Next Issue
Volume 20, July-1
Previous Issue
Volume 20, June-1
sensors-logo

Journal Browser

Journal Browser

Table of Contents

Sensors, Volume 20, Issue 12 (June-2 2020) – 275 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story (view full-size image) Smart sensors and smartphones are becoming increasingly prevalent. These devices are capable of [...] Read more.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Up-Conversion Sensing of 2D Spatially-Modulated Infrared Information-Carrying Beams with Si-Based Cameras
Sensors 2020, 20(12), 3610; https://doi.org/10.3390/s20123610 - 26 Jun 2020
Viewed by 564
Abstract
Up-conversion sensing based on optical heterodyning of an IR (infrared) image with a local oscillator laser wave in a nonlinear optical sum-frequency mixing (SFM) process is a practical solution to circumvent some limitations of IR image sensors in terms of signal-to-noise ratio, speed, [...] Read more.
Up-conversion sensing based on optical heterodyning of an IR (infrared) image with a local oscillator laser wave in a nonlinear optical sum-frequency mixing (SFM) process is a practical solution to circumvent some limitations of IR image sensors in terms of signal-to-noise ratio, speed, resolution, or cooling needs in some demanding applications. In this way, the spectral content of an IR image can become spectrally shifted to the visible/near infrared (VIS/NWIR) and then detected with silicon focal plane arrayed sensors (Si-FPA), such as CCD/CMOS (charge-coupled and complementary metal-oxide-semiconductor devices). This work is an extension of a previous study where we recently introduced this technique in the context of optical communications, in particular in FSOC (free-space optical communications). Herein, we present an image up-conversion system based on a 1064 nm Nd3+: YVO4 solid-state laser with a KTP (potassium titanyl phosphate) nonlinear crystal located intra-cavity where a laser beam at 1550 nm 2D spatially-modulated with a binary Quick Response (QR) code is mixed, giving an up-converted code image at 631 nm that is detected with an Si-based camera. The underlying technology allows for the extension of other IR spectral allocations, construction of compact receivers at low cost, and provides a natural way for increased protection against eavesdropping. Full article
Show Figures

Figure 1

Open AccessLetter
Stone Paper as a New Substrate to Fabricate Flexible Screen-Printed Electrodes for the Electrochemical Detection of Dopamine
Sensors 2020, 20(12), 3609; https://doi.org/10.3390/s20123609 - 26 Jun 2020
Viewed by 339
Abstract
Flexible screen-printed electrodes (HP) were fabricated on stone paper substrate and amperometrically modified with gold nanoparticles (HP-AuNPs). The modified electrode displayed improved electronic transport properties, reflected in a low charge-transfer resistance (1220 Ω) and high apparent heterogeneous electron transfer rate constant (1.94 × [...] Read more.
Flexible screen-printed electrodes (HP) were fabricated on stone paper substrate and amperometrically modified with gold nanoparticles (HP-AuNPs). The modified electrode displayed improved electronic transport properties, reflected in a low charge-transfer resistance (1220 Ω) and high apparent heterogeneous electron transfer rate constant (1.94 × 10−3 cm/s). The voltammetric detection of dopamine (DA) was tested with HP and HP-AuNPs electrodes in standard laboratory solutions (pH 6 phosphate-buffered saline (PBS)) containing various concentrations of analyte (10−7–10−3 M). As expected, the modified electrode exhibits superior performances in terms of linear range (10−7–10−3 M) and limit of detection (3 × 10−8 M), in comparison with bare HP. The determination of DA was tested with HP-AuNPs in spiked artificial urine and in pharmaceutical drug solution (ZENTIVA) that contained dopamine hydrochloride (5 mg/mL). The results obtained indicated a very good DA determination in artificial urine without significant matrix effects. In the case of the pharmaceutical drug solution, the DA determination was affected by the interfering species present in the vial, such as sodium metabisulfite, maleic acid, sodium chloride, and propylene glycol. Full article
(This article belongs to the Special Issue Graphene-Based Sensors for Pharmaceutical and Biomedical Analysis)
Show Figures

