

Open AccessEditor’s ChoiceArticle

Concrete Crack Monitoring Using a Novel Strain Transfer Model for Distributed Fiber Optics Sensors

by Antoine Bassil, Xavier Chapeleau, Dominique Leduc and Odile Abraham

Sensors 2020, 20(8), 2220; https://doi.org/10.3390/s20082220 - 15 Apr 2020

Cited by 81 | Viewed by 8404

Abstract

In this paper, we study the strain transfer mechanism between a host material and an optical fiber. A new analytical model handling imperfect bonding between layers is proposed. A general expression of the crack-induced strain transfer from fractured concrete material to optical fiber [...] Read more.

In this paper, we study the strain transfer mechanism between a host material and an optical fiber. A new analytical model handling imperfect bonding between layers is proposed. A general expression of the crack-induced strain transfer from fractured concrete material to optical fiber is established in the case of a multilayer system. This new strain transfer model is examined through performing wedge splitting tests on concrete specimens instrumented with embedded and surface-mounted fiber optic cables. The experimental results showed the validity of the crack-induced strain expression fitted to the distributed strains measured using an Optical Backscattering Reflectometry (OBR) system. As a result, precise estimations of the crack openings next to the optical cable location were achieved, as well as the monitoring of the optical cable response through following the strain lag parameter. Full article

(This article belongs to the Section Optical Sensors)

► Show Figures

Figure 1

18 pages, 1337 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

by Dimitrios Dechouniotis, Nikolaos Athanasopoulos, Aris Leivadeas, Nathalie Mitton, Raphael Jungers and Symeon Papavassiliou

Sensors 2020, 20(8), 2191; https://doi.org/10.3390/s20082191 - 13 Apr 2020

Cited by 36 | Viewed by 6188

Abstract

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and [...] Read more.

The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Full article

(This article belongs to the Special Issue Optimization and Communication in UAV Networks)

► Show Figures

Graphical abstract

26 pages, 2231 KiB

Open AccessEditor’s ChoiceArticle

Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals

by Faisal Jamil, Shabir Ahmad, Naeem Iqbal and Do-Hyeun Kim

Sensors 2020, 20(8), 2195; https://doi.org/10.3390/s20082195 - 13 Apr 2020

Cited by 270 | Viewed by 23631

Abstract

Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has [...] Read more.

Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data. Full article

(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)

► Show Figures

Figure 1

24 pages, 5501 KiB

Open AccessEditor’s ChoiceArticle

LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture

by Gaia Codeluppi, Antonio Cilfone, Luca Davoli and Gianluigi Ferrari

Sensors 2020, 20(7), 2028; https://doi.org/10.3390/s20072028 - 4 Apr 2020

Cited by 153 | Viewed by 21777

Abstract

Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable [...] Read more.

Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture. Full article

(This article belongs to the Special Issue Metrology for Agriculture and Forestry 2019)

► Show Figures

Figure 1

17 pages, 684 KiB

Open AccessEditor’s ChoiceArticle

Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis

by Marica Amadeo, Claudia Campolo, Giuseppe Ruggeri, Gianmarco Lia and Antonella Molinaro

Sensors 2020, 20(7), 1985; https://doi.org/10.3390/s20071985 - 2 Apr 2020

Cited by 30 | Viewed by 4225

Abstract

Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however [...] Read more.

Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however not considered the fact that multiple vehicular contents can be transient, i.e., they expire after a certain time period since they were generated, the so-called FreshnessPeriod in NDN. In this paper, we study the effects of caching transient contents in Vehicular NDN and present a simple yet effective freshness-driven caching decision strategy that vehicles can implement autonomously. Performance evaluation in ndnSIM shows that the FreshnessPeriod is a crucial parameter that deeply influences the cache hit ratio and, consequently, the data dissemination performance. Full article

(This article belongs to the Special Issue Vehicular Sensor Networks: Applications, Advances and Challenges)

