Sensors — Open Access Journal

Direct acting solenoid on–off valves are key fluid power components whose efficiency is dependent upon the state of the spool’s axial motion. By sensing the trajectory of the valve spool, more efficient control schemes can be implemented. Therefore, the goal of this study is to derive an analytical model for spool displacement sensing based on coil inductance. First, a mathematical model of the coil inductance as a function of air gap width and lumped magnetic reluctance is derived. Second, to solve the inductance from coil current, an optimization to obtain an initial value based on physical constraints is proposed. Furthermore, an experiment using a laser triangulation sensor is designed to correlate the magnetic reluctance to the air gap. Lastly, using the obtained empirical reluctance model to eliminate unknowns from the proposed air gap-inductance model, the model in atmosphere or hydraulic oil environments was tested. Initial results showed that the proposed model is capable of calculating the spool displacement based on the coil current, and the estimation errors compared to the laser measurement are within ±7% in air environment. Full article

This study investigates the electroacoustic behavior of a piezoelectric multilayered structure for ultrasonic sensors using the equivalent circuit method (ECM). We first derived the vertical deflection of the multilayered structure as a function of pressure and voltage using equilibrium equations of the structure. The deflection derived in this work is novel in that it includes the effect of piezoelectricity as well as the external pressure on the radiating surface. Subsequently, the circuit parameters were derived from the vertical deflection. The acoustic characteristics of the structure were then analyzed using the electro-acoustical model of an ultrasonic sensor for in-air application. Using the equivalent circuit, we analyzed the effects of various structural parameters on the acoustic properties of the structure such as resonance frequency, radiated sound pressure, and beam pattern. The validity of the ECM was verified initially by comparing the results with those from the finite element analysis (FEA) of the same structure. Furthermore, experimental testing of an actual ultrasonic sensor was carried out to verify the efficacy of the ECM. The ECM presented in this study can estimate the performance characteristics of a piezoelectric multilayered structure with high rapidity and efficiency. Full article

Under the main features required on portable devices in electrochemical instrumentation is to have a small size, low power consumption, economically affordable and precision in the measurements. This paper describes the development of a programmable Embedded Potentiostat System (EPS) capable of performing electrochemical sensing over system-on-a-chip platforms. Furthermore, the study explains a circuit design and develops some validation of the entire system. The hardware validation is performed by electrochemical experiments such as Double Step Chronoamperometry (DSC), Linear Sweep Voltammetry (LSV) and Cyclic Voltammetry (CV); moreover, a comparison of the experimental signals between a commercial potentiostat and the EPS was done by analysis of errors on the response signal. Results illustrate that the EPS is capable of handling currents in the range of absolute values of 86.44 to 3000 nA and having control voltages in the range of ±2 V. The device can support from 50 to 2000 samples per second. The EPS capabilities were compared with other compact potentiostats. The programmable EPS is an original approach which hugely reduces the hardware complexity and leads the way to create new applications for Point-of-Care or industrial developments with a reusable full electronics module. Full article

In the present study, we design a tunable plasmonic refractive index sensor with nanoring-strip graphene arrays. The calculations prove that the nanoring-strip have two transmission dips. By changing the strip length L of the present structure, we find that the nanoring-strip graphene arrays have a wide range of resonances (resonance wavelength increases from 17.73 μm to 28.15 μm). When changing the sensing medium refractive index n_med, the sensitivity of mode A and B can reach 2.97 μm/RIU and 5.20 μm/RIU. By changing the doping level n_g, we notice that the transmission characteristics can be tuned flexibly. Finally, the proposed sensor also shows good angle tolerance for both transverse magnetic (TM) and transverse electric (TE) polarizations. The proposed nanoring-strip graphene arrays along with the numerical results could open a new avenue to realize various tunable plasmon devices and have a great application prospect in biosensing, detection, and imaging. Full article

This paper presents a simple relaxation generator, suitable for a sensor interface, operating as a transducer of capacitance to frequency/period. The proposed circuit employs a current feedback operational amplifier, fabricated in I3T25 0.35 $\mathsf{\mu }$m ON Semiconductor CMOS process, and four passive elements including a grounded capacitor (the sensed parameter). It offers a low-impedance voltage output of the generated square wave. Additional frequency to DC voltage converter offers output information in the form of voltage. The experimental capacitance variation from 6.8 nF to 100 nF yields voltage change in the range from 21 mV to 106 mV with error below 5% and sensitivity 0.912 mV/nF evaluated over the full range of change. These values are in good agreement with simulation results obtained from the Mathcad model of frequency to DC voltage transducer passive circuit. Full article

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm⁻¹. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels. Full article

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective. Full article

A radio frequency identifier (RFID) tag sensor for the real time monitoring of cracks in civil engineering building is presented in this work. The RFID tag is equipped with a piezoelectric sensor able to detect small movements of crack in order to prevent collapses of buildings or civil engineering structures. The information is delivered by using the modulated scattering technique (MST) which permits to obtain high operative ranges. The sensor is passive, the power supply is provided by means of a suitable rect-antenna and a Chockcroft-Walton multiplier circuit powered by means of the impinging interrogating electromagnetic wave. A system prototype, operating in the X band at 10 GHz, has been designed fabricated and experimentally assessed in a realistic scenario obtaining promising results. Full article

Convolutional Long Short-Term Memory Neural Networks (CNN-LSTM) are a variant of recurrent neural networks (RNN) that can extract spatial features in addition to classifying or making predictions from sequential data. In this paper, we analyzed the use of CNN-LSTM for gas source localization (GSL) in outdoor environments using time series data from a gas sensor network and anemometer. CNN-LSTM is used to estimate the location of a gas source despite the challenges created from inconsistent airflow and gas distribution in outdoor environments. To train CNN-LSTM for GSL, we used temporal data taken from a 5 × 6 metal oxide semiconductor (MOX) gas sensor array, spaced 1.5 m apart, and an anemometer placed in the center of the sensor array in an open area outdoors. The output of the CNN-LSTM is one of thirty cells approximating the location of a gas source. We show that by using CNN-LSTM, we were able to determine the location of a gas source from sequential data. In addition, we compared several artificial neural network (ANN) architectures as well as trained them without wind vector data to estimate the complexity of the task. We found that ANN is a promising prospect for GSL tasks. Full article

Global Navigation Satellite Systems (GNSSs) have become a ubiquitous tool for our modern society to carry out vital tasks such as transportation, civil engineering or precision agriculture. This breath has reached the realm of safety-critical applications such as time management of critical infrastructures or autonomous vehicles, in which GNSS is an essential tool nowadays. Unfortunately, current GNSS performance is not enough to fulfill the requirements of these professional and critical applications. For this reason, the FANTASTIC project was launched to boost the adoption of these applications. The project was funded by the European GNSS agency (GSA) in order to enhance the robustness and accuracy of GNSS in harsh environments. This paper presents the part related to the development of a weighting and exclusion function with a dual circularly polarized antenna. The idea is to reduce the effects of multipath by weighting and/or excluding those measurements affected by multipath. The observables and other metrics obtained from a dual polarized antenna will be exploited to define an exclusion threshold and to provide the weights. Real-world experiments will show the improvement in the positioning solution, using all available constellations, obtained with the developed technique. Full article

This paper presents a novel measuring system for partial discharge (PD) measurements in Gas Insulated Systems (GIS) using high frequency current transformers (HFCT). The system is based on the measurement of the induced PD currents in the GIS enclosure. In opposition to the existing antenna technologies that measure the radiated energy in the very high frequency/ultra-high frequency (VHF/UHF) range, the proposed system measures the PD conducted currents in the high frequency (HF) range and below. The foundation of the measurements together with a detailed explanation of the sensor installed conveniently at the bolts of the GIS spacer are presented. An experimental study on the current distribution in the GIS enclosure is described to evaluate the impact of the sensor on the measurements. Laboratory experiments have been performed that show the suitability of this method to properly measure particle discharges caused by corona, surface and free moving particle discharges in SF₆. Discharges in the range of 1 to 4 pC have been properly measured. An analysis to evaluate the performance of the method is shown, in comparison to VHF/UHF antenna measurements. The potential benefits of this novel technique rely on the small attenuation of PD signals in the GIS components in the HF range and sample rate reductions. Finally, a discussion on the potential applicability of present cluster and charge calculation techniques to the proposed PD GIS measurement using HFCT is presented. Full article

Wireless sensor networks are widely used in many applications such as environmental monitoring, health care, smart grid and surveillance. Many security protocols have been proposed and intensively studied due to the inherent nature of wireless networks. In particular, Wu et al. proposed a promising authentication scheme which is sufficiently robust against various attacks. However, according to our analysis, Wu et al.’s scheme has two serious security weaknesses against malicious outsiders. First, their scheme can lead to user impersonation attacks. Second, user anonymity is not preserved in their scheme. In this paper, we present these vulnerabilities of Wu et al.’s scheme in detail. We also propose a new scheme to complement their weaknesses. We improve and speed up the vulnerability of the Wu et al. scheme. Security analysis is analyzed by Proverif and informal analysis is performed for various attacks. Full article

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner. Full article

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C₁₈ reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25). Full article

A tilted fiber Bragg grating (TFBG) hydrogen sensor coated with a palladium (Pd) membrane by the electroless plating method is proposed in this paper. A uniform layer of Pd metal is fabricated in aqueous solutions by the chemical coating method, which is used as the sensitive element to detect the change of the surrounding refractive index (SRI) caused by hydrogen absorption. The change in SRI causes an unsynchronized change of the cladding modes and the Bragg peak in the TFBG transmission spectrum, thereby eliminating the cross-sensitivity due to membrane expansion and is able to simultaneously monitor the presence of cracks in the pipe, as well as the hydrogen leakage. By subtracting the wavelength shift caused by fiber expansion, the change of SRI, i.e., the information from the H₂ level, can be separately obtained. The drifted wavelength is measured for the H₂ concentration below the hydrogen explosion limit between 1% and 4%. The chemical-based coating has the advantages of a low cost, a simple operation, and being suitable for coating on long fiber structures. The proposed sensor is able to detect the H₂ signal in 5 min at a 1% H₂ concentration. The proposed sensor is proved to be able to monitor the hydrogen level without the cross-sensitivity of temperature variation and expansion strains, so could be a good candidate for security applications in industry. Full article

Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation. Full article

An underwater imaging system was investigated for automotive use in highly scattered underwater environments. The purpose of the system is the driver’s information about hidden obstacles, such as stones, driftwood, open sewer hatches. A comparison of various underwater vision methods was presented by the way they are implemented, the range reached, and the cost of implementation. It has been experimentally shown that a conventional active system can provide a maximum visibility range of up to three light attenuation lengths. In most practical cases of turbid waters during floods, this corresponds to distances of about 1 meter. From the presented analysis it follows that advanced extended range imaging methods allow increasing of the visibility range up to 2 meters. Full article

Signal-In-Space User Range Errors (SIS UREs) are assumed to be overbounded by a normal distribution with a standard deviation represented by the User Range Accuracy (URA). The BeiDou Navigation Satellite System (BDS) broadcast URA is not compatible with the historical SIS URE performance that affects the Advanced Receiver Autonomous Integrity Monitoring (ARAIM) False Alert Probability (Pfa) and availability evaluation. This study compares the BDS broadcast and precise ephemeris from 1 March 2013 to 1 March 2017 to obtain SIS UREs. Through analyzing the statistical characteristics of the SIS UREs, we obtain the standard deviation σ_URE for the accuracy and continuity and σ_URA used for the integrity of the SIS UREs. The results show that the broadcast σ_URA of 2 m cannot completely overbound SIS UREs for all BDS satellites, but the σ_URA of 2.4 m can. Then, we use the σ_URA of 2.4 m to evaluate the ARAIM Pfa and availability. The results show that the Pfa may increase to 2 × 10⁻⁵ and exceed its limit by an order of magnitude. We also consider the differences between the SIS UREs of Geostationary Earth Orbit (GEO), Inclined Geo-Synchronous Orbit (IGSO), and Medium Earth Orbit (MEO). The results indicate that all Pfa values calculated by the computed σ_URE are less than the Pfa in the Integrity Support Message (ISM) for the worst-performing GEO satellite. The approximately 55% Pfa calculated by the computed σ_URE is less than the Pfa in ISM for the worst-performing IGSO satellite. Most Pfa values calculated by the computed σ_URE is less than the Pfa in the ISM for the worst-performing MEO satellite. For BDS satellites, the Pfa is mainly affected by σ_URE. When the σ_URA of 2.4 m is used to evaluate the availability, the computed availability is lower than the availability calculated by the broadcast σ_URA/σ_URE and the greatest degradation can reach 25%. Full article

Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future. Full article