The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate, and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s, and it has been applied in many fields. For example, manufacturing and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this special issue is to report innovative ideas and solutions for multi-sensor information fusion in the emerging applications era, focusing on development, adoption, and applications. Full article

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH₄, CO, SO₂, and H₂S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R² and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. Full article

The investigation of planar coils of differing topologies, when combined with a magnetostrictive amorphous ribbon to form a stress-sensitive self-inductor, is an active research area for applications as stress or pressure sensors. Four topologies of planar coil (Circular, Mesh, Meander, and Square) have been constructed using copper track on 30 mm wide PCB substrate. The coils are energized to draw 0.4 A and the resulting magnetic field distribution is observed with a newly developed three-dimensional magnetic field scanner. The system is based on a variably angled Micromagnetics^® STJ-020 tunneling magneto-resistance sensor with a spatial resolution of 5–10 µm and sensitivity to fields of less than 10 A/m. These experimental results are compared with the fields computed by ANSYS Maxwell^® finite element modelling of the same topologies. Measured field shape and strength correspond well with the results of modelling, including direct observation of corner and edge effects. Three-dimensional analysis of the field shape produced by the square coil, isolating the components H_(x) and H_(z), is compared with the three-dimensional field solutions from modelling. The finite element modelling is validated and the accuracy and utility of the new system for three-dimensional scanning of general stray fields is confirmed. Full article

A novel fiber-optic based earth pressure sensor (FPS) with an adjustable measurement range and high sensitivity is developed to measure earth pressures for civil infrastructures. The new FPS combines a cantilever beam with fiber Bragg grating (FBG) sensors and a flexible membrane. Compared with a traditional pressure transducer with a dual diaphragm design, the proposed FPS has a larger measurement range and shows high accuracy. The working principles, parameter design, fabrication methods, and laboratory calibration tests are explained in this paper. A theoretical solution is derived to obtain the relationship between the applied pressure and strain of the FBG sensors. In addition, a finite element model is established to analyze the mechanical behavior of the membrane and the cantilever beam and thereby obtain optimal parameters. The cantilever beam is 40 mm long, 15 mm wide, and 1 mm thick. The whole FPS has a diameter of 100 mm and a thickness of 30 mm. The sensitivity of the FPS is 0.104 kPa/με. In addition, automatic temperature compensation can be achieved. The FPS’s sensitivity, physical properties, and response to applied pressure are extensively examined through modeling and experiments. The results show that the proposed FPS has numerous potential applications in soil pressure measurement. Full article

Automatic component detection of spacecraft can assist in on-orbit operation and space situational awareness. Spacecraft are generally composed of solar panels and cuboidal or cylindrical modules. These components can be simply represented by geometric primitives like plane, cuboid and cylinder. Based on this prior, we propose a robust automatic detection scheme to automatically detect such basic components of spacecraft in three-dimensional (3D) point clouds. In the proposed scheme, cylinders are first detected in the iteration of the energy-based geometric model fitting and cylinder parameter estimation. Then, planes are detected by Hough transform and further described as bounded patches with their minimum bounding rectangles. Finally, the cuboids are detected with pair-wise geometry relations from the detected patches. After successive detection of cylinders, planar patches and cuboids, a mid-level geometry representation of the spacecraft can be delivered. We tested the proposed component detection scheme on spacecraft 3D point clouds synthesized by computer-aided design (CAD) models and those recovered by image-based reconstruction, respectively. Experimental results illustrate that the proposed scheme can detect the basic geometric components effectively and has fine robustness against noise and point distribution density. Full article

Because little is known about the atomization theory of a micro-tapered aperture atomizer, we investigated the vibration characteristics of this type of atomizer. The atomization mechanism of a micro-tapered aperture atomizer was described, and the atomization rate equation was deduced. As observed via microscopy, the angle of the micro-tapered aperture changes with the applied voltage, which proved the existence of a dynamic cone angle. The forward and reverse atomization rates were measured at various voltages, and the influence of the micro-tapered aperture and its variation on the atomization rate was characterized. The resonance frequency of the piezoelectric vibrator was obtained using a laser vibrometer, and the atomization rates were measured at each resonance frequency. From experiments, we found that the atomization rates at the first five resonance frequencies increased as the working frequency increased. At the fifth resonance frequency (121.1 kHz), the atomization rate was maximized (0.561 mL/min), and at the sixth resonance frequency (148.3 kHz), the atomization rate decreased significantly (0.198 mL/min). The experimental results show that the vibration characteristics of the piezoelectric vibrator have a relatively strong impact on the atomization rate. This research is expected to contribute to the manufacture of micro-tapered aperture atomizers. Full article