Figure 1

Open AccessArticle
Advantages of Highly Spherical Gold Nanoparticles as Labels for Lateral Flow Immunoassay
Sensors 2020, 20(12), 3608; https://doi.org/10.3390/s20123608 - 26 Jun 2020
Viewed by 296
Abstract
The use of lateral flow immunoassays (LFIAs) for rapid on-site testing is restricted by their relatively high limit of detection (LoD). One possible way to decrease the LoD is to optimize nanoparticle properties that are used as labels. We compare two types of [...] Read more.
The use of lateral flow immunoassays (LFIAs) for rapid on-site testing is restricted by their relatively high limit of detection (LoD). One possible way to decrease the LoD is to optimize nanoparticle properties that are used as labels. We compare two types of Au nanoparticles: usual quasispherical gold nanoparticles (C-GNPs), obtained by the Turkevich–Frens method, and superspherical gold nanoparticles (S-GNPs), obtained by a progressive overgrowth technique. Average diameters were 18.6–47.5 nm for C-GNPs and 20.2–90.4 nm for S-GNPs. Cardiomarker troponin I was considered as the target analyte. Adsorption and covalent conjugation with antibodies were tested for both GNP types. For C-GNPs, the minimal LoD was obtained with 33.7 nm nanoparticles, reaching 12.7 ng/mL for covalent immobilization and 9.9 ng/mL for adsorption. The average diameter of S-GNPs varied from 20.2 to 64.5 nm, which resulted in a decrease in LoD for an LFIA of troponin I from 3.4 to 1.2 ng/mL for covalent immobilization and from 2.9 to 2.0 ng/mL for adsorption. Thus, we obtained an 8-fold decrease in LoD (9.9 to 1.2 ng/mL) by using S-GNPs. This effect can be related to more effective antibody immobilization and improved S-GNP optical properties. The obtained results can improve LFIAs for various practically significant analytes. Full article
(This article belongs to the Special Issue Biomedical Optical Nanosensors)
Show Figures

Figure 1

Open AccessArticle
An Experimental Test Proposal to Study Human Behaviour in Fires Using Virtual Environments
Sensors 2020, 20(12), 3607; https://doi.org/10.3390/s20123607 - 26 Jun 2020
Viewed by 297
Abstract
Human behavior in an emergency situation is the starting point for all evacuation planning projects. A better understanding of the decisions made by the occupants during an emergency can help to develop calculation tools that can create more efficient forms of visual and [...] Read more.
Human behavior in an emergency situation is the starting point for all evacuation planning projects. A better understanding of the decisions made by the occupants during an emergency can help to develop calculation tools that can create more efficient forms of visual and audio communication and implement better procedures for evacuating people. The difficulty in studying human behavior lies in the very nature of emergencies, as they are unpredictable, somewhat exceptional and not reproducible. Fire drills play a role in training emergency teams and building occupants, but they cannot be used to collect real data on people’s behavior unless the drill is so realistic that it could endanger the occupants’ safety. In the procedure described here, through the use of a Virtual Reality device that encompasses all critical phases, including user characterization data before the virtual experience, building design parameters and fire scenario, key variables of human behavior can be recorded in order to evaluate each user’s experience satisfactorily. This research shows that the average delay in starting an evacuation is greater than one minute, that anxiety levels and heart rates increase during a fire and that people do not pay attention to evacuation signals. Further analysis of the quantitative data may also provide the causes for decision-making. The use of devices that create realistic virtual environments is a solution for conducting “what if” tests to study and record the decisions taken by the users who undergo the experience in a way that is completely safe for them. Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Graphical abstract

Open AccessArticle
C2DAN: An Improved Deep Adaptation Network with Domain Confusion and Classifier Adaptation
Sensors 2020, 20(12), 3606; https://doi.org/10.3390/s20123606 - 26 Jun 2020
Viewed by 335
Abstract
Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to [...] Read more.
Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to align the feature distribution in a reproducing kernel Hilbert space. However, DAN does not perform very well in feature level transfer, and the assumption that source and target domain share classifiers is too strict in different adaptation scenarios. In this paper, we further improve the adaptability of DAN by incorporating Domain Confusion (DC) and Classifier Adaptation (CA). To achieve this, we propose a novel domain adaptation method named C2DAN. Our approach first enables Domain Confusion (DC) by using a domain discriminator for adversarial training. For Classifier Adaptation (CA), a residual block is added to the source domain classifier in order to learn the difference between source classifier and target classifier. Beyond validating our framework on the standard domain adaptation dataset office-31, we also introduce and evaluate on the Comprehensive Cars (CompCars) dataset, and the experiment results demonstrate the effectiveness of the proposed framework C2DAN. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Graphical abstract

Open AccessLetter
High-Sensitivity Goos-Hänchen Shifts Sensor Based on BlueP-TMDCs-Graphene Heterostructure
Sensors 2020, 20(12), 3605; https://doi.org/10.3390/s20123605 - 26 Jun 2020
Viewed by 299
Abstract
Surface plasmon resonance (SPR) with two-dimensional (2D) materials is proposed to enhance the sensitivity of sensors. A novel Goos–Hänchen (GH) shift sensing scheme based on blue phosphorene (BlueP)/transition metal dichalogenides (TMDCs) and graphene structure is proposed. The significantly enhanced GH shift is obtained [...] Read more.
Surface plasmon resonance (SPR) with two-dimensional (2D) materials is proposed to enhance the sensitivity of sensors. A novel Goos–Hänchen (GH) shift sensing scheme based on blue phosphorene (BlueP)/transition metal dichalogenides (TMDCs) and graphene structure is proposed. The significantly enhanced GH shift is obtained by optimizing the layers of BlueP/TMDCs and graphene. The maximum GH shift of the hybrid structure of Ag-Indium tin oxide (ITO)-BlueP/WS2–graphene is −2361λ with BlueP/WS2 four layers and a graphene monolayer. Furthermore, the GH shift can be positive or negative depending on the layer number of BlueP/TMDCs and graphene. For sensing performance, the highest sensitivity of 2.767 × 107λ/RIU is realized, which is 5152.7 times higher than the traditional Ag-SPR structure, 2470.5 times of Ag-ITO, 2159.2 times of Ag-ITO-BlueP/WS2, and 688.9 times of Ag-ITO–graphene. Therefore, such configuration with GH shift can be used in various chemical, biomedical and optical sensing fields. Full article
(This article belongs to the Special Issue 2D Material Based Plasmonic Biosensors)
Show Figures

Figure 1

Open AccessArticle
Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology
Sensors 2020, 20(12), 3604; https://doi.org/10.3390/s20123604 - 26 Jun 2020
Viewed by 324
Abstract
The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which [...] Read more.
The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc.) round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value. Full article
(This article belongs to the Special Issue Recent Advances of Blockchain Technologies in Sensor Networks)
Show Figures

Figure 1

Open AccessLetter
Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification
Sensors 2020, 20(12), 3603; https://doi.org/10.3390/s20123603 - 26 Jun 2020
Viewed by 308
Abstract
Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. [...] Read more.
Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets. Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Figure 1

Open AccessLetter
QCL-Based Dual-Comb Spectrometer for Multi-Species Measurements at High Temperatures and High Pressures
Sensors 2020, 20(12), 3602; https://doi.org/10.3390/s20123602 - 26 Jun 2020
Viewed by 338
Abstract
Rapid multi-species sensing is an overarching goal in time-resolved studies of chemical kinetics. Most current laser sources cannot achieve this goal due to their narrow spectral coverage and/or slow wavelength scanning. In this work, a novel mid-IR dual-comb spectrometer is utilized for chemical [...] Read more.
Rapid multi-species sensing is an overarching goal in time-resolved studies of chemical kinetics. Most current laser sources cannot achieve this goal due to their narrow spectral coverage and/or slow wavelength scanning. In this work, a novel mid-IR dual-comb spectrometer is utilized for chemical kinetic investigations. The spectrometer is based on two quantum cascade laser frequency combs and provides rapid (4 µs) measurements over a wide spectral range (~1175–1235 cm−1). Here, the spectrometer was applied to make time-resolved absorption measurements of methane, acetone, propene, and propyne at high temperatures (>1000 K) and high pressures (>5 bar) in a shock tube. Such a spectrometer will be of high value in chemical kinetic studies of future fuels. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Open AccessArticle
Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation
Sensors 2020, 20(12), 3601; https://doi.org/10.3390/s20123601 - 26 Jun 2020
Viewed by 317
Abstract
Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for [...] Read more.
Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets. However, for clinical acceptance, easy interpretability of the AI models is crucial. The goal of the present study was to investigate whether a multi-parameter sensor could be used during a standardized activity test to interpret functional capacity in the longitudinal follow-up of CR patients. A total of 129 patients were followed for 3 months during CR using 6-min walking tests (6MWT) equipped with a wearable ECG and accelerometer device. Functional capacity was assessed based on 6MWT distance (6MWD). Linear and nonlinear interpretable models were explored to predict 6MWD. The t-distributed stochastic neighboring embedding (t-SNE) technique was exploited to embed and visualize high dimensional data. The performance of support vector machine (SVM) models, combining different features and using different kernel types, to predict functional capacity was evaluated. The SVM model, using chronotropic response and effort as input features, showed a mean absolute error of 42.8 m (±36.8 m). The 3D-maps derived using the t-SNE technique visualized the relationship between sensor-derived biomarkers and functional capacity, which enables tracking of the evolution of patients throughout the CR program. The current study showed that wearable monitoring combined with interpretable ML can objectively track clinical progression in a CR population. These results pave the road towards ambulatory CR. Full article
Show Figures