► Show Figures

Figure 1

12 pages, 2264 KiB

Open AccessEditor’s ChoiceArticle

A Syringe-Based Biosensor to Rapidly Detect Low Levels of Escherichia Coli (ECOR13) in Drinking Water Using Engineered Bacteriophages

by Troy C. Hinkley, Spencer Garing, Paras Jain, John Williford, Anne-Laure M. Le Ny, Kevin P. Nichols, Joseph E. Peters, Joey N. Talbert and Sam R. Nugen

Sensors 2020, 20(7), 1953; https://doi.org/10.3390/s20071953 - 31 Mar 2020

Cited by 22 | Viewed by 4927

Abstract

A sanitized drinking water supply is an unconditional requirement for public health and the overall prosperity of humanity. Potential microbial and chemical contaminants of drinking water have been identified by a joint effort between the World Health Organization (WHO) and the United Nations [...] Read more.

A sanitized drinking water supply is an unconditional requirement for public health and the overall prosperity of humanity. Potential microbial and chemical contaminants of drinking water have been identified by a joint effort between the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF), who together establish guidelines that define, in part, that the presence of Escherichia coli (E. coli) in drinking water is an indication of inadequate sanitation and a significant health risk. As E. coli is a nearly ubiquitous resident of mammalian gastrointestinal tracts, no detectable counts in 100 mL of drinking water is the standard used worldwide as an indicator of sanitation. The currently accepted EPA method relies on filtration, followed by growth on selective media, and requires 24–48 h from sample to results. In response, we developed a rapid bacteriophage-based detection assay with detection limit capabilities comparable to traditional methods in less than a quarter of the time. We coupled membrane filtration with selective enrichment using genetically engineered bacteriophages to identify less than 20 colony forming units (CFU) E. coli in 100 mL drinking water within 5 h. The combination of membrane filtration with phage infection produced a novel assay that demonstrated a rapid, selective, and sensitive detection of an indicator organism in large volumes of drinking water as recommended by the leading world regulatory authorities. Full article

(This article belongs to the Section Biosensors)

► Show Figures

Figure 1

16 pages, 7429 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

by Samuel Ortega, Martin Halicek, Himar Fabelo, Rafael Camacho, María de la Luz Plaza, Fred Godtliebsen, Gustavo M. Callicó and Baowei Fei

Sensors 2020, 20(7), 1911; https://doi.org/10.3390/s20071911 - 30 Mar 2020

Cited by 80 | Viewed by 8060

Abstract

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful [...] Read more.

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies. Full article

(This article belongs to the Special Issue Sensors, Signal and Image Processing in Biomedicine and Assisted Living)

► Show Figures

Figure 1

20 pages, 3986 KiB

Open AccessEditor’s ChoiceArticle

Wearable Hand Module and Real-Time Tracking Algorithms for Measuring Finger Joint Angles of Different Hand Sizes with High Accuracy Using FBG Strain Sensor

by Jun Sik Kim, Byung Kook Kim, Minsu Jang, Kyumin Kang, Dae Eun Kim, Byeong-Kwon Ju and Jinseok Kim

Sensors 2020, 20(7), 1921; https://doi.org/10.3390/s20071921 - 30 Mar 2020

Cited by 46 | Viewed by 8855

Abstract

This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move [...] Read more.

This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move continuously according to the size of the hand and the bending of the joint. Representatively, four algorithms were proposed; point strain (PTS), area summation (AREA), proportional summation (PS), and PS/interference (PS/I or PS/I_

α

). For more accurate and efficient assessments, 3D printed hand replica with different finger sizes was adopted and quantitative evaluations were performed for index~little fingers (77 to 117 mm) and thumb (68~78 mm). For index~little fingers, the optimized algorithms were PS and PS/I_

α

. For thumb, the optimized algorithms were PS/I_

α

and AREA. The average error angle of the wearable hand module was observed to be 0.47 ± 2.51° and mean absolute error (MAE) was achieved at 1.63 ± 1.97°. These results showed that more accurate hand modules than other glove modules applied to different hand sizes can be manufactured using FBG strain sensors which move continuously and algorithms for tracking this movable FBG sensors. Full article

(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors and Systems)

► Show Figures

Figure 1

23 pages, 5564 KiB

Open AccessEditor’s ChoiceArticle

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

by Han-Shin Jo, Chanshin Park, Eunhyoung Lee, Haing Kun Choi and Jaedon Park

Sensors 2020, 20(7), 1927; https://doi.org/10.3390/s20071927 - 30 Mar 2020

Cited by 125 | Viewed by 8442

Abstract

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a [...] Read more.

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model. Full article

(This article belongs to the Special Issue Machine Learning Models for Wireless Network Monitoring and Data Analysis)

► Show Figures

Figure 1

23 pages, 4894 KiB

Open AccessEditor’s ChoiceArticle

Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition

by Dami Jeong, Byung-Gyu Kim and Suh-Yeon Dong

Sensors 2020, 20(7), 1936; https://doi.org/10.3390/s20071936 - 30 Mar 2020

Cited by 85 | Viewed by 6580

Abstract

Understanding a person’s feelings is a very important process for the affective computing. People express their emotions in various ways. Among them, facial expression is the most effective way to present human emotional status. We propose efficient deep joint spatiotemporal features for facial [...] Read more.

Understanding a person’s feelings is a very important process for the affective computing. People express their emotions in various ways. Among them, facial expression is the most effective way to present human emotional status. We propose efficient deep joint spatiotemporal features for facial expression recognition based on the deep appearance and geometric neural networks. We apply three-dimensional (3D) convolution to extract spatial and temporal features at the same time. For the geometric network, 23 dominant facial landmarks are selected to express the movement of facial muscle through the analysis of energy distribution of whole facial landmarks.We combine these features by the designed joint fusion classifier to complement each other. From the experimental results, we verify the recognition accuracy of 99.21%, 87.88%, and 91.83% for CK+, MMI, and FERA datasets, respectively. Through the comparative analysis, we show that the proposed scheme is able to improve the recognition accuracy by 4% at least. Full article

(This article belongs to the Special Issue Deep Learning Methods for Human Activity Recognition and Emotion Detection)

► Show Figures

Figure 1

17 pages, 3381 KiB

Open AccessEditor’s ChoiceArticle

Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site

by Sabina Licen, Alessia Di Gilio, Jolanda Palmisani, Stefania Petraccone, Gianluigi de Gennaro and Pierluigi Barbieri

Sensors 2020, 20(7), 1887; https://doi.org/10.3390/s20071887 - 29 Mar 2020

Cited by 27 | Viewed by 5531

Abstract

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of [...] Read more.

Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses. Full article

(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)

► Show Figures

Figure 1

19 pages, 3749 KiB

Open AccessEditor’s ChoiceArticle

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

by Rafia Nishat Toma, Alexander E. Prosvirin and Jong-Myon Kim

Sensors 2020, 20(7), 1884; https://doi.org/10.3390/s20071884 - 28 Mar 2020

Cited by 224 | Viewed by 13989

Abstract

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich [...] Read more.

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults. Full article

(This article belongs to the Special Issue Sensor Applications in Fault Diagnosis and Monitoring of Electrical Machines)

► Show Figures

Figure 1

23 pages, 4257 KiB

Open AccessEditor’s ChoiceArticle

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

by Dat Tien Nguyen, Jin Kyu Kang, Tuyen Danh Pham, Ganbayar Batchuluun and Kang Ryoung Park

Sensors 2020, 20(7), 1822; https://doi.org/10.3390/s20071822 - 25 Mar 2020

Cited by 93 | Viewed by 13068

Abstract

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the [...] Read more.

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods. Full article

(This article belongs to the Special Issue Image and Video Processing and Recognition Based on Artificial Intelligence)

► Show Figures

Figure 1

17 pages, 15903 KiB

Open AccessEditor’s ChoiceArticle

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

by Ritesh Kumar Singh, Michiel Aernouts, Mats De Meyer, Maarten Weyn and Rafael Berkvens

Sensors 2020, 20(7), 1827; https://doi.org/10.3390/s20071827 - 25 Mar 2020

Cited by 91 | Viewed by 10763

Abstract

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use [...] Read more.

The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design. Full article

(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)

► Show Figures

Figure 1

20 pages, 2037 KiB

Open AccessEditor’s ChoiceArticle

Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things

by Yuta Nakamura, Yuanyu Zhang, Masahiro Sasabe and Shoji Kasahara

Sensors 2020, 20(6), 1793; https://doi.org/10.3390/s20061793 - 24 Mar 2020

Cited by 62 | Viewed by 7555

Abstract

Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what [...] Read more.

Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost. Full article

(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)

► Show Figures

Figure 1

21 pages, 11730 KiB

Open AccessEditor’s ChoiceArticle

A Novel Fabry-Pérot Optical Sensor for Guided Wave Signal Acquisition

by Cheng Xu and Zahra Sharif Khodaei

Sensors 2020, 20(6), 1728; https://doi.org/10.3390/s20061728 - 19 Mar 2020

Cited by 16 | Viewed by 4237

Abstract

In this paper, a novel hybrid damage detection system is proposed, which utilizes piezoelectric actuators for guided wave excitation and a new fibre optic (FO) sensor based on Fabry-Perot (FP) and Fiber Bragg Grating (FBG). By replacing the FBG sensors with FBG-based FP [...] Read more.

In this paper, a novel hybrid damage detection system is proposed, which utilizes piezoelectric actuators for guided wave excitation and a new fibre optic (FO) sensor based on Fabry-Perot (FP) and Fiber Bragg Grating (FBG). By replacing the FBG sensors with FBG-based FP sensors in the hybrid damage detection system, a higher strain resolution is achieved, which results in higher damage sensitivity and higher reliability in diagnosis. To develop the novel sensor, optimum parameters such as reflectivity, a wavelength spectrum, and a sensor length were chosen carefully through an analytical model of the sensor, which has been validated with experiments. The sensitivity of the new FBG-based FP sensors was compared to FBG sensors to emphasize the superiority of the new sensors in measuring micro-strains. Lastly, the new FBG-based FP sensor was utilized for recording guided waves in a hybrid setup and compared to the conventional FBG hybrid sensor network to demonstrate their improved performance for a structural health monitoring (SHM) application. Full article

(This article belongs to the Section Optical Sensors)

► Show Figures

Figure 1

25 pages, 7743 KiB

Open AccessEditor’s ChoiceArticle

A Wristwatch-Based Wireless Sensor Platform for IoT Health Monitoring Applications

by Sanjeev Kumar, John L. Buckley, John Barton, Melusine Pigeon, Robert Newberry, Matthew Rodencal, Adhurim Hajzeraj, Tim Hannon, Ken Rogers, Declan Casey, Donal O’Sullivan and Brendan O’Flynn

Sensors 2020, 20(6), 1675; https://doi.org/10.3390/s20061675 - 17 Mar 2020

Cited by 49 | Viewed by 17822

Abstract

A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application [...] Read more.

A wristwatch-based wireless sensor platform for IoT wearable health monitoring applications is presented. The paper describes the platform in detail, with a particular focus given to the design of a novel and compact wireless sub-system for 868 MHz wristwatch applications. An example application using the developed platform is discussed for arterial oxygen saturation (SpO₂) and heart rate measurement using optical photoplethysmography (PPG). A comparison of the wireless performance in the 868 MHz and the 2.45 GHz bands is performed. Another contribution of this work is the development of a highly integrated 868 MHz antenna. The antenna structure is printed on the surface of a wristwatch enclosure using laser direct structuring (LDS) technology. At 868 MHz, a low specific absorption rate (SAR) of less than 0.1% of the maximum permissible limit in the simulation is demonstrated. The measured on-body prototype antenna exhibits a −10 dB impedance bandwidth of 36 MHz, a peak realized gain of −4.86 dBi and a radiation efficiency of 14.53% at 868 MHz. To evaluate the performance of the developed 868 MHz sensor platform, the wireless communication range measurements are performed in an indoor environment and compared with a commercial Bluetooth wristwatch device. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems in the IOT)

► Show Figures

Figure 1

15 pages, 2237 KiB

Open AccessEditor’s ChoiceArticle

Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors

by Jung-Yeon Kim, Geunsu Park, Seong-A Lee and Yunyoung Nam

Sensors 2020, 20(6), 1622; https://doi.org/10.3390/s20061622 - 14 Mar 2020

Cited by 49 | Viewed by 5816

Abstract

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale [...] Read more.

Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable. Full article

(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)

► Show Figures

Figure 1

11 pages, 1521 KiB

Open AccessEditor’s ChoiceArticle

Surface Plasmon Resonance-Based Sensing Utilizing Spatial Phase Modulation in an Imaging Interferometer

by Roman Kaňok, Dalibor Ciprian and Petr Hlubina

Sensors 2020, 20(6), 1616; https://doi.org/10.3390/s20061616 - 13 Mar 2020

Cited by 9 | Viewed by 4204

Abstract

Spatial phase modulation in an imaging interferometer is utilized in surface plasmon resonance (SPR) based sensing of liquid analytes. In the interferometer, a collimated light beam from a laser diode irradiating at 637.1 nm is passing through a polarizer and is reflected from [...] Read more.

Spatial phase modulation in an imaging interferometer is utilized in surface plasmon resonance (SPR) based sensing of liquid analytes. In the interferometer, a collimated light beam from a laser diode irradiating at 637.1 nm is passing through a polarizer and is reflected from a plasmonic structure of SF10/Cr/Au attached to a prism in the Kretschmann configuration. The beam passes through a combination of a Wollaston prism, a polarizer and a lens, and forms an interference pattern on a CCD sensor of a color camera. Interference patterns obtained for different liquid analytes are acquired and transferred to the computer for data processing. The sensing concept is based on the detection of a refractive index change, which is transformed via the SPR phenomenon into an interference fringe phase shift. By calculating the phase shift for the plasmonic structure of SF10/Cr/Au of known parameters we demonstrate that this technique can detect different weight concentrations of ethanol diluted in water, or equivalently, different changes in the refractive index. The sensitivity to the refractive index and the detection limit obtained are −278 rad/refractive-index-unit (RIU) and 3.6

\times 10^{- 6}

RIU, respectively. The technique is demonstrated in experiments with the same liquid analytes as in the theory. Applying an original approach in retrieving the fringe phase shift, we revealed good agreement between experiment and theory, and the measured sensitivity to the refractive index and the detection limit reached −226 rad/RIU and 4.4

\times 10^{- 6}

RIU, respectively. These results suggest that the SPR interferometer with the detection of a fringe phase shift is particularly useful in applications that require measuring refractive index changes with high sensitivity. Full article

(This article belongs to the Section Optical Sensors)

► Show Figures

Figure 1

15 pages, 5286 KiB

Open AccessEditor’s ChoiceArticle

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

by Xiaoming Lv, Fajie Duan, Jia-jia Jiang, Xiao Fu and Lin Gan

Sensors 2020, 20(6), 1562; https://doi.org/10.3390/s20061562 - 11 Mar 2020

Cited by 318 | Viewed by 17845

Abstract

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this [...] Read more.

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection. Full article

(This article belongs to the Collection Signal Processing, Control, and Estimation for Intelligent Sensor Systems)

► Show Figures

Figure 1

21 pages, 3548 KiB

Open AccessEditor’s ChoiceArticle

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

by Berthold K.P. Horn

Sensors 2020, 20(5), 1489; https://doi.org/10.3390/s20051489 - 9 Mar 2020

Cited by 54 | Viewed by 5977

Abstract

Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m [...] Read more.

Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable—standard deviations of 1–2 m in distance measurement under favorable circumstances—limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent “noise gain” of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces “frequency diversity,” a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation)

► Show Figures

Figure 1

14 pages, 4032 KiB

Open AccessEditor’s ChoiceArticle

Motion Artifact Reduction in Wearable Photoplethysmography Based on Multi-Channel Sensors with Multiple Wavelengths

by Jongshill Lee, Minseong Kim, Hoon-Ki Park and In Young Kim

Sensors 2020, 20(5), 1493; https://doi.org/10.3390/s20051493 - 9 Mar 2020

Cited by 94 | Viewed by 13020

Abstract

Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel [...] Read more.

Photoplethysmography (PPG) is an easy and convenient method by which to measure heart rate (HR). However, PPG signals that optically measure volumetric changes in blood are not robust to motion artifacts. In this paper, we develop a PPG measuring system based on multi-channel sensors with multiple wavelengths and propose a motion artifact reduction algorithm using independent component analysis (ICA). We also propose a truncated singular value decomposition for 12-channel PPG signals, which contain direction and depth information measured using the developed multi-channel PPG measurement system. The performance of the proposed method is evaluated against the R-peaks of an electrocardiogram in terms of sensitivity (Se), positive predictive value (PPV), and failed detection rate (FDR). The experimental results show that Se, PPV, and FDR were 99%, 99.55%, and 0.45% for walking, 96.28%, 99.24%, and 0.77% for fast walking, and 82.49%, 99.83%, and 0.17% for running, respectively. The evaluation shows that the proposed method is effective in reducing errors in HR estimation from PPG signals with motion artifacts in intensive motion situations such as fast walking and running. Full article

(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)

► Show Figures

Figure 1

17 pages, 6667 KiB

Open AccessEditor’s ChoiceArticle

Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops

by Chongyuan Zhang, Wilson A. Craine, Rebecca J. McGee, George J. Vandemark, James B. Davis, Jack Brown, Scot H. Hulbert and Sindhuja Sankaran

Sensors 2020, 20(5), 1450; https://doi.org/10.3390/s20051450 - 6 Mar 2020

Cited by 30 | Viewed by 5281

Abstract

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, [...] Read more.

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops. Full article

(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)

► Show Figures

Figure 1

16 pages, 4119 KiB

Open AccessEditor’s ChoiceArticle

Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

by Zulfiqar Ahmad Khan, Tanveer Hussain, Amin Ullah, Seungmin Rho, Miyoung Lee and Sung Wook Baik

Sensors 2020, 20(5), 1399; https://doi.org/10.3390/s20051399 - 4 Mar 2020

Cited by 166 | Viewed by 11312

Abstract

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is [...] Read more.

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting. Full article

(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)

► Show Figures

Figure 1

14 pages, 15615 KiB

Open AccessEditor’s ChoiceArticle

Water Management for Sustainable Irrigation Systems Using Internet-of-Things

by André Glória, Carolina Dionisio, Gonçalo Simões, João Cardoso and Pedro Sebastião

Sensors 2020, 20(5), 1402; https://doi.org/10.3390/s20051402 - 4 Mar 2020

Cited by 49 | Viewed by 9984

Abstract

This paper introduces a new way of managing water in irrigation systems, which can be applied to gardens or agricultural fields, replacing human intervention with Wireless Sensor Networks. A typical irrigation system wastes on average 30% of the water used, due to poor [...] Read more.

This paper introduces a new way of managing water in irrigation systems, which can be applied to gardens or agricultural fields, replacing human intervention with Wireless Sensor Networks. A typical irrigation system wastes on average 30% of the water used, due to poor management and configuration. This sustainable irrigation system allows a better efficiency in the process of irrigation that can lead to savings for the end user, not only monetary but also in natural resources, such as water and energy, leading to a more sustainable environment. The system can retrieve real time data and use them to determinate the correct amount of water to be used in a garden. With this solution, it is possible to save up to 34% of water when using sensor data from temperature, humidity and soil moisture, or up to 26% when using only temperature inputs. Besides a detailed system architecture, this paper includes a real case scenario implementation and results discussion. Full article

(This article belongs to the Special Issue Sensing and Instrumentation in IoT Era)

► Show Figures

Figure 1

31 pages, 10669 KiB

Open AccessEditor’s ChoiceArticle

The Field Monitoring Experiment of the Roof Strata Movement in Coal Mining Based on DFOS

by Tao Hu, Gongyu Hou and Zixiang Li

Sensors 2020, 20(5), 1318; https://doi.org/10.3390/s20051318 - 28 Feb 2020

Cited by 45 | Viewed by 5271

Abstract

Mining deformation of roof strata is the main cause of methane explosion, water inrush, and roof collapse accidents amid underground coal mining. To ensure the safety of coal mining, the distributed optical fiber sensor (DFOS) technology has been applied in the 150,313 working [...] Read more.

Mining deformation of roof strata is the main cause of methane explosion, water inrush, and roof collapse accidents amid underground coal mining. To ensure the safety of coal mining, the distributed optical fiber sensor (DFOS) technology has been applied in the 150,313 working face by Yinying Coal Mine in Shanxi Province, north China to monitor the roof strata movement, so as to grasp the movement law of roof strata and make it serve for production. The optical fibers are laid out in the holes drilled through the overlying strata on the roadway roof and BOTDR technique is utilized to carry out the on-site monitoring. Prior to the on-site test, the coupling test of the fiber strain in the concrete anchorage, the calibration test of the fiber strain coefficient of the 5-mm steel strand (SS) fiber, and the test of the strain transfer performance of the SS fiber were carried out in the laboratory. The approaches for fiber laying-out in the holes and fiber’s spatial positioning underground the coal mine have been optimized in the field. The indoor test results show that the high-strength SS optical fiber has a high strain transfer performance, which can be coupled with the concrete anchor with uniform deformation. This demonstrated the feasibility of SS fiber for monitoring strata movement theoretically and experimentally; and the law of roof strata fracturing and collapse is obtained from the field test results. This paper is a trial to study the whole process of dynamic movement of the deformation of roof strata. Eventually the study results will help Yinying Coal Mine to optimize mining design, prevent coal mine accidents, and provide detailed test basis for DFOS monitoring technique of roof strata movement. Full article

(This article belongs to the Special Issue Optical Fiber Sensors and Photonic Devices)

► Show Figures

Figure 1

18 pages, 7367 KiB

Open AccessEditor’s ChoiceArticle

Miniaturized 0.13-μm CMOS Front-End Analog for AlN PMUT Arrays

by Iván Zamora, Eyglis Ledesma, Arantxa Uranga and Núria Barniol

Sensors 2020, 20(4), 1205; https://doi.org/10.3390/s20041205 - 22 Feb 2020

Cited by 37 | Viewed by 8030

Abstract

This paper presents an analog front-end transceiver for an ultrasound imaging system based on a high-voltage (HV) transmitter, a low-noise front-end amplifier (RX), and a complementary-metal-oxide-semiconductor, aluminum nitride, piezoelectric micromachined ultrasonic transducer (CMOS-AlN-PMUT). The system was designed using the 0.13-μm Silterra CMOS process [...] Read more.

This paper presents an analog front-end transceiver for an ultrasound imaging system based on a high-voltage (HV) transmitter, a low-noise front-end amplifier (RX), and a complementary-metal-oxide-semiconductor, aluminum nitride, piezoelectric micromachined ultrasonic transducer (CMOS-AlN-PMUT). The system was designed using the 0.13-μm Silterra CMOS process and the MEMS-on-CMOS platform, which allowed for the implementation of an AlN PMUT on top of the CMOS-integrated circuit. The HV transmitter drives a column of six 80-μm-square PMUTs excited with 32 V in order to generate enough acoustic pressure at a 2.1-mm axial distance. On the reception side, another six 80-μm-square PMUT columns convert the received echo into an electric charge that is amplified by the receiver front-end amplifier. A comparative analysis between a voltage front-end amplifier (VA) based on capacitive integration and a charge-sensitive front-end amplifier (CSA) is presented. Electrical and acoustic experiments successfully demonstrated the functionality of the designed low-power analog front-end circuitry, which outperformed a state-of-the art front-end application-specific integrated circuit (ASIC) in terms of power consumption, noise performance, and area. Full article

(This article belongs to the Special Issue Electronics for Sensors)

► Show Figures

Figure 1

16 pages, 2130 KiB

Open AccessEditor’s ChoiceArticle

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

by María Teresa García-Ordás, José Alberto Benítez-Andrades, Isaías García-Rodríguez, Carmen Benavides and Héctor Alaiz-Moretón

Sensors 2020, 20(4), 1214; https://doi.org/10.3390/s20041214 - 22 Feb 2020

Cited by 90 | Viewed by 8129

Abstract

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases [...] Read more.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification. Full article

(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Editor’s Choice Articles

Article

Further Information

Guidelines

MDPI Initiatives

Follow MDPI