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Sensors, Volume 20, Issue 10 (May-2 2020) – 261 articles

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Cover Story (view full-size image) Diffuse optical tomography is a non-invasive photonics-based imaging technology suited to [...] Read more.
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Open AccessArticle
Modeling and Analysis of Capacitive Relaxation Quenching in a Single Photon Avalanche Diode (SPAD) Applied to a CMOS Image Sensor
Sensors 2020, 20(10), 3007; https://doi.org/10.3390/s20103007 - 25 May 2020
Viewed by 577
Abstract
We present an analysis of carrier dynamics of the single-photon detection process, i.e., from Geiger mode pulse generation to its quenching, in a single-photon avalanche diode (SPAD). The device is modeled by a parallel circuit of a SPAD and a capacitance representing both [...] Read more.
We present an analysis of carrier dynamics of the single-photon detection process, i.e., from Geiger mode pulse generation to its quenching, in a single-photon avalanche diode (SPAD). The device is modeled by a parallel circuit of a SPAD and a capacitance representing both space charge accumulation inside the SPAD and parasitic components. The carrier dynamics inside the SPAD is described by time-dependent bipolar-coupled continuity equations (BCE). Numerical solutions of BCE show that the entire process completes within a few hundreds of picoseconds. More importantly, we find that the total amount of charges stored on the series capacitance gives rise to a voltage swing of the internal bias of SPAD twice of the excess bias voltage with respect to the breakdown voltage. This, in turn, gives a design methodology to control precisely generated charges and enables one to use SPADs as conventional photodiodes (PDs) in a four transistor pixel of a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) with short exposure time and without carrier overflow. Such operation is demonstrated by experiments with a 6 µm size 400 × 400 pixels SPAD-based CIS designed with this methodology. Full article
(This article belongs to the Special Issue Photon Counting Image Sensors)
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Open AccessArticle
A Comparison of Different Counting Methods for a Holographic Particle Counter: Designs, Validations and Results
Sensors 2020, 20(10), 3006; https://doi.org/10.3390/s20103006 - 25 May 2020
Viewed by 291
Abstract
Digital Inline Holography (DIH) is used in many fields of Three-Dimensional (3D) imaging to locate micro or nano-particles in a volume and determine their size, shape or trajectories. A variety of different wavefront reconstruction approaches have been developed for 3D profiling and tracking [...] Read more.
Digital Inline Holography (DIH) is used in many fields of Three-Dimensional (3D) imaging to locate micro or nano-particles in a volume and determine their size, shape or trajectories. A variety of different wavefront reconstruction approaches have been developed for 3D profiling and tracking to study particles’ morphology or visualize flow fields. The novel application of Holographic Particle Counters (HPCs) requires observing particle densities in a given sampling volume which does not strictly necessitate the reconstruction of particles. Such typically spherical objects yield circular intereference patterns—also referred to as fringe patterns—at the hologram plane which can be detected by simpler Two-Dimensional (2D) image processing means. The determination of particle number concentrations (number of particles/unit volume [#/cm 3 ]) may therefore be based on the counting of fringe patterns at the hologram plane. In this work, we explain the nature of fringe patterns and extract the most relevant features provided at the hologram plane. The features aid the identification and selection of suitable pattern recognition techniques and its parameterization. We then present three different techniques which are customized for the detection and counting of fringe patterns and compare them in terms of detection performance and computational speed. Full article
(This article belongs to the Special Issue Photonics-Based Sensors for Environment and Pollution Monitoring)
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Open AccessLetter
Terahertz Gas-Phase Spectroscopy Using a Sub-Wavelength Thick Ultrahigh-Q Microresonator
Sensors 2020, 20(10), 3005; https://doi.org/10.3390/s20103005 - 25 May 2020
Viewed by 298
Abstract
The terahertz spectrum provides tremendous opportunities for broadband gas-phase spectroscopy, as numerous molecules exhibit strong fundamental resonances in the THz frequency range. However, cutting-edge THz gas-phase spectrometer require cumbersome multi-pass gas cells to reach sufficient sensitivity for trace level gas detection. Here, we [...] Read more.