In a group of general geometric primitives, plane-based features are widely used for indoor localization because of their robustness against noises. However, a lack of linearly independent planes may lead to a non-trivial estimation. This in return can cause a degenerate state from which all states cannot be estimated. To solve this problem, this paper first proposed a degeneracy detection method. A compensation method that could fix orientations by projecting an inertial measurement unit’s (IMU) information was then explained. Experiments were conducted using an IMU-Kinect v2 integrated sensor system prone to fall into degenerate cases owing to its narrow field-of-view. Results showed that the proposed framework could enhance map accuracy by successful detection and compensation of degenerated orientations. Full article

In this paper, a novel time-frequency signature using resonance-based sparse signal decomposition (RSSD), phase space reconstruction (PSR), time-frequency distribution (TFD) and manifold learning is proposed for feature extraction of ship-radiated noise, which is called resonance-based time-frequency manifold (RTFM). This is suitable for analyzing signals with oscillatory, non-stationary and non-linear characteristics in a situation of serious noise pollution. Unlike the traditional methods which are sensitive to noise and just consider one side of oscillatory, non-stationary and non-linear characteristics, the proposed RTFM can provide the intact feature signature of all these characteristics in the form of a time-frequency signature by the following steps: first, RSSD is employed on the raw signal to extract the high-oscillatory component and abandon the low-oscillatory component. Second, PSR is performed on the high-oscillatory component to map the one-dimensional signal to the high-dimensional phase space. Third, TFD is employed to reveal non-stationary information in the phase space. Finally, manifold learning is applied to the TFDs to fetch the intrinsic non-linear manifold. A proportional addition of the top two RTFMs is adopted to produce the improved RTFM signature. All of the case studies are validated on real audio recordings of ship-radiated noise. Case studies of ship-radiated noise on different datasets and various degrees of noise pollution manifest the effectiveness and robustness of the proposed method. Full article

A constrained least-squares (CLS) 3D source localization method is presented for acoustic sensor networks with sensor position errors. The proposed approach uses angles of arrivals (AOAs) and gain ratios of arrival (GROAs) measured simultaneously at each node to estimate the source position jointly. Compared to AOA-only localization methods, the GROAs can be used in conjunction with AOA measurements so as to get more accurate results by exploiting the geometrical relationship between these two measurements. Compared to time difference of arrival localization methods, the proposed algorithm does not require accurate time synchronization over different nodes. The theoretical mean-square error matrices of the proposed approach are derived and they are exactly equal to the Cramér–Rao bound for Gaussian noise under the small error condition. Simulations validate the performance of the proposed estimator. Full article

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems. Full article

In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context. Full article

The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination. Full article

Spotlight synthetic aperture radar (SAR) is a proven technique, which can provide high-resolution images as compared to those produced by traditional stripmap SAR. This paper addresses a high-resolution SAR focusing experiment based on Gaofen-3 satellite (GF-3) staring data with about 55 cm azimuth resolution and 240 MHz range bandwidth. In staring spotlight (ST) mode, the antenna always illuminates the same scene on the ground, which can extend the synthetic aperture. Based on a two-step processing algorithm, some special aspects such as curved-orbit model error correction, stop-and-go correction, and antenna pattern demodulation must be considered in image focusing. We provide detailed descriptions of all these aspects and put forward corresponding solutions. Using these suggested methods directly in an imaging module without any modification for other data processing software can make the most of the existing ground data processor. Finally, actual data acquired in GF-3 ST mode is used to validate these methodologies, and a well-focused, high-resolution image is obtained as a result of this focusing experiment. Full article

The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ${}^{\circledR }$ camera, orthorectified in Headwall SpectralView ${}^{\circledR }$ , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools. Full article

In this paper we present a multi-sensor floating system designed to monitor marine energy parameters, in order to sample wind, wave, and marine current energy resources. For this purpose, a set of dedicated sensors to measure the height and period of the waves, wind, and marine current intensity and direction have been selected and installed in the system. The floating device incorporates wind and marine current turbines for renewable energy self-consumption and to carry out complementary studies on the stability of such a system. The feasibility, safety, sensor communications, and buoy stability of the floating device have been successfully checked in real operating conditions. Full article

Human tau protein is one of the most advanced and accepted biomarkers for AD and tauopathies diagnosis in general. In this work, a quartz crystal balance (QCM) immunosensor was developed for the detection of human tau protein in buffer and artificial cerebrospinal fluid (aCSF), through both direct and sandwich assays. Starting from a conventional immuno-based sandwich strategy, two monoclonal antibodies recognizing different epitopes of tau protein were used, achieving a detection limit for the direct assay in nanomolar range both in HBES-EP and aCSF. Afterward, for exploring alternative specific receptors as secondary recognition elements for tau protein biosensing, we tested tubulin and compared its behavior to a conventional secondary antibody in the sandwich assay. Tau–tubulin binding has shown an extended working range coupled to a signal improvement in comparison with the conventional secondary antibody-based approach, showing a dose–response trend at lower tau concentration than is usually investigated and closer to the physiological levels in the reference matrix for protein tau biomarker. Our results open up new and encouraging perspectives for the use of tubulin as an alternative receptor for tau protein with interesting features due to the possibility of taking advantage of its polymerization and reversible binding to this key hallmark of Alzheimer’s disease. Full article

Equivalent circuits of piezoelectric structures such as bimorphs and unimorphs conventionally focus on the bending vibration modes. However, the longitudinal vibration modes are rarely considered even though they also play a remarkable role in piezoelectric devices. Losses, especially elastic loss in the metal substrate, are also generally neglected, which leads to discrepancies compared with experiments. In this paper, a novel equivalent circuit with four kinds of losses is proposed for a beamlike piezoelectric structure under the longitudinal vibration mode. This structure consists of a slender beam as the metal substrate, and a piezoelectric patch which covers a partial length of the beam. In this approach, first, complex numbers are used to deal with four kinds of losses—elastic loss in the metal substrate, and piezoelectric, dielectric, and elastic losses in the piezoelectric patch. Next in this approach, based on Mason’s model, a new equivalent circuit is developed. Using MATLAB, impedance curves of this structure are simulated by the equivalent circuit method. Experiments are conducted and good agreements are revealed between experiments and equivalent circuit results. It is indicated that the introduction of four losses in an equivalent circuit can increase the result accuracy considerably. Full article

In this work, SnO₂ nanoflowers synthesized by a hydrothermal method were employed as hydrogen sensing materials. The as-synthesized SnO₂ nanoflowers consisted of cuboid-like SnO₂ nanorods with tetragonal structures. A great increase in the relative content of surface-adsorbed oxygen was observed after the vacuum annealing treatment, and this increase could have been due to the increase in surface oxygen vacancies serving as preferential adsorption sites for oxygen species. Annealing treatment resulted in an 8% increase in the specific surface area of the samples. Moreover, the conductivity of the sensors decreased after the annealing treatment, which should be attributed to the increase in electron scattering around the defects and the compensated donor behavior of the oxygen vacancies due to the surface oxygen adsorption. The hydrogen sensors of the annealed samples, compared to those of the unannealed samples, exhibited a much higher sensitivity and faster response rate. The sensor response factor and response rate increased from 27.1% to 80.2% and 0.34%/s to 1.15%/s, respectively. This remarkable enhancement in sensing performance induced by the annealing treatment could be attributed to the larger specific surface areas and higher amount of surface-adsorbed oxygen, which provides a greater reaction space for hydrogen. Moreover, the sensors with annealed SnO₂ nanoflowers also exhibited high selectivity towards hydrogen against CH₄, CO, and ethanol. Full article

There exists a common problem in the localization of underwater control networks that the precision of the absolute coordinates of known points obtained by marine absolute measurement is poor, and it seriously affects the precision of the whole network in traditional constraint adjustment. Therefore, considering that the precision of underwater baselines is good, we use it to carry out quasi-stable adjustment to amend known points before constraint adjustment so that the points fit the network shape better. In addition, we add unconstrained adjustment for quality control of underwater baselines, the observations of quasi-stable adjustment and constrained adjustment, to eliminate the unqualified baselines and improve the results’ accuracy of the two adjustments. Finally, the modified method is applied to a practical LBL (Long Baseline) experiment and obtains a mean point location precision of 0.08 m, which improves by 38% compared with the traditional method. Full article

For micromotion scatterers with small rotating radii, the micro-Doppler (m-D) effect interferes with cross-range compression in inverse synthetic aperture radar (ISAR) imaging and leads to a blurred main body image. In this paper, a novel method is proposed to remove the m-D effect by promoting the joint sparsity in the time-frequency domain. Firstly, to obtain the time-frequency representations of the limited measurements, the short-time Fourier transform (STFT) was modelled by an underdetermined equation. Then, a new objective function was used to measure the joint sparsity of the STFT entries so that the joint sparse recovery problem could be formulated as a constrained minimization problem. Similar to the smoothed $l_0$ (SL0) algorithm, a steepest descend approach was used to minimize the new objective function, where the projection step was tailored to make it suitable for m-D effect removal. Finally, we utilized the recovered STFT entries to obtain the main body echoes, based on which cross-range compression could be realized without m-D interference. After all contaminated range cells were processed by the proposed method, a clear main body image could be achieved. Experiments using both the point-scattering model and electromagnetic (EM) computation validated the performance of the proposed method. Full article

Underwater acoustic target recognition based on ship-radiated noise belongs to the small-sample-size recognition problems. A competitive deep-belief network is proposed to learn features with more discriminative information from labeled and unlabeled samples. The proposed model consists of four stages: (1) A standard restricted Boltzmann machine is pretrained using a large number of unlabeled data to initialize its parameters; (2) the hidden units are grouped according to categories, which provides an initial clustering model for competitive learning; (3) competitive training and back-propagation algorithms are used to update the parameters to accomplish the task of clustering; (4) by applying layer-wise training and supervised fine-tuning, a deep neural network is built to obtain features. Experimental results show that the proposed method can achieve classification accuracy of 90.89%, which is 8.95% higher than the accuracy obtained by the compared methods. In addition, the highest accuracy of our method is obtained with fewer features than other methods. Full article

The design and validation of a continuously stretchable and flexible skin sensor for collaborative robotic applications is outlined. The skin consists of a PDMS skin doped with Carbon Nanotubes and the addition of conductive fabric, connected by only five wires to a simple microcontroller. The accuracy is characterized in position as well as force, and the skin is also tested under uniaxial stretch. There are also two examples of practical implementations in collaborative robotic applications. The stationary position estimate has an RMSE of $7.02$ mm, and the sensor error stays within $2.5\pm 1.5$ mm even under stretch. The skin consistently provides an emergency stop command at only $0.5$ N of force and is shown to maintain a collaboration force of 10 N in a collaborative control experiment. Full article

In this paper, we present a work based on the computational load distribution among the homogeneous nodes and the Hub/Sink of Wireless Sensor Networks (WSNs). The main contribution of the paper is an early decision support framework helping WSN designers to take decisions about computational load distribution for those WSNs where power consumption is a key issue (when we refer to “framework” in this work, we are considering it as a support tool to make decisions where the executive judgment can be included along with the set of mathematical tools of the WSN designer; this work shows the need to include the load distribution as an integral component of the WSN system for making early decisions regarding energy consumption). The framework takes advantage of the idea that balancing sensors nodes and Hub/Sink computational load can lead to improved energy consumption for the whole or at least the battery-powered nodes of the WSN. The approach is not trivial and it takes into account related issues such as the required data distribution, nodes, and Hub/Sink connectivity and availability due to their connectivity features and duty-cycle. For a practical demonstration, the proposed framework is applied to an agriculture case study, a sector very relevant in our region. In this kind of rural context, distances, low costs due to vegetable selling prices and the lack of continuous power supplies may lead to viable or inviable sensing solutions for the farmers. The proposed framework systematize and facilitates WSN designers the required complex calculations taking into account the most relevant variables regarding power consumption, avoiding full/partial/prototype implementations, and measurements of different computational load distribution potential solutions for a specific WSN. Full article

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications. Full article

In this paper, we fabricated p-Co₃O₄/n-TiO₂ heterostructures and investigated their gas sensing properties. The structural and morphological characterization were performed by scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy analysis (XPS). The electrical properties of the heterostructure were studied within the temperature range from 293 K to 423 K. Changes in electrical properties and sensing behavior against reducing and oxidizing gases were attributed to the formation of p–n heterojunctions at the Co₃O₄ and TiO₂ interface. In comparison with sensing performed with pristine TiO₂ nanotubes (NTs), a significant improvement in H₂ sensing at 200 °C was observed, while the sensing response against NO₂ decreased for the heterostructures. Additionally, a response against toluene gas, in contrast to pristine TiO₂ NTs, appeared in the Co₃O₄/TiO₂ heterostructure samples. Full article

Because aggressive driving often causes large-scale loss of life and property, techniques for advance detection of adverse driver emotional states have become important for the prevention of aggressive driving behaviors. Previous studies have primarily focused on systems for detecting aggressive driver emotion via smart-phone accelerometers and gyro-sensors, or they focused on methods of detecting physiological signals using electroencephalography (EEG) or electrocardiogram (ECG) sensors. Because EEG and ECG sensors cause discomfort to drivers and can be detached from the driver’s body, it becomes difficult to focus on bio-signals to determine their emotional state. Gyro-sensors and accelerometers depend on the performance of GPS receivers and cannot be used in areas where GPS signals are blocked. Moreover, if driving on a mountain road with many quick turns, a driver’s emotional state can easily be misrecognized as that of an aggressive driver. To resolve these problems, we propose a convolutional neural network (CNN)-based method of detecting emotion to identify aggressive driving using input images of the driver’s face, obtained using near-infrared (NIR) light and thermal camera sensors. In this research, we conducted an experiment using our own database, which provides a high classification accuracy for detecting driver emotion leading to either aggressive or smooth (i.e., relaxed) driving. Our proposed method demonstrates better performance than existing methods. Full article

An In₂O₃/ITO thin film thermocouple was prepared via screen printing. Glass additives were added to improve the sintering process and to increase the density of the In₂O₃/ITO films. The surface and cross-sectional images indicate that both the grain size and densification of the ITO and In₂O₃ films increased with the increase in annealing time. The thermoelectric voltage of the In₂O₃/ITO thermocouple was 53.5 mV at 1270 °C at the hot junction. The average Seebeck coefficient of the thermocouple was calculated as 44.5 μV/°C. The drift rate of the In₂O₃/ITO thermocouple was 5.44 °C/h at a measuring time of 10 h at 1270 °C. Full article

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market. Full article

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. Full article

It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63–87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6–89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network. Full article

The arc welding process is widely used in industry but its automatic control is limited by the difficulty in measuring the weld bead geometry and closing the control loop on the arc, which has adverse environmental conditions. To address this problem, this work proposes a system to capture the welding variables and send stimuli to the Gas Metal Arc Welding (GMAW) conventional process with a constant voltage power source, which allows weld bead geometry estimation with an open-loop control. Dynamic models of depth and width estimators of the weld bead are implemented based on the fusion of thermographic data, welding current and welding voltage in a multilayer perceptron neural network. The estimators were trained and validated off-line with data from a novel algorithm developed to extract the features of the infrared image, a laser profilometer was implemented to measure the bead dimensions and an image processing algorithm that measures depth by making a longitudinal cut in the weld bead. These estimators are optimized for embedded devices and real-time processing and were implemented on a Field-Programmable Gate Array (FPGA) device. Experiments to collect data, train and validate the estimators are presented and discussed. The results show that the proposed method is useful in industrial and research environments. Full article

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage. Full article

Strong fluorescent carbon nanodots (FCNs) were synthesized with a green approach using gardenia as a carbon source through a one-step hydrothermal method. FCNs were characterized by their UV-vis absorption spectra, photoluminescence (PL), Fourier transform infrared spectroscopy (FTIR) as well as X-ray photoelectron spectroscopy (XPS). We further explored the use of as-synthesized FCNs as an effective probe for the detection of metronidazole (MNZ), which is based on MNZ-induced fluorescence quenching of FCNