sensors-logo

Journal Browser

Journal Browser

Editor's Choice Articles

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

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
Ultra Stable Molecular Sensors by Submicron Referencing and Why They Should Be Interrogated by Optical Diffraction—Part II. Experimental Demonstration
Sensors 2021, 21(1), 9; https://doi.org/10.3390/s21010009 - 22 Dec 2020
Cited by 6
Abstract
Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental [...] Read more.
Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental noise sources. In this paper, we demonstrate that sensors that apply the spatial affinity lock-in principle (part I) and perform readout by diffraction overcome the drawbacks of established refractometric biosensors. We show this with a direct comparison of the cover refractive index jump sensitivity as well as the surface mass resolution of an unstabilized diffractometric biosensor with a state-of-the-art Biacore 8k. A combined refractometric diffractometric biosensor demonstrates that a refractometric sensor requires a much higher measurement precision than the diffractometric to achieve the same resolution. In a conceptual and quantitative discussion, we elucidate the physical reasons behind and define the figure of merit of diffractometric biosensors. Because low-precision unstabilized diffractometric devices achieve the same resolution as bulky stabilized refractometric sensors, we believe that label-free optical sensors might soon move beyond the drug discovery lab as miniaturized, mass-produced environmental/medical sensors. In fact, combined with the right surface chemistry and recognition element, they might even bring the senses of smell/taste to our smart devices. Full article
(This article belongs to the Special Issue Advanced Biophotonic Sensors)
Show Figures

Figure 1

Article
Sensing the Generation of Intracellular Free Electrons Using the Inactive Catalytic Subunit of Cytochrome P450s as a Sink
Sensors 2020, 20(14), 4050; https://doi.org/10.3390/s20144050 - 21 Jul 2020
Cited by 5
Abstract
Cytochrome P450 reductase (CPR) abstracts electrons from Nicotinamide adenine dinucleotide phosphate H (NADPH), transferring them to an active Cytochrome P450 (CYP) site to provide a functional CYP. In the present study, a yeast strain was genetically engineered to delete the endogenous CPR gene. [...] Read more.
Cytochrome P450 reductase (CPR) abstracts electrons from Nicotinamide adenine dinucleotide phosphate H (NADPH), transferring them to an active Cytochrome P450 (CYP) site to provide a functional CYP. In the present study, a yeast strain was genetically engineered to delete the endogenous CPR gene. A human CYP expressed in a CPR-null (yRD) strain was inactive. It was queried if Bax—which induces apoptosis in yeast and human cells by generating reactive oxygen species (ROS)—substituted for the absence of CPR. Since Bax-generated ROS stems from an initial release of electrons, is it possible for these released electrons to be captured by an inactive CYP to make it active once again? In this study, yeast cells that did not contain any CPR activity (i.e., because the yeasts’ CPR gene was completely deleted) were used to show that (a) human CYPs produced within CPR-null (yRD-) yeast cells were inactive and (b) low levels of the pro-apoptotic human Bax protein could activate inactive human CYPs within this yeast cells. Surprisingly, Bax activated three inactive CYP proteins, confirming that it could compensate for CPR’s absence within yeast cells. These findings could be useful in research, development of bioassays, bioreactors, biosensors, and disease diagnosis, among others. Full article
(This article belongs to the Section Biosensors)
Show Figures

Figure 1

Article
A Spatially Distributed Fiber-Optic Temperature Sensor for Applications in the Steel Industry
Sensors 2020, 20(14), 3900; https://doi.org/10.3390/s20143900 - 13 Jul 2020
Cited by 7
Abstract
This paper presents a spatially distributed fiber-optic sensor system designed for demanding applications, like temperature measurements in the steel industry. The sensor system employed optical frequency domain reflectometry (OFDR) to interrogate Rayleigh backscattering signals in single-mode optical fibers. Temperature measurements employing the OFDR [...] Read more.
This paper presents a spatially distributed fiber-optic sensor system designed for demanding applications, like temperature measurements in the steel industry. The sensor system employed optical frequency domain reflectometry (OFDR) to interrogate Rayleigh backscattering signals in single-mode optical fibers. Temperature measurements employing the OFDR system were compared with conventional thermocouple measurements, accentuating the spatially distributed sensing capability of the fiber-optic system. Experiments were designed and conducted to test the spatial thermal mapping capability of the fiber-optic temperature measurement system. Experimental simulations provided evidence that the optical fiber system could resolve closely spaced temperature features, due to the high spatial resolution and fast measurement rates of the OFDR system. The ability of the fiber-optic system to perform temperature measurements in a metal casting was tested by monitoring aluminum solidification in a sand mold. The optical fiber, encased in a stainless steel tube, survived both mechanically and optically at temperatures exceeding 700 °C. The ability to distinguish between closely spaced temperature features that generate information-rich thermal maps opens up many applications in the steel industry. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Article
Antiresonant Hollow-Core Fiber-Based Dual Gas Sensor for Detection of Methane and Carbon Dioxide in the Near- and Mid-Infrared Regions
Sensors 2020, 20(14), 3813; https://doi.org/10.3390/s20143813 - 08 Jul 2020
Cited by 11
Abstract
In this work, we present for the first time a laser-based dual gas sensor utilizing a silica-based Antiresonant Hollow-Core Fiber (ARHCF) operating in the Near- and Mid-Infrared spectral region. A 1-m-long fiber with an 84-µm diameter air-core was implemented as a low-volume absorption [...] Read more.
In this work, we present for the first time a laser-based dual gas sensor utilizing a silica-based Antiresonant Hollow-Core Fiber (ARHCF) operating in the Near- and Mid-Infrared spectral region. A 1-m-long fiber with an 84-µm diameter air-core was implemented as a low-volume absorption cell in a sensor configuration utilizing the simple and well-known Wavelength Modulation Spectroscopy (WMS) method. The fiber was filled with a mixture of methane (CH4) and carbon dioxide (CO2), and a simultaneous detection of both gases was demonstrated targeting their transitions at 3.334 µm and 1.574 µm, respectively. Due to excellent guidance properties of the fiber and low background noise, the proposed sensor reached a detection limit down to 24 parts-per-billion by volume for CH4 and 144 parts-per-million by volume for CO2. The obtained results confirm the suitability of ARHCF for efficient use in gas sensing applications for over a broad spectral range. Thanks to the demonstrated low loss, such fibers with lengths of over one meter can be used for increasing the laser-gas molecules interaction path, substituting bulk optics-based multipass cells, while delivering required flexibility, compactness, reliability and enhancement in the sensor’s sensitivity. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Article
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
Cited by 4
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
Show Figures

Figure 1

Article
An Efficient Distributed Area Division Method for Cooperative Monitoring Applications with Multiple UAVs
Sensors 2020, 20(12), 3448; https://doi.org/10.3390/s20123448 - 18 Jun 2020
Cited by 5
Abstract
This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal [...] Read more.
This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal solution, following a frequency-based approach. Based on the “coordination variables” concept and on a strict neighborhood relation to share information (left, right, above and below neighbors), this technique defines a distributed division protocol to determine coherently the size and shape of the sub-area assigned to each UAV. Theoretically, the convergence time of the proposed solution depends linearly on the number of UAVs. Validation results, comparing the proposed approach with other distributed techniques, are provided to evaluate and analyze its performance following a convergence time criterion. Full article
(This article belongs to the Special Issue UAV-Based Smart Sensor Systems and Applications)
Show Figures

Figure 1

Article
Layered Double Hydroxide-Modified Organic Electrochemical Transistor for Glucose and Lactate Biosensing
Sensors 2020, 20(12), 3453; https://doi.org/10.3390/s20123453 - 18 Jun 2020
Cited by 9
Abstract
Biosensors based on Organic Electrochemical Transistors (OECTs) are developed for the selective detection of glucose and lactate. The transistor architecture provides signal amplification (gain) with respect to the simple amperometric response. The biosensors are based on a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) channel and the gate [...] Read more.
Biosensors based on Organic Electrochemical Transistors (OECTs) are developed for the selective detection of glucose and lactate. The transistor architecture provides signal amplification (gain) with respect to the simple amperometric response. The biosensors are based on a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) channel and the gate electrode is functionalised with glucose oxidase (GOx) or lactate oxidase (LOx) enzymes, which are immobilised within a Ni/Al Layered Double Hydroxide (LDH) through a one-step electrodeposition procedure. The here-designed OECT architecture allows minimising the required amount of enzyme during electrodeposition. The output signal of the biosensor is the drain current (Id), which decreases as the analyte concentration increases. In the optimised conditions, the biosensor responds to glucose in the range of 0.1–8.0 mM with a limit of detection (LOD) of 0.02 mM. Two regimes of proportionality are observed. For concentrations lower than 1.0 mM, a linear response is obtained with a mean gain of 360, whereas for concentrations higher than 1.0 mM, Id is proportional to the logarithm of glucose concentration, with a gain of 220. For lactate detection, the biosensor response is linear in the whole concentration range (0.05–8.0 mM). A LOD of 0.04 mM is reached, with a net gain equal to 400. Full article
(This article belongs to the Special Issue New Generation of Electrochemical Sensors)
Show Figures

Graphical abstract

Article
Development of Taste Sensor to Detect Non-Charged Bitter Substances
Sensors 2020, 20(12), 3455; https://doi.org/10.3390/s20123455 - 18 Jun 2020
Cited by 3
Abstract
A taste sensor with lipid/polymer membranes is one of the devices that can evaluate taste objectively. However, the conventional taste sensor cannot measure non-charged bitter substances, such as caffeine contained in coffee, because the taste sensor uses the potentiometric measurement based mainly on [...] Read more.
A taste sensor with lipid/polymer membranes is one of the devices that can evaluate taste objectively. However, the conventional taste sensor cannot measure non-charged bitter substances, such as caffeine contained in coffee, because the taste sensor uses the potentiometric measurement based mainly on change in surface electric charge density of the membrane. In this study, we aimed at the detection of typical non-charged bitter substances such as caffeine, theophylline and theobromine included in beverages and pharmaceutical products. The developed sensor is designed to detect the change in the membrane potential by using a kind of allosteric mechanism of breaking an intramolecular hydrogen bond between the carboxy group and hydroxy group of aromatic carboxylic acid (i.e., hydroxy-, dihydroxy-, and trihydroxybenzoic acids) when non-charged bitter substances are bound to the hydroxy group. As a result of surface modification by immersing the sensor electrode in a modification solution in which 2,6-dihydroxybenzoic acid was dissolved, it was confirmed that the sensor response increased with the concentration of caffeine as well as allied substances. The threshold and increase tendency were consistent with those of human senses. The detection mechanism is discussed by taking into account intramolecular and intermolecular hydrogen bonds, which cause allostery. These findings suggest that it is possible to evaluate bitterness caused by non-charged bitter substances objectively by using the taste sensor with allosteric mechanism. Full article
(This article belongs to the Section Chemical Sensors)
Show Figures

Figure 1

Article
Strain Transfer in Surface-Bonded Optical Fiber Sensors
Sensors 2020, 20(11), 3100; https://doi.org/10.3390/s20113100 - 30 May 2020
Cited by 9
Abstract
Fiber optic sensors represent one of the most promising technologies for the monitoring of various engineering structures. A major challenge in the field is to analyze and predict the strain transfer to the fiber core reliably. Many authors developed analytical models of a [...] Read more.
Fiber optic sensors represent one of the most promising technologies for the monitoring of various engineering structures. A major challenge in the field is to analyze and predict the strain transfer to the fiber core reliably. Many authors developed analytical models of a coated optical fiber, assuming null strain at the ends of the bonding length. However, this configuration only partially reflects real experimental setups in which the cable structure can be more complex and the strains do not drastically reduce to zero. In this study, a novel strain transfer model for surface-bonded sensing cables with multilayered structure was developed. The analytical model was validated both experimentally and numerically, considering two surface-mounted cable prototypes with three different bonding lengths and five load cases. The results demonstrated the capability of the model to predict the strain profile and, differently from the available strain transfer models, that the strain values at the extremities of the bonded fiber length are not null. Full article
(This article belongs to the Special Issue Fiber Optic Sensing Technology)
Show Figures

Figure 1

Article
SPR Biosensor Based on Polymer Multi-Mode Optical Waveguide and Nanoparticle Signal Enhancement
Sensors 2020, 20(10), 2889; https://doi.org/10.3390/s20102889 - 20 May 2020
Cited by 11
Abstract
We present a surface plasmon resonance (SPR) biosensor that is based on a planar-optical multi-mode (MM) polymer waveguide structure applied for the detection of biomolecules in the lower nano-molar (nM) range. The basic sensor shows a sensitivity of 608.6 nm/RIU when exposed to [...] Read more.
We present a surface plasmon resonance (SPR) biosensor that is based on a planar-optical multi-mode (MM) polymer waveguide structure applied for the detection of biomolecules in the lower nano-molar (nM) range. The basic sensor shows a sensitivity of 608.6 nm/RIU when exposed to refractive index changes with a measurement resolution of 4.3 × 10−3 RIU. By combining the SPR sensor with an aptamer-functionalized, gold-nanoparticle (AuNP)-enhanced sandwich assay, the detection of C-reactive protein (CRP) in a buffer solution was achieved with a response of 0.118 nm/nM. Due to the multi-mode polymer waveguide structure and the simple concept, the reported biosensor is well suited for low-cost disposable lab-on-a-chip applications and can be used with rather simple and economic devices. In particular, the sensor offers the potential for fast and multiplexed detection of several biomarkers on a single integrated platform. Full article
(This article belongs to the Special Issue Integrated Photonics for Novel Sensing and Measurement Applications)
Show Figures

Figure 1

Article
Hollow-Core Photonic Crystal Fiber Mach–Zehnder Interferometer for Gas Sensing
Sensors 2020, 20(10), 2807; https://doi.org/10.3390/s20102807 - 15 May 2020
Cited by 8
Abstract
A novel and compact interferometric refractive index (RI) point sensor is developed using hollow-core photonic crystal fiber (HC-PCF) and experimentally demonstrated for high sensitivity detection and measurement of pure gases. To construct the device, the sensing element fiber (HC-PCF) was placed between two [...] Read more.
A novel and compact interferometric refractive index (RI) point sensor is developed using hollow-core photonic crystal fiber (HC-PCF) and experimentally demonstrated for high sensitivity detection and measurement of pure gases. To construct the device, the sensing element fiber (HC-PCF) was placed between two single-mode fibers with airgaps at each side. Great measurement repeatability was shown in the cyclic test for the detection of various gases. The RI sensitivity of 4629 nm/RIU was demonstrated in the RI range of 1.0000347–1.000436 for the sensor with an HC-PCF length of 3.3 mm. The sensitivity of the proposed Mach–Zehnder interferometer (MZI) sensor increases when the length of the sensing element decreases. It is shown that response and recovery times of the proposed sensor inversely change with the length of HC-PCF. Besides, spatial frequency analysis for a wide range of air-gaps revealed information on the number and power distribution of modes. It is shown that the power is mainly carried by two dominant modes in the proposed structure. The proposed sensors have the potential to improve current technology’s ability to detect and quantify pure gases. Full article
(This article belongs to the Special Issue Fiber Optic Sensors in Chemical and Biological Applications)
Show Figures

Figure 1

Article
Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)
Sensors 2020, 20(10), 2749; https://doi.org/10.3390/s20102749 - 12 May 2020
Cited by 12
Abstract
The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the [...] Read more.
The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources. Full article
(This article belongs to the Special Issue Remote Sensing of Geohazards)
Show Figures

Graphical abstract

Article
Examination of Multi-Receiver GPS/EGNOS Positioning with Kalman Filtering and Validation Based on CORS Stations
Sensors 2020, 20(9), 2732; https://doi.org/10.3390/s20092732 - 11 May 2020
Cited by 11
Abstract
This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. [...] Read more.
This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. Next, the Kalman filter was employed to give the final solution. It was proven that EGNOS positioning allows to obtain an accuracy in the range of about 0.5–1.5 m. The proposed solution involving the use of three mobile receivers and Kalman filtering allowed to reduce the 3D error to a level below 0.3 m. Such an accuracy was achieved using only GPS L1 code observations and EGNOS corrections. Additionally, a reliable monitoring of quality of GPS/EGNOS positioning in the test area based on CORS stations was presented. Full article
(This article belongs to the Special Issue GNSS Sensors in Aerial Navigation)
Show Figures

Figure 1

Article
Multi-Addressed Fiber Bragg Structures for Microwave-Photonic Sensor Systems
Sensors 2020, 20(9), 2693; https://doi.org/10.3390/s20092693 - 09 May 2020
Cited by 8
Abstract
The new theory and technique of Multi-Addressed Fiber Bragg Structure (MAFBS) usage in Microwave Photonics Sensor Systems (MPSS) is presented. This theory is the logical evolution of the theory of Addressed Fiber Bragg Structure (AFBS) usage as sensors in MPSS. The mathematical model [...] Read more.
The new theory and technique of Multi-Addressed Fiber Bragg Structure (MAFBS) usage in Microwave Photonics Sensor Systems (MPSS) is presented. This theory is the logical evolution of the theory of Addressed Fiber Bragg Structure (AFBS) usage as sensors in MPSS. The mathematical model of additive response from a single MAFBS is presented. The MAFBS is a special type of Fiber Bragg Gratings (FBG), the reflection spectrum of which has three (or more) narrow notches. The frequencies of narrow notches are located in the infrared range of electromagnetic spectrum, while differences between them are located in the microwave frequency range. All cross-differences between optical frequencies of single MAFBS are called the address frequencies set. When the additive optical response from a single MAFBS, passed through an optic filter with an oblique amplitude–frequency characteristic, is received on a photodetector, the complex electrical signal, which consists of all cross-frequency beatings of all optical frequencies, which are included in this optical signal, is taken at its output. This complex electrical signal at the photodetector’s output contains enough information to determine the central frequency shift of the MAFBS. The method of address frequencies analysis with the microwave-photonic measuring conversion method, which allows us to define the central frequency shift of a single MAFBS, is discussed in the work. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors and Systems)
Show Figures

Figure 1

Article
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Sensors 2020, 20(9), 2530; https://doi.org/10.3390/s20092530 - 29 Apr 2020
Cited by 21
Abstract
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, [...] Read more.
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers. Full article
(This article belongs to the Special Issue Metrology for Agriculture and Forestry 2019)
Show Figures

Figure 1

Article
Development of an Aptamer Based Luminescent Optical Fiber Sensor for the Continuous Monitoring of Hg2+ in Aqueous Media
Sensors 2020, 20(8), 2372; https://doi.org/10.3390/s20082372 - 22 Apr 2020
Cited by 5
Abstract
A fluorescent optical fiber sensor for the detection of mercury (Hg2+) ions in aqueous solutions is presented in this work. The sensor was based on a fluorophore-labeled thymine (T)-rich oligodeoxyribonucleotide (ON) sequence that was directly immobilized onto the tip of a [...] Read more.
A fluorescent optical fiber sensor for the detection of mercury (Hg2+) ions in aqueous solutions is presented in this work. The sensor was based on a fluorophore-labeled thymine (T)-rich oligodeoxyribonucleotide (ON) sequence that was directly immobilized onto the tip of a tapered optical fiber. In the presence of mercury ions, the formation of T–Hg2+-T mismatches quenches the fluorescence emission by the labeled fluorophore, which enables the measurement of Hg2+ ions in aqueous solutions. Thus, in contrast to commonly designed sensors, neither a fluorescence quencher nor a complementary ON sequence is required. The sensor presented a response time of 24.8 seconds toward 5 × 10−12 M Hg2+. It also showed both good reversibility (higher than the 95.8%) and selectivity: the I0/I variation was 10 times higher for Hg2+ ions than for Mn2+ ions. Other contaminants examined (Co2+, Ag+, Cd2+, Ni2+, Ca2+, Pb2+, Mn2+, Zn2+, Fe3+, and Cu2+) presented an even lower interference. The limit of detection of the sensor was 4.73 × 10−13 M Hg2+ in buffer solution and 9.03 × 10−13 M Hg2+ in ultrapure water, and was also able to detect 5 × 10−12 M Hg2+ in tap water. Full article
(This article belongs to the Special Issue Calibration of Chemical Sensors Based on Photoluminescence)
Show Figures

Figure 1

Article
Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
Sensors 2020, 20(8), 2249; https://doi.org/10.3390/s20082249 - 16 Apr 2020
Cited by 6
Abstract
In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power [...] Read more.
In this paper, we present a privacy-preserving scheme for Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed Demand Response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. To monitor the power consumption of the buildings, while respecting the privacy of the users, we extend our previous Overgrid algorithms to provide privacy preserving data aggregation (PP-Overgrid). This new technique combines a distributed data aggregation scheme with the Secure Multi-Party Computation paradigm. First, we use the energy profiles of hundreds of buildings, classifying the amount of “flexible” energy consumption, i.e., the quota which could be potentially exploited for DR programs. Second, we consider renewable energy sources and apply the DR scheme to match the flexible consumption with the available energy. Finally, to show the feasibility of our approach, we validate the PP-Overgrid algorithm in simulation for a large network of smart buildings. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
Show Figures

Figure 1

Article
Concrete Crack Monitoring Using a Novel Strain Transfer Model for Distributed Fiber Optics Sensors
Sensors 2020, 20(8), 2220; https://doi.org/10.3390/s20082220 - 15 Apr 2020
Cited by 17
Abstract
In this paper, we study the strain transfer mechanism between a host material and an optical fiber. A new analytical model handling imperfect bonding between layers is proposed. A general expression of the crack-induced strain transfer from fractured concrete material to optical fiber [...] Read more.
In this paper, we study the strain transfer mechanism between a host material and an optical fiber. A new analytical model handling imperfect bonding between layers is proposed. A general expression of the crack-induced strain transfer from fractured concrete material to optical fiber is established in the case of a multilayer system. This new strain transfer model is examined through performing wedge splitting tests on concrete specimens instrumented with embedded and surface-mounted fiber optic cables. The experimental results showed the validity of the crack-induced strain expression fitted to the distributed strains measured using an Optical Backscattering Reflectometry (OBR) system. As a result, precise estimations of the crack openings next to the optical cable location were achieved, as well as the monitoring of the optical cable response through following the strain lag parameter. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Article
Towards a Remote Monitoring of Patient Vital Signs Based on IoT-Based Blockchain Integrity Management Platforms in Smart Hospitals
Sensors 2020, 20(8), 2195; https://doi.org/10.3390/s20082195 - 13 Apr 2020
Cited by 48
Abstract
Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has [...] Read more.
Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data. Full article
(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)
Show Figures

Figure 1

Article
Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective
Sensors 2020, 20(8), 2191; https://doi.org/10.3390/s20082191 - 13 Apr 2020
Cited by 7
Abstract
The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and [...] Read more.
The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities. Full article
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
Show Figures

Graphical abstract

Article
LoRaFarM: A LoRaWAN-Based Smart Farming Modular IoT Architecture
Sensors 2020, 20(7), 2028; https://doi.org/10.3390/s20072028 - 04 Apr 2020
Cited by 26
Abstract
Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable [...] Read more.
Presently, the adoption of Internet of Things (IoT)-related technologies in the Smart Farming domain is rapidly emerging. The ultimate goal is to collect, monitor, and effectively employ relevant data for agricultural processes, with the purpose of achieving an optimized and more environmentally sustainable agriculture. In this paper, a low-cost, modular, and Long-Range Wide-Area Network (LoRaWAN)-based IoT platform, denoted as “LoRaWAN-based Smart Farming Modular IoT Architecture” (LoRaFarM), and aimed at improving the management of generic farms in a highly customizable way, is presented. The platform, built around a core middleware, is easily extensible with ad-hoc low-level modules (feeding the middleware with data coming from the sensors deployed in the farm) or high-level modules (providing advanced functionalities to the farmer). The proposed platform has been evaluated in a real farm in Italy, collecting environmental data (air/soil temperature and humidity) related to the growth of farm products (namely grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture. Full article
(This article belongs to the Special Issue Metrology for Agriculture and Forestry 2019)
Show Figures

Figure 1

Article
Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
Sensors 2020, 20(7), 1985; https://doi.org/10.3390/s20071985 - 02 Apr 2020
Cited by 10
Abstract
Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however [...] Read more.
Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however not considered the fact that multiple vehicular contents can be transient, i.e., they expire after a certain time period since they were generated, the so-called FreshnessPeriod in NDN. In this paper, we study the effects of caching transient contents in Vehicular NDN and present a simple yet effective freshness-driven caching decision strategy that vehicles can implement autonomously. Performance evaluation in ndnSIM shows that the FreshnessPeriod is a crucial parameter that deeply influences the cache hit ratio and, consequently, the data dissemination performance. Full article
(This article belongs to the Special Issue Vehicular Sensor Networks: Applications, Advances and Challenges)
Show Figures

Figure 1

Article
A Syringe-Based Biosensor to Rapidly Detect Low Levels of Escherichia Coli (ECOR13) in Drinking Water Using Engineered Bacteriophages
Sensors 2020, 20(7), 1953; https://doi.org/10.3390/s20071953 - 31 Mar 2020
Cited by 8
Abstract
A sanitized drinking water supply is an unconditional requirement for public health and the overall prosperity of humanity. Potential microbial and chemical contaminants of drinking water have been identified by a joint effort between the World Health Organization (WHO) and the United Nations [...] Read more.
A sanitized drinking water supply is an unconditional requirement for public health and the overall prosperity of humanity. Potential microbial and chemical contaminants of drinking water have been identified by a joint effort between the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF), who together establish guidelines that define, in part, that the presence of Escherichia coli (E. coli) in drinking water is an indication of inadequate sanitation and a significant health risk. As E. coli is a nearly ubiquitous resident of mammalian gastrointestinal tracts, no detectable counts in 100 mL of drinking water is the standard used worldwide as an indicator of sanitation. The currently accepted EPA method relies on filtration, followed by growth on selective media, and requires 24–48 h from sample to results. In response, we developed a rapid bacteriophage-based detection assay with detection limit capabilities comparable to traditional methods in less than a quarter of the time. We coupled membrane filtration with selective enrichment using genetically engineered bacteriophages to identify less than 20 colony forming units (CFU) E. coli in 100 mL drinking water within 5 h. The combination of membrane filtration with phage infection produced a novel assay that demonstrated a rapid, selective, and sensitive detection of an indicator organism in large volumes of drinking water as recommended by the leading world regulatory authorities. Full article
(This article belongs to the Section Biosensors)
Show Figures

Figure 1

Article
Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks
Sensors 2020, 20(7), 1911; https://doi.org/10.3390/s20071911 - 30 Mar 2020
Cited by 13
Abstract
Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful [...] Read more.
Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies. Full article
Show Figures

Figure 1

Article
Wearable Hand Module and Real-Time Tracking Algorithms for Measuring Finger Joint Angles of Different Hand Sizes with High Accuracy Using FBG Strain Sensor
Sensors 2020, 20(7), 1921; https://doi.org/10.3390/s20071921 - 30 Mar 2020
Cited by 9
Abstract
This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move [...] Read more.
This paper presents a wearable hand module which was made of five fiber Bragg grating (FBG) strain sensor and algorithms to achieve high accuracy even when worn on different hand sizes of users. For real-time calculation with high accuracy, FBG strain sensors move continuously according to the size of the hand and the bending of the joint. Representatively, four algorithms were proposed; point strain (PTS), area summation (AREA), proportional summation (PS), and PS/interference (PS/I or PS/I_ α ). For more accurate and efficient assessments, 3D printed hand replica with different finger sizes was adopted and quantitative evaluations were performed for index~little fingers (77 to 117 mm) and thumb (68~78 mm). For index~little fingers, the optimized algorithms were PS and PS/I_ α . For thumb, the optimized algorithms were PS/I_ α and AREA. The average error angle of the wearable hand module was observed to be 0.47 ± 2.51° and mean absolute error (MAE) was achieved at 1.63 ± 1.97°. These results showed that more accurate hand modules than other glove modules applied to different hand sizes can be manufactured using FBG strain sensors which move continuously and algorithms for tracking this movable FBG sensors. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors and Systems)
Show Figures

Figure 1

Article
Pattern Recognition and Anomaly Detection by Self-Organizing Maps in a Multi Month E-nose Survey at an Industrial Site
Sensors 2020, 20(7), 1887; https://doi.org/10.3390/s20071887 - 29 Mar 2020
Cited by 4
Abstract
Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of [...] Read more.
Currently people are aware of the risk related to pollution exposure. Thus odor annoyances are considered a warning about the possible presence of toxic volatile compounds. Malodor often generates immediate alarm among citizens, and electronic noses are convenient instruments to detect mixture of odorant compounds with high monitoring frequency. In this paper we present a study on pattern recognition on ambient air composition in proximity of a gas and oil pretreatment plant by elaboration of data from an electronic nose implementing 10 metal-oxide-semiconductor (MOS) sensors and positioned outdoor continuously during three months. A total of 80,017 e-nose vectors have been elaborated applying the self-organizing map (SOM) algorithm and then k-means clustering on SOM outputs on the whole data set evidencing an anomalous data cluster. Retaining data characterized by dynamic responses of the multisensory system, a SOM with 264 recurrent sensor responses to air mixture sampled at the site and four main air type profiles (clusters) have been identified. One of this sensor profiles has been related to the odor fugitive emissions of the plant, by using ancillary data from a total volatile organic compound (VOC) detector and wind speed and direction data. The overall and daily cluster frequencies have been evaluated, allowing us to identify the daily duration of presence at the monitoring site of air related to industrial emissions. The refined model allowed us to confirm the anomaly detection of the sensor responses. Full article
(This article belongs to the Special Issue Electronic Noses and Tongues for Environmental Monitoring)
Show Figures

Figure 1

Article
Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things
Sensors 2020, 20(6), 1793; https://doi.org/10.3390/s20061793 - 24 Mar 2020
Cited by 15
Abstract
Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what [...] Read more.
Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost. Full article
(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)
Show Figures

Figure 1

Article
Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors
Sensors 2020, 20(6), 1622; https://doi.org/10.3390/s20061622 - 14 Mar 2020
Cited by 11
Abstract
Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale [...] Read more.
Spasticity is a frequently observed symptom in patients with neurological impairments. Spastic movements of their upper and lower limbs are periodically measured to evaluate functional outcomes of physical rehabilitation, and they are quantified by clinical outcome measures such as the modified Ashworth scale (MAS). This study proposes a method to determine the severity of elbow spasticity, by analyzing the acceleration and rotation attributes collected from the elbow of the affected side of patients and machine-learning algorithms to classify the degree of spastic movement; this approach is comparable to assigning an MAS score. We collected inertial data from participants using a wearable device incorporating inertial measurement units during a passive stretch test. Machine-learning algorithms—including decision tree, random forests (RFs), support vector machine, linear discriminant analysis, and multilayer perceptrons—were evaluated in combinations of two segmentation techniques and feature sets. A RF performed well, achieving up to 95.4% accuracy. This work not only successfully demonstrates how wearable technology and machine learning can be used to generate a clinically meaningful index but also offers rehabilitation patients an opportunity to monitor the degree of spasticity, even in nonhealthcare institutions where the help of clinical professionals is unavailable. Full article
(This article belongs to the Special Issue Wearable Sensors and Systems for Rehabilitation)
Show Figures

Figure 1

Article
Surface Plasmon Resonance-Based Sensing Utilizing Spatial Phase Modulation in an Imaging Interferometer
Sensors 2020, 20(6), 1616; https://doi.org/10.3390/s20061616 - 13 Mar 2020
Cited by 1
Abstract
Spatial phase modulation in an imaging interferometer is utilized in surface plasmon resonance (SPR) based sensing of liquid analytes. In the interferometer, a collimated light beam from a laser diode irradiating at 637.1 nm is passing through a polarizer and is reflected from [...] Read more.
Spatial phase modulation in an imaging interferometer is utilized in surface plasmon resonance (SPR) based sensing of liquid analytes. In the interferometer, a collimated light beam from a laser diode irradiating at 637.1 nm is passing through a polarizer and is reflected from a plasmonic structure of SF10/Cr/Au attached to a prism in the Kretschmann configuration. The beam passes through a combination of a Wollaston prism, a polarizer and a lens, and forms an interference pattern on a CCD sensor of a color camera. Interference patterns obtained for different liquid analytes are acquired and transferred to the computer for data processing. The sensing concept is based on the detection of a refractive index change, which is transformed via the SPR phenomenon into an interference fringe phase shift. By calculating the phase shift for the plasmonic structure of SF10/Cr/Au of known parameters we demonstrate that this technique can detect different weight concentrations of ethanol diluted in water, or equivalently, different changes in the refractive index. The sensitivity to the refractive index and the detection limit obtained are −278 rad/refractive-index-unit (RIU) and 3.6 × 10 6 RIU, respectively. The technique is demonstrated in experiments with the same liquid analytes as in the theory. Applying an original approach in retrieving the fringe phase shift, we revealed good agreement between experiment and theory, and the measured sensitivity to the refractive index and the detection limit reached −226 rad/RIU and 4.4 × 10 6 RIU, respectively. These results suggest that the SPR interferometer with the detection of a fringe phase shift is particularly useful in applications that require measuring refractive index changes with high sensitivity. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Article
Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
Sensors 2020, 20(5), 1450; https://doi.org/10.3390/s20051450 - 06 Mar 2020
Cited by 8
Abstract
The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, [...] Read more.
The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6–2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
Show Figures

Figure 1

Article
Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework
Sensors 2020, 20(5), 1399; https://doi.org/10.3390/s20051399 - 04 Mar 2020
Cited by 24
Abstract
Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is [...] Read more.
Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters’ data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder to generate encoded sequences. The encoded sequences are decoded by another following LSTM-decoder to advance it to the final dense layer for energy prediction. The experimental results using different evaluation metrics show that the proposed hybrid model works well. Also, it records the smallest value for mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) when compared to other state-of-the-art forecasting methods over the UCI residential building dataset. Furthermore, we conducted experiments on Korean commercial building data and the results indicate that our proposed hybrid model is a worthy contribution to energy forecasting. Full article
(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)
Show Figures

Figure 1

Article
A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems
Sensors 2020, 20(4), 1125; https://doi.org/10.3390/s20041125 - 19 Feb 2020
Cited by 12
Abstract
A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly [...] Read more.
A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
Show Figures

Figure 1

Article
Embedded Fiber Sensors to Monitor Temperature and Strain of Polymeric Parts Fabricated by Additive Manufacturing and Reinforced with NiTi Wires
Sensors 2020, 20(4), 1122; https://doi.org/10.3390/s20041122 - 19 Feb 2020
Cited by 5
Abstract
This paper focuses on three main issues regarding Material Extrusion (MEX) Additive Manufacturing (AM) of thermoplastic composites reinforced by pre-functionalized continuous Nickel–Titanium (NiTi) wires: (i) Evaluation of the effect of the MEX process on the properties of the pre-functionalized NiTi, (ii) evaluation of [...] Read more.
This paper focuses on three main issues regarding Material Extrusion (MEX) Additive Manufacturing (AM) of thermoplastic composites reinforced by pre-functionalized continuous Nickel–Titanium (NiTi) wires: (i) Evaluation of the effect of the MEX process on the properties of the pre-functionalized NiTi, (ii) evaluation of the mechanical and thermal behavior of the composite material during usage, (iii) the inspection of the parts by Non-Destructive Testing (NDT). For this purpose, an optical fiber sensing network, based on fiber Bragg grating and a cascaded optical fiber sensor, was successfully embedded during the 3D printing of a polylactic acid (PLA) matrix reinforced by NiTi wires. Thermal and mechanical perturbations were successfully registered as a consequence of thermal and mechanical stimuli. During a heating/cooling cycle, a maximum contraction of ≈100 µm was detected by the cascaded sensor in the PLA material at the end of the heating step (induced by Joule effect) of NiTi wires and a thermal perturbation associated with the structural transformation of austenite to R-phase was observed during the natural cooling step, near 33.0 °C. Regarding tensile cycling tests, higher increases in temperature arose when the applied force ranged between 0.7 and 1.1 kN, reaching a maximum temperature variation of 9.5 ± 0.1 °C. During the unload step, a slope change in the temperature behavior was detected, which is associated with the material transformation of the NiTi wire (martensite to austenite). The embedded optical sensing methodology presented here proved to be an effective and precise tool to identify structural transformations regarding the specific application as a Non-Destructive Testing for AM. Full article
(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)
Show Figures

Figure 1

Article
Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection
Sensors 2020, 20(4), 1029; https://doi.org/10.3390/s20041029 - 14 Feb 2020
Cited by 8
Abstract
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to [...] Read more.
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving. Full article
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
Show Figures

Graphical abstract

Article
Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications
Sensors 2020, 20(4), 1021; https://doi.org/10.3390/s20041021 - 14 Feb 2020
Cited by 16
Abstract
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking [...] Read more.
The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments. Full article
(This article belongs to the Special Issue Sensors for Construction Automation and Management)
Show Figures

Figure 1

Article
Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images
Sensors 2020, 20(4), 1005; https://doi.org/10.3390/s20041005 - 13 Feb 2020
Cited by 16
Abstract
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus [...] Read more.
Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Sensors)
Show Figures

Figure 1

Article
Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions
Sensors 2020, 20(4), 984; https://doi.org/10.3390/s20040984 - 12 Feb 2020
Cited by 4
Abstract
Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users [...] Read more.
Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world. Full article
(This article belongs to the Special Issue Remote Sensing and Geoinformatics in Wildfire Management)
Show Figures

Figure 1

Article
Secure Authentication and Credential Establishment in Narrowband IoT and 5G
Sensors 2020, 20(3), 882; https://doi.org/10.3390/s20030882 - 07 Feb 2020
Cited by 7
Abstract
Security is critical in the deployment and maintenance of novel IoT and 5G networks. The process of bootstrapping is required to establish a secure data exchange between IoT devices and data-driven platforms. It entails, among other steps, authentication, authorization, and credential management. Nevertheless, [...] Read more.
Security is critical in the deployment and maintenance of novel IoT and 5G networks. The process of bootstrapping is required to establish a secure data exchange between IoT devices and data-driven platforms. It entails, among other steps, authentication, authorization, and credential management. Nevertheless, there are few efforts dedicated to providing service access authentication in the area of constrained IoT devices connected to recent wireless networks such as narrowband IoT (NB-IoT) and 5G. Therefore, this paper presents the adaptation of bootstrapping protocols to be compliant with the 3GPP specifications in order to enable the 5G feature of secondary authentication for constrained IoT devices. To allow the secondary authentication and key establishment in NB-IoT and 4G/5G environments, we have adapted two Extensible Authentication Protocol (EAP) lower layers, i.e., PANATIKI and LO-CoAP-EAP. In fact, this approach presents the evaluation of both aforementioned EAP lower layers, showing the contrast between a current EAP lower layer standard, i.e., PANA, and one specifically designed with the constraints of IoT, thus providing high flexibility and scalability in the bootstrapping process in 5G networks. The proposed solution is evaluated to prove its efficiency and feasibility, being one of the first efforts to support secure service authentication and key establishment for constrained IoT devices in 5G environments. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
Show Figures

Figure 1

Article
Modelling Remote Sensing Reflectance to Detect Dispersed Oil at Sea
Sensors 2020, 20(3), 863; https://doi.org/10.3390/s20030863 - 06 Feb 2020
Cited by 7
Abstract
This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote [...] Read more.
This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote sensing reflectance (Rrs). Analyses were performed for the optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea). The case of seawater contaminated by dispersed oil at a concentration of 10 ppm was also discussed for different wind speeds. Two types of oils with extremely different optical properties (refraction and absorption coefficients) were taken into account for consideration. The optical properties (absorption and scattering coefficients and angular light scattering distribution) of the oil-in-water dispersion system were determined using the Mie theory. The spectral index for oil detection in seawater for different wind conditions was determined based on the results obtained for reflectance at selected wavelengths in the range 412–676 nm. The determined spectral index for seawater free of oil achieves higher values for seawater contaminated by oil. The analysis of the values of the spectral indices calculated for 28 combinations of wavelengths was used to identify the most universal spectral index of Rrs for 555 nm/440 nm for dispersed oil detection using any optical parameters. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)
Show Figures

Figure 1

Article
5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps
Sensors 2020, 20(3), 828; https://doi.org/10.3390/s20030828 - 04 Feb 2020
Cited by 40
Abstract
Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current [...] Read more.
Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps. Full article
(This article belongs to the Section Internet of Things)
Show Figures

Figure 1

Article
Accuracy of Trajectory Tracking Based on Nonlinear Guidance Logic for Hydrographic Unmanned Surface Vessels
Sensors 2020, 20(3), 832; https://doi.org/10.3390/s20030832 - 04 Feb 2020
Cited by 12
Abstract
A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect [...] Read more.
A new trend in recent years for hydrographic measurement in water bodies is the use of unmanned surface vehicles (USVs). In the process of navigation by USVs, it is particularly important to control position precisely on the measuring profile. Precise navigation with respect to the measuring profile avoids registration of redundant data and thus saves time and survey costs. This article addresses the issue of precise navigation of the hydrographic unit on the measuring profile with the use of a nonlinear adaptive autopilot. The results of experiments concerning hydrographic measurements performed in real conditions using an USV are discussed. Full article
(This article belongs to the Special Issue Remote Sensing in Vessel Detection and Navigation)
Show Figures

Figure 1

Article
Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
Sensors 2020, 20(3), 814; https://doi.org/10.3390/s20030814 - 03 Feb 2020
Cited by 8
Abstract
Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 [...] Read more.
Understanding the factors that might intentionally influence the reception of global navigation satellite system (GNSS) signals can be a challenging topic today. The focus of this research is to evaluate the vulnerability of geodetic GNSS receivers under the use of a low-cost L1/E1 frequency jammer. A suitable area for testing was established in Slovenia. Nine receivers from different manufacturers were under consideration in this study. While positioning, intentional 3-minute jammings were performed by a jammer that was located statically at different distances from receivers. Furthermore, kinematic disturbances were performed using a jammer placed in a vehicle that passed the testing area at various speeds. An analysis of different scenarios indicated that despite the use of an L1/E1 jammer, the GLONASS (Russian: Globalnaya Navigatsionnaya Sputnikovaya Sistema) and Galileo signals were also affected, either due to the increased carrier-to-noise-ratio (C/N0) or, in the worst cases, by a loss-of-signal. A jammer could substantially affect the position, either with a lack of any practical solution or even with a wrong position. Maximal errors in the carrier-phase positions, which should be considered a concern for geodesy, differed by a few metres from the exact solution. The factor that completely disabled the signal reception was the proximity of a jammer, regardless of its static or kinematic mode. Full article
(This article belongs to the Special Issue GNSS Data Processing and Navigation)
Show Figures

Figure 1

Article
IoT Based Architecture for Model Predictive Control of HVAC Systems in Smart Buildings
Sensors 2020, 20(3), 781; https://doi.org/10.3390/s20030781 - 31 Jan 2020
Cited by 27
Abstract
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control [...] Read more.
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. Full article
(This article belongs to the Special Issue Sensor Based Smart Grid in Internet of Things Era)
Show Figures

Figure 1

Article
Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine
Sensors 2020, 20(3), 765; https://doi.org/10.3390/s20030765 - 30 Jan 2020
Cited by 14
Abstract
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient [...] Read more.
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%. Full article
Show Figures

Figure 1

Article
Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
Sensors 2020, 20(3), 726; https://doi.org/10.3390/s20030726 - 28 Jan 2020
Cited by 14
Abstract
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, [...] Read more.
An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%. Full article
(This article belongs to the Special Issue Imaging Sensor Systems for Analyzing Subsea Environment and Life)
Show Figures

Figure 1

Article
SeisMote: A Multi-Sensor Wireless Platform for Cardiovascular Monitoring in Laboratory, Daily Life, and Telemedicine
Sensors 2020, 20(3), 680; https://doi.org/10.3390/s20030680 - 26 Jan 2020
Cited by 13
Abstract
This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from [...] Read more.
This article presents a new wearable platform, SeisMote, for the monitoring of cardiovascular function in controlled conditions and daily life. It consists of a wireless network of sensorized nodes providing simultaneous multiple measures of electrocardiogram (ECG), acceleration, rotational velocity, and photoplethysmogram (PPG) from different body areas. A custom low-power transmission protocol was developed to allow the concomitant real-time monitoring of 32 signals (16 bit @200 Hz) from up to 12 nodes with a jitter in the among-node time synchronization lower than 0.2 ms. The BluetoothLE protocol may be used when only a single node is needed. Data can also be collected in the off-line mode. Seismocardiogram and pulse transit times can be derived from the collected data to obtain additional information on cardiac mechanics and vascular characteristics. The employment of the system in the field showed recordings without data gaps caused by transmission errors, and the duration of each battery charge exceeded 16 h. The system is currently used to investigate strategies of hemodynamic regulation in different vascular districts (through a multisite assessment of ECG and PPG) and to study the propagation of precordial vibrations along the thorax. The single-node version is presently exploited to monitor cardiac patients during telerehabilitation. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
Show Figures

Figure 1

Article
An IoT Architecture for Water Resource Management in Agroindustrial Environments: A Case Study in Almería (Spain)
Sensors 2020, 20(3), 596; https://doi.org/10.3390/s20030596 - 21 Jan 2020
Cited by 10
Abstract
The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which [...] Read more.
The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which are difficult to implement as the different agents are usually geographically separated. In this sense, the use of enabling technologies such as the Internet of Things can be essential to the proper operation of these agroindustrial systems. In this paper, an Internet of Things cloud architecture based on the FIWARE standard is proposed for interconnecting the several agents that make up the agroindustrial system. In addition, this architecture includes an efficient management method based on a model predictive control technique, which is aimed at minimizing operating costs. A case study inspired by three real facilities located in Almería (southeast of Spain) is used as the simulation test bed. The obtained results show how around 75% of the total operating costs can be saved with the application of the proposed approach, which could be very significant to decrease the costs of desalinated water and, therefore, to maintain the sustainability of the agricultural system. Full article
(This article belongs to the Special Issue Selected Papers from the 3rd Global IoT Summit)
Show Figures

Figure 1

Article
Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data
Sensors 2020, 20(2), 571; https://doi.org/10.3390/s20020571 - 20 Jan 2020
Cited by 11
Abstract
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently [...] Read more.
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently captured by control devices. In the experimental part, we work on real measurement data and events from a coal fired power plant. The main research objective is to implement a model that detects deviations from the normal operation state based on regression and to check which events or failures can be detected by it. The presented technique allows the creation of a predictive system working on the basis of the available data with a minimal requirement of expert knowledge, in particular the knowledge related to the categorization of failures and the exact time of their occurrence, which is sometimes difficult to identify. The paper shows that with modern technologies, such as the Internet of Things, big data, and cloud computing, it is possible to integrate automation systems, designed in the past only to control the production process, with IT systems that make all processes more efficient through the use of advanced analytic tools. Full article
Show Figures

Graphical abstract

Article
A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers
Sensors 2020, 20(2), 536; https://doi.org/10.3390/s20020536 - 18 Jan 2020
Cited by 21
Abstract
Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These [...] Read more.
Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively. Full article
Show Figures

Figure 1

Article
Fused-Deposition-Material 3D-Printing Procedure and Algorithm Avoiding Use of Any Supports
Sensors 2020, 20(2), 470; https://doi.org/10.3390/s20020470 - 14 Jan 2020
Cited by 1
Abstract
The three-dimensional printing of complex shapes without using supporting structures is the most attractive factor of merit in current additive manufacturing because it allows to drastically reduce printing time, and ideally nullify postprocessing and waste material. In this work, we present an innovative [...] Read more.
The three-dimensional printing of complex shapes without using supporting structures is the most attractive factor of merit in current additive manufacturing because it allows to drastically reduce printing time, and ideally nullify postprocessing and waste material. In this work, we present an innovative procedure and algorithm (Print on Air, PoA) for additive manufacturing that, relying on sensing systems embedded into the three-dimensional (3D) printer (e.g., temperature and speed sensors), aims at generating a printing sequence capable of a self-sustaining bridge and overhang structures. This feature was achieved by splitting the actual floating area of the layer where the aforementioned structures are in many subsections. Each is generated with a negligible floating surface and printed in a well-determined sequence with accurate temperature and speed profiles. Therefore, each subsection is formed without the need for scaffolding, simultaneously acting as a supporting structure for the following subsection. The array of subsections constitutes the actual bridge or overhang structure. The proposed method can be used for any object, including very long bridges or convex surfaces. The revolutionary method is here reported and evaluated in order to show its applicability in any condition. Although the study was conducted in a Fused Deposition Material (FDM) environment, it can certainly be adapted to other manufacturing environments with adequate modifications. Full article
Show Figures

Figure 1

Article
An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel
Sensors 2020, 20(2), 431; https://doi.org/10.3390/s20020431 - 12 Jan 2020
Cited by 13
Abstract
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location [...] Read more.
Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Data in Water Resources Management)
Show Figures

Figure 1

Article
Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units
Sensors 2020, 20(2), 412; https://doi.org/10.3390/s20020412 - 11 Jan 2020
Cited by 14
Abstract
DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in [...] Read more.
DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network. Full article
Show Figures

Figure 1

Article
Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification
Sensors 2020, 20(2), 374; https://doi.org/10.3390/s20020374 - 09 Jan 2020
Cited by 1
Abstract
In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely [...] Read more.
In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications. Full article
(This article belongs to the Special Issue Sensor Systems for Satellite Attitude Determination)
Show Figures

Figure 1

Article
Distributed Optical Fiber-Based Approach for Soil–Structure Interaction
Sensors 2020, 20(1), 321; https://doi.org/10.3390/s20010321 - 06 Jan 2020
Cited by 5
Abstract
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the [...] Read more.
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the innovative optical frequency domain reflectometry (OFDR) was used to evaluate the effect of scour by performing distributed strain measurements along a rod under static lateral loads. An analytical analysis based on the Winkler model of the soil was carefully established and used to evaluate the accuracy of the fiber optic sensors and helped interpret the measurements results. Dynamic tests were also performed and results from static and dynamic tests were compared using an equivalent cantilever model. Full article
(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)
Show Figures

Graphical abstract

Article
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
Sensors 2020, 20(1), 322; https://doi.org/10.3390/s20010322 - 06 Jan 2020
Cited by 15
Abstract
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on [...] Read more.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
Show Figures

Figure 1

Article
Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications
Sensors 2020, 20(1), 288; https://doi.org/10.3390/s20010288 - 04 Jan 2020
Cited by 13
Abstract
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle [...] Read more.
Driver inattention is one of the leading causes of traffic crashes worldwide. Providing the driver with an early warning prior to a potential collision can significantly reduce the fatalities and level of injuries associated with vehicle collisions. In order to monitor the vehicle surroundings and predict collisions, on-board sensors such as radar, lidar, and cameras are often used. However, the driving environment perception based on these sensors can be adversely affected by a number of factors such as weather and solar irradiance. In addition, potential dangers cannot be detected if the target is located outside the limited field-of-view of the sensors, or if the line of sight to the target is occluded. In this paper, we propose an approach for designing a vehicle collision warning system based on fusion of multisensors and wireless vehicular communications. A high-level fusion of radar, lidar, camera, and wireless vehicular communication data was performed to predict the trajectories of remote targets and generate an appropriate warning to the driver prior to a possible collision. We implemented and evaluated the proposed vehicle collision system in virtual driving environments, which consisted of a vehicle–vehicle collision scenario and a vehicle–pedestrian collision scenario. Full article
(This article belongs to the Special Issue Intelligent Vehicles)
Show Figures

Figure 1

Article
Impact of SCHC Compression and Fragmentation in LPWAN: A Case Study with LoRaWAN
Sensors 2020, 20(1), 280; https://doi.org/10.3390/s20010280 - 03 Jan 2020
Cited by 8
Abstract
The dawn of the Internet of Things (IoT) paradigm has brought about a series of novel services never imagined until recently. However, certain deployments such as those employing Low-Power Wide-Area Network (LPWAN)-based technologies may present severe network restrictions in terms of throughput and [...] Read more.
The dawn of the Internet of Things (IoT) paradigm has brought about a series of novel services never imagined until recently. However, certain deployments such as those employing Low-Power Wide-Area Network (LPWAN)-based technologies may present severe network restrictions in terms of throughput and supported packet length. This situation prompts the isolation of LPWAN systems on islands with limited interoperability with the Internet. For that reason, the IETF’s LPWAN working group has proposed a Static Context Header Compression (SCHC) scheme that permits compression and fragmentation of and IPv6/UDP/CoAP packets with the aim of making them suitable for transmission over the restricted links of LPWANs. Given the impact that such a solution can have in many IoT scenarios, this paper addresses its real evaluation in terms not only of latency and delivery ratio improvements, as a consequence of different compression and fragmentation levels, but also of the overhead in end node resources and useful payload sent per fragment. This has been carried out with the implementation of middleware and using a real testbed implementation of a LoRaWAN-to-IPv6 architecture together with a publish/subscribe broker for CoAP. The attained results show the advantages of SCHC, and sustain discussion regarding the impact of different SCHC and LoRaWAN configurations on the performance. It is highlighted that necessary end node resources are low as compared to the benefit of delivering long IPv6 packets over the LPWAN links. In turn, fragmentation can impose a lack of efficiency in terms of data and energy and, hence, a cross-layer solution is needed in order to obtain the best throughput of the network. Full article
(This article belongs to the Special Issue Pub/Sub Solutions for IoT)
Show Figures

Figure 1

Article
Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution
Sensors 2019, 19(11), 2596; https://doi.org/10.3390/s19112596 - 07 Jun 2019
Cited by 8
Abstract
Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach [...] Read more.
Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach for determining phosphate concentrations in a paprika nutrient solution. As a conventional multivariate analysis approach using spectral data, partial least squares regression (PLSR) and principal components regression (PCR) models were developed using 56 samples for calibration and 24 samples for evaluation. The R2 values of estimation models using PCR and PLSR ranged from 0.44 to 0.64. Furthermore, an estimation model using raw electromotive force (EMF) data from cobalt electrodes gave R2 values of 0.58–0.71. To improve the model performance, a data fusion method was developed to estimate phosphate concentration using near infrared (NIR) spectral and cobalt electrochemical data. Raw EMF data from cobalt electrodes and principle component values from the spectral data were combined. Results of calibration and evaluation tests using an artificial neural network estimation model showed that R2 = 0.90 and 0.89 and root mean square error (RMSE) = 96.70 and 119.50 mg/L, respectively. These values are sufficiently high for application to measuring phosphate concentration in hydroponic solutions. Full article
(This article belongs to the Special Issue Ion Selective Electrodes and Optodes)
Show Figures

Figure 1

Article
Deep CT to MR Synthesis Using Paired and Unpaired Data
Sensors 2019, 19(10), 2361; https://doi.org/10.3390/s19102361 - 22 May 2019
Cited by 34
Abstract
Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This [...] Read more.
Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach. Full article
(This article belongs to the Special Issue Biomedical Imaging and Sensing)
Show Figures

Figure 1

Article
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
Sensors 2019, 19(9), 2068; https://doi.org/10.3390/s19092068 - 03 May 2019
Cited by 23
Abstract
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable [...] Read more.
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm. Full article
Show Figures

Figure 1

Article
Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
Sensors 2019, 19(9), 1977; https://doi.org/10.3390/s19091977 - 27 Apr 2019
Cited by 55
Abstract
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end [...] Read more.
Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively. Full article
(This article belongs to the Special Issue Security and Privacy in Internet of Things)
Show Figures

Figure 1

Article
Sperm-Cultured Gate Ion-Sensitive Field-Effect Transistor for Non-Optical and Live Monitoring of Sperm Capacitation
Sensors 2019, 19(8), 1784; https://doi.org/10.3390/s19081784 - 14 Apr 2019
Cited by 8
Abstract
We have successfully monitored the effect of progesterone and Ca2+ on artificially induced sperm capacitation in a real-time, noninvasive and label-free manner using an ion-sensitive field-effect transistor (ISFET) sensor. The sperm activity can be electrically detected as a change in pH generated [...] Read more.
We have successfully monitored the effect of progesterone and Ca2+ on artificially induced sperm capacitation in a real-time, noninvasive and label-free manner using an ion-sensitive field-effect transistor (ISFET) sensor. The sperm activity can be electrically detected as a change in pH generated by sperm respiration based on the principle of the ISFET sensor. Upon adding mouse sperm to the gate of the ISFET sensor in the culture medium with progesterone, the pH decreases with an increasing concentration of progesterone from 1 to 40 μM. This is because progesterone induces Ca2+ influx into spermatozoa and triggers multiple Ca2+-dependent physiological responses, which subsequently activates sperm respiration. Moreover, this pH response of the ISFET sensor is not observed for a Ca2+-free medium even when progesterone is introduced, which means that Ca2+ influx is necessary for sperm activation that results in sperm capacitation. Thus, a platform based on the ISFET sensor system can provide a simple method of evaluating artificially induced sperm capacitation in the field of male infertility treatment. Full article
(This article belongs to the Special Issue Potentiometric Bio/Chemical Sensing)
Show Figures

Graphical abstract

Article
A Smart Wireless Ear-Worn Device for Cardiovascular and Sweat Parameter Monitoring During Physical Exercise: Design and Performance Results
Sensors 2019, 19(7), 1616; https://doi.org/10.3390/s19071616 - 04 Apr 2019
Cited by 16
Abstract
Wearable biomedical technology has gained much support lately as devices have become more affordable to the general public and they can easily interact with mobile phones and other platforms. The feasibility and accuracy of the data generated by these devices so as to [...] Read more.
Wearable biomedical technology has gained much support lately as devices have become more affordable to the general public and they can easily interact with mobile phones and other platforms. The feasibility and accuracy of the data generated by these devices so as to replace the standard medical methods in use today is still under scrutiny. In this paper, we present an ear-worn device to measure cardiovascular and sweat parameters during physical exercise. ECG bipolar recordings capture the electric potential around both ears, whereas sweat rate is estimated by the impedance method over one segment of tissue closer to the left ear, complemented by the measurement of the lactate and pH levels using amperiometric and potentiometric sensors, respectively. Together with head acceleration, the acquired data is sent to a mobile phone via BLE, enabling extended periods of signal recording. Results obtained by the device have shown a SNR level of 18 dB for the ECG signal recorded around the ears, a THD value of −20.46 dB for the excitation signal involved in impedance measurements, sweat conductivity of 0.08 S/m at 1 kHz and sensitivities of 50 mV/pH and 0.8 μA/mM for the pH and lactate acquisition channels, respectively. Testing of the device was performed in human subjects during indoors cycling with characteristic level changes. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
Show Figures

Figure 1

Article
A Simple Procedure to Assess Limit of Detection for Multisensor Systems
Sensors 2019, 19(6), 1359; https://doi.org/10.3390/s19061359 - 18 Mar 2019
Cited by 16
Abstract
Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach [...] Read more.
Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems. Full article
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
Show Figures

Figure 1

Article
Formation of Interstitial Hot-Spots Using the Reduced Gap-Size between Plasmonic Microbeads Pattern for Surface-Enhanced Raman Scattering Analysis
Sensors 2019, 19(5), 1046; https://doi.org/10.3390/s19051046 - 01 Mar 2019
Cited by 8
Abstract
To achieve an effective surface-enhanced Raman scattering (SERS) sensor with periodically distributed “hot spots” on wafer-scale substrates, we propose a hybrid approach combining physical nano-imprint lithography and a chemical deposition method to form a silver microbead array. Nano-imprint lithography (NIL) can lead to [...] Read more.
To achieve an effective surface-enhanced Raman scattering (SERS) sensor with periodically distributed “hot spots” on wafer-scale substrates, we propose a hybrid approach combining physical nano-imprint lithography and a chemical deposition method to form a silver microbead array. Nano-imprint lithography (NIL) can lead to mass-production and high throughput, but is not appropriate for generating strong “hot-spots.” However, when we apply electrochemical deposition to an NIL substrate and the reaction time was increased to 45 s, periodical “hot-spots” between the microbeads were generated on the substrates. It contributed to increasing the enhancement factor (EF) and lowering the detection limit of the substrates to 4.40 × 106 and 1.0 × 10−11 M, respectively. In addition, this synthetic method exhibited good substrate-to-substrate reproducibility (RSD < 9.4%). Our research suggests a new opportunity for expanding the SERS application. Full article
(This article belongs to the Special Issue SERS Sensing)
Show Figures

Figure 1

Article
The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study
Sensors 2019, 19(5), 983; https://doi.org/10.3390/s19050983 - 26 Feb 2019
Cited by 21
Abstract
Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given [...] Read more.
Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments. Full article
(This article belongs to the Special Issue Sensor Technology for Smart Homes)
Show Figures

Figure 1

Article
Deep Learning-Based Framework for In Vivo Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain
Sensors 2019, 19(4), 920; https://doi.org/10.3390/s19040920 - 22 Feb 2019
Cited by 42
Abstract
The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in [...] Read more.
The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure. Full article
(This article belongs to the Special Issue Advanced Spectroscopy, Imaging and Sensing in Biomedicine)
Show Figures

Figure 1

Article
Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
Sensors 2019, 19(4), 838; https://doi.org/10.3390/s19040838 - 18 Feb 2019
Cited by 45
Abstract
LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present [...] Read more.
LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present an approach to increase network throughput through a Slotted-ALOHA overlay on LoRaWAN networks. To increase the single channel capacity, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA variant on the top of the Pure-ALOHA approach used by the standard; thus, no modification in pre-existing libraries is necessary. Our method is based on an innovative synchronization service that is suitable for low-cost wireless sensor nodes. We modelled the LoRaWAN channel with extensive measurement on hardware platforms, and we quantified the impact of tuning parameters on physical and medium access control layers, as well as the packet collision rate. Results show that Slotted-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput. Full article
Show Figures

Figure 1

Article
Waste Coffee Ground Biochar: A Material for Humidity Sensors
Sensors 2019, 19(4), 801; https://doi.org/10.3390/s19040801 - 15 Feb 2019
Cited by 19
Abstract
Worldwide consumption of coffee exceeds 11 billion tons/year. Used coffee grounds end up as landfill. However, the unique structural properties of its porous surface make coffee grounds popular for the adsorption of gaseous molecules. In the present work, we demonstrate the use of [...] Read more.
Worldwide consumption of coffee exceeds 11 billion tons/year. Used coffee grounds end up as landfill. However, the unique structural properties of its porous surface make coffee grounds popular for the adsorption of gaseous molecules. In the present work, we demonstrate the use of coffee grounds as a potential and cheap source for biochar carbon. The produced coffee ground biochar (CGB) was investigated as a sensing material for developing humidity sensors. CGB was fully characterized by using laser granulometry, X-ray diffraction (XRD), Raman spectroscopy, field emission-scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and the Brunnauer Emmett Teller (BET) technique in order to acquire a complete understanding of its structural and surface properties and composition. Subsequently humidity sensors were screen printed using an ink-containing CGB with polyvinyl butyral (PVB) acting as a temporary binder and ethylene glycol monobutyral ether, Emflow, as an organic vehicle so that the proper rheological characteristics were achieved. Screen-printed films were the heated at 300 °C in air. Humidity tests were performed under a flow of 1.7 L/min in the relative humidity range 0–100% at room temperature. The initial impedance of the film was 25.2 ± 0.15 MΩ which changes to 12.3 MΩ under 98% humidity exposure. A sensor response was observed above 20% relative humidity (RH). Both the response and recovery times were reasonably fast (less than 2 min). Full article
(This article belongs to the Special Issue Functional Materials for the Applications of Advanced Gas Sensors)
Show Figures

Graphical abstract

Article
Enhanced Hydrogen Detection in ppb-Level by Electrospun SnO2-Loaded ZnO Nanofibers
Sensors 2019, 19(3), 726; https://doi.org/10.3390/s19030726 - 11 Feb 2019
Cited by 11
Abstract
High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The [...] Read more.
High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The xSnO2-loaded (x = 0.05, 0.1 and 0.15) ZnO NFs were fabricated using an electrospinning technique followed by calcination at high temperature. Microscopic analyses demonstrated the formation of NFs with expected morphology and chemical composition. Hydrogen sensing studies were performed at various temperatures and the optimal working temperature was selected as 300 °C. The optimal gas sensor (0.1 SnO2 loaded ZnO NFs) not only showed a high response to 50 ppb hydrogen gas, but also showed an excellent selectivity to hydrogen gas. The excellent performance of the gas sensor to hydrogen gas was mainly related to the formation of SnO2-ZnO heterojunctions and the metallization effect of ZnO. Full article
(This article belongs to the Section Chemical Sensors)
Show Figures

Figure 1

Article
An Improved Routing Schema with Special Clustering Using PSO Algorithm for Heterogeneous Wireless Sensor Network
Sensors 2019, 19(3), 671; https://doi.org/10.3390/s19030671 - 07 Feb 2019
Cited by 97
Abstract
Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads [...] Read more.
Energy efficiency and energy balancing are crucial research issues as per routing protocol designing for self-organized wireless sensor networks (WSNs). Many literatures used the clustering algorithm to achieve energy efficiency and energy balancing, however, there are usually energy holes near the cluster heads (CHs) because of the heavy burden of forwarding. As the clustering problem in lossy WSNs is proved to be a NP-hard problem, many metaheuristic algorithms are utilized to solve the problem. In this paper, a special clustering method called Energy Centers Searching using Particle Swarm Optimization (EC-PSO) is presented to avoid these energy holes and search energy centers for CHs selection. During the first period, the CHs are elected using geometric method. After the energy of the network is heterogeneous, EC-PSO is adopted for clustering. Energy centers are searched using an improved PSO algorithm and nodes close to the energy center are elected as CHs. Additionally, a protection mechanism is also used to prevent low energy nodes from being the forwarder and a mobile data collector is introduced to gather the data. We conduct numerous simulations to illustrate that our presented EC-PSO outperforms than some similar works in terms of network lifetime enhancement and energy utilization ratio. Full article
Show Figures

Figure 1

Article
Embedded Smart Antenna for Non-Destructive Testing and Evaluation (NDT&E) of Moisture Content and Deterioration in Concrete
Sensors 2019, 19(3), 547; https://doi.org/10.3390/s19030547 - 28 Jan 2019
Cited by 43
Abstract
Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To [...] Read more.
Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To date, a lot of research is ongoing to develop different instruments to inspect concrete quality. Detection of moisture ingress is important in the structural monitoring of concrete. This paper presents a novel sensing technique using a smart antenna for the non-destructive evaluation of moisture content and deterioration inspection in concrete blocks. Two different standard concrete samples (United Kingdom and Malaysia) were investigated in this research. An electromagnetic (EM) sensor was designed and embedded inside the concrete to detect the moisture content within the structure. In addition, CST microwave studio was used to validate the theoretical model of the EM sensor against the test data. The results demonstrated that the EM sensor at 2.45 GHz is capable of detecting the moisture content in the concrete with linear regression of R2 = 0.9752. Furthermore, identification of different mix ratios of concrete were successfully demonstrated in this paper. In conclusion, the EM sensor is capable of detecting moisture content non-destructively and could be a potential technique for maintenance and quality control of the building performance. Full article
(This article belongs to the Special Issue RF Technology for Sensor Applications)
Show Figures

Graphical abstract

Article
Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley
Sensors 2019, 19(3), 535; https://doi.org/10.3390/s19030535 - 28 Jan 2019
Cited by 33
Abstract
Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass [...] Read more.
Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m2 were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height ( R 2 > 0.80 ) for both fields, and with dry biomass ( R 2 = 0.88 ), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement ( R 2 = 0.87 ). The UAV-based system showed a weaker estimation capability for plant height and dry biomass ( R 2 < 0.6 ). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands. Full article
(This article belongs to the Special Issue Emerging Sensor Technology in Agriculture)
Show Figures

Figure 1

Article
Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
Sensors 2019, 19(3), 478; https://doi.org/10.3390/s19030478 - 24 Jan 2019
Cited by 39
Abstract
This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up [...] Read more.
This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments. Full article
Show Figures

Figure 1

Article
Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals
Sensors 2019, 19(1), 210; https://doi.org/10.3390/s19010210 - 08 Jan 2019
Cited by 47
Abstract
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an [...] Read more.
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI. Full article
Show Figures

Figure 1

Article
Synthesis of Cu2O/CuO Nanocrystals and Their Application to H2S Sensing
Sensors 2019, 19(1), 211; https://doi.org/10.3390/s19010211 - 08 Jan 2019
Cited by 24
Abstract
Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu2O and CuO using [...] Read more.
Semiconducting metal oxide nanocrystals are an important class of materials that have versatile applications because of their useful properties and high stability. Here, we developed a simple route to synthesize nanocrystals (NCs) of copper oxides such as Cu2O and CuO using a hot-soap method, and applied them to H2S sensing. Cu2O NCs were synthesized by simply heating a copper precursor in oleylamine in the presence of diol at 160 °C under an Ar flow. X-ray diffractometry (XRD), dynamic light scattering (DLS), and transmission electron microscopy (TEM) results indicated the formation of monodispersed Cu2O NCs having approximately 5 nm in crystallite size and 12 nm in colloidal size. The conversion of the Cu2O NCs to CuO NCs was undertaken by straightforward air oxidation at room temperature, as confirmed by XRD and UV-vis analyses. A thin film Cu2O NC sensor fabricated by spin coating showed responses to H2S in dilute concentrations (1–8 ppm) at 50–150 °C, but the stability was poor because of the formation of metallic Cu2S in a H2S atmosphere. We found that Pd loading improved the stability of the sensor response. The Pd-loaded Cu2O NC sensor exhibited reproducible responses to H2S at 200 °C. Based on the gas sensing mechanism, it is suggested that Pd loading facilitates the reaction of adsorbed oxygen with H2S and suppresses the irreversible formation of Cu2S. Full article
(This article belongs to the Special Issue Functional Materials for the Applications of Advanced Gas Sensors)
Show Figures

Figure 1

Review

Jump to: Research

Review
InAsSb-Based Infrared Photodetectors: Thirty Years Later On
Sensors 2020, 20(24), 7047; https://doi.org/10.3390/s20247047 - 09 Dec 2020
Cited by 6
Abstract
In 1989, one author of this paper (A.R.) published the very first review paper on InAsSb infrared detectors. During the last thirty years, many scientific breakthroughs and technological advances for InAsSb-based photodetectors have been made. Progress in advanced epitaxial methods contributed considerably to [...] Read more.
In 1989, one author of this paper (A.R.) published the very first review paper on InAsSb infrared detectors. During the last thirty years, many scientific breakthroughs and technological advances for InAsSb-based photodetectors have been made. Progress in advanced epitaxial methods contributed considerably to the InAsSb improvement. Current efforts are directed towards the photodetector’s cut-off wavelength extension beyond lattice-available and lattice-strained binary substrates. It is suspected that further improvement of metamorphic buffers for epitaxial layers will lead to lower-cost InAsSb-based focal plane arrays on large-area alternative substrates like GaAs and silicon. Most photodetector reports in the last decade are devoted to the heterostructure and barrier architectures operating in high operating temperature conditions. In the paper, at first InAsSb growth methods are briefly described. Next, the fundamental material properties are reviewed, stressing electrical and optical aspects limiting the photodetector performance. The last part of the paper highlights new ideas in design of InAsSb-based bulk and superlattice infrared detectors and focal plane arrays. Their performance is compared with the state-of-the-art infrared detector technologies. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Review
Recent Progress in Plasmonic Biosensing Schemes for Virus Detection
Sensors 2020, 20(17), 4745; https://doi.org/10.3390/s20174745 - 22 Aug 2020
Cited by 17
Abstract
The global burden of coronavirus disease 2019 (COVID-19) to public health and global economy has stressed the need for rapid and simple diagnostic methods. From this perspective, plasmonic-based biosensing can manage the threat of infectious diseases by providing timely virus monitoring. In recent [...] Read more.
The global burden of coronavirus disease 2019 (COVID-19) to public health and global economy has stressed the need for rapid and simple diagnostic methods. From this perspective, plasmonic-based biosensing can manage the threat of infectious diseases by providing timely virus monitoring. In recent years, many plasmonics’ platforms have embraced the challenge of offering on-site strategies to complement traditional diagnostic methods relying on the polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA). This review compiled recent progress on the development of novel plasmonic sensing schemes for the effective control of virus-related diseases. A special focus was set on the utilization of plasmonic nanostructures in combination with other detection formats involving colorimetric, fluorescence, luminescence, or Raman scattering enhancement. The quantification of different viruses (e.g., hepatitis virus, influenza virus, norovirus, dengue virus, Ebola virus, Zika virus) with particular attention to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was reviewed from the perspective of the biomarker and the biological receptor immobilized on the sensor chip. Technological limitations including selectivity, stability, and monitoring in biological matrices were also reviewed for different plasmonic-sensing approaches. Full article
(This article belongs to the Special Issue Applications of Plasmonic Biosensing in Clinical Diagnosis)
Show Figures

Figure 1

Review
Electrochemical Biosensors Based on Nanomaterials for Early Detection of Alzheimer’s Disease
Sensors 2020, 20(17), 4748; https://doi.org/10.3390/s20174748 - 22 Aug 2020
Cited by 6
Abstract
Alzheimer’s disease (AD) is an untreatable neurodegenerative disease that initially manifests as difficulty to remember recent events and gradually progresses to cognitive impairment. The incidence of AD is growing yearly as life expectancy increases, thus early detection is essential to ensure a better [...] Read more.
Alzheimer’s disease (AD) is an untreatable neurodegenerative disease that initially manifests as difficulty to remember recent events and gradually progresses to cognitive impairment. The incidence of AD is growing yearly as life expectancy increases, thus early detection is essential to ensure a better quality of life for diagnosed patients. To reach that purpose, electrochemical biosensing has emerged as a cost-effective alternative to traditional diagnostic techniques, due to its high sensitivity and selectivity. Of special relevance is the incorporation of nanomaterials in biosensors, as they contribute to enhance electron transfer while promoting the immobilization of biological recognition elements. Moreover, nanomaterials have also been employed as labels, due to their unique electroactive and electrocatalytic properties. The aim of this review is to add value in the advances achieved in the detection of AD biomarkers, the strategies followed for the incorporation of nanomaterials and its effect in biosensors performance. Full article
(This article belongs to the Section Biosensors)
Show Figures

Figure 1

Review
Lab-On-Fiber Technology: A Roadmap toward Multifunctional Plug and Play Platforms
Sensors 2020, 20(17), 4705; https://doi.org/10.3390/s20174705 - 20 Aug 2020
Cited by 10
Abstract
This review presents an overview of the “lab-on-fiber technology” vision and the main milestones set in the technological roadmap to achieve the ultimate objective of developing flexible, multifunctional plug and play fiber-optic platforms designed for specific applications. The main achievements, obtained with nanofabrication [...] Read more.
This review presents an overview of the “lab-on-fiber technology” vision and the main milestones set in the technological roadmap to achieve the ultimate objective of developing flexible, multifunctional plug and play fiber-optic platforms designed for specific applications. The main achievements, obtained with nanofabrication strategies for unconventional substrates, such as optical fibers, are discussed here. The perspectives and challenges that lie ahead are highlighted with a special focus on full spatial control at the nanoscale and high-throughput production scenarios. The rapid progress in the fabrication stage has opened new avenues toward the development of multifunctional plug and play platforms, discussed here with particular emphasis on new functionalities and unparalleled figures of merit, to demonstrate the potential of this powerful technology in many strategic application scenarios. The paper also analyses the benefits obtained from merging lab-on-fiber (LOF) technology objectives with the emerging field of optomechanics, especially at the microscale and the nanoscale. We illustrate the main advances at the fabrication level, describe the main achievements in terms of functionalities and performance, and highlight future directions and related milestones. All achievements reviewed and discussed clearly suggest that LOF technology is much more than a simple vision and could play a central role not only in scenarios related to diagnostics and monitoring but also in the Information and Communication Technology (ICT) field, where optical fibers have already yielded remarkable results. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Figure 1

Review
3D-Printed Immunosensor Arrays for Cancer Diagnostics
Sensors 2020, 20(16), 4514; https://doi.org/10.3390/s20164514 - 12 Aug 2020
Cited by 9
Abstract
Detecting cancer at an early stage of disease progression promises better treatment outcomes and longer lifespans for cancer survivors. Research has been directed towards the development of accessible and highly sensitive cancer diagnostic tools, many of which rely on protein biomarkers and biomarker [...] Read more.
Detecting cancer at an early stage of disease progression promises better treatment outcomes and longer lifespans for cancer survivors. Research has been directed towards the development of accessible and highly sensitive cancer diagnostic tools, many of which rely on protein biomarkers and biomarker panels which are overexpressed in body fluids and associated with different types of cancer. Protein biomarker detection for point-of-care (POC) use requires the development of sensitive, noninvasive liquid biopsy cancer diagnostics that overcome the limitations and low sensitivities associated with current dependence upon imaging and invasive biopsies. Among many endeavors to produce user-friendly, semi-automated, and sensitive protein biomarker sensors, 3D printing is rapidly becoming an important contemporary tool for achieving these goals. Supported by the widely available selection of affordable desktop 3D printers and diverse printing options, 3D printing is becoming a standard tool for developing low-cost immunosensors that can also be used to make final commercial products. In the last few years, 3D printing platforms have been used to produce complex sensor devices with high resolution, tailored towards researchers’ and clinicians’ needs and limited only by their imagination. Unlike traditional subtractive manufacturing, 3D printing, also known as additive manufacturing, has drastically reduced the time of sensor and sensor array development while offering excellent sensitivity at a fraction of the cost of conventional technologies such as photolithography. In this review, we offer a comprehensive description of 3D printing techniques commonly used to develop immunosensors, arrays, and microfluidic arrays. In addition, recent applications utilizing 3D printing in immunosensors integrated with different signal transduction strategies are described. These applications include electrochemical, chemiluminescent (CL), and electrochemiluminescent (ECL) 3D-printed immunosensors. Finally, we discuss current challenges and limitations associated with available 3D printing technology and future directions of this field. Full article
(This article belongs to the Special Issue Biosensors – Recent Advances and Future Challenges)
Show Figures

Figure 1

Review
Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis
Sensors 2020, 20(16), 4433; https://doi.org/10.3390/s20164433 - 08 Aug 2020
Cited by 5
Abstract
Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic [...] Read more.
Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in ‘point of care’ diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases. Full article
Show Figures

Figure 1

Review
Enzyme-Based Biosensors: Tackling Electron Transfer Issues
Sensors 2020, 20(12), 3517; https://doi.org/10.3390/s20123517 - 21 Jun 2020
Cited by 21
Abstract
This review summarizes the fundamentals of the phenomenon of electron transfer (ET) reactions occurring in redox enzymes that were widely employed for the development of electroanalytical devices, like biosensors, and enzymatic fuel cells (EFCs). A brief introduction on the ET observed in proteins/enzymes [...] Read more.
This review summarizes the fundamentals of the phenomenon of electron transfer (ET) reactions occurring in redox enzymes that were widely employed for the development of electroanalytical devices, like biosensors, and enzymatic fuel cells (EFCs). A brief introduction on the ET observed in proteins/enzymes and its paradigms (e.g., classification of ET mechanisms, maximal distance at which is observed direct electron transfer, etc.) are given. Moreover, the theoretical aspects related to direct electron transfer (DET) are resumed as a guideline for newcomers to the field. Snapshots on the ET theory formulated by Rudolph A. Marcus and on the mathematical model used to calculate the ET rate constant formulated by Laviron are provided. Particular attention is devoted to the case of glucose oxidase (GOx) that has been erroneously classified as an enzyme able to transfer electrons directly. Thereafter, all tools available to investigate ET issues are reported addressing the discussions toward the development of new methodology to tackle ET issues. In conclusion, the trends toward upcoming practical applications are suggested as well as some directions in fundamental studies of bioelectrochemistry. Full article
(This article belongs to the Special Issue Biosensors – Recent Advances and Future Challenges)
Show Figures

Figure 1

Review
Wireless Power Transfer Techniques for Implantable Medical Devices: A Review
Sensors 2020, 20(12), 3487; https://doi.org/10.3390/s20123487 - 19 Jun 2020
Cited by 21
Abstract
Wireless power transfer (WPT) systems have become increasingly suitable solutions for the electrical powering of advanced multifunctional micro-electronic devices such as those found in current biomedical implants. The design and implementation of high power transfer efficiency WPT systems are, however, challenging. The size [...] Read more.
Wireless power transfer (WPT) systems have become increasingly suitable solutions for the electrical powering of advanced multifunctional micro-electronic devices such as those found in current biomedical implants. The design and implementation of high power transfer efficiency WPT systems are, however, challenging. The size of the WPT system, the separation distance between the outside environment and location of the implanted medical device inside the body, the operating frequency and tissue safety due to power dissipation are key parameters to consider in the design of WPT systems. This article provides a systematic review of the wide range of WPT systems that have been investigated over the last two decades to improve overall system performance. The various strategies implemented to transfer wireless power in implantable medical devices (IMDs) were reviewed, which includes capacitive coupling, inductive coupling, magnetic resonance coupling and, more recently, acoustic and optical powering methods. The strengths and limitations of all these techniques are benchmarked against each other and particular emphasis is placed on comparing the implanted receiver size, the WPT distance, power transfer efficiency and tissue safety presented by the resulting systems. Necessary improvements and trends of each WPT techniques are also indicated per specific IMD. Full article
(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)
Show Figures

Figure 1

Review
MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey
Sensors 2020, 20(11), 3243; https://doi.org/10.3390/s20113243 - 07 Jun 2020
Cited by 15
Abstract
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be [...] Read more.
Many neurological diseases and delineating pathological regions have been analyzed, and the anatomical structure of the brain researched with the aid of magnetic resonance imaging (MRI). It is important to identify patients with Alzheimer’s disease (AD) early so that preventative measures can be taken. A detailed analysis of the tissue structures from segmented MRI leads to a more accurate classification of specific brain disorders. Several segmentation methods to diagnose AD have been proposed with varying complexity. Segmentation of the brain structure and classification of AD using deep learning approaches has gained attention as it can provide effective results over a large set of data. Hence, deep learning methods are now preferred over state-of-the-art machine learning methods. We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD. Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe the state-of-the-art approaches, and summarize their results using publicly available datasets. Finally, we provide insight into current issues and discuss possible future research directions in building a computer-aided diagnostic system for AD. Full article
(This article belongs to the Section Biomedical Sensors)
Show Figures

Figure 1

Review
How Reliable Is the Electrochemical Readout of MIP Sensors?
Sensors 2020, 20(9), 2677; https://doi.org/10.3390/s20092677 - 08 May 2020
Cited by 7
Abstract
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin [...] Read more.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration. Full article
Show Figures

Graphical abstract

Review
A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues
Sensors 2020, 20(9), 2464; https://doi.org/10.3390/s20092464 - 27 Apr 2020
Cited by 16
Abstract
Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with [...] Read more.
Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research. Full article
Show Figures

Figure 1

Review
The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms
Sensors 2020, 20(6), 1751; https://doi.org/10.3390/s20061751 - 21 Mar 2020
Cited by 14
Abstract
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, [...] Read more.
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals. Full article
(This article belongs to the Special Issue Underwater Sensor Networks)
Show Figures

Figure 1

Review
Sensor-Integrated Microfluidic Approaches for Liquid Biopsies Applications in Early Detection of Cancer
Sensors 2020, 20(5), 1317; https://doi.org/10.3390/s20051317 - 28 Feb 2020
Cited by 12
Abstract
Cancer represents one of the conditions with the most causes of death worldwide. Common methods for its diagnosis are based on tissue biopsies—the extraction of tissue from the primary tumor, which is used for its histological analysis. However, this technique represents a risk [...] Read more.
Cancer represents one of the conditions with the most causes of death worldwide. Common methods for its diagnosis are based on tissue biopsies—the extraction of tissue from the primary tumor, which is used for its histological analysis. However, this technique represents a risk for the patient, along with being expensive and time-consuming and so it cannot be frequently used to follow the progress of the disease. Liquid biopsy is a new cancer diagnostic alternative, which allows the analysis of the molecular information of the solid tumors via a body fluid draw. This fluid-based diagnostic method displays relevant advantages, including its minimal invasiveness, lower risk, use as often as required, it can be analyzed with the use of microfluidic-based platforms with low consumption of reagent, and it does not require specialized personnel and expensive equipment for the diagnosis. In recent years, the integration of sensors in microfluidics lab-on-a-chip devices was performed for liquid biopsies applications, granting significant advantages in the separation and detection of circulating tumor nucleic acids (ctNAs), circulating tumor cells (CTCs) and exosomes. The improvements in isolation and detection technologies offer increasingly sensitive and selective equipment’s, and the integration in microfluidic devices provides a better characterization and analysis of these biomarkers. These fully integrated systems will facilitate the generation of fully automatized platforms at low-cost for compact cancer diagnosis systems at an early stage and for the prediction and prognosis of cancer treatment through the biomarkers for personalized tumor analysis. Full article
(This article belongs to the Special Issue Sensors Integration in Organ-on-chip & Microfluidic Systems)
Show Figures

Figure 1

Review
Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone
Sensors 2020, 20(5), 1251; https://doi.org/10.3390/s20051251 - 25 Feb 2020
Cited by 33
Abstract
Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. [...] Read more.
Annual deaths in the U.S. attributed to diabetes are expected to increase from 280,210 in 2015 to 385,840 in 2030. The increase in the number of people affected by diabetes has made it one of the major public health challenges around the world. Better management of diabetes has the potential to decrease yearly medical costs and deaths associated with the disease. Non-invasive methods are in high demand to take the place of the traditional finger prick method as they can facilitate continuous glucose monitoring. Research groups have been trying for decades to develop functional commercial non-invasive glucose measurement devices. The challenges associated with non-invasive glucose monitoring are the many factors that contribute to inaccurate readings. We identify and address the experimental and physiological challenges and provide recommendations to pave the way for a systematic pathway to a solution. We have reviewed and categorized non-invasive glucose measurement methods based on: (1) the intrinsic properties of glucose, (2) blood/tissue properties and (3) breath acetone analysis. This approach highlights potential critical commonalities among the challenges that act as barriers to future progress. The focus here is on the pertinent physiological aspects, remaining challenges, recent advancements and the sensors that have reached acceptable clinical accuracy. Full article
(This article belongs to the Special Issue Bioimaging and Biosensing in Telemedicine)
Show Figures

Figure 1

Review
Applications of Graphene Quantum Dots in Biomedical Sensors
Sensors 2020, 20(4), 1072; https://doi.org/10.3390/s20041072 - 16 Feb 2020
Cited by 33
Abstract
Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in [...] Read more.
Due to the proliferative cancer rates, cardiovascular diseases, neurodegenerative disorders, autoimmune diseases and a plethora of infections across the globe, it is essential to introduce strategies that can rapidly and specifically detect the ultralow concentrations of relevant biomarkers, pathogens, toxins and pharmaceuticals in biological matrices. Considering these pathophysiologies, various research works have become necessary to fabricate biosensors for their early diagnosis and treatment, using nanomaterials like quantum dots (QDs). These nanomaterials effectively ameliorate the sensor performance with respect to their reproducibility, selectivity as well as sensitivity. In particular, graphene quantum dots (GQDs), which are ideally graphene fragments of nanometer size, constitute discrete features such as acting as attractive fluorophores and excellent electro-catalysts owing to their photo-stability, water-solubility, biocompatibility, non-toxicity and lucrativeness that make them favorable candidates for a wide range of novel biomedical applications. Herein, we reviewed about 300 biomedical studies reported over the last five years which entail the state of art as well as some pioneering ideas with respect to the prominent role of GQDs, especially in the development of optical, electrochemical and photoelectrochemical biosensors. Additionally, we outline the ideal properties of GQDs, their eclectic methods of synthesis, and the general principle behind several biosensing techniques. Full article
(This article belongs to the Special Issue Nanoimmunosensor)
Show Figures

Graphical abstract

Review
Electrochemical Paper-Based Biosensor Devices for Rapid Detection of Biomarkers
Sensors 2020, 20(4), 967; https://doi.org/10.3390/s20040967 - 11 Feb 2020
Cited by 20
Abstract
In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians [...] Read more.
In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians in decision-making. During the last decade, paper-based analytical devices, combining paper substrates and electrochemical detection components, have emerged as important point-of-need diagnostic tools. This review highlights significant works on this topic over the last five years, from 2015 to 2019. The most relevant articles published in 2018 and 2019 are examined in detail, focusing on device fabrication techniques and materials applied to the production of paper fluidic and electrochemical cell architectures as well as on the final device assembly. Two main approaches were identified, that are, on one hand, those ones where the fabrication of the electrochemical cell is done on the paper substrate, where the fluidic structures are also defined, and, on the other hand, the fabrication of those ones where the electrochemical cell and liquid-driving paper component are defined on different substrates and then heterogeneously assembled. The main limitations of the current technologies are outlined and an outlook on the current technology status and future prospects is given. Full article
(This article belongs to the Special Issue Electrochemical Nanobiosensors)
Show Figures

Figure 1

Review
Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors
Sensors 2020, 20(3), 737; https://doi.org/10.3390/s20030737 - 29 Jan 2020
Cited by 5
Abstract
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last [...] Read more.
Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables. Full article
(This article belongs to the Special Issue Sensor Applications on Marine Recognition)
Show Figures

Figure 1

Review
Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review
Sensors 2020, 20(3), 673; https://doi.org/10.3390/s20030673 - 26 Jan 2020
Cited by 16
Abstract
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be [...] Read more.
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings. Full article
(This article belongs to the Special Issue Inertial Sensors)
Show Figures

Figure 1

Review
Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review
Sensors 2020, 20(2), 479; https://doi.org/10.3390/s20020479 - 15 Jan 2020
Cited by 40
Abstract
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA [...] Read more.
The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research. Full article
(This article belongs to the Special Issue Skin Sensors)
Show Figures

Figure 1

Review
Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
Sensors 2020, 20(2), 488; https://doi.org/10.3390/s20020488 - 15 Jan 2020
Cited by 19
Abstract
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, [...] Read more.
Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment. Full article
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
Show Figures

Figure 1

Review
Tapered Optical Fibre Sensors: Current Trends and Future Perspectives
Sensors 2019, 19(10), 2294; https://doi.org/10.3390/s19102294 - 17 May 2019
Cited by 40
Abstract
The development of reliable, affordable and efficient sensors is a key step in providing tools for efficient monitoring of critical environmental parameters. This review focuses on the use of tapered optical fibres as an environmental sensing platform. Tapered fibres allow access to the [...] Read more.
The development of reliable, affordable and efficient sensors is a key step in providing tools for efficient monitoring of critical environmental parameters. This review focuses on the use of tapered optical fibres as an environmental sensing platform. Tapered fibres allow access to the evanescent wave of the propagating mode, which can be exploited to facilitate chemical sensing by spectroscopic evaluation of the medium surrounding the optical fibre, by measurement of the refractive index of the medium, or by coupling to other waveguides formed of chemically sensitive materials. In addition, the reduced diameter of the tapered section of the optical fibre can offer benefits when measuring physical parameters such as strain and temperature. A review of the basic sensing platforms implemented using tapered optical fibres and their application for development of fibre-optic physical, chemical and bio-sensors is presented. Full article
(This article belongs to the Special Issue Sensors for Emerging Environmental Markers and Contaminants)
Show Figures

Figure 1

Review
Review on Wearable Technology Sensors Used in Consumer Sport Applications
Sensors 2019, 19(9), 1983; https://doi.org/10.3390/s19091983 - 28 Apr 2019
Cited by 76
Abstract
This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows [...] Read more.
This review paper discusses the trends and projections for wearable technology in the consumer sports sector (excluding professional sport). Analyzing the role of wearable technology for different users and why there is such a need for these devices in everyday lives. It shows how different sensors are influential in delivering a variety of readings that are useful in many ways regarding sport attributes. Wearables are increasing in function, and through integrating technology, users are gathering more data about themselves. The amount of wearable technology available is broad, each having its own role to play in different industries. Inertial measuring unit (IMU) and Global Positioning System (GPS) sensors are predominantly present in sport wearables but can be programmed for different needs. In this review, the differences are displayed to show which sensors are compatible and which ones can evolve sensor technology for sport applications. Full article
(This article belongs to the Special Issue Wearable Wireless Sensors)
Show Figures

Figure 1

Review
Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review
Sensors 2019, 19(7), 1555; https://doi.org/10.3390/s19071555 - 31 Mar 2019
Cited by 57
Abstract
Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made [...] Read more.
Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made inertial measurement sensors (M/IMU) are potentially good alternatives to the laboratory-based systems, and recent technology improvements required a synthesis of the current evidence. The aim of this systematic review was to determine the criterion validity and reliability of M/IMU for each body joint and for tasks of different levels of complexity. Five different databases were screened (Pubmed, Cinhal, Embase, Ergonomic abstract, and Compendex). Two evaluators performed independent selection, quality assessment (consensus-based standards for the selection of health measurement instruments [COSMIN] and quality appraisal tools), and data extraction. Forty-two studies were included. Reported validity varied according to task complexity (higher validity for simple tasks) and the joint evaluated (better validity for lower limb joints). More studies on reliability are needed to make stronger conclusions, as the number of studies addressing this psychometric property was limited. M/IMU should be considered as a valid tool to assess whole body range of motion, but further studies are needed to standardize technical procedures to obtain more accurate data. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Review
Advanced Micro- and Nano-Gas Sensor Technology: A Review
Sensors 2019, 19(6), 1285; https://doi.org/10.3390/s19061285 - 14 Mar 2019
Cited by 113
Abstract
Micro- and nano-sensors lie at the heart of critical innovation in fields ranging from medical to environmental sciences. In recent years, there has been a significant improvement in sensor design along with the advances in micro- and nano-fabrication technology and the use of [...] Read more.
Micro- and nano-sensors lie at the heart of critical innovation in fields ranging from medical to environmental sciences. In recent years, there has been a significant improvement in sensor design along with the advances in micro- and nano-fabrication technology and the use of newly designed materials, leading to the development of high-performance gas sensors. Advanced micro- and nano-fabrication technology enables miniaturization of these sensors into micro-sized gas sensor arrays while maintaining the sensing performance. These capabilities facilitate the development of miniaturized integrated gas sensor arrays that enhance both sensor sensitivity and selectivity towards various analytes. In the past, several micro- and nano-gas sensors have been proposed and investigated where each type of sensor exhibits various advantages and limitations in sensing resolution, operating power, response, and recovery time. This paper presents an overview of the recent progress made in a wide range of gas-sensing technology. The sensing functionalizing materials, the advanced micro-machining fabrication methods, as well as their constraints on the sensor design, are discussed. The sensors’ working mechanisms and their structures and configurations are reviewed. Finally, the future development outlook and the potential applications made feasible by each category of the sensors are discussed. Full article
(This article belongs to the Section Physical Sensors)
Show Figures

Figure 1

Review
Electrochemical Deposition of Nanomaterials for Electrochemical Sensing
Sensors 2019, 19(5), 1186; https://doi.org/10.3390/s19051186 - 08 Mar 2019
Cited by 27
Abstract
The most commonly used methods to electrodeposit nanomaterials on conductive supports or to obtain electrosynthesis nanomaterials are described. Au, layered double hydroxides (LDHs), metal oxides, and polymers are the classes of compounds taken into account. The electrochemical approach for the synthesis allows one [...] Read more.
The most commonly used methods to electrodeposit nanomaterials on conductive supports or to obtain electrosynthesis nanomaterials are described. Au, layered double hydroxides (LDHs), metal oxides, and polymers are the classes of compounds taken into account. The electrochemical approach for the synthesis allows one to obtain nanostructures with well-defined morphologies, even without the use of a template, and of variable sizes simply by controlling the experimental synthesis conditions. In fact, parameters such as current density, applied potential (constant, pulsed or ramp) and duration of the synthesis play a key role in determining the shape and size of the resulting nanostructures. This review aims to describe the most recent applications in the field of electrochemical sensors of the considered nanomaterials and special attention is devoted to the analytical figures of merit of the devices. Full article
(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)
Show Figures

Figure 1

Review
Visible Light Communication: A System Perspective—Overview and Challenges
Sensors 2019, 19(5), 1153; https://doi.org/10.3390/s19051153 - 07 Mar 2019
Cited by 54
Abstract
Visible light communication (VLC) is a new paradigm that could revolutionise the future of wireless communication. In VLC, information is transmitted through modulating the visible light spectrum (400–700 nm) that is used for illumination. Analytical and experimental work has shown the potential of [...] Read more.
Visible light communication (VLC) is a new paradigm that could revolutionise the future of wireless communication. In VLC, information is transmitted through modulating the visible light spectrum (400–700 nm) that is used for illumination. Analytical and experimental work has shown the potential of VLC to provide high-speed data communication with the added advantage of improved energy efficiency and communication security/privacy. VLC is still in the early phase of research. There are fewer review articles published on this topic mostly addressing the physical layer research. Unlike other reviews, this article gives a system prespective of VLC along with the survey on existing literature and potential challenges toward the implementation and integration of VLC. Full article
(This article belongs to the Special Issue Advanced Technologies on Green Radio Networks)
Show Figures

Figure 1

Review
EM-Wave Biosensors: A Review of RF, Microwave, mm-Wave and Optical Sensing
Sensors 2019, 19(5), 1013; https://doi.org/10.3390/s19051013 - 27 Feb 2019
Cited by 44
Abstract
This article presents a broad review on optical, radio-frequency (RF), microwave (MW), millimeter wave (mmW) and terahertz (THz) biosensors. Biomatter-wave interaction modalities are considered over a wide range of frequencies and applications such as detection of cancer biomarkers, biotin, neurotransmitters and heart rate [...] Read more.
This article presents a broad review on optical, radio-frequency (RF), microwave (MW), millimeter wave (mmW) and terahertz (THz) biosensors. Biomatter-wave interaction modalities are considered over a wide range of frequencies and applications such as detection of cancer biomarkers, biotin, neurotransmitters and heart rate are presented in detail. By treating biological tissue as a dielectric substance, having a unique dielectric signature, it can be characterized by frequency dependent parameters such as permittivity and conductivity. By observing the unique permittivity spectrum, cancerous cells can be distinguished from healthy ones or by measuring the changes in permittivity, concentration of medically relevant biomolecules such as glucose, neurotransmitters, vitamins and proteins, ailments and abnormalities can be detected. In case of optical biosensors, any change in permittivity is transduced to a change in optical properties such as photoluminescence, interference pattern, reflection intensity and reflection angle through techniques like quantum dots, interferometry, surface enhanced raman scattering or surface plasmon resonance. Conversely, in case of RF, MW, mmW and THz biosensors, capacitive sensing is most commonly employed where changes in permittivity are reflected as changes in capacitance, through components like interdigitated electrodes, resonators and microstrip structures. In this paper, interactions of EM waves with biomatter are considered, with an emphasis on a clear demarcation of various modalities, their underlying principles and applications. Full article
Show Figures

Figure 1

Review
The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors
Sensors 2019, 19(4), 800; https://doi.org/10.3390/s19040800 - 15 Feb 2019
Cited by 108
Abstract
Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget. The non-invasive glucose monitoring technology overcomes these limitations, for which this topic is significantly being researched and [...] Read more.
Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget. The non-invasive glucose monitoring technology overcomes these limitations, for which this topic is significantly being researched and represents an exciting and highly sought after market for many companies. This review aims to offer an up-to-date report on the leading technologies for non-invasive (NI) and minimally-invasive (MI) glucose monitoring sensors, devices currently available in the market, regulatory framework for accuracy assessment, new approaches currently under study by representative groups and developers, and algorithm types for signal enhancement and value prediction. The review also discusses the future trend of glucose detection by analyzing the usage of the different bands in the electromagnetic spectrum. The review concludes that the adoption and use of new technologies for glucose detection is unavoidable and closer to become a reality. Full article
(This article belongs to the Special Issue Electromagnetic Medical Sensing)
Show Figures

Figure 1

Review
Layer-by-Layer Nano-assembly: A Powerful Tool for Optical Fiber Sensing Applications
Sensors 2019, 19(3), 683; https://doi.org/10.3390/s19030683 - 07 Feb 2019
Cited by 20
Abstract
The ability to tune the composition of nanostructured thin films is a hot topic for the design of functional coatings with advanced properties for sensing applications. The control of the structure at the nanoscale level enables an improvement of intrinsic properties (optical, chemical [...] Read more.
The ability to tune the composition of nanostructured thin films is a hot topic for the design of functional coatings with advanced properties for sensing applications. The control of the structure at the nanoscale level enables an improvement of intrinsic properties (optical, chemical or physical) in comparison with the traditional bulk materials. In this sense, among all the known nanofabrication techniques, the layer-by-layer (LbL) nano-assembly method is a flexible, easily-scalable and versatile approach which makes possible precise control of the coating thickness, composition and structure. The development of sensitive nanocoatings has shown an exceptional growth in optical fiber sensing applications due to their self-assembling ability with oppositely charged components in order to obtain a multilayer structure. This nanoassembly technique is a powerful tool for the incorporation of a wide variety of species (polyelectrolytes, metal/metal oxide nanoparticles, hybrid particles, luminescent materials, dyes or biomolecules) in the resultant multilayer structure for the design of high-performance optical fiber sensors. In this work we present a review of applications related to optical fiber sensors based on advanced LbL coatings in two related research areas of great interest for the scientific community, namely chemical sensing (pH, gases and volatile organic compounds detection) as well as biological/biochemical sensing (proteins, immunoglobulins, antibodies or DNA detection). Full article
(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)
Show Figures

Figure 1

Review
Fluorescent Sensors for the Detection of Heavy Metal Ions in Aqueous Media
Sensors 2019, 19(3), 599; https://doi.org/10.3390/s19030599 - 31 Jan 2019
Cited by 71
Abstract
Due to the risks that water contamination implies for human health and environmental protection, monitoring the quality of water is a major concern of the present era. Therefore, in recent years several efforts have been dedicated to the development of fast, sensitive, and [...] Read more.
Due to the risks that water contamination implies for human health and environmental protection, monitoring the quality of water is a major concern of the present era. Therefore, in recent years several efforts have been dedicated to the development of fast, sensitive, and selective sensors for the detection of heavy metal ions. In particular, fluorescent sensors have gained in popularity due to their interesting features, such as high specificity, sensitivity, and reversibility. Thus, this review is devoted to the recent advances in fluorescent sensors for the monitoring of these contaminants, and special focus is placed on those devices based on fluorescent aptasensors, quantum dots, and organic dyes. Full article
(This article belongs to the Special Issue Optical Chemical Nanosensors)
Show Figures

Figure 1

Review
Nanostructured Chemiresistive Gas Sensors for Medical Applications
Sensors 2019, 19(3), 462; https://doi.org/10.3390/s19030462 - 23 Jan 2019
Cited by 26
Abstract
Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required [...] Read more.
Treating diseases at their earliest stages significantly increases the chance of survival while decreasing the cost of treatment. Therefore, compared to traditional blood testing methods it is the goal of medical diagnostics to deliver a technique that can rapidly predict and if required non-invasively monitor illnesses such as lung cancer, diabetes, melanoma and breast cancer at their very earliest stages, when the chance of recovery is significantly higher. To date human breath analysis is a promising candidate for fulfilling this need. Here, we highlight the latest key achievements on nanostructured chemiresistive sensors for disease diagnosis by human breath with focus on the multi-scale engineering of both composition and nano-micro scale morphology. We critically assess and compare state-of-the-art devices with the intention to provide direction for the next generation of chemiresistive nanostructured sensors. Full article
(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)
Show Figures

Figure 1

Review
Non-Covalent Functionalization of Carbon Nanotubes for Electrochemical Biosensor Development
Sensors 2019, 19(2), 392; https://doi.org/10.3390/s19020392 - 18 Jan 2019
Cited by 81
Abstract
Carbon nanotubes (CNTs) have been widely studied and used for the construction of electrochemical biosensors owing to their small size, cylindrical shape, large surface-to-volume ratio, high conductivity and good biocompatibility. In electrochemical biosensors, CNTs serve a dual purpose: they act as immobilization support [...] Read more.
Carbon nanotubes (CNTs) have been widely studied and used for the construction of electrochemical biosensors owing to their small size, cylindrical shape, large surface-to-volume ratio, high conductivity and good biocompatibility. In electrochemical biosensors, CNTs serve a dual purpose: they act as immobilization support for biomolecules as well as provide the necessary electrical conductivity for electrochemical transduction. The ability of a recognition molecule to detect the analyte is highly dependent on the type of immobilization used for the attachment of the biomolecule to the CNT surface, a process also known as biofunctionalization. A variety of biofunctionalization methods have been studied and reported including physical adsorption, covalent cross-linking, polymer encapsulation etc. Each method carries its own advantages and limitations. In this review we provide a comprehensive review of non-covalent functionalization of carbon nanotubes with a variety of biomolecules for the development of electrochemical biosensors. This method of immobilization is increasingly being used in bioelectrode development using enzymes for biosensor and biofuel cell applications. Full article
(This article belongs to the Special Issue Nanostructured Surfaces in Sensing Systems)
Show Figures

Figure 1

Review
A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions
Sensors 2019, 19(1), 126; https://doi.org/10.3390/s19010126 - 02 Jan 2019
Cited by 104
Abstract
Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has [...] Read more.
Cognitive radio technology has the potential to address the shortage of available radio spectrum by enabling dynamic spectrum access. Since its introduction, researchers have been working on enabling this innovative technology in managing the radio spectrum. As a result, this research field has been progressing at a rapid pace and significant advances have been made. To help researchers stay abreast of these advances, surveys and tutorial papers are strongly needed. Therefore, in this paper, we aimed to provide an in-depth survey on the most recent advances in spectrum sensing, covering its development from its inception to its current state and beyond. In addition, we highlight the efficiency and limitations of both narrowband and wideband spectrum sensing techniques as well as the challenges involved in their implementation. TV white spaces are also discussed in this paper as the first real application of cognitive radio. Last but by no means least, we discuss future research directions. This survey paper was designed in a way to help new researchers in the field to become familiar with the concepts of spectrum sensing, compressive sensing, and machine learning, all of which are the enabling technologies of the future networks, yet to help researchers further improve the efficiently of spectrum sensing. Full article
(This article belongs to the Section Sensor Networks)
Show Figures

Figure 1

Back to TopTop