The present study investigates an algorithm for the calculation of 3D joint angles based on inertial measurement units (IMUs), omitting magnetometer data. Validity, test-retest reliability, and long-term stability are evaluated in reference to an optical motion capture (OMC) system. Twenty-eight healthy subjects performed a 6 min walk test. Three-dimensional joint kinematics of the lower extremity was recorded simultaneously by means of seven IMUs and an OptiTrack OMC system. To evaluate the performance, the root mean squared error (RMSE), mean range of motion error (ROME), coefficient of multiple correlations (CMC), Bland-Altman (BA) analysis, and intraclass correlation coefficient (ICC) were calculated. For all joints, the RMSE was lower than 2.40°, and the ROME was lower than 1.60°. The CMC revealed good to excellent waveform similarity. Reliability was moderate to excellent with ICC values of 0.52–0.99 for all joints. Error measures did not increase over time. When considering soft tissue artefacts, RMSE and ROME increased by an average of 2.2° ± 1.5° and 2.9° ± 1.7°. This study revealed an excellent correspondence of a magnetometer-free IMU system with an OMC system when excluding soft tissue artefacts. Full article

The article provides a review of the state-of-art non-destructive testing (NDT) methods used for evaluation of integrated circuit (IC) packaging. The review identifies various types of the defects and the capabilities of most common NDT methods employed for defect detection. The main aim of this paper is to provide a detailed review on the common NDT methods for IC packaging addressing their principles of operation, advantages, limitations and suggestions for improvement. The current methods such as, X-ray, scanning acoustic microscopy (SAM), infrared thermography (IRT), magnetic current imaging (MCI) and surface acoustic waves (SAW) are explicitly reviewed. The uniqueness of the paper lies in comprehensive comparison of the current NDT methods, recommendations for the improvements, and introduction of new candidate NDT technologies, which can be adopted for IC packaging. Full article

This paper proposes a low-profile textile-modified meander line Inverted-F Antenna (IFA) with variable width and spacing meanders, for Industrial Scientific Medical (ISM) 2.4-GHz Wireless Body Area Networks (WBAN), optimized with a novel metaheuristic algorithm. Specifically, a metaheuristic known as Coral Reefs Optimization with Substrate Layer (CRO-SL) is used to obtain an optimal antenna for sensor systems, which allows covering properly and resiliently the 2.4–2.45-GHz industrial scientific medical bandwidth. Flexible pad foam has been used to make the designed prototype with a 1.1-mm thickness. We have used a version of the algorithm that is able to combine different searching operators within a single population of solutions. This approach is ideal to deal with hard optimization problems, such as the design of the proposed meander line IFA. During the optimization phase with the CRO-SL, the proposed antenna has been simulated using CST Microwave Studio software, linked to the CRO-SL by means of MATLAB implementation and Visual Basic Applications (VBA) code. We fully describe the antenna design process, the adaptation of the CRO-SL approach to this problem and several practical aspects of the optimization and details on the algorithm’s performance. To validate the simulation results, we have constructed and measured two prototypes of the antenna, designed with the proposed algorithm. Several practical aspects such as sensitivity during the antenna manufacturing or the agreement between the simulated and constructed antenna are also detailed in the paper. Full article

Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message. Full article

Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities. Full article

Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images. In our approach, perceptual reconstruction loss is used to establish a loss equation that continuously optimizes a min-max game theoretic model to obtain better UAV image denoising results. The generated denoised images by the proposed method enjoy clearer ground objects edges and more detailed textures of ground objects. In addition to the traditional comparison method, denoised UAV images and corresponding original clean UAV images were employed to perform image matching based on local features. At the same time, the classification experiment on the denoised images was also conducted to compare the denoising results of UAV images with others. The proposed method had achieved better results in these comparison experiments. Full article

A highly sensitive nitrite (NO₂⁻) electrochemical sensor is fabricated using glassy carbon electrode modified with Au nanoparticle and grapheme oxide. Briefly, this electrochemical sensor was prepared by drop-coating graphene oxide-chitosan mixed film on the surface of the electrode and then electrodepositing a layer of Au nanoparticle using cyclic voltammetry. The electrochemical behavior of NO₂⁻ on the sensor was investigated by cyclic voltammetry and amperometric i-t curve. The results showed that the sensor exhibited better electrocatalytic activity for NO₂⁻ in 0.1 mol/L phosphate buffer solution (PBS) (pH 5.0). The oxidation peak current was positively correlated with NO₂⁻ concentration in the ranges of 0.9 µM to 18.9 µM. The detection limit was estimated to be 0.3 µM. In addition, the interference of some common ions (e.g., NO₃⁻, CO₃²⁻, SO₄²⁻, Cl⁻, Ca²⁺ and Mg²⁺) and oxidizable compound including sodium sulfite and ascorbic acid in the detection of nitrite was also studied. The results show that this sensor is more sensitive and selective to NO₂⁻. Therefore, this electrochemical sensor provided an effective tool for the detection of NO₂⁻. Full article

By using a multi-scale measurement technique, a high-sensitivity and large dynamic-range sensor array, which consisted of a single resonator and a series of cascaded resonators with a sensing ring and a reference ring, was modeled, and its transmission properties were investigated theoretically and numerically. We also set forth the principle of a multi-scale measurement technique based on the transmission spectrum of a resonator. This sensor array could have a nearly tenfold increase in sensitivity, and an improved dynamic range in an arrow wavelength range. The simulated results were in good agreement with the theoretical analysis. Full article

The intermittence of the shape detection signal associated with an entire roller-embedded shapemeter roll, used in a seven-pass cold reversible rolling process, is considered. A transient interference at the sensor top surface and the distance between the sensor top surface and the roll outer surface are developed, and a sensor reliability evaluation model is derived. The reliability of the sensor is evaluated via the random perturbation method, and the reliability sensitivity of design variables is proposed. The analysis reveals that the reliability is smallest in the third rolling pass. Of the design variables considered, the initial interference exhibits the largest reliability sensitivity and has the greatest influence on the sensor reliability. A reliability robust design model of the initial interference is therefore developed. A new shapemeter roll is fabricated and tested in a 1050 reversible cold rolling mill. The test results are consistent with the theoretical results, thereby validating the proposed model. The selection of an appropriate initial interference provides an important means of overcoming the adverse effects associated with the thermal deformation of sensor contact surfaces. Full article

Imaging spectrometers show great potential for environmental and biomedical sensing applications. Selfie sticks, which are tools used to take photographs or videos, have gained global popularity in recent years. Few people have connected these two objects, and few people have researched the application of imaging spectrometers to perform scientific monitoring in point-of-use scenarios. In this paper, we develop a compact imaging spectrometer (35 g in weight, 18 mm in diameter, and 72 mm in length) that can be equipped on a motorized selfie stick to perform remote sensing. We applied this system to perform environmental and facial remote sensing via motorized scanning. The absorption of chlorophyll and hemoglobin can be found in the reflectance spectra, indicating that our system can be used in urban greening monitoring and point-of-care testing. In addition, this compact imaging spectrometer was also easily attached to an underwater dome port and a quad-rotor unmanned aerial vehicle to perform underwater and airborne spectral detection. Our system offers a route toward mobile imaging spectrometers used in daily life. Full article

The synthesized LaFeO₃ nanocrystalline sensor powders show positive response to sensing acetone vapor at 200 °C. The responses to acetone vapor (at 0.5, 1, 2, 5, 10 ppm) are 1.18, 1.22, 1.89, 3.2 and 7.83. To make the sensor operate at a lower optimum temperature, UV light illumination 365 nm is performed. Response of the sensor has a larger improvement under 365 nm UV light illumination than without it. The responses to acetone vapor (at 0.5, 1, 2, 5, 10 ppm) are 1.37, 1.85, 3.16, 8.32 and 14.1. Furthermore, the optimum operating temperature is reduced to 170 °C. As the relative humidity increases, the resistance and sensitivity of sensor are reduced. The sensor shows good selectivity toward acetone when compared with other gases. Since the detection of ultralow concentrations of acetone vapor is possible, the sensor can be used to preliminarily judge diabetes in the general public, as a high concentration of acetone is exhaled in breath of diabetic patients. The sensor shows a good stability, which is further enhanced under UV light illumination. The sensor shows better stability when under 365 nm UV light illumination. Whether under light illumination or not. The LaFeO₃ material shows good performance as a sensor when exposed to acetone vapor. Full article

Ambient Assisted Living (AAL) systems aim to enable the elderly people to stay active and live independently into older age by monitoring their behaviour, provide the needed assistance and detect early signs of health status deterioration. Non-intrusive sensors are preferred by the elderly to be used for the monitoring purposes. However, false positive or negative triggers of those sensors could lead to a misleading interpretation of the status of the elderlies. This paper presents a systematic literature review of the sensor failure detection and fault tolerance in AAL equipped with non-intrusive, event-driven, binary sensors. The existing works are discussed, and the limitations and research gaps are highlighted. Full article

This paper introduces a methodology applying an imaging sonar for three-dimensional (3D) target tracking underwater. The key process in this work involves obtaining the target’s position in space using two images of the same scene, acquired by an adaptive resolution imaging sonar (ARIS) at different positions. A data association algorithm was designed to connect the same target in image sequences. The goal of this work was to track multiple targets in 3D space. The ARIS provides sequences of bi-dimensional images from the backscattered energy according to the range and azimuth. The challenge involved determining the missing elevation information for the observed object within the sonar detection range. By computing the geometrical transformation between the acquisition planar images and the cubical space, using only the sonar information that included the posture and moving speed of the ARIS, the target’s elevation information was obtained. To evaluate the performance of the proposed method, an indoor experiment was conducted using the ARIS. On the basis of the experimental results, we confirmed that the proposed method effectively obtained the target’s position in 3D space. A moving target simulation was also conducted, and the results showed that this method was effective for moving targets. Finally, a field experiment was performed to obtain the vertical distribution and track the 3D trajectories of fish. Full article

Dual-motor Electric Drive Tracked Vehicles (DDTVs) have attracted increasing attention due to their high transmission efficiency and economical fuel consumption. A test bench for the development and validation of new DDTV technologies is necessary and urgent. How to load the vehicle on a DDTV test bench exactly the same as on a real road is a crucial issue when designing the bench. This paper proposes a novel dynamic load emulation method to address this problem. The method adopts dual dynamometers to simulate both the road load and the inertia load that are imposed on the dual independent drive systems. The vehicle’s total inertia equivalent to the drive wheels is calculated with separate consideration of vehicle body, tracks and road wheels to obtain a more accurate inertia load. A speed tracking control strategy with feedforward compensation is implemented to control the dual dynamometers, so as to make the real-time dynamic load emulation possible. Additionally, a MATLAB/Simulink model of the test bench is built based on a dynamics analysis of the platform. Experiments are finally carried out on this test bench under different test conditions. The outcomes show that the proposed load emulation method is effective, and has good robustness and adaptability to complex driving conditions. Besides, the accuracy of the established test bench model is also demonstrated by comparing the results obtained from the simulation model and experiments. Full article

The detection of the toxic gas carbon monoxide (CO) in the low ppm range is required in different applications. We present a study of the reactivity of different gasochromic rhodium complexes towards the toxic gas carbon monoxide (CO). Therefore, variations of binuclear rhodium complexes with different ligands were prepared. They were characterized by FTIR spectroscopy, ¹H NMR spectroscopy, and differential scanning calorimetry. All complexes are spectroscopically distinguishable and temperature stable up to at least 187 °C. The gasochromic behavior of all different compounds was tested. Therefore, the compounds were dissolved in toluene and exposed to 100 ppm CO for 10 min to investigate their gas sensitivity and reaction velocity. The changes in the transmission spectra were recorded by UV/vis spectroscopy. Furthermore, a significant influence of the solvent to the color dyes’ gasochromic reaction and behavior was observed. After characterization, one complex was transferred as sensing element into an optical gas sensor. Two different measurement principles (reflection- and waveguide-based) were built up and tested towards their capability as gasochromic CO sensors. Finally, different gas-dependent measurements were carried out. Full article

With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency, and their environmental robustness is not strong enough. In this paper, we have introduced an RFID positioning algorithm based on the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM), which is called the GSOS-ELM algorithm. The GSOS-ELM algorithm automatically adjusts the regularization weights of the SOS-ELM algorithm through the GSO algorithm, so that it can quickly obtain the optimal regularization weights under different initial conditions; at the same time, the semi-supervised characteristics of the GSOS-ELM algorithm can significantly reduce the number of labeled reference tags and reduce the cost of positioning systems. In addition, the online learning phase of the GSOS-ELM algorithm can continuously update the system to perceive changes in the environment and resist the environmental interference. We have carried out experiments to study the influence factors and validate the performance, both the simulation and testbed experiment results show that compared with other algorithms, our proposed GSOS-ELM localization system can achieve more accurate positioning results and has certain adaptability to the changes of the environment. Full article

Radiometric calibration for imaging sensors is a crucial procedure to ensure imagery quality. One of the challenges in relative radiometric calibration is to correct detector-level artifacts due to the fluctuation in discrepant responses (spatial) and electronic instability (temporal). In this paper, the integration of the empirical mode decomposition (EMD) with Hilbert–Huang transform (HHT) in relative radiometric calibration was explored for a new sensor, FS-5 RSI (remote sensing instrument onboard the FORMOSAT-5 satellite). The key intrinsic mode functions (IMFs) analyzed by HHT were examined with the pre-flight datasets of the FS-5 RSI in temporal and spatial variations. The results show that the EMD–HHT method can stabilize and improve the radiometric quality of the FS-5 imagery as well as boost its application ability to a new level. It is noticed that the IMFs of the spatial variation would be disturbed by the instability of the temporal variation. The relative response discrepancies among detector chips can be well calibrated after considering the temporal effect. Taking a test imagery dataset of gain setting G2 as an example, the standard deviation (STD) of the discrepancy in the digital number after calibration was dramatically scaled down compared to the original ones (e.g., PAN: 66.31 to 1.85; B1: 54.19 to 1.90; B2: 36.50 to 1.49; B3: 32.43 to 1.56; B4: 37.67 to 1.20). The good performance of pre-flight imagery indicates that the EMD–HHT approach could be highly practical to the on-orbit relative radiometric calibration of the FS-5 RSI sensor and is applicable to other optical sensors. To our knowledge, the proposed EMD–HHT approach is used for the first time to explore relative radiometric calibration for optical sensors. Full article

Monitoring nitric oxide at the trace level is required in a large range of applications. We report on a trace gas analyzer optimized for nitric oxide measurements by Optical Feedback Cavity Enhanced Absorption Spectroscopy with an interband cascade laser at 5.3 µm. The short response time of the instrument allows for reaching the level of 50 ppt in only 180 ms. Its stability enables averaging up to 12 min to reach a detection limit of 0.9 ppt. Absolute concentration calibration requires to account for the optical saturation effect that results from the intense absorption line intensity addressed here, in the mid infrared region, in contrast to instruments that are operating in the near infrared region. Full article

Due to the importance of sensors in control strategy and safety, early detection of faults in sensors has become a key point to improve the availability of railway traction drives. The presented sensor fault reconstruction is based on sliding mode observers and equivalent injection signals, and it allows detecting defective sensors and isolating faults. Moreover, the severity of faults is provided. The proposed on-board fault reconstruction has been validated in a hardware-in-the-loop platform, composed of a real-time simulator and a commercial traction control unit for a tram. Low computational resources, robustness to measurement noise, and easiness to tune are the main requirements for industrial acceptance. As railway applications are not safety-critical systems, compared to aerospace applications, a fault evaluation procedure is proposed, since there is enough time to perform diagnostic tasks. This procedure analyses the fault reconstruction in the steady state, delaying the decision-making in some seconds, but minimising false detections. Full article

The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones. Full article

A correction approach for the inclined array of hydrophones is proposed to prevent decline of the image quality in SAS. In this approach, the 2-way exact acoustic propagation path of the inclined array is transformed into the sum of a single root term and an offset term, where the single root term is the 2-way ideal propagation path and the offset term contains all errors cause by the inclined array. The correction for the offset term is separated into two steps: phase correction and delay correction. The phase correction is performed on the echo signal of each receiving hydrophone in the 2-D time domain by a phase multiplication and the delay correction is performed on the echo signal of each receiving hydrophone in the range frequency domain by a phase multiplication with a linear function of range frequency at the reference range. Finally, the effectiveness of the proposed approach is examined by the simulation experiments. Full article

In this paper, an investigation into detecting immunosuppressive medicine in aqueous solutions using a spectrometry-based technique is described. Using optical transmissive spectrometry, absorbance measurements in the spectra range from 250 nm to 1000 nm were carried out for different cyclosporine A (CsA) concentrations in aqueous solutions. The experiment was conducted for samples both with and without interferent substances—glucose and sodium chloride. Using a dedicated algorithm, the measured data was analyzed and a high correlation coefficient R² = 0.8647 was achieved. The experiment showed that the described technique allowed for the detection of various CsA concentration levels in a selective, label-free and simple way. This method could be used in medicine, veterinary medicine and laboratory diagnostics. Full article

The demand for a harmless noncontact trapping and transportation method in manipulation and measurement of biological micro objects waits to be met. In this paper, a novel micromanipulation method named “Hydrodynamic Tweezers” using the vortex induced by oscillating a single piezoelectric actuator is introduced. The piezoelectric actuator is set between a micropipette and a copper beam. Oscillating the actuator at a certain frequency causes the resonance of the copper beam and extend 1D straight oscillation of the piezoelectric actuator to 2D circular oscillation of the micropipette, which induces a micro vortex after putting the micropipette into fluid. The induced vortex featuring low pressure in its core area can trap the object nearby. A robotic micromanipulator is utilized to transport the trapped objects together with the micropipette. Experiments of trapping and transportation microbeads are carried out to characterize the key parameters. The results show that the trapping force can be controlled by adjusting peak-peak voltage of the sinusoidal voltage input into the piezoelectric actuator. Full article

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computational time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring. Full article

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches. Full article

Standard computer vision methods are usually based on powerful contact-less measurement approaches but applications, especially at the micro-scale, are restricted by finite depth-of-field and fixed working distance of imaging devices. Digital holography is a lensless, indirect imaging method recording the optical wave diffracted by the object onto the image sensor. The object is reconstructed numerically by propagating the recorded wavefront backward. The object distance becomes a computation parameter that can be chosen arbitrarily and adjusted to match the object position. No refractive lens is used and usual depth-of-field and working distance limitations are replaced by less restrictive ones tied to the laser-source coherence-length and to the size and resolution of the camera sensor. This paper applies digital holography to artificial visual in-plane position sensing with an extra-large range-to-resolution ratio. The object is made of a pseudoperiodic pattern allowing a subpixel resolution as well as a supra field-of-observation displacement range. We demonstrate an in-plane resolution of 50 nm and $0.002$deg. in X, Y and $\theta$ respectively, over a working distance range of more than 15 cm. The allowed workspace extends over $12\times 10\times 150{\mathrm{mm}}^3$. Digital holography extends the field of application of computer vision by allowing an extra-large range of working distances inaccessible to refractive imaging systems. Full article

Hydrogen-sulfide gas is a toxic, colorless gas with a pungent odor that occurs naturally as a decomposition by-product. It is critical to monitor the concentration of hydrogen sulfide. Multivariate optical computing (MOC) is a method that can monitor analytes while minimizing responses to interferences. MOC is a technique by which an analogue calculation is performed entirely in the optical domain, which simplifies instrument design, prevents the drift of a calibration, and increases the strength and durability of spectroscopic instrumentation against physical perturbation when used for chemical detection and identification. This paper discusses the detection of hydrogen-sulfide gas in the ultraviolet (UV) spectral region in the presence of interfering gaseous species. A laboratory spectroscopic measurement system was set up to acquire the UV spectra of H₂S and interference gas mixtures in high-pressure/high-temperature (HPHT) conditions. These spectra were used to guide the design and fabrication of a multivariate optical element (MOE), which has an expected measurement relative accuracy of 3.3% for H₂S, with a concentration in the range of 0–150 nmol/mL. An MOC validation system with the MOE was used to test three samples of H₂S and mercaptans mixtures under various pressures, and the relative accuracy of H₂S measurement was determined to be 8.05%. Full article

The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately. Full article

Cardiovascular disease remains the leading cause of death in Western society. Recent technological advances have opened the opportunity of developing new and innovative smart stent devices that have advanced electrical properties that can improve diagnosis and even treatment of previously intractable conditions, such as central line access failure, atherosclerosis and reporting on vascular grafts for renal dialysis. Here we review the latest advances in the field of cardiovascular medical implants, providing a broad overview of the application of their use in the context of cardiovascular disease rather than an in-depth analysis of the current state of the art. We cover their powering, communication and the challenges faced in their fabrication. We focus specifically on those devices required to maintain vascular access such as ones used to treat arterial disease, a major source of heart attacks and strokes. We look forward to advances in these technologies in the future and their implementation to improve the human condition. Full article

In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide copper minerals such as chalcopyrite (CuFeS₂), bornite (Cu₅FeS₄) and pyrite (FeS₂). Different combustion conditions are addressed, such as sulfur-copper ratio and oxygen excess. Temperature and spectral emissivity features are estimated for each case by using the two wavelength method and radiometric models. The most relevant results have shown an increasing intensity behavior for higher sulfur-copper ratios and oxygen contents, where emissivity is almost constant along the visible spectrum range for all cases, which validates the gray body assumption. CuO and FeO emission line features along the visible spectrum appear to be a sensing alternative for describing the combustion reactions. Full article

Electrochemical enzyme-linked immunosorbent assay (ELISA)-based immunoassays for cancer biomarker detection have recently attracted much interest owing to their higher sensitivity, amplification of signal, ease of handling, potential for automation and combination with miniaturized analytical systems, low cost and comparative simplicity for mass production. Their developments have considerably improved the sensitivity required for detection of low concentrations of cancer biomarkers present in bodily fluids in the early stages of the disease. Recently, various attempts have been made in their development and several methods and processes have been described for their development, amplification strategies and testing. The present review mainly focuses on the development of ELISA-based electrochemical immunosensors that may be utilized for cancer diagnosis, prognosis and therapy monitoring. Various fabrication methods and signal enhancement strategies utilized during the last few years for the development of ELISA-based electrochemical immunosensors are described. Full article

In nature, arthropods have evolved to utilize a multiaperture vision system with a micro-optical structure which has advantages, such as compact size and wide-angle view, compared to that of a single-aperture vision system. In this paper, we present a multiaperture imaging system using a microlens array fabricated by a patterned-layer integrating soft lithography (PLISL) process which is based on a molding technique that can transfer three-dimensional structures and a gold screening layer simultaneously. The imaging system consists of a microlens array, a lens-adjusting jig, and a conventional (charge-coupled device) CCD image sensor. The microlens array has a light screening layer patterned among all the microlenses by the PLISL process to prevent light interference. The three-dimensionally printed jig adjusts the microlens array on the conventional CCD sensor for the focused image. The manufactured imaging system has a thin optic system and a large field-of-view of 100 degrees. The developed imaging system takes multiple images at once. To show its possible applications, multiple depth plane images were reconstructed based on the taken subimages with a single shot. Full article

Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network. Full article

With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection. Full article

Chinese Gaofen-3 (GF-3), a vital satellite for high-resolution earth observation, was the first C-band polarimetric synthetic aperture radar (SAR) launched in China with a resolution of up to one meter. Polarimetric SAR can obtain the complete physical scattering mechanisms of targets, thereby having the potential to differentiate objects. In this paper, several classification methods are briefly summarized and the types of features that should be chosen during classification are discussed. A pre-classification step is introduced to reduce the workload of precise labeling. The Random Forest classifier, which performs well for many other classification tasks, is used for the initial land cover classification. Then, based on a polarimetric constant false-alarm rate (CFAR) edge detector, a fast super-pixel generation method for polarimetric SAR image is proposed, which does not require the adjustment of parameters in advance. Following that, majority vote is conducted on the initial classification result based on the super-pixels, so that the classification result can be optimized to better meet the mapping requirements. The experimental results based on GF-3 polarimetric SAR data verify the effectiveness of proposed procedure and demonstrate that GF-3 data has excellent performance in land cover classification. Full article

Lubricant and hydraulic fluid ageing impacts the performance of the machines, gears, transmissions or automatisms where they are being used. This manuscript describes the work accomplished for bringing an innovative measurement concept for analysing the physical- chemical properties of these fluids, to a real industrial product ready to be integrated into different industrial equipment. The steps taken to deal with uncertainties and evolving requirements while progressing in the sensor development are described, covering the stages of theoretical formulation of the problem, optical and fluidic simulations, sensor prototype development and tests. The sensor working principle is based on a combination of transmittance and diffuse reflectance photonic inspection of the fluid sample that is collected in a microcavity through a standard hydraulic fitting. Photonics, electronics, micro-mechanics, fluidics, data processing and analysis has been merged with a deep knowledge in the lubricant degradation process to develop a sensor solution that is able to measure the Oil Degradation Index, Oil Oxidation, Acid Number, Ruler and Membrane Patch Colorimetry data from an inservice lubricating oil sample. The photonic micro sensor presented here offers a powerful tool that operates directly immersed in the fluid, at an economic cost and compacted size for inline oil degradation monitoring. Full article

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction”. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided. Full article

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage. Full article

More and more Internet of Things (IoT) wireless devices have been providing ubiquitous services over the recent years. Since most of these devices are powered by batteries, a fundamental trade-off to be addressed is the depleted energy and the achieved data throughput in wireless data transmission. By exploiting the rate-adaptive capacities of wireless devices, most existing works on energy-efficient data transmission try to design rate-adaptive transmission policies to maximize the amount of transmitted data bits under the energy constraints of devices. Such solutions, however, cannot apply to scenarios where data packets have respective deadlines and only integrally transmitted data packets contribute. Thus, this paper introduces a notion of weighted throughput, which measures how much total value of data packets are successfully and integrally transmitted before their own deadlines. By designing efficient rate-adaptive transmission policies, this paper aims to make the best use of the energy and maximize the weighted throughput. What is more challenging but with practical significance, we consider the fading effect of wireless channels in both offline and online scenarios. In the offline scenario, we develop an optimal algorithm that computes the optimal solution in pseudo-polynomial time, which is the best possible solution as the problem undertaken is NP-hard. In the online scenario, we propose an efficient heuristic algorithm based on optimal properties derived for the optimal offline solution. Simulation results validate the efficiency of the proposed algorithm. Full article

A plenoptic cameras is a sensor that records the 4D light-field distribution of target scenes. The surface errors of a microlens array (MLA) can cause the degradation and distortion of the raw image captured by a plenoptic camera, resulting in the confusion or loss of light-field information. To address this issue, we propose a method for the local rectification of distorted images using white light-field images. The method consists of microlens center calibration, geometric rectification, and grayscale rectification. The scope of its application to different sized errors and the rectification accuracy of three basic surface errors, including the overall accuracy and the local accuracy, are analyzed through simulation of imaging experiments. The rectified images have a significant improvement in quality, demonstrating the provision of precise light-field data for reconstruction of real objects. Full article

Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles. Full article

Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring. Full article

We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns. Full article

This paper proposes a novel micro-electromechanical system (MEMS) piezoresistive pressure sensor with a four-petal membrane combined with narrow beams and a center boss (PMNBCB) for low-pressure measurements. The stresses induced in the piezoresistors and deflection of the membrane were calculated using the finite element method (FEM). The functions of the relationship between the dimension variables and mechanical performance were determined based on the curve fitting method, which can provide an approach for geometry optimization of the sensor. In addition, the values in the equations were varied to determine the optimal dimensions for the proposed membrane. Then, to further improve the sensitivity of the sensor, a series of rectangular grooves was created at the position of the piezoresistors. The proposed diaphragm was compared to existing diaphragms, and a considerable increase in the sensitivity and a considerable decrease in nonlinearity error could be achieved by using the proposed sensor. The simulation results suggest that the sensor with the PMNBCB structure obtained a high sensitivity of 34.67 mV/kPa and a low nonlinearity error of 0.23% full-scale span (FSS) for the pressure range of 0–5 kPa. The proposed sensor structure is a suitable selection for MEMS piezoresistive pressure sensors. Full article

In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and process area as it utilizes the sparsity and measurement matrix to reconstruct the compressible signal from limited samples successfully. In this paper, CS is introduced into an efficient numerical method, multilevel fast multipole acceleration (MLFMA), for the electromagnetic (EM) scattering problem over a wide incident angle. This allows composition of a new kind of incident wave, which obtains efficient and reliable data for scattering centers extraction with low complexity. The resulting data from CS-based MLFMA are processed for ISAR) imaging. Simulation results show the received data for ISAR imaging from MLFMA with CS can outperform the data from MLFMA, which achieves a similar quality of ISAR imaging. Additionally, the computation complexity is improved by CS through the reduced matrix computation for fewer incident waves. It makes ISAR imaging using real data feasible and meaningful. Full article

In many Internet of Things (IoT) applications, large numbers of small sensor data are delivered in the network, which may cause heavy traffics. To reduce the number of messages delivered from the sensor devices to the IoT server, a promising approach is to aggregate several small IoT messages into a large packet before they are delivered through the network. When the packets arrive at the destination, they are disaggregated into the original IoT messages. In the existing solutions, packet aggregation/disaggregation is performed by software at the server, which results in long delays and low throughputs. To resolve the above issue, this paper utilizes the programmable Software Defined Networking (SDN) switch to program quick packet aggregation and disaggregation. Specifically, we consider the Programming Protocol-Independent Packet Processor (P4) technology. We design and develop novel P4 programs for aggregation and disaggregation in commercial P4 switches. Our study indicates that packet aggregation can be achieved in a P4 switch with its line rate (without extra packet processing cost). On the other hand, to disaggregate a packet that combines N IoT messages, the processing time is about the same as processing N individual IoT messages. Our implementation conducts IoT message aggregation at the highest bit rate (100 Gbps) that has not been found in the literature. We further propose to provide a small buffer in the P4 switch to significantly reduce the processing power for disaggregating a packet. Full article

Recent advances in unmanned aerial system (UAS) sensed imagery, sensor quality/size, and geospatial image processing can enable UASs to rapidly and continually monitor coral reefs, to determine the type of coral and signs of coral bleaching. This paper describes an unmanned aerial vehicle (UAV) remote sensing methodology to increase the efficiency and accuracy of existing surveillance practices. The methodology uses a UAV integrated with advanced digital hyperspectral, ultra HD colour (RGB) sensors, and machine learning algorithms. This paper describes the combination of airborne RGB and hyperspectral imagery with in-water survey data of several types in-water survey of coral under diverse levels of bleaching. The paper also describes the technology used, the sensors, the UAS, the flight operations, the processing workflow of the datasets, the methods for combining multiple airborne and in-water datasets, and finally presents relevant results of material classification. The development of the methodology for the collection and analysis of airborne hyperspectral and RGB imagery would provide coral reef researchers, other scientists, and UAV practitioners with reliable data collection protocols and faster processing techniques to achieve remote sensing objectives. Full article

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people. Full article

Cronobacter sakazakii is a foodborne pathogen that can cause a rare, septicemia, life-threatening meningitis, and necrotizing enterocolitis in infants. In general, standard methods for pathogen detection rely on culture, plating, colony counting and polymerase chain reaction DNA-sequencing for identification, which are time, equipment and skill demanding. Recently, nanoparticle- and surface-based immunoassays have increasingly been explored for pathogen detection. We investigate the functionalization of gold nanoparticles optimized for irreversible and specific binding to C. sakazakii and their use for spectroscopic detection of the pathogen. We demonstrate how 40-nm gold nanoparticles grafted with a poly(ethylene glycol) brush and functionalized with polyclonal antibodies raised against C. sakazakii can be used to specifically target C. sakazakii. The strong extinction peak of the Au nanoparticle plasmon polariton resonance in the optical range is used as a label for detection of the pathogens. Individual binding of the nanoparticles to the C. sakazakii surface is also verified by transmission electron microscopy. We show that a high degree of surface functionalization with anti-C. sakazakii optimizes the detection and leads to a detection limit as low as 10 CFU/mL within 2 h using a simple cuvette-based UV-Vis spectrometric readout that has great potential for further optimization. Full article

A surface plasmon resonance (SPR) sensor based on a side-polished single mode fiber coated with polyvinyl alcohol (PVA) is demonstrated for relative humidity (RH) sensing. The SPR sensor exhibits a resonant dip in the transmission spectrum in ambient air after PVA film coating, and the resonant wavelength shifts to longer wavelengths as the thickness of the PVA film increases. When RH changes, the resonant dip of the sensor with different film-thicknesses exhibits interesting characteristics for optical spectrum evolution. For sensors with initial wavelengths between 550 nm and 750 nm, the resonant dip shifts to longer wavelengths with increasing RH. The averaged sensitivity increases firstly and then drops, and shows a maximal sensitivity of 1.01 nm/RH%. Once the initial wavelength of the SPR sensor exceeds 850 nm, an inflection point of the resonant wavelength shift can be observed with RH increasing, and the resonant dip shifts to shorter wavelengths for RH values exceeding this point, and sensitivity as high as −4.97 nm/RH% can be obtained in the experiment. The sensor is expected to have potential applications in highly sensitive and cost effective humidity sensing. Full article

This paper proposes a decoupled six degrees of freedom (DOF) displacement measurement methodology, which is accomplished by utilizing six pairs of permanent magnets and six Hall sensors. Firstly, the coordinate transformation was mathematically derived, which represented the relationships between the main coordinate system of the motion system and each body coordinate system of the Hall sensors. With the aid of an ellipsoid function and the least squares method, only the output voltages of the six Hall sensors were required to decouple the six-DOF displacement and inclination of the motion platform with high accuracy. Finally, the experimental measurements demonstrate the effectiveness of the six DOF displacement measurement methodology, based on which the maximum errors of displacements can reach 0.23 mm and the maximum errors of inclinations can reach 0.07°. Full article

Previous studies have shown that in many wireless sensor network applications the IEEE 802.15.4 carrier sense multiple access with collision avoidance (CSMA/CA) mechanism with default parameters cannot guarantee the constraints of reliability, time efficiency, or energy efficiency. Although many adaptive parameter tuning algorithms have been proposed, many of them cannot correctly identify the changes of the network condition and are unable to effectively perform the parameter tuning operation. Considering the randomness that CSMA/CA brings about, for most of the proposed algorithms, it is a challenge to distinguish significant violations that were caused by actual changes of the network from the general fluctuations that were due to CSMA/CA. In this paper, we propose a lightweight algorithm called the network equivalent adaptive parameter tuning (NEAPT) algorithm. It is fully distributed and can work without any predefined information or acknowledgement. NEAPT not only takes reliability as an evaluation of a network condition, but it proposes a synthetic value, called the equivalent node number, and takes it as another reference for a network condition. Simulation results show that by taking both reliability and the equivalent node number into consideration, NEAPT can effectively identify the network changes and provide adequate and steady performances for wireless sensor networks (WSNs) in both stationary and dynamic conditions. Full article

The emerging mHealth applications, incorporating wearable sensors, enables continuous monitoring of physical activity (PA). This study aimed at analyzing the relevance of a multivariate complexity metric in assessment of functional change in younger older adults. Thirty individuals (60–70 years old) participated in a 4-week home-based exercise intervention. The Community Balance and Mobility Scale (CBMS) was used for clinical assessment of the participants’ functional balance and mobility performance pre- and post- intervention. Accelerometers worn on the low back were used to register PA of one week before and in the third week of the intervention. Changes in conventional univariate PA metrics (percentage of walking and sedentary time, step counts, mean cadence) and complexity were compared to the change as measured by the CBMS. Statistical analyses (21 participants) showed significant rank correlation between the change as measured by complexity and CBMS (ρ = 0.47, p = 0.03). Smoothing the activity output improved the correlation (ρ = 0.58, p = 0.01). In contrast, change in univariate PA metrics did not show correlations. These findings demonstrate the high potential of the complexity metric being useful and more sensitive than conventional PA metrics for assessing functional changes in younger older adults. Full article

Natural seepage of methane into the oceans is considerable, and plays a role in the global carbon cycle. Estimating the amount of this greenhouse gas entering the water column is important in order to understand their environmental impact. In addition, leakage from man-made structures such as gas pipelines may have environmental and economical consequences and should be promptly detected. Split beam echo sounders (SBES) detect hydroacoustic plumes due to the significant contrast in acoustic impedance between water and free gas. SBES are also powerful tools for plume characterization, with the ability to provide absolute acoustic measurements, estimate bubble trajectories, and capture the frequency dependent response of bubbles. However, under challenging conditions such as deep water and considerable background noise, it can be difficult to detect the presence of gas seepage from the acoustic imagery alone. The spatial coherence of the wavefield measured across the split beam sectors, quantified by the coherence factor (CF), is a computationally simple, easily available quantity which complements the acoustic imagery and may ease the ability to automatically or visually detect bubbles in the water column. We demonstrate the benefits of CF processing using SBES data from the Hudson Canyon, acquired using the Simrad EK80 SBES. We observe that hydroacoustic plumes appear more clearly defined and are easier to detect in the CF imagery than in the acoustic backscatter images. Full article

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities. Full article

In this work, we propose an electrochemical DNA aptasensor for the detection of profenofos, an organophosphorus pesticide, based on a competitive format and disposable graphite screen-printed electrodes (GSPEs). A thiol-tethered DNA capture probe, which results to be complementary to the chosen aptamer sequence, was immobilised on gold nanoparticles/polyaniline composite film-modified electrodes (AuNPs/PANI/GSPE). Different profenofos solutions containing a fixed amount of the biotinylated DNA aptamer were dropped onto the realized aptasensors. The hybridisation reaction was measured using a streptavidin-alkaline phosphatase enzyme conjugate, which catalyses the hydrolysis of 1-naphthyl -phosphate. The 1-naphtol enzymatic product was detected by means of differential pulse voltammetry (DPV). The aptasensor showed itself to work as a signal off sensor, according to the competitive format used. A dose response curve was obtained between 0.10 μM and 10 μM with a detection limit of 0.27 μM. Full article

Optical coupling between subwavelength grating pairs allows for the precise measurement of lateral or vertical displacement of grating elements and gives rise to different types of displacement and inertial sensors. In this paper, we demonstrate a design for a nano-optomechanical accelerometer based on a subwavelength grating pair that can be easily fabricated by a single Silicon-on-insulator (SOI) chip. The parameters of the subwavelength grating pair-based optical readout, including period, duty cycle, thickness of grating and metal film, and the distance of the air gap, were optimized by combining a genetic algorithm and rigorous coupled wavelength analysis (RCWA) to obtain the optimal sensitivity to the displacement of suspended grating element and the acceleration. A corresponding mechanical design was also completed to meet the highly sensitive acceleration measurement requirement while considering the mechanical cross-axis sensitivity, dynamic range, bandwidth, and fabrication feasibility. This device was verified by both RCWA and finite-different-time-domain methods, and a tolerance analysis was also completed to confirm that it is able to achieve the extremely high optical displacement sensitivity of 1.8%/nm, acceleration-displacement sensitivity of 1.56 nm/mg, and acceleration measurement sensitivity of more than 2.5%/mg, which is almost one order of magnitude higher than any reported counterparts. This work enables a single SOI-based high performance accelerometer, and provides a theoretical basis and fabrication guides for the design. Full article

Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach. Full article

A significant amount of noteworthy articles reviewing different label-free biosensors are being published in the last years. Most of the times, the comparison among the different biosensors is limited by the procedure used of calculating the limit of detection and the measurement uncertainty. This article clarifies and establishes a simple procedure to determine the calibration function and the uncertainty of the concentration measured at any point of the measuring interval of a generic label-free biosensor. The value of the limit of detection arises naturally from this model as the limit at which uncertainty tends when the concentration tends to zero. The need to provide additional information, such as the measurement interval and its linearity, among others, on the analytical systems and biosensor in addition to the detection limit is pointed out. Finally, the model is applied to curves that are typically obtained in immunoassays and a discussion is made on the application validity of the model and its limitations. Full article

This paper presents a new Strap-down Inertial Navigation System/Spectrum Red-Shift/Star Sensor (SINS/SRS/SS) system integration methodology to improve the autonomy and reliability of spacecraft navigation using the spectrum red-shift information from natural celestial bodies such as the Sun, Jupiter and the Earth. The system models for SINS/SRS/SS integration are established. The information fusion of SINS/SRS/SS integration is designed as the structure of the federated Kalman filter to fuse the local estimations of SINS/SRS and SINS/SS integrated subsystems to generate the global state estimation for spacecraft navigation. A new robust adaptive unscented particle filter is also developed to obtain the local state estimations of SINS/SRS and SINS/SS integrated subsystems in a parallel manner. The simulation results demonstrate that the proposed methodology for SINS/SRS/SS integration can effectively calculate navigation solutions, leading to strong autonomy and high reliability for spacecraft navigation. Full article

The complex nature of rubbing faults makes it difficult to use traditional signal analysis methods for feature extraction. Various time-frequency analysis approaches based on signal decomposition, such as empirical mode decomposition (EMD) and ensemble EMD (EEMD), have been widely utilized recently to analyze rub-impact faults. However, traditional EMD suffers from “mode-mixing”, and in both EMD and EEMD the relevance of the extracted components to rubbing processes must be determined. In this paper, we introduce a new informative intrinsic mode function (IMF) selection method for EEMD and a hybrid feature model for diagnosing rub-impact faults of various intensities. Our method uses a novel selection procedure that combines the degree-of-presence ratio of rub impact and a Kullback–Leibler divergence-based similarity measure into an IMF quality metric with adaptive threshold-based selection to pick the meaningful signal-dominant modes. Signals reconstructed using the selected IMFs contained explicit information about the rubbing faults and are used for hybrid feature extraction. Experimental results demonstrated that the proposed approach effectively defines meaningful IMFs for rubbing processes, and the presented hybrid feature model allows for the classification of rub-impact faults of various intensities with good accuracy. Full article

This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in $t+1$, by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption. Full article

We have synthesized novel coumarin-based fluorescent chemosensors for detection of fluoride ions in aqueous solution. The detection mechanism relied on a fluoride-mediated desilylation triggering fluorogenic reaction and a strong interaction between fluoride and the silicon center. In this work, the hydroxyl-decorated coumarins containing oxysilyl moiety have been synthesized through the aldehyde-functionalized coumarins. The optical responses toward fluoride, as well as aqueous stability studies of both aldehyde and hydroxyl functionalized coumarins, have been investigated. Due to the highest fluorescence enhancement upon the addition of fluoride and good stability in aqueous solution, the hydroxyl-decorated coumarin connected with the bulky tert-butyldiphenyloxysilyl group (-OSitBuPh₂) has been selected for further investigation of its potential as a fluoride sensor. This hydroxyl-decorated coumarin can selectively sense fluoride ions in aqueous media (contain 0.8% MeCN) with desirable response times (40 min). The limit of detection of this compound was determined as 0.043 ppm, satisfying the standard fluoride level (0.7 ppm) in drinking water recommended by U.S. Department of Health and Human Services. The application of this silyl-capped coumarin derivative for fluoride analysis in collected water samples displayed satisfactory analytical accuracy (<5% error). Finally, this compound was successfully employed in fluorescence bioimaging of fluoride ions in human liver cancer cells, indicating its excellent cell permeability, ability to retain inside the living cells, and good stability under physiological conditions. Full article

The proliferation of Internet-of-Things (IoT) technology and its reliance on the license-free Industrial, Scientific, and Medical (ISM) bands have rendered radio spectrum scarce. The IoT can nevertheless obtain great advantage from Cognitive Radio (CR) technology for efficient use of a spectrum, to be implemented in IEEE 802.11af-based primary networks. However, such networks require a geolocation database and a centralized architecture to communicate white space information on channels. On the other hand, in spectrum sensing, CR presents various challenges such as the Hidden Primary Terminal (HPT) problem. To this end, we focus on the most recently released standard, i.e., IEEE 802.11ah, in which IoT stations can first be classified into multiple groups to reduce collisions and then they can periodically access the channel. Therein, both services are similarly supported by a centralized server that requires signaling overhead to control the groups of stations. In addition, more regroupings are required over time due to the frequent variations in the number of participating stations, which leads to more overhead. In this paper, we propose a new Multiple Access Control (MAC) protocol for CR-based IEEE 802.11ah systems, called Restricted Access with Collision and Interference Resolution (RACIR). We introduce a decentralized group split algorithm that distributes the participating stations into multiple groups based on a probabilistic estimation in order to resolve collisions. Furthermore, we propose a decentralized channel access procedure that avoids the HPT problem and resolves interference with the incumbent receiver. We analyze the performance of our proposed MAC protocol in terms of normalized throughput, packet delay and energy consumption with the Markov model and analytic expressions. The results are quite promising, which makes the RACIR protocol a strong candidate for the CR-based IoT environment. Full article

The problem of vibrations of rotating rings has been of interest for its wide applications in engineering, such as the vibratory ring gyroscopes. For the vibratory ring gyroscopes, the vibration of a micro ring is usually actuated and sensed by means of electrostatics. The analytical models of electrostatic microstructures are complicated due to their non-linear electromechanical coupling behavior. Therefore, this paper presents for the first time the free vibration of a rotating ring under uniform electrical field and the results will be helpful for extending our knowledge on the problem of vibrations of rotating rings, helping the design of vibratory ring gyroscopes, and inspiring the feasibilities of other engineering applications. An analytical model, based on thin-ring theory, is derived by means of energy method for a rotating ring under uniformly distributed electrical field. After that, the closed form solutions of the natural frequencies and modes are obtained by means of modal expansion method. Some valuable conclusions are made according to the results of the present analytical model. The electrical field causes not only an electrostatic force but also an equivalently negative electrical-stiffness. The equivalent negative electrical-stiffness will reduce either the natural frequencies or critical speeds of the rotating ring. It is known that the ring will buckle when its rotational speed equals its natural frequencies. The introduction of electrical field will further reduce the buckling speeds to a value less than the natural frequencies. The rotation effect will induce the so-called traveling modes, each one travels either in the same direction as the rotating ring or in the opposite direction with respect to stationary coordinate system. The electrical field will reduce the traveling velocities of the traveling modes. Full article

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance. Full article

Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to be a challenging problem due to the unpredictability of various real-time situations, such as illumination variations, occlusion, fast motion, deformation, and scale variation, even though we only know the initial target position. To address these matters, we used a kernelized-correlation-filter-based translation filter with the integration of multiple features such as histogram of oriented gradients (HOG) and color attributes. These powerful features are useful to differentiate the target from the surrounding background and are effective for motion blur and illumination variations. To minimize the scale variation problem, we designed a correlation-filter-based scale filter. The proposed adaptive model’s updating and dynamic learning rate strategies based on a peak-to-sidelobe ratio effectively reduce model-drifting problems by avoiding noisy appearance changes. The experiment results show that our method provides the best performance compared to other methods, with a distance precision score of 79.9%, overlap success score of 59.0%, and an average running speed of 74 frames per second on the object tracking benchmark (OTB-2015). Full article

A highly sensitive quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor based on a custom quartz tuning fork (QTF) with a small-gap of 200 μm was demonstrated. With the help of the finite element modeling (FEM) simulation software COMSOL, the change tendency of the QEPAS signal under the influence of the laser beam vertical position and the length of the micro-resonator (mR) were calculated theoretically. Water vapor (H₂O) was selected as the target analyte. The experimental results agreed well with those of the simulation, which verified the correctness of the theoretical model. An 11-fold signal enhancement was achieved with the addition of an mR with an optimal length of 5 mm in comparison to the bare QTF. Finally, the H₂O-QEPAS sensor, which was based on a small-gap QTF, achieved a minimum detection limit (MDL) of 1.3 ppm, indicating an improvement of the sensor performance when compared to the standard QTF that has a gap of 300 μm. Full article

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle. Full article

The present work provides a detailed description on quantitative 3D magnetic field reconstruction using a scanning magnetoresistance microscopy setup incorporating a 19.5 $\mathsf{\mu }$m × 2.5 $\mathsf{\mu }$m magnetoresistive sensor. Therefore, making use of a rotation stage, 11 nm thick ferromagnetic CoFe elements with 20 $\mathsf{\mu }$m × 5 $\mathsf{\mu }$m planar size were measured along different sensor axes and converted into cartesian coordinate magnetic field components by use of the analytical coordinate transform equations. The reconstruction steps were followed and validated by numerical simulations based on a field averaging model caused by a non-negligible sensor volume. Detailed in-plane magnetic component reconstruction with ability to reconstruct sub-micrometer features is achieved. A discussion on the limiting factors for optimal resolution is presented. Full article

We employed a single-mode, widely tunable (~300 cm⁻¹) external-cavity quantum cascade laser operating around 8 µm for broadband direct absorption spectroscopy and wavelength modulation spectroscopy where a modulation frequency of 50 kHz was employed with high modulation amplitudes of up to 10 GHz. Using a compact multipass cell, we measured the entire molecular absorption band of acetone at ~7.4 µm with a spectral resolution of ~1 cm⁻¹. In addition, to demonstrate the high modulation dynamic range of the laser, we performed direct absorption (DAS) and second harmonic wavelength modulation spectroscopy (WMS-2f) of the Q-branch peak of acetone molecular absorption band (HWHM ~10 GHz) near 1365 cm⁻¹. With WMS-2f, a minimum detection limit of 15 ppbv in less than 10 s is achieved, which yields a noise equivalent absorption sensitivity of 1.9 × 10⁻⁸ cm⁻¹ Hz^−1/2. Full article

The characteristics of the dual-core photonic crystal fiber (PCF) sensor are studied using the finite element method (FEM), and the structure is improved according to the numerical simulation results. The results show that whether or not the four large air holes far away from the geometry center of the PCF are filled with analyte has no influence on the wavelength sensitivity of the sensor which means those holes can be replaced by small air holes. The wavelength sensitivity can be tuned by adjusting the sizes of the other large air holes which are as for liquid holes. The dynamic detection range of the refractive index (RI) is from 1.33 to 1.51. In particular, high linearity is obtained in the range of 1.44 to 1.51. The sensitivity is as high as 6021 nm/RIU when the liquid holes are the smallest. When liquid holes are tangential with the envelope of first layer air holes, the wavelength sensitivity is 4028 nm/RIU, and the coefficient of determination (R²) is 0.99822 when the RI of the analyte varies from 1.44 to 1.51 which shows that high sensitivity and good linearity are both obtained. Full article

This article presents a Virtual Transducer Electronic Data Sheet (VTEDS)-based framework for the development of intelligent sensor nodes with plug-and-play capabilities in order to contribute to the evolution of the Internet of Things (IoT) toward the Web of Things (WoT). It makes use of new lightweight protocols that allow sensors to self-describe, auto-calibrate, and auto-register. Such protocols enable the development of novel IoT solutions while guaranteeing low latency, low power consumption, and the required Quality of Service (QoS). Thanks to the developed human-centered tools, it is possible to configure and modify dynamically IoT device firmware, managing the active transducers and their communication protocols in an easy and intuitive way, without requiring any prior programming knowledge. In order to evaluate the performance of the system, it was tested when using Bluetooth Low Energy (BLE) and Ethernet-based smart sensors in different scenarios. Specifically, user experience was quantified empirically (i.e., how fast the system shows collected data to a user was measured). The obtained results show that the proposed VTED architecture is very fast, with some smart sensors (located in Europe) able to self-register and self-configure in a remote cloud (in South America) in less than 3 s and to display data to remote users in less than 2 s. Full article

In this study, the influence of two critical geometrical parameters (i.e., angles of wind fairing, α; and lower inclined web, β) in the aerodynamic performance of closed-box girder bridges was systematically investigated through conducting a theoretical analysis and wind tunnel testing using laser displacement sensors. The results show that, for a particular inclined web angle β, a closed-box girder with a sharper wind fairing angle of α = 50° has better flutter and vortex-induced vibration (VIV) performance than that with α = 60°, while an inclined web angle of β = 14° produces the best VIV performance. In addition, the results from particle image velocimetry (PIV) tests indicate that a wind fairing angle of α = 50° produces a better flutter performance by inducing a single vortex structure and a balanced distribution of the strength of vorticity in both upper and lower parts of the wake region. Furthermore, two-dimensional three-degrees-of-freedom (2D-3DOF) analysis results demonstrate that the absolute values of Part A (with a reference of flutter derivative A₂^*) and Part D (with a reference of A₁^*H₃^*) generally decrease with the increase of β, while the change of the participation level of heaving degrees of freedom (DOF) in torsion-dominated coupled flutter initially increases, reaches its peak, and then decreases with the increase of β. Full article

We present the results of the theoretical modeling of the elastic shear properties of a magnetic gel, consisting of soft matrix and embedded, fine magnetizable particles, which are united in linear chain-like structures. We suppose that the composite is placed in a magnetic field, perpendicular to the direction of the sample shear. Our results show that the field can significantly enhance the mechanical rigidity of the soft composite. Theoretical results are in quantitative agreement with the experiments. Full article

The scale factor drifts and other long-term instability drifts of Micro-Electro-Mechanical System (MEMS) inertial sensors are the main contributors of the position and orientation errors in high dynamic environments. In this paper, a novel high dynamic micro vibrator, which could provide high acceleration and high angular rate rotation with integrated optical displacement detector, is proposed. Commercial MEMS inertial sensors, including 3-axis accelerometer and 6-axis inertial measurement unit which is about 3 mm * 3 mm * 1 mm with 19 mg, could be bonded on the vibration platform of the micro vibrator to perform in-situ during the self-calibration procedure. The high dynamic micro vibrator is fabricated by a fully-integrated MEMS process, including lead zirconate titanate (PZT) film deposition, PZT and electrodes patterning, and structural ion etching. The optical displacement detector, using vertical-cavity surface-emitting laser (VCSEL) and photoelectric diodes (PD), is integrated on the top of the package to measure the 6-DOF vibrating displacement with the detecting resolution of 150 nm in the range of 500 μm. The maximum out-of-plane acceleration of the z-axis vibrating platform loaded with commercial 3-axis accelerometer (H3LIS331DL) achieves above 16 g and the maximum angular velocity achieves above 720°/s when the driving voltage is ±6 V. Full article

In this work, a surface plasmon resonance (SPR) biosensor based on two-dimensional transition metal dichalcogenides (TMDCs) is proposed to improve the biosensor’s sensitivity. In this sensor, different kinds of two-dimensional TMDCs are coated on both surfaces of metal film. By optimizing the structural parameters, the angular sensitivity can reach as high as 315.5 Deg/RIU with 7-layers WS₂ and 36 nm Al thin film, which is 3.3 times of the conventional structure based on single Al thin film. We also obtain maximum phase sensitivity (3.85 × 10⁶ Deg/RIU) with bilayer WS₂ and 35 nm Al thin film. The phase sensitivity can be further improved by employing Ag and removing air layer. The proposed configuration is of great potential for biochemical sensing. Full article

As one of the major methods for the diagnosis and treatment of cancers in their early stages, the percutaneous puncture technique has bright prospect in biopsy, ablation, proximity radiotherapy, and drug delivery. Recent years, researchers found the flexible needle cannot realize feedback control during the puncture surgeries only by path planning. To solve this problem, the flexible needle is tried to achieve real-time detection in this paper. Compared with previous methods, the strain gauges glued on the needle surface rather than the medical imaging techniques is used to collect the information to reconstruct the needle curve, which is benefit to integrate the whole system and obtain a more simple and accurate closed-loop control. This paper presented the math model of curve fitting and analyzed the causes of curve fitting errors. To verify the feasibility of this method, an experiment setup was built. Results from the experiments validated the solution in this paper to be effective. Full article

Sensitive trace gas detection plays an important role in current challenges occurring in areas such as industrial process control and environmental monitoring. In particular, for medical breath analysis and for the detection of illegal substances, e.g., drugs and explosives, a selective and sensitive detection of trace gases in real-time is required. We report on a compact and transportable multi-component system (RES-Q-Trace) for molecular trace gas detection based on cavity-enhanced techniques in the mid-infrared (MIR). The RES-Q-Trace system can operate four independent continuous wave quantum or interband cascade lasers each combined with an optical cavity. Twice the method of off-axis cavity-enhanced absorption spectroscopy (OA-CEAS) was used, twice the method of optical feedback cavity-enhanced absorption spectroscopy (OF-CEAS), respectively. Multi-functional software has been implemented (i) for the general system control; (ii) to drive the four different laser sources and (iii) to analyze the detector signals for concentration determination of several molecular species. For the validation of the versatility and the performance of the RES-Q-Trace instrument the species NO, N₂O, CH₄, C₂H₄ and C₃H₆O, with relevance in the fields of breath gas analysis and the detection of explosives have been monitored in the MIR with detection limits at atmospheric pressure in the ppb and ppt range. Full article

Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. Full article

Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems. Full article

Vibrational spectroscopy is a powerful tool for probing molecular structures and dynamics since it offers a unique fingerprint that allows molecular identification. One of important aspects of applying vibrational spectroscopy is to develop the probes that can characterize the related properties of molecules such as the conformation and intermolecular interaction. Many examples of vibrational probes have appeared in the literature, including the azide group (–N₃), amide group (–CONH₂), nitrile groups (–CN), hydroxyl group (–OH), –CH group and so on. Among these probes, the –CH group is an excellent one since it is ubiquitous in organic and biological molecules and the C–H stretching vibrational spectrum is extraordinarily sensitive to the local molecular environment. However, one challenge encountered in the application of C–H probes arises from the difficulty in the accurate assignment due to spectral congestion in the C–H stretching region. In this paper, recent advances in the complete assignment of C–H stretching spectra of aliphatic alcohols and the utility of C–H vibration as a probe of the conformation and weak intermolecular interaction are outlined. These results fully demonstrated the potential of the –CH chemical group as a molecular probe. Full article

The electrochemical properties of ion-exchanger-based solvent polymeric ion-selective electrodes (ISEs)—bulk and interfacial resistance, capacitance, and polarization under a galvanostatic current step—are studied, with a nitrate ISE based on tetradecylammonium nitrate (TDANO₃) as a model system. The study is performed by chronopotentiometric and impedance measurements, and focuses on the dependence of the aforementioned properties on the concentration of NO₃⁻ anions in solution. The impacts from the bulk and the interfacial charge transfer to the overall membrane resistance are revealed. It is shown that the bulk resistance of the membranes decreases over an increase of NO₃⁻ concentration within the range of a Nernstian potentiometric response of the ISE. This fact, also reported earlier for K⁺- and Ca²⁺-selective ISEs, is not in line with current views of the mechanism of the ISE response, or of the role of ion exchange in particular. The origin of this effect is unclear. Estimates are made for the concentration of ionized species (NO₃⁻ and TDA⁺) and, respectively, for the TDANO₃ association constant, as well as for the species diffusion coefficients in the membrane. Full article

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO₂ flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis. Full article

The motivation of this work is to explore the possibility of typhoon wave retrieval (the main parameter is significant wave height (SWH)) for C-band Gaofen (GF-3) synthetic aperture radar (SAR) with a wide swath coverage (>400 km). We aim to establish an analysis of a typhoon wave in the subresolution-scale (approximately 20 × 20 km²) on GF-3 SAR through SAR-measured parameters, including a normalized radar cross section (NRCS) and variance of the normalized SAR image (herein called cvar), which are the basic variables in an empirical wave retrieval algorithm and are independent of visible wave streaks. Several typhoons around the China Seas were captured by Chinese GF-3 SAR in 2017; e.g., Noru, Doksuri, Talim and Hato. The wave fields simulated from the third-generation numerical wave model WAVEWATCH-III (WW3) are collocated with these images. In general, the distribution patterns of the typhoon waves from the WW3 model are consistent with wave fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 0.125° grids, indicating that the simulation results from the WW3 model are suitable for our study. In addition to winds retrieved from GF-3 SAR images in vertical-horizontal (VH) polarization, the characteristics of the typhoon wave on vertical-vertical (VV) polarization GF-3 SAR images are studied. It is found that SWH has a linear relationship with NRCS and cvar, however, SWH fluctuates with wind speed at all incidence angles. Based on the analyzed results, we simply tune two empirical wave retrieval algorithms for GF-3 SAR in typhoons. Although the correlation (COR) reaches 0.5 taking account into the NRCS term, a more accurate retrieval algorithm, including more related terms, is anticipated for further development for GF-3 SAR and validated through more typhoon images. Full article

It is a fact that surface electron accumulation layer with sheet electron density in the magnitude of ~10¹³ cm⁻² on InN, either as-grown or Mg-doped, makes InN an excellent candidate for sensing application. In this paper, the response of hydrogen sensors based on Mg-doped InN films (InN:Mg) grown by molecular beam epitaxy has been investigated. The sensor exhibits a resistance variation ratio of 16.8% with response/recovery times of less than 2 min under exposure to 2000 ppm H₂/air at 125 °C, which is 60% higher in the magnitude of response than the one based on the as-grown InN film. Hall-effect measurement shows that the InN:Mg with suitable Mg doping level exhibits larger sheet resistance, which accords with buried p-type conduction in the InN bulk. This work shows the advantage of InN:Mg and signifies its potential for sensing application. Full article

Eye-to-eye contact and facial expressions are key communicators, yet there has been little done to evaluate the basic properties of face-to-face; mutual head orientation behaviors. This may be because there is no practical device available to measure the behavior. This paper presents a novel headband-type wearable device called FaceLooks, used for measuring the time of the face-to-face state with identity of the partner, using an infrared emitter and receiver. It can also be used for behavioral healthcare applications, such as for children with developmental disorders who exhibit difficulties with the behavior, by providing awareness through the visual feedback from the partner’s device. Two laboratory experiments showed the device’s detection range and response time, tested with a pair of dummy heads. Another laboratory experiment was done with human participants with gaze trackers and showed the device’s substantial agreement with a human observer. We then conducted two field studies involving children with intellectual disabilities and/or autism spectrum disorders. The first study showed that the devices could be used in the school setting, observing the children did not remove the devices. The second study showed that the durations of children’s face-to-face behavior could be increased under a visual feedback condition. The device shows its potential to be used in therapy and experimental fields because of its wearability and its ability to quantify and shape face-to-face behavior. Full article

Due to the narrow space and a harsh chemical environment in the sterilization processes for the end-effector of surgical robots, it is difficult to install and integrate suitable sensors for the purpose of effective and precise force control. This paper presents an innovative tension sensor for estimation of grasping force in our laparoscope surgical robot. The proposed sensor measures the tension of cable using fiber gratings (FBGs) which are pasted in the grooves on the inclined cantilevers of the sensor. By exploiting the stain measurement characteristics of FBGs, the small deformation of the inclined cantilevers caused by the cable tension can be measured. The working principle and the sensor model are analyzed. Based on the sensor model, the dimensions of the sensor are designed and optimized. A dedicated experimental setup is established to calibrate and test the sensor. The results of experiments for estimation the grasping force validate the sensor. Full article

This work develops a speech recognition system that uses two procedures of proposed noise detection and combined noise reduction. The system can be used in applications that require interactive robots to recognize the contents of speech that includes ambient noise. The system comprises two stages, which are the threshold-based noise detection and the noise reduction procedure. In the first stage, the proposed system automatically determines when to enhance the quality of speech based on the signal-to-noise ratio (SNR) values of the collected speech at all times. In the second stage, independent component analysis (ICA) and subspace speech enhancement (SSE) are employed for noise reduction. Experimental results reveal that the SNR values of the enhanced speech exceed those of the received noisy speech by approximately 20 dB to 25 dB. The noise reduction procedure improves the speech recognition rates by around 15% to 25%. The experimental results indicate that the proposed system can reduce the effect of noise in numerous noisy environments and improve the quality of speech for recognition purposes. Full article

To address the problem of low accuracy for the regular filter algorithm in SINS/DVL integrated navigation, a square-root unscented information filter (SR-UIF) is presented in this paper. The proposed method: (1) adopts the state probability approximation instead of the Taylor model linearization in EKF algorithm to improve the accuracy of filtering estimation; (2) selects the most suitable parameter form at each filtering stage to simply the calculation complexity; (3) transforms the square root to ensure the symmetry and positive definiteness of the covariance matrix or information matrix, and then to enhance the stability of the filter. The simulation results indicate that the estimation accuracy of SR-UIF is higher than that of EKF, and similar to UKF; meanwhile the computational complexity of SR-UIF is lower than that of UKF. Full article

An on-line machine learning approach integrating the genetic algorithm (GA) and jitter measurements is proposed to learn the write strategy for the infrared diode of ultra-speed CD-RW recorders. The recording performance differs significantly for the CD-RW discs recorded for the first, second, or third time above. It is difficult to learn one set of write strategy parameters for the infrared diode of ultra-speed CD-RW recorder that satisfies the recording specifications for three different types of discs. The GA is applied to the on-line learning of write strategy. However, the convergence of GA stagnates at the final stage of the learning process due to the fact that the write strategy parameters learned by the GA need to satisfy the recording specifications for discs recorded for the first time, second time and third time within one recording trial. To overcome this difficulty, a scheme called dynamic parameter encoding is proposed. This scheme improves the GA convergence and explores the search space much better than the conventional GA. Full article

This work presents a solution to localize Unmanned Autonomous Vehicles with respect to pipes and other cylindrical elements found in inspection and maintenance tasks both in industrial and civilian infrastructures. The proposed system exploits the different features of vision and laser based sensors, combining them to obtain accurate positioning of the robot with respect to the cylindrical structures. A probabilistic (RANSAC-based) procedure is used to segment possible cylinders found in the laser scans, and this is used as a seed to accurately determine the robot position through a computer vision system. The priors obtained from the laser scan registration help to solve the problem of determining the apparent contour of the cylinders. In turn this apparent contour is used in a degenerate quadratic conic estimation, enabling to visually estimate the pose of the cylinder. Full article

Ultraviolet (UV) detectors have attracted considerable attention in the past decade due to their extensive applications in the civil and military fields. Wide bandgap semiconductor-based UV detectors can detect UV light effectively, and nanowire structures can greatly improve the sensitivity of sensors with many quantum effects. This review summarizes recent developments in the classification and principles of UV detectors, i.e., photoconductive type, Schottky barrier type, metal-semiconductor-metal (MSM) type, p-n junction type and p-i-n junction type. The current state of the art in wide bandgap semiconductor materials suitable for producing nanowires for use in UV detectors, i.e., metallic oxide, III-nitride and SiC, during the last five years is also summarized. Finally, novel types of UV detectors such as hybrid nanostructure detectors, self-powered detectors and flexible detectors are introduced. Full article

In this paper, based on the reactive component of the vertical intensity, the method for target depth resolution has been improved. In the previous existing research results, using the reactive component of vertical intensity, the research objects for target depth resolution in shallow water, can only be the targets whose frequencies can only excite the first two normal modes, and the depth of targets whose frequencies excite more than two normal modes cannot be correctly identified. The basic idea of the improved method is to classify targets on the foundation of the lower-mode correlation quantity of the vertical intensity. Based on the improved method, we can realize depth resolution of the targets whose frequency can excite the first three normal modes so as to effectively expand the working band useful for target depth resolution. Finally, we can realize the three-dimensional target depth resolution so as to distinguish the aerial, surface and underwater targets. The feasibility of the algorithm is verified by simulation and experimental data processing. Full article

Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed. Full article

In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage. Full article

The evolving role of modern navies has required increasingly higher levels of capability in the Radio Frequency (RF) shipboard systems that provide radar, communications, Electronic Attack (EA) and Electronic Support (ES) functions. The result has been a proliferation of topside antennas and associated hardware on naval vessels. The notion of MultiFunction RF (MFRF) systems has drawn considerable interest as an approach to reversing this trend. In a MFRF system, RF functions are consolidated within a shared set of electronics and antenna apertures that utilize Active Electronically Scanned Array (AESA) technology. This paper highlights a number of issues to be considered in the design and implementation of a naval MFRF system. Specifically, the key requirements of the RF functions of interest are first reviewed, and MFRF system design trade-offs resulting from costs and/or performance limitations in existing hardware technology are then discussed. It is found that limitations in hardware technology constrain the implementation of practical MFRF systems. MFRF system prototype development programs that have been conducted in other countries are described. MFRF resource allocation management is identified as an important future research topic. Full article

Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400–1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400–1000 nm (visible range) and 900–1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way. Full article

Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes’ locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node’ location is computed based on its previous location; in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial–temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP. Full article

The currently accepted interval of weekly cast changes in the treatment of clubfeet seems unsubstantiated. A force sensor is needed to determine the adaptation rate of a clubfoot to establish what cast change interval would be most effective and efficient. We developed a force sensor based on the principle that the resonance frequency of an LC-tank changes when a metal target is brought in close proximity. A thin rubber ring between the LC-tank and the metal target transformed this proximity sensor into a force sensor. With a static load test and an incremental load test, the performance of the constructed force sensors was characterized. The custom-made sensor showed excellent sensitivity (($1.7\pm 0.8\times {10}^5$) counts/N), resolution (($0.15\pm 0.06$) mN), and accuracy (($3.5\pm 3.0$) %) for the application. The observed drift was ($2.1\pm 0.7$) %/log${}_{10}$(h), which is lower than other thin force sensors. Preliminary results of measurements in the treatment of Dupuytren fingers and clubfeet show good functioning for long-term force measurements. Full article

Given that facial features contain a wide range of identification information and cannot be completely represented by a single feature, the fusion of multiple features is particularly significant for achieving a robust face recognition performance, especially when there is a big difference between the test sets and the training sets. This has been proven in both traditional and deep learning approaches. In this work, we proposed a novel method named C2D-CNN (color 2-dimensional principal component analysis (2DPCA)-convolutional neural network). C2D-CNN combines the features learnt from the original pixels with the image representation learnt by CNN, and then makes decision-level fusion, which can significantly improve the performance of face recognition. Furthermore, a new CNN model is proposed: firstly, we introduce a normalization layer in CNN to speed up the network convergence and shorten the training time. Secondly, the layered activation function is introduced to make the activation function adaptive to the normalized data. Finally, probabilistic max-pooling is applied so that the feature information is preserved to the maximum extent while maintaining feature invariance. Experimental results show that compared with the state-of-the-art method, our method shows better performance and solves low recognition accuracy caused by the difference between test and training datasets. Full article

For a running freely land-vehicle strapdown inertial navigation system (SINS), the problems of self-calibration and attitude alignment need to be solved simultaneously. This paper proposes a complete alignment algorithm for the land vehicle navigation using Inertial Measurement Units (IMUs) and an odometer. A self-calibration algorithm is proposed based on the global observability analysis to calibrate the odometer scale factor and IMU misalignment angle, and the initial alignment and calibration method based on optimal algorithm is established to estimate the attitude and other system parameters. This new algorithm has the capability of self-initialization and calibration without any prior attitude and sensor noise information. Computer simulation results show that the performance of the proposed algorithm is superior to the extended Kalman filter (EKF) method during the oscillating attitude motions, and the vehicle test validates its advantages. Full article

Consistent observations of lakes and reservoirs that comprise the majority of surface freshwater globally are limited, especially in Africa where water bodies are exposed to unfavorable climatic conditions and human interactions. Publicly available satellite imagery has increased the ability to monitor water bodies of various sizes without much financial hassle. Landsat 7 and 8 images were used in this study to estimate area changes around Lake Chad. The Automated Water Extraction Index (AWEI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) were compared for the remote sensing retrieval process of surface water. Otsu threshold method was used to separate water from non-water features. With an overall accuracy of ~96% and an inter-rater agreement (kappa coefficient) of 0.91, the MNDWI was a better indicator for mapping recent area changes in Lake Chad and was used to estimate the lake’s area changes from 2003–2016. Extracted monthly areas showed an increasing trend and ranged between ~1242 km² and 2231 km² indicating high variability within the 13-year period, 2003–2016. In addition, we combined Landsat measurements with Total Water Storage Anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) satellites. This combination is well matched with our estimated surface area trends. This work not only demonstrates the importance of remote sensing in sparsely gauged developing countries, it also suggests the use of freely available high-quality imagery data to address existing lake crisis. Full article

Optimal nitrogen (N) management is essential for profitable vegetable crop production and to minimize N losses to the environment that are a consequence of an excessive N supply. Proximal optical sensors placed in contact with or close to the crop can provide a rapid assessment of a crop N status. Three types of proximal optical sensors (chlorophyll meters, canopy reflectance sensors, and fluorescence-based flavonols meters) for monitoring the crop N status of vegetable crops are reviewed, addressing practical caveats and sampling considerations and evaluating the practical use of these sensors for crop N management. Research over recent decades has shown strong relationships between optical sensor measurements, and different measures of crop N status and of yield of vegetable species. However, the availability of both: (a) Sufficiency values to assess crop N status and (b) algorithms to translate sensor measurements into N fertilizer recommendations are limited for vegetable crops. Optical sensors have potential for N management of vegetable crops. However, research should go beyond merely diagnosing crop N status. Research should now focus on the determination of practical fertilization recommendations. It is envisaged that the increasing environmental and societal pressure on sustainable crop N management will stimulate progress in this area. Full article

A novel lightweight diagnostic film with sensors/actuators and a multiple-path wiring option using inkjet printing was developed. The diagnostic film allows for systematic, accurate, and repeatable sensor placement. Furthermore, the film is highly flexible and adaptable for placement on complex configurations. The film can be attached to the surface of the structure through a uniform secondary boundary procedure or embedded within the composite layup during curing. The surface-mounted film can simply be peeled off for repair or replacement without scratching or damaging the part. The film offers significant weight reduction compared to other available technologies. A set of extreme temperature, altitude, and vibration environment test profiles were carried out following the Radio Technical Commission for Aeronautics (RTCA) DO-160 document to assess the durability and performance of the diagnostic film for onboard application. The diagnostic film was shown to be durable and reliable in withstanding the variable operational and harsh environmental conditions of tests representing the conditions of regional aircraft. Full article

In this paper, two-to-one energy and spectrum dual-cooperation (ESDC) is investigated for cognitive cooperation networks. Specifically, the energy and spectrum of two primary users (PUs) are both transferred or authorized to one multi-antenna secondary user (SU) in exchange for its aid in the signal relaying to guarantee the successful data transmission, whilst the SU, which originally owns no spectrum access privilege and limited energy storage, is also able to concurrently transmit its own data through spatial multiplexing. Moreover, network-coding is also adopted to further compress the data size and hence reduce the power consumption at SU. The formulated problem for the aforementioned two-to-one ESDC model is non-convex and intractable to solve directly. To solve the problem effectively, the Lagrangian dual methods plus fixed-point iteration methods and semidefinite relation methods are employed, and the optimal solution could be achieved through iterative optimization. Simulation results show that, compared with the traditional spectrum-only cooperation, the proposed two-to-one ESDC paradigms can greatly improve the successful transmission probability for PUs and achievable transmission rate for SU. Meanwhile, the proposed two-to-one dual-cooperation modes are significantly superior to the one-to-one cooperation mode, in terms of spectrum efficiency and energy efficiency. Full article

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors. Full article

Gasoline engine oil (SAE 5W-20) was subjected to thermal oxidization (TO) for four periods of time (0 h, 48 h, 96 h and 144 h) and exposed to THz-time domain spectroscopy (TDS) measurement. Error contributions from various error sources, such as repeatability errors, assembly errors of the probe volume and errors caused by the TDS system were evaluated with respect to discernibility and significance of measurement results. The most significant error source was due to modifications of the TDS setup, causing errors in the range of 0.13% of the refractive index for samples with a refractive index around 1.467 and a probe volume length between 5 and 15 mm at 1 THz. The absorption coefficient error was in the range of 8.49% for an absorption around 0.6 cm⁻¹. While the average of measurements taken with different setup configurations did not yield significant differences for different TO times, a single, fixed setup would be able to discern all investigated oil species across the entire frequency range of 0.5–2.5 THz. The absorption coefficient measurement showed greater discernibility than the measurement of the refractive index. Full article

This study investigates the simple reaction time (SRT) and response time (RT) to a vibrotactile stimulus presented on two body locations at the lower extremity of the foot on different types of surface during walking. We determined RTs while walking on Concrete, Foam, Sand, and gravel surface. Also, for RT, we evaluated two vibrotactile stimulus (VS) locations on the lower extremity: the ankle (AL) and under the foot plantar (FP). A total of 21 young adult participants (n = 21), aged mean 24 ± 2.9 years, took part in a two-session experiment with two main conditions (at rest and while walking on four types of surface). The control session included 2016 repeated measures, with one-way and two-way ANOVA analyses. The findings have consistently revealed slowness of RT to VS, in particular on sand and gravel surface. In addition, we found that body location has a significant effect on RT in certain surfaces. These results showed that RTs increased with environment changes during the performance of dual tasks. Full article

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal—End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support. Full article

We developed an automated approach to differentiate between different types of arrhythmic episodes in electrocardiogram (ECG) signals, because, in real-life scenarios, a software application does not know in advance the type of arrhythmia a patient experiences. Our approach has four main stages: (1) Classification of ventricular fibrillation (VF) versus non-VF segments—including atrial fibrillation (AF), ventricular tachycardia (VT), normal sinus rhythm (NSR), and sinus arrhythmias, such as bigeminy, trigeminy, quadrigeminy, couplet, triplet—using four image-based phase plot features, one frequency domain feature, and the Shannon entropy index. (2) Classification of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize the nonlinearity of the data. (4) Determination of the PVC patterns, if present, to categorize distinct types of sinus arrhythmias and NSR. We used the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, Creighton University’s VT arrhythmia database, the MIT-BIH atrial fibrillation database, and the MIT-BIH malignant ventricular arrhythmia database to test our algorithm. Binary decision tree (BDT) and support vector machine (SVM) classifiers were used in both stage 1 and stage 3. We also compared our proposed algorithm’s performance to other published algorithms. Our VF detection algorithm was accurate, as in balanced datasets (and unbalanced, in parentheses) it provided an accuracy of 95.1% (97.1%), sensitivity of 94.5% (91.1%), and specificity of 94.2% (98.2%). The AF detection was accurate, as the sensitivity and specificity in balanced datasets (and unbalanced, in parentheses) were found to be 97.8% (98.6%) and 97.21% (97.1%), respectively. Our PVC detection algorithm was also robust, as the accuracy, sensitivity, and specificity were found to be 99% (98.1%), 98.0% (96.2%), and 98.4% (99.4%), respectively, for balanced and (unbalanced) datasets. Full article

Increasing the number of inspection sources creates an opportunity to combine information in order to properly set the operation of the entire system, not only in terms of such factors as reliability, confidence, or accuracy, but inspection time as well. In this paper, a magnetic sensor-array-based nondestructive system was applied to inspect defects inside circular-shaped steel elements. The experiments were carried out for various sensor network strategies, followed by the fusion of multisensor data for each case. In order to combine the measurements, first data registration and then four algorithms based on spatial and transformed representations of sensor signals were applied. In the case of spatial representation, the data were combined using an algorithm operating directly on input signals, allowing pooling of information. To build the transformed representation, a multiresolution analysis based on the Laplacian pyramid was used. Finally, the quality of the obtained results was assessed. The details of algorithms are given and the results are presented and discussed. It is shown that the application of data fusion rules for magnetic multisensor inspection systems can result in the growth of reliability of proper identification and classification of defects in steel elements depending on the utilized configuration of the sensor network. Full article

Nowadays, the preservation, maintenance, rehabilitation, and improvement of road networks are key issues. Pavement condition is highly affected by environmental factors such as temperature and humidity, hence the importance of building databases enriched with real-time information from monitoring systems that enable the analysis and modeling of the road properties. Information and communication technologies, and specifically wireless sensor networks and computational intelligence methods, are enabling the design of new monitoring systems. The main goal of this work is the design of a pavement monitoring system for measuring temperature at internal layers. The proposed solution is based on low-cost and robust temperature sensors, vehicle-to-infrastructure communications, allowing one to transmit information directly from probes to a moving auscultation vehicle, and a neural network-based model for prediction pavement temperature. User requirements drive probes’ design to a modular device, with easy installation, low cost, and reduced energy consumption. Results of the test and validation experiments show both the benefits and viability of the proposed system, which reflect in an accuracy improvement and reduction in routine test duration. Finally, data collected over a year is applied to assess the performance of BELLS3 models and the suggested neural network for predicting pavement temperature. The dynamic behavior of the predicted temperature and the mean absolute error of the neural network-based model are better than the BELL3 model, demonstrating the suitability of the proposed pavement monitoring system. Full article

With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision. Full article

Debonding defects are common and they are the main reason for the failure of hidden frame supported glass curtain walls, which are widely used as an external enclosure and decorative structure. In this paper, a debonding detection method for hidden frame supported glass curtain walls is developed based on nonlinear ultrasonic modulation and piezoceramic transducers. First, the excitation frequency was determined according to the response characteristics. Then, empirical mode decomposition (EMD) was applied to extract the feature components. After discrete Fourier transform (DFT), the nonlinear coefficients were calculated to evaluate the debonding defect. Finally, the experimental setup was established and a series of experiments were carried out to verify the feasibility and effectiveness of the nonlinear ultrasonic modulation method. The nonlinear harmonics detection method was also investigated and it was compared with the nonlinear ultrasonic modulation method. The detection effect at different temperatures and impact were studied. The results showed that the nonlinear coefficient increases with the debonding length. The mean squared error (MSE) of the nonlinear ultrasonic modulation method was improved by 41% compared with the nonlinear harmonics method. The nonlinear ultrasonic modulation method can successfully detect debonding defects in hidden frame supported glass curtain walls at different temperatures and impact. Full article

One potential method to estimate noninvasive cuffless blood pressure (BP) is through measurement of pulse wave velocity (PWV), which can be characterized by measuring the distance and the transit time of the pulse between two arterial sites. To obtain the pulse waveform, bioimpedance (BI) measurement is a promising approach because it continuously reflects the change in BP through the change in the arterial cross-sectional area. Several studies have investigated BI channels in a vertical direction with electrodes located along the wrist and the finger to calculate PWV and convert to BP; however, the measurement systems were relatively large in size. In order to reduce the total device size for use in a PWV-based BP smartwatch, this study proposes and examines a horizontal BI structure. The BI device is also designed to apply in a very small body area. Our proposed structure is based on two sets of four-electrode BI interface attached around the wrist. The effectiveness of our system and approach is evaluated on 15 human subjects; the PWV values are obtained with various distances between two BI channels to assess the efficacy. The results show that our BI system can monitor pulse rate efficiently in only a 0.5 × 1.75 cm2 area of the body. The correlation of pulse rate from the proposed design against the reference is 0.98 ± 0.07 (p < 0.001). Our structure yields higher detection ratios for PWV measurements of 99.0 ± 2.2%, 99.0 ± 2.1%, and 94.8 ± 3.7% at 1, 2, and 3 cm between two BI channels, respectively. The measured PWVs correlate well with the BP standard device at 0.81 ± 0.08 and 0.84 ± 0.07 with low root-mean-squared-errors at 7.47 ± 2.15 mmHg and 5.17 ± 1.81 mmHg for SBP and DBP, respectively. Our results inform future designs of smart watches capable of measuring blood pressure. Full article

Various watermarking algorithms have been studied to better enable the copyright protection of remote sensing images. The robustness of such algorithms against image cropping attacks has subsequently been verified mainly by various experiments. However, to date, the experimental results are subject to the differences in experimental factors and computational resource costs. Hence, the study presented in this paper proposes a robustness computation model of watermarking remote sensing images in terms of the image cropping attack. The robustness computation model consists of three parts: analysis principles, an evaluation index, and a computation method. The robustness analysis principles are provided based on the salient features of watermarking remote sensing images and attacking properties. A probability-based evaluation index is then defined to more comprehensively measure the robustness of different algorithms. The computation method developed in this study is based on permutations and the inclusion-exclusion principle to theoretically calculate robustness. The experiments conducted to verify the effectiveness of the computation model, revealed true closeness between both the calculated and experimental results. Finally, the relationships between the robustness and the different parameters used in the watermarking algorithms are investigated by using the proposed computation model. Full article

Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is often laborious and costly in practice, whereas there are abundant unlabeled samples which also imply health information of bearings. Thus, it is worthwhile to develop semi-supervised methods of fault diagnosis to make effective use of the plentiful unlabeled samples. Nevertheless, considering the normal data are much more than the faulty ones, the problem of imbalanced data exists among unlabeled samples for fault diagnosis. Besides, in practice, bearings often work under uncertain and variable operation conditions, which would also have negative influence on fault diagnosis. To solve these issues, a novel hybrid method for bearing fault diagnosis is proposed in this paper: (1) Inspired by visibility graph, a novel fault feature extraction method named visibility graph feature (VGF) is proposed. The obtained features by VGF are natively insensitive to variable conditions, which has been validated by a simulation experiment in this paper; (2) On basis of VGF, to deal with imbalanced unlabeled data, graph-based rebalance semi-supervised learning (GRSSL) for fault diagnosis is proposed. In GRSSL, a graph based on a weighted sparse adjacency matrix is constructed by the k-nearest neighbors and Gaussian Kernel weighting algorithm by means of the samples. Then, a bivariate cost function over classification and normalized label variable is built up to rebalance the importance of labels. Finally, the proposed VGF-GRSSL method was verified by data collected from Case Western Reserve University Bearing Data Center. The experiment results show that the proposed method of bearing fault diagnosis performs effectively to deal with the imbalanced unlabeled data under variable conditions. Full article

Transparent conducting oxides (TCOs) have appeared in the past few years as potential plasmonic materials for the development of optical devices in the near infrared regime (NIR). However, the performance of biosensors with TCOs has been limited in sensitivity and figure of merit (FOM). To improve the performance of the biosensors with TCOs, a biosensor based on long-range surface plasmon with Ga-doped zinc oxide (GZO) is proposed. It is shown that a larger FOM with a 2~7 times enhancement compared to the traditional surface plasmon polaritons (SPPs) sensor and higher detection accuracy (DA) can be realized in our proposed sensor compared with the surface plasmon resonance (SPR) sensor with GZO. Therefore, this sensor can be used to detect biological activity or chemical reactions in the near infrared region. Full article

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities. Full article

With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor route planning, in this paper, we focus on the connections inside buildings and propose a data model, which uses OSM primitives (nodes, ways and relations) and tags to capture horizontal and vertical indoor components, as well as the connection between indoor and outdoor environments. A set of new approaches is developed to support indoor modeling and mapping. Based on the proposed data model, we present a workflow that enables automatic generation of a routing graph and provide an algorithm to calculate integrated indoor-outdoor routes. We applied our data model to a set of test cases. The application results demonstrate the capability of our data model in modeling built environments and its feasibility for the integration of indoor and outdoor navigation. Full article

Placing the needle inside the epidural space for locoregional anesthesia is a challenging procedure, which even today is left to the expertise of the operator. Recently, we have demonstrated that the use of optically sensorized needles significantly improves the effectiveness of this procedure. Here, we propose an optimized configuration, where the optical fiber strain sensor is directly integrated inside the epidural catheter. The new design allows the solving of the biocompatibility issues and increases the versatility of the former configuration. Through an in vivo study carried out on a porcine model, we confirm the reliability of our approach, which also opens the way to catheter monitoring during insertion inside biological spaces. Full article