The terahertz spectrum provides tremendous opportunities for broadband gas-phase spectroscopy, as numerous molecules exhibit strong fundamental resonances in the THz frequency range. However, cutting-edge THz gas-phase spectrometer require cumbersome multi-pass gas cells to reach sufficient sensitivity for trace level gas detection. Here, we report on the first demonstration of a THz gas-phase spectrometer using a sub-wavelength thick ultrahigh-Q THz disc microresonator. Leveraging the microresonator’s ultrahigh quality factor in excess of 120,000 as well as the intrinsically large evanescent field, allows for the implementation of a very compact spectrometer without the need for complex multi-pass gas cells. Water vapour concentrations as low as 4 parts per million at atmospheric conditions have been readily detected in proof-of-concept experiments. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Characterisation of Ex Vivo Liver Thermal Properties for Electromagnetic-Based Hyperthermic Therapies
Sensors 2020, 20(10), 3004; https://doi.org/10.3390/s20103004 - 25 May 2020
Viewed by 286
Abstract
Electromagnetic-based hyperthermic therapies induce a controlled increase of temperature in a specific tissue target in order to increase the tissue perfusion or metabolism, or even to induce cell necrosis. These therapies require accurate knowledge of dielectric and thermal properties to optimise treatment plans. [...] Read more.
Electromagnetic-based hyperthermic therapies induce a controlled increase of temperature in a specific tissue target in order to increase the tissue perfusion or metabolism, or even to induce cell necrosis. These therapies require accurate knowledge of dielectric and thermal properties to optimise treatment plans. While dielectric properties have been well investigated, only a few studies have been conducted with the aim of understanding the changes of thermal properties as a function of temperature; i.e., thermal conductivity, volumetric heat capacity and thermal diffusivity. In this study, we experimentally investigate the thermal properties of ex vivo ovine liver in the hyperthermic temperature range, from 25 °C to 97 °C. A significant increase in thermal properties is observed only above 90 °C. An analytical model is developed to model the thermal properties as a function of temperature. Thermal properties are also investigated during the natural cooling of the heated tissue. A reversible phenomenon of the thermal properties is observed; during the cooling, thermal properties followed the same behaviour observed in the heating process. Additionally, tissue density and water content are evaluated at different temperatures. Density does not change with temperature; mass and volume losses change proportionally due to water vaporisation. A 30% water loss was observed above 90 °C. Full article
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Open AccessArticle
Sensor Based on PZT Ceramic Resonator with Lateral Electric Field for Immunodetectionof Bacteria in the Conducting Aquatic Environment †
Sensors 2020, 20(10), 3003; https://doi.org/10.3390/s20103003 - 25 May 2020
Viewed by 261
Abstract
A biological sensor for detection and identification of bacterial cells, including a resonator with a lateral electric field based on PZT ceramics was experimentally investigated. For bacterial immunodetection the frequency dependencies of the electric impedance of the sensor with a suspension of microbial [...] Read more.
A biological sensor for detection and identification of bacterial cells, including a resonator with a lateral electric field based on PZT ceramics was experimentally investigated. For bacterial immunodetection the frequency dependencies of the electric impedance of the sensor with a suspension of microbial cells were measured before and after adding the specific antibodies. It was found that the addition of specific antibodies to a suspension of microbial cells led to a significant change in these frequency dependencies due to the increase in the conductivity of suspension. The analysis of microbial cells was carried out in aqueous solutions with a conductivity of 4.5–1000 μS/cm, as well as in the tap and drinking water. The detection limit of microbial cells was found to be 103 cells/mL and the analysis time did not exceed 4 min. Experiments with non-specific antibodies were also carried out and it was shown that their addition to the cell suspension did not lead to a change in the analytical signal of the sensor. This confirms the ability to not only detect, but also identify bacterial cells in suspensions. Full article
(This article belongs to the Special Issue Acoustic Wave Sensors for Gaseous and Liquid Environments)
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Open AccessComment
Comment on the Article "A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection"
Sensors 2020, 20(10), 3002; https://doi.org/10.3390/s20103002 - 25 May 2020
Viewed by 269
Abstract
This reply aims to correct some incomplete/incorrect information provided in the article "A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection", when the authors compare their results with some state-of-the-art contributions. Full article
(This article belongs to the Section Remote Sensors)
Open AccessReply
Reply to Comments: A Lightweight and Low-Power UAV-Borne Ground Penetrating Radar Design for Landmine Detection
Sensors 2020, 20(10), 3001; https://doi.org/10.3390/s20103001 - 25 May 2020
Viewed by 262
Abstract
In this brief note, we respond to the comments made by Dr [...] Full article
(This article belongs to the Section Remote Sensors)
Open AccessReview
Selfishness in Vehicular Delay-Tolerant Networks: A Review
Sensors 2020, 20(10), 3000; https://doi.org/10.3390/s20103000 - 25 May 2020
Viewed by 335
Abstract
Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the [...] Read more.
Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future. Full article
(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)
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Open AccessArticle
Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar
Sensors 2020, 20(10), 2999; https://doi.org/10.3390/s20102999 - 25 May 2020
Viewed by 265
Abstract
In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has [...] Read more.
In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Residual Energy Analysis in Cognitive Radios with Energy Harvesting UAV under Reliability and Secrecy Constraints
Sensors 2020, 20(10), 2998; https://doi.org/10.3390/s20102998 - 25 May 2020
Viewed by 258
Abstract
The integration of unmanned aerial vehicles (UAVs) with a cognitive radio (CR) technology can improve the spectrum utilization. However, UAV network services demand reliable and secure communications, along with energy efficiency to prolong battery life. We consider an energy harvesting UAV (e.g., surveillance [...] Read more.
The integration of unmanned aerial vehicles (UAVs) with a cognitive radio (CR) technology can improve the spectrum utilization. However, UAV network services demand reliable and secure communications, along with energy efficiency to prolong battery life. We consider an energy harvesting UAV (e.g., surveillance drone) flying periodically in a circular track around a ground-mounted primary transmitter. The UAV, with limited-energy budget, harvests radio frequency energy and uses the primary spectrum band opportunistically. To obtain intuitive insight into the performance of energy-harvesting, and reliable and secure communications, the closed-form expressions of the residual energy, connection outage probability, and secrecy outage probability, respectively, are analytically derived. We construct the optimization problems of residual energy with reliable and secure communications, under scenarios without and with an eavesdropper, respectively, and the analytical solutions are obtained with the approximation of perfect sensing. The numerical simulations verify the analytical results and identify the requirements of length of sensing phase and transmit power for the maximum residual energy in both reliable and secure communication scenarios. Additionally, it is shown that the residual energy in secure communication is lower than that in reliable communication. Full article
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Open AccessReview
A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems
Sensors 2020, 20(10), 2997; https://doi.org/10.3390/s20102997 - 25 May 2020
Viewed by 216
Abstract
Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera [...] Read more.
Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments. Full article
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Open AccessReview
Hollow-Core Photonic Crystal Fiber Gas Sensing
Sensors 2020, 20(10), 2996; https://doi.org/10.3390/s20102996 - 25 May 2020
Viewed by 261
Abstract
Fiber gas sensing techniques have been applied for a wide range of industrial applications. In this paper, the basic fiber gas sensing principles and the development of different fibers have been introduced. In various specialty fibers, hollow-core photonic crystal fibers (HC-PCFs) can overcome [...] Read more.
Fiber gas sensing techniques have been applied for a wide range of industrial applications. In this paper, the basic fiber gas sensing principles and the development of different fibers have been introduced. In various specialty fibers, hollow-core photonic crystal fibers (HC-PCFs) can overcome the fundamental limits of solid fibers and have attracted intense interest recently. Here, we focus on the review of HC-PCF gas sensing, including the light-guiding mechanisms of HC-PCFs, various sensing configurations, microfabrication approaches, and recent research advances including the mid-infrared gas sensors via hollow core anti-resonant fibers. This review gives a detailed and deep understanding of HC-PCF gas sensors and will promote more practical applications of HC-PCFs in the near future. Full article
(This article belongs to the Special Issue Photonic Crystal Fiber Gas Sensor)
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Open AccessComment
Comment on “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor”
Sensors 2020, 20(10), 2995; https://doi.org/10.3390/s20102995 - 25 May 2020
Viewed by 268
Abstract
The recent paper “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor” (Sensors 2020, 20, 354) proposes a wearable system based on a foot-worn miniature inertial measurement unit (MIMU) and different methods to detect [...] Read more.
The recent paper “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor” (Sensors 2020, 20, 354) proposes a wearable system based on a foot-worn miniature inertial measurement unit (MIMU) and different methods to detect hurdle clearance and to identify the leading leg during 400-m hurdle races. Furthermore, the presented system identifies changes in contact time, flight time, running speed, and step frequency throughout the race. In this comment, we discuss the original paper with a focus on the ecological validity and the applicability of MIMU systems for field-based settings, such as training or competition for elite athletes. Full article
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Open AccessArticle
Efficient Resource Allocation for Backhaul-Aware Unmanned Air Vehicles-to-Everything (U2X)
Sensors 2020, 20(10), 2994; https://doi.org/10.3390/s20102994 - 25 May 2020
Viewed by 250
Abstract
Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable [...] Read more.
Unmanned aerial vehicles (UAVs) allow better coverage, enhanced connectivity, and elongated lifetime when used in telecommunications. However, these features are predominately affected by the policies used for sharing resources amongst the involved nodes. Moreover, the architecture and deployment strategies also have a considerable impact on their functionality. Recently, many researchers have suggested using layer-based UAV deployment, which allows better communications between the entities. Regardless of these solutions, there are a limited number of studies which focus on connecting layered-UAVs to everything (U2X). In particular, none of them have actually addressed the aspect of resource allocation. This paper considers the issue of resource allocation and helps decide the optimal number of transfers amongst the UAVs, which can conserve the maximum amount of energy while increasing the overall probability of resource allocation. The proposed approach relies on mutual-agreement based reward theory, which considers Minkowski distance as a decisive metric and helps attain efficient resource allocation for backhaul-aware U2X. The effectiveness of the proposed solution is demonstrated using Monte-Carlo simulations. Full article
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Open AccessReply
Reply to Comments: Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor
Sensors 2020, 20(10), 2993; https://doi.org/10.3390/s20102993 - 25 May 2020
Viewed by 252
Abstract
The current document answers the comment addressed by Schmidt, M [...] Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Routing Based Multi-Agent System for Network Reliability in the Smart Microgrid
Sensors 2020, 20(10), 2992; https://doi.org/10.3390/s20102992 - 25 May 2020
Viewed by 263
Abstract
Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this [...] Read more.
Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids. Full article
(This article belongs to the Special Issue Smart Energy City with AI, IoT and Big Data)
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Open AccessArticle
Quality Control and Pre-Analysis Treatment of the Environmental Datasets Collected by an Internet Operated Deep-Sea Crawler during Its Entire 7-Year Long Deployment (2009–2016)
Sensors 2020, 20(10), 2991; https://doi.org/10.3390/s20102991 - 25 May 2020
Viewed by 220
Abstract
Deep-sea environmental datasets are ever-increasing in size and diversity, as technological advances lead monitoring studies towards long-term, high-frequency data acquisition protocols. This study presents examples of pre-analysis data treatment steps applied to the environmental time series collected by the Internet Operated Deep-sea Crawler [...] Read more.
Deep-sea environmental datasets are ever-increasing in size and diversity, as technological advances lead monitoring studies towards long-term, high-frequency data acquisition protocols. This study presents examples of pre-analysis data treatment steps applied to the environmental time series collected by the Internet Operated Deep-sea Crawler “Wally” during a 7-year deployment (2009–2016) in the Barkley Canyon methane hydrates site, off Vancouver Island (BC, Canada). Pressure, temperature, electrical conductivity, flow, turbidity, and chlorophyll data were subjected to different standardizing, normalizing, and de-trending methods on a case-by-case basis, depending on the nature of the treated variable and the range and scale of the values provided by each of the different sensors. The final pressure, temperature, and electrical conductivity (transformed to practical salinity) datasets are ready for use. On the other hand, in the cases of flow, turbidity, and chlorophyll, further in-depth processing, in tandem with data describing the movement and position of the crawler, will be needed in order to filter out all possible effects of the latter. Our work evidences challenges and solutions in multiparametric data acquisition and quality control and ensures that a big step is taken so that the available environmental data meet high quality standards and facilitate the production of reliable scientific results. Full article
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Open AccessArticle
IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning
Sensors 2020, 20(10), 2990; https://doi.org/10.3390/s20102990 - 25 May 2020
Viewed by 315
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)
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Open AccessArticle
A RPCA-Based ISAR Imaging Method for Micromotion Targets
Sensors 2020, 20(10), 2989; https://doi.org/10.3390/s20102989 - 25 May 2020
Viewed by 237
Abstract
Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler [...] Read more.
Micro-Doppler generated by the micromotion of a target contaminates the inverse synthetic aperture radar (ISAR) image heavily. To acquire a clear ISAR image, removing the Micro-Doppler is an indispensable task. By exploiting the sparsity of the ISAR image and the low-rank of Micro-Doppler signal in the Range-Doppler (RD) domain, a novel Micro-Doppler removal method based on the robust principal component analysis (RPCA) framework is proposed. We formulate the model of sparse ISAR imaging for micromotion target in the framework of RPCA. Then, the imaging problem is decomposed into iterations between the sub-problem of sparse imaging and Micro-Doppler extraction. The alternative direction method of multipliers (ADMM) approach is utilized to seek for the solution of each sub-problem. Furthermore, to improve the computational efficiency and numerical robustness in the Micro-Doppler extraction, an SVD-free method is presented to further lessen the calculative burden. Experimental results with simulated data validate the effectiveness of the proposed method. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessViewpoint
Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective
Sensors 2020, 20(10), 2988; https://doi.org/10.3390/s20102988 - 25 May 2020
Viewed by 398
Abstract
In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still [...] Read more.
In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand—where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development. Full article
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Open AccessArticle
Bragg Peak Localization with Piezoelectric Sensors for Proton Therapy Treatment
Sensors 2020, 20(10), 2987; https://doi.org/10.3390/s20102987 - 25 May 2020
Viewed by 264
Abstract
A full chain simulation of the acoustic hadrontherapy monitoring for brain tumours is presented in this work. For the study, a proton beam of 100 MeV is considered. In the first stage, Geant4 is used to simulate the energy deposition and to study [...] Read more.
A full chain simulation of the acoustic hadrontherapy monitoring for brain tumours is presented in this work. For the study, a proton beam of 100 MeV is considered. In the first stage, Geant4 is used to simulate the energy deposition and to study the behaviour of the Bragg peak. The energy deposition in the medium produces local heating that can be considered instantaneous with respect to the hydrodynamic time scale producing a sound pressure wave. The resulting thermoacoustic signal has been subsequently obtained by solving the thermoacoustic equation. The acoustic propagation has been simulated by FEM methods in the brain and the skull, where a set of piezoelectric sensors are placed. Last, the final received signals in the sensors have been processed in order to reconstruct the position of the thermal source and, thus, to determine the feasibility and accuracy of acoustic beam monitoring in hadrontherapy. Full article
(This article belongs to the Special Issue Piezoelectric Transducers)
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Open AccessArticle
Building Dynamic Communities of Interest for Internet of Things in Smart Cities
Sensors 2020, 20(10), 2986; https://doi.org/10.3390/s20102986 - 25 May 2020
Viewed by 254
Abstract
The Internet of things (IoT) is a growing area of research in the context of smart cities. It links a city’s physical objects that are equipped with embedded sensing, communicating, and computing technology. These objects possess the capability to connect and share data [...] Read more.
The Internet of things (IoT) is a growing area of research in the context of smart cities. It links a city’s physical objects that are equipped with embedded sensing, communicating, and computing technology. These objects possess the capability to connect and share data with minimal human intervention, which creates the potential to establish social relationships among them. However, it is challenging for an object to discover, communicate, and collaborate dynamically with other objects, such as social entities, and provide services to humans. This is due to the increase in the number of objects and the complexity in defining social-like relationships among them. The current research aims to address this by introducing an object architecture and defining a Dynamic Community of Interest Model (DCIM) for IoT objects. The proposed model will help IoT objects to socialize and build communities amongst themselves based on different criteria. In this approach, objects belonging to a community will collaborate with each other to collect, manipulate, and share interesting content and provide services to enhance the quality of human interactions in smart cities. Full article
(This article belongs to the Special Issue IoT-Enabled Smart Cities)
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Open AccessArticle
A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information
Sensors 2020, 20(10), 2985; https://doi.org/10.3390/s20102985 - 25 May 2020
Viewed by 238
Abstract
To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor [...] Read more.
To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13–6.69 cm/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71–100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83–0.88, and the RAD value was 8.05–9.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards. Full article
(This article belongs to the Special Issue Sensors in Agriculture 2020)
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Open AccessArticle
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques
Sensors 2020, 20(10), 2984; https://doi.org/10.3390/s20102984 - 25 May 2020
Viewed by 314
Abstract
Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which [...] Read more.
Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is difficult to perform using traditional image analysis, which is more important for long-term yield prediction. Therefore, tomato detection that can generalize well in real tomato cultivation scenes and is robust to issues such as fruit occlusion and variable lighting conditions is highly desired. In this study, we build a tomato detection model to automatically detect intact green tomatoes regardless of occlusions or fruit growth stage using deep learning approaches. The tomato detection model used faster region-based convolutional neural network (R-CNN) with Resnet-101 and transfer learned from the Common Objects in Context (COCO) dataset. The detection on test dataset achieved high average precision of 87.83% (intersection over union ≥ 0.5) and showed a high accuracy of tomato counting (R2 = 0.87). In addition, all the detected boxes were merged into one image to compile the tomato location map and estimate their size along one row in the greenhouse. By tomato detection, counting, location and size estimation, this method shows great potential for ripeness and yield prediction. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb
Sensors 2020, 20(10), 2983; https://doi.org/10.3390/s20102983 - 25 May 2020
Viewed by 278
Abstract
The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This [...] Read more.
The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion. Full article
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors)
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Open AccessArticle
BIM in People2People and Things2People Interactive Process
Sensors 2020, 20(10), 2982; https://doi.org/10.3390/s20102982 - 24 May 2020
Viewed by 365
Abstract
In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and [...] Read more.
In this research work, we present an IoT solution to environment variables using a LoRa transmission technology to give real-time information to users in a Things2People process and achieve savings by promoting behavior changes in a People2People process. These data are stored and later processed to identify patterns and integrate with visualization tools, which allow us to develop an environmental perception while using the system. In this project, we implemented a different approach based on the development of a 3D visualization tool that presents the system collected data, warnings, and other users’ perception in an interactive 3D model of the building. This data representation introduces a new People2People interaction approach to achieve savings in shared spaces like public buildings by combining sensor data with the users’ individual and collective perception. This approach was validated at the ISCTE-IUL University Campus, where this 3D IoT data representation was presented in mobile devices, and from this, influenced user behavior toward meeting campus sustainability goals. Full article
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Open AccessArticle
Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method
Sensors 2020, 20(10), 2981; https://doi.org/10.3390/s20102981 - 24 May 2020
Viewed by 304
Abstract
Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and [...] Read more.
Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone. Full article
(This article belongs to the Section Communications)
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Open AccessArticle
Directional Response of Randomly Dispersed Carbon Nanotube Strain Sensors
Sensors 2020, 20(10), 2980; https://doi.org/10.3390/s20102980 - 24 May 2020
Viewed by 354
Abstract
Tests on a double lap bonded joint, with transverse strips of randomly oriented carbon nanotubes (CNT) sprayed onto an epoxy adhesive film, showed a positive increment in electrical resistance under tensile load, even though the transverse strains were negative. Other experiments included in [...] Read more.
Tests on a double lap bonded joint, with transverse strips of randomly oriented carbon nanotubes (CNT) sprayed onto an epoxy adhesive film, showed a positive increment in electrical resistance under tensile load, even though the transverse strains were negative. Other experiments included in this work involved placing longitudinal and transversal CNT sensors in a tensile loaded aluminum plate, and, as reported by other authors, the results confirm that the resistance change is not only dependent on the strains oriented with the electrode line, while the other strain components also influence the response. This behavior is quite different to that of conventional strain gages which have a near zero sensitivity to strains not aligned to the sensor direction. The dependence of the electrical response on all the strain components makes it quite difficult, possibly unfeasible, to experimentally determine the individual strain components with this kind of sensors; however, the manufacturing of aligned CNT sensors could deal with this issue. Full article
(This article belongs to the Special Issue Carbon Nanotube and Graphene-based Sensors)
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Open AccessArticle
Ferroelectret-based Hydrophone Employed in Oil Identification—A Machine Learning Approach
Sensors 2020, 20(10), 2979; https://doi.org/10.3390/s20102979 - 24 May 2020
Viewed by 376
Abstract
This work focuses on acoustic analysis as a way of discriminating mineral oil, providing a robust technique, immune to electromagnetic noise, and in some cases, depending on the applied sensor, a low-cost technique. Thus, we propose a new method for the diagnosis of [...] Read more.
This work focuses on acoustic analysis as a way of discriminating mineral oil, providing a robust technique, immune to electromagnetic noise, and in some cases, depending on the applied sensor, a low-cost technique. Thus, we propose a new method for the diagnosis of the quality of mineral oil used in electrical transformers, integrating a ferroelectric-based hydrophone and an acoustic transducer. Our classification solution is based on a supervised machine learning technique applied to the signals generated by an in-home built hydrophone. A total of three statistical datasets entries were collected during the acoustic experiments on four types of oils. The first, the second, and third datasets contain 180, 240, and 420 entries, respectively. Eighty-four features were considered from each dataset to apply to two classification approaches. The first classification approach is able to distinguish the oils from the four possible classes with a classification error less than 2%, while the second approach is able to successfully classify the oils without errors (e.g., with a score of 100%). Full article
(This article belongs to the Special Issue Acoustic Wave Sensors for Gaseous and Liquid Environments)
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Open AccessArticle
Cognitive States Matter: Design Guidelines for Driving Situation Awareness in Smart Vehicles
Sensors 2020, 20(10), 2978; https://doi.org/10.3390/s20102978 - 24 May 2020
Viewed by 352
Abstract
Situation awareness (SA) is crucial for safe driving. It is all about perception, comprehension of current situations and projection of the future status. It is demanding for drivers to constantly maintain SA by checking for potential hazards while performing the primary driving tasks. [...] Read more.
Situation awareness (SA) is crucial for safe driving. It is all about perception, comprehension of current situations and projection of the future status. It is demanding for drivers to constantly maintain SA by checking for potential hazards while performing the primary driving tasks. As vehicles in the future will be equipped with more sensors, it is likely that an SA aiding system will present complex situational information to drivers. Although drivers have difficulty to process a variety of complex situational information due to limited cognitive capabilities and perceive the information differently depending upon their cognitive states, the well-known SA design principles by Endsley only provide general guidelines. The principles lack detailed guidelines for dealing with limited human cognitive capabilities. Cognitive capability is a mental capability including planning, complex idea comprehension, and learning from experience. A cognitive state can be regarded as a condition of being (e.g., the state of being aware of the situation). In this paper, we investigate the key cognitive attributes related to SA in driving contexts (i.e., attention focus, mental model, workload, and memory). Endsley proposed that those key cognitive attributes are the main factors that influence SA. In those with higher levels of attributes, we found eight cognitive states which mainly influence a human driver in achieving SA. These are the focused attention state, inattentional blindness state, unfamiliar situation state, familiar situation state, insufficient mental resource state, sufficient mental resource state, high time pressure state, and low time pressure state. We then propose cognitive state aware SA design guidelines that can help designers to effectively convey situation information to drivers. As a case study, we demonstrated the usefulness of our cognitive state aware SA design guidelines by conducting controlled experiments where an existing SA interface is compared with a new SA interface designed following the key guidelines. We used the Situation Awareness Global Assessment Technique (SAGAT) and Decision-Making Questionnaire (DMQ) to measure the SA and decision-making style scores, respectively. Our results show that the new guidelines allowed participants to achieve significantly higher SA and exhibit better decision making performance. Full article
(This article belongs to the Section Intelligent Sensors)
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