Figure 1

Open AccessArticle
Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
Sensors 2020, 20(12), 3600; https://doi.org/10.3390/s20123600 - 26 Jun 2020
Viewed by 290
Abstract
Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the [...] Read more.
Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation. Full article
(This article belongs to the Special Issue Sensor-Based Systems for Kinematics and Kinetics)
Show Figures

Figure 1

Open AccessArticle
Speech Quality Feature Analysis for Classification of Depression and Dementia Patients
Sensors 2020, 20(12), 3599; https://doi.org/10.3390/s20123599 - 26 Jun 2020
Viewed by 323
Abstract
Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an [...] Read more.
Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Figure 1

Open AccessArticle
Quantitative 3D Reconstruction from Scanning Electron Microscope Images Based on Affine Camera Models
Sensors 2020, 20(12), 3598; https://doi.org/10.3390/s20123598 - 26 Jun 2020
Viewed by 321
Abstract
Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed [...] Read more.
Scanning electron microscopes (SEMs) are versatile imaging devices for the micro- and nanoscale that find application in various disciplines such as the characterization of biological, mineral or mechanical specimen. Even though the specimen’s two-dimensional (2D) properties are provided by the acquired images, detailed morphological characterizations require knowledge about the three-dimensional (3D) surface structure. To overcome this limitation, a reconstruction routine is presented that allows the quantitative depth reconstruction from SEM image sequences. Based on the SEM’s imaging properties that can be well described by an affine camera, the proposed algorithms rely on the use of affine epipolar geometry, self-calibration via factorization and triangulation from dense correspondences. To yield the highest robustness and accuracy, different sub-models of the affine camera are applied to the SEM images and the obtained results are directly compared to confocal laser scanning microscope (CLSM) measurements to identify the ideal parametrization and underlying algorithms. To solve the rectification problem for stereo-pair images of an affine camera so that dense matching algorithms can be applied, existing approaches are adapted and extended to further enhance the yielded results. The evaluations of this study allow to specify the applicability of the affine camera models to SEM images and what accuracies can be expected for reconstruction routines based on self-calibration and dense matching algorithms. Full article
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision)
Show Figures

Figure 1

Open AccessArticle
Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System
Sensors 2020, 20(12), 3597; https://doi.org/10.3390/s20123597 - 25 Jun 2020
Viewed by 365
Abstract
Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To [...] Read more.
Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using ϵ -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system. Full article
(This article belongs to the Special Issue Sensor-Based, Context-Aware Recommender Systems)
Show Figures

Figure 1

Open AccessArticle
Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance
Sensors 2020, 20(12), 3596; https://doi.org/10.3390/s20123596 - 25 Jun 2020
Viewed by 390
Abstract
The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an [...] Read more.
The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares Ψs and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: ‘Regina’ and ‘Sweetheart’. For this purpose, a water stress–recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on Ψs levels. The water status of trees was measured weekly using Ψs, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of Ψs and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI’s from both cameras were 11 ± 0.27%. However, these CWSI’s were not able to detect differences among irrigation treatments as compared to Ψs and gs. Full article
(This article belongs to the Section Remote Sensors)
Show Figures

Figure 1

Open AccessLetter
Doubly Covariance Matrix Reconstruction Based Blind Beamforming for Coherent Signals
Sensors 2020, 20(12), 3595; https://doi.org/10.3390/s20123595 - 25 Jun 2020
Viewed by 336
Abstract
This paper proposes a beamforming method in the presence of coherent multipath arrivals at the array. The proposed method avoids the prior knowledge or estimation of the directions of arrival (DOAs) of the direct path signal and the multipath signals. The interferences are [...] Read more.
This paper proposes a beamforming method in the presence of coherent multipath arrivals at the array. The proposed method avoids the prior knowledge or estimation of the directions of arrival (DOAs) of the direct path signal and the multipath signals. The interferences are divided into two groups based on their powers and the interference-plus-noise covariance matrix (INCM) is reconstructed through the doubly covariance matrix reconstruction concept. The composite steering vector (CSV) that accounts for the direct path signal and multipath signals is estimated as the principal eigenvector of the sample covariance matrix with interferences and noise removed. The optimal weight vector is finally computed using the INCM and the CSV. The proposed method involves no spatial smoothing and avoids reduction in the degree of freedom. Simulation results demonstrate the improved performance of the proposed method. Full article
Show Figures

Figure 1

Open AccessLetter
Measurement of In-Plane Motions in MEMS
Sensors 2020, 20(12), 3594; https://doi.org/10.3390/s20123594 - 25 Jun 2020
Viewed by 319
Abstract
We report a technique to measure in-plane and out-of-plane motions of MEMS using typical out-of-plane (single-axis) Laser Doppler Vibrometers (LDVs). The efficacy of the technique is demonstrated by evaluating the in-plane and out-of-plane modal response and frequency response of an interdigitated comb-drive actuator. [...] Read more.
We report a technique to measure in-plane and out-of-plane motions of MEMS using typical out-of-plane (single-axis) Laser Doppler Vibrometers (LDVs). The efficacy of the technique is demonstrated by evaluating the in-plane and out-of-plane modal response and frequency response of an interdigitated comb-drive actuator. We also investigate the validity of observing planar modes of vibration outside their dominant plane of motion and find that it leads to erroneous results. Planar modes must be evaluated in their plan of motion. Full article
(This article belongs to the Special Issue MEMS and NEMS Sensors)
Show Figures

Figure 1

Open AccessArticle
A Low-Cost, Disposable and Portable Inkjet-Printed Biochip for the Developing World
Sensors 2020, 20(12), 3593; https://doi.org/10.3390/s20123593 - 25 Jun 2020
Viewed by 341
Abstract
Electrowetting on dielectric-based digital microfluidic platforms (EWOD-DMF) have a potential to impact point-of-care diagnostics. Conventionally, EWOD-DMF platforms are manufactured in cleanrooms by expert technicians using costly and time consuming micro-nanofabrication processes such as optical lithography, depositions and etching. However, such high-end microfabrication facilities [...] Read more.
Electrowetting on dielectric-based digital microfluidic platforms (EWOD-DMF) have a potential to impact point-of-care diagnostics. Conventionally, EWOD-DMF platforms are manufactured in cleanrooms by expert technicians using costly and time consuming micro-nanofabrication processes such as optical lithography, depositions and etching. However, such high-end microfabrication facilities are extremely challenging to establish in resource-poor and low-income countries, due to their high capital investment and operating costs. This makes the fabrication of EWOD-DMF platforms extremely challenging in low-income countries, where such platforms are most needed for many applications such as point-of-care testing applications. To address this challenge, we present a low-cost and simple fabrication procedure for EWOD-DMF electrode arrays, which can be performed anywhere with a commercial office inkjet printer without the need of expensive cleanroom facilities. We demonstrate the utility of our platform to move and mix droplets of different reagents and physiologically conductive buffers, thereby showing its capability to potentially perform a variety of biochemical assays. By combining our low-cost, inkjet-printed EWOD-DMF platform with smartphone imaging technology and a compact control system for droplet manipulation, we also demonstrate a portable and hand-held device which can be programmed to potentially perform a variety of biochemical assays. Full article
(This article belongs to the Section Biosensors)
Show Figures

Figure 1

Open AccessArticle
Sensing HIV Protease and Its Inhibitor Using “Helical Epitope”—Imprinted Polymers
Sensors 2020, 20(12), 3592; https://doi.org/10.3390/s20123592 - 25 Jun 2020
Viewed by 274
Abstract
A helical epitope-peptide (lle85-Gly94) was selected from the α-helix structure of the HIV protease (PR) as the template, which represents an intricate interplay between structure conformation and dimerization. The peptide template was mixed with water, trifluoroethanol (TFE), and acetonitrile [...] Read more.
A helical epitope-peptide (lle85-Gly94) was selected from the α-helix structure of the HIV protease (PR) as the template, which represents an intricate interplay between structure conformation and dimerization. The peptide template was mixed with water, trifluoroethanol (TFE), and acetonitrile (ACN) at a certain ratio to enlarge the helical conformation in the solution for the fabrication of helical epitope-mediated molecularly imprinted polymers (HEMIPs) on a quartz crystal microbalance (QCM) chip. The template molecules were then removed under equilibrium batch rebinding conditions involving 5% acetic acid/water. The resulting HEMIPs chip exhibited a high affinity toward template peptide HIV PR85–94, His-tagged HIV PR, and HIV PR, with dissociation constants (Kd) as 160, 43.3, and 78.5 pM, respectively. The detection limit of the developed HIV PR85–94 QCM sensor is 0.1 ng/mL. The HEMIPs chip exhibited a high affinity and selectivity to bind HIV PR and subsequently to an inhibitor of HIV PR (nelfinavir). The HIV PR binding site was properly oriented on the HEMIPs-chip to develop a HIV PR/HEMIPs chip, which can effectively bind nelfinavir to establish a sandwich assay. The nelfinavir then attached to the HIV PR/HEMIPs chip, which can be easily removed involving 0.8% acetic acid/water. Therefore, HIV PR/HEMIPs chip can be useful to screen for other HIV PR inhibitors. This technique may improve drug targeting for HIV therapy and also strengthen investigations into other virus assays. Full article
(This article belongs to the Special Issue Molecularly Imprinted Polymer Sensing Platforms)
Show Figures

Figure 1

Open AccessLetter
Real-Time Moving Object Detection in High-Resolution Video Sensing
Sensors 2020, 20(12), 3591; https://doi.org/10.3390/s20123591 - 25 Jun 2020
Viewed by 376
Abstract
This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on [...] Read more.
This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Figure 1

Open AccessReview
Bluetooth Low Energy Mesh Networks: Survey of Communication and Security Protocols
Sensors 2020, 20(12), 3590; https://doi.org/10.3390/s20123590 - 25 Jun 2020
Viewed by 446
Abstract
Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support [...] Read more.
Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support has increased the vulnerability of BLE to security threats, since a larger number of devices can participate in a BLE Mesh network. To address these concerns, BLE version 5 enhanced existing BLE security features to deal with various authenticity, integrity, and confidentiality issues. However, there is still a lack of detailed studies related to these new security features. This survey examines the most recent BLE-based mesh network protocols and related security issues. In the first part, the latest BLE-based mesh communication protocols are discussed. The analysis shows that the implementation of BLE pure mesh protocols remains an open research issue. Moreover, there is a lack of auto-configuration mechanisms in order to support bootstrapping of BLE pure mesh networks. In the second part, recent BLE-related security issues and vulnerabilities are highlighted. Strong Intrusion Detection Systems (IDS) are essential for detecting security breaches in order to protect against zero-day exploits. Nonetheless, viable IDS solutions for BLE Mesh networks remain a nascent research area. Consequently, a comparative survey of IDS approaches for related low-power wireless protocols was used to map out potential approaches for enhancing IDS solutions for BLE Mesh networks. Full article
(This article belongs to the Special Issue Wireless Communication in Internet of Things)
Show Figures

Figure 1

Open AccessLetter
In-Situ Measurement of Fresh Produce Respiration Using a Modular Sensor-Based System
Sensors 2020, 20(12), 3589; https://doi.org/10.3390/s20123589 - 25 Jun 2020
Viewed by 329
Abstract
In situ, continuous and real-time monitoring of respiration (R) and respiratory quotient (RQ) are crucial for identifying the optimal conditions for the long-term storage of fresh produce. This study reports the application of a gas sensor (RMS88) and a modular respirometer for in [...] Read more.
In situ, continuous and real-time monitoring of respiration (R) and respiratory quotient (RQ) are crucial for identifying the optimal conditions for the long-term storage of fresh produce. This study reports the application of a gas sensor (RMS88) and a modular respirometer for in situ real-time monitoring of gas concentrations and respiration rates of strawberries during storage in a lab-scale controlled atmosphere chamber (190 L) and of Pinova apples in a commercial storage facility (170 t). The RMS88 consisted of wireless O2 (0% to 25%) and CO2 sensors (0% to 0.5% and 0% to 5%). The modular respirometer (3.3 L for strawberries and 7.4 L for apples) consisted of a leak-proof arrangement with a water-containing base plate and a glass jar on top. Gas concentrations were continuously recorded by the RMS88 at regular intervals of 1 min for strawberries and 5 min for apples and, in real-time, transferred to a terminal program to calculate respiration rates ( R O 2 and R CO 2 ) and RQ. Respiration measurement was done in cycles of flushing and measurement period. A respiration measurement cycle with a measurement period of 2 h up to 3 h was shown to be useful for strawberries under air at 10 °C. The start of anaerobic respiration of strawberries due to low O2 concentration (1%) could be recorded in real-time. R O 2 and R CO 2 of Pinova apples were recorded every 5 min during storage and mean values of 1.6 and 2.7 mL kg−1 h−1, respectively, were obtained when controlled atmosphere (CA) conditions (2% O2, 1.3% CO2 and 2 °C) were established. The modular respirometer was found to be useful for in situ real-time monitoring of respiration rate during storage of fresh produce and offers great potential to be incorporated into RQ-based dynamic CA storage system. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Open AccessArticle
Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces
Sensors 2020, 20(12), 3588; https://doi.org/10.3390/s20123588 - 25 Jun 2020
Viewed by 355
Abstract
Brain–computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography [...] Read more.
Brain–computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs. Full article
Show Figures

Figure 1

Open AccessReview
Wireless Body Sensor Communication Systems Based on UWB and IBC Technologies: State-of-the-Art and Open Challenges
Sensors 2020, 20(12), 3587; https://doi.org/10.3390/s20123587 - 25 Jun 2020
Viewed by 357
Abstract
In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband [...] Read more.
In recent years there has been an increasing need for miniature, low-cost, commercially accessible, and user-friendly sensor solutions for wireless body area networks (WBAN), which has led to the adoption of new physical communication interfaces providing distinctive advantages over traditional wireless technologies. Ultra-wideband (UWB) and intrabody communication (IBC) have been the subject of intensive research in recent years due to their promising characteristics as means for short-range, low-power, and low-data-rate wireless interfaces for interconnection of various sensors and devices placed on, inside, or in the close vicinity of the human body. The need for safe and standardized solutions has resulted in the development of two relevant standards, IEEE 802.15.4 (for UWB) and IEEE 802.15.6 (for UWB and IBC), respectively. This paper presents an in-depth overview of recent studies and advances in the field of application of UWB and IBC technologies for wireless body sensor communication systems. Full article
(This article belongs to the Special Issue Wireless Body Sensors)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
Sensors 2020, 20(12), 3586; https://doi.org/10.3390/s20123586 - 25 Jun 2020
Viewed by 311
Abstract
Global Navigation Satellite Systems (GNSS) are the main source of position, navigation, and timing (PNT) information and will be a key player in the next-generation intelligent transportation systems and safety-critical applications, but several limitations need to be overcome to meet the stringent performance [...] Read more.
Global Navigation Satellite Systems (GNSS) are the main source of position, navigation, and timing (PNT) information and will be a key player in the next-generation intelligent transportation systems and safety-critical applications, but several limitations need to be overcome to meet the stringent performance requirements. One of the open issues is how to provide precise PNT solutions in harsh propagation environments. Under nominal conditions, the former is typically achieved by exploiting carrier phase information through precise positioning techniques, but these methods are very sensitive to the quality of phase observables. Another option that is gaining interest in the scientific community is the use of large bandwidth signals, which allow obtaining a better baseband resolution, and therefore more precise code-based observables. Two options may be considered: (i) high-order binary offset carrier (HO-BOC) modulations or (ii) the concept of GNSS meta-signals. In this contribution, we assess the time-delay and phase maximum likelihood (ML) estimation performance limits of such signals, together with the performance translation into the position domain, considering single point positioning (SPP) and RTK solutions, being an important missing point in the literature. A comprehensive discussion is provided on the estimators’ behavior, the corresponding ML threshold regions, the impact of good and bad satellite constellation geometries, and final conclusions on the best candidates, which may lead to precise solutions under harsh conditions. It is found that if the receiver is constrained by the receiver bandwidth, the best choices are the L1-M or E6-Public Regulated Service (PRS) signals. If the receiver is able to operate at 60 MHz, it is recommended to exploit the full-bandwidth Galileo E5 signal. In terms of robustness and performance, if the receiver can operate at 135 MHz, the best choice is to use the GNSS meta-signals E5 + E6 or B2 + B3, which provide the best overall performances regardless of the positioning method used, the satellite constellation geometry, or the propagation conditions. Full article
(This article belongs to the Special Issue Recent Advances in GNSS-based High Precision Positioning Technology)
Show Figures

Figure 1

Open AccessArticle
Characterization of Low-Cost Capacitive Soil Moisture Sensors for IoT Networks
Sensors 2020, 20(12), 3585; https://doi.org/10.3390/s20123585 - 25 Jun 2020
Viewed by 370
Abstract
The rapid development and wide application of the IoT (Internet of Things) has pushed toward the improvement of current practices in greenhouse technology and agriculture in general, through automation and informatization. The experimental and accurate determination of soil moisture is a matter of [...] Read more.
The rapid development and wide application of the IoT (Internet of Things) has pushed toward the improvement of current practices in greenhouse technology and agriculture in general, through automation and informatization. The experimental and accurate determination of soil moisture is a matter of great importance in different scientific fields, such as agronomy, soil physics, geology, hydraulics, and soil mechanics. This paper focuses on the experimental characterization of a commercial low-cost “capacitive” coplanar soil moisture sensor that can be housed in distributed nodes for IoT applications. It is shown that at least for a well-defined type of soil with a constant solid matter to volume ratio, this type of capacitive sensor yields a reliable relationship between output voltage and gravimetric water content. Full article
(This article belongs to the Special Issue Soil Moisture Sensors for Irrigation Management)
Show Figures

Figure 1

Open AccessArticle
Window-Modulated Compounding Nakagami Parameter Ratio Approach for Assessing Muscle Perfusion with Contrast-Enhanced Ultrasound Imaging
Sensors 2020, 20(12), 3584; https://doi.org/10.3390/s20123584 - 24 Jun 2020
Viewed by 374
Abstract
The assessment of microvascular perfusion is essential for the diagnosis of a specific muscle disease. In comparison with the current available medical modalities, the contrast-enhanced ultrasound imaging is the simplest and fastest means for probing the tissue perfusion. Specifically, the perfusion parameters estimated [...] Read more.
The assessment of microvascular perfusion is essential for the diagnosis of a specific muscle disease. In comparison with the current available medical modalities, the contrast-enhanced ultrasound imaging is the simplest and fastest means for probing the tissue perfusion. Specifically, the perfusion parameters estimated from the ultrasound time-intensity curve (TIC) and statistics-based time–Nakagami parameter curve (TNC) approaches were found able to quantify the perfusion. However, due to insufficient tolerance on tissue clutters and subresolvable effects, these approaches remain short of reproducibility and robustness. Consequently, the window-modulated compounding (WMC) Nakagami parameter ratio imaging was proposed to alleviate these effects, by taking the ratio of WMC Nakagami parameters corresponding to the incidence of two different acoustic pressures from an employed transducer. The time–Nakagami parameter ratio curve (TNRC) approach was also developed to estimate perfusion parameters. Measurements for the assessment of muscle perfusion were performed from the flow phantom and animal subjects administrated with a bolus of ultrasound contrast agents. The TNRC approach demonstrated better sensitivity and tolerance of tissue clutters than those of TIC and TNC. The fusion image with the WMC Nakagami parameter ratio and B-mode images indicated that both the tissue structures and perfusion properties of ultrasound contrast agents may be better discerned. Full article
Show Figures

Figure 1

Open AccessArticle
Bipolar Optical Code Division Multiple Access Techniques Using a Dual Electro-Optical Modulator Implemented in Free-Space Optics Communications
Sensors 2020, 20(12), 3583; https://doi.org/10.3390/s20123583 - 24 Jun 2020
Viewed by 338
Abstract
This study developed a bipolar optical code division multiple access (Bi-OCDMA) technique based on spectral amplitude coding for the formation and transmission of optical-polarized and coded signals over wireless optical channels. Compared with conventional Bi-OCDMA schemes, the proposed free-space optics communication system that [...] Read more.
This study developed a bipolar optical code division multiple access (Bi-OCDMA) technique based on spectral amplitude coding for the formation and transmission of optical-polarized and coded signals over wireless optical channels. Compared with conventional Bi-OCDMA schemes, the proposed free-space optics communication system that uses a dual electro-optical modulator design improves the transmission rate. In theory, multiple access interference can be removed by using correlation subtraction schemes. The experiment results revealed that the proposed system can be employed to accurately extract codewords from an M-sequence and subsequently reconstruct the desired original data. Moreover, the proposed architecture can be implemented easily in simple and cost-effective designs and may be beneficial for broadening the use of Bi-OCDMA schemes in wireless optical communications. Full article
Show Figures

Figure 1

Open AccessArticle
Developing of Low-Cost Air Pollution Sensor—Measurements with the Unmanned Aerial Vehicles in Poland
Sensors 2020, 20(12), 3582; https://doi.org/10.3390/s20123582 - 24 Jun 2020
Viewed by 523
Abstract
This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the [...] Read more.
This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM2.5 and PM10 suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area. Full article
(This article belongs to the Special Issue Air Quality and Sensor Networks)
Show Figures

Figure 1

Open AccessArticle
TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud
Sensors 2020, 20(12), 3581; https://doi.org/10.3390/s20123581 - 24 Jun 2020
Viewed by 387
Abstract
In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a [...] Read more.
In the recent past, the number of surveillance cameras placed in the public has increased significantly, and an enormous amount of visual data is produced at an alarming rate. Resultantly, there is a demand for a distributed system for video analytics. However, a majority of existing research on video analytics focuses on improving video content management and rely on a traditional client/server framework. In this paper, we develop a scalable and flexible framework called TORNADO on top of general-purpose big data technologies for intelligent video big data analytics in the cloud. The proposed framework acquires video streams from device-independent data-sources utilizing distributed streams and file management systems. High-level abstractions are provided to allow the researcher to develop and deploy video analytics algorithms and services in the cloud under the as-a-service paradigm. Furthermore, a unified IR Middleware has been proposed to orchestrate the intermediate results being generated during video big data analytics in the cloud. We report results demonstrating the performance of the proposed framework and the viability of its usage in terms of better scalability, less fault-tolerance, and better performance. Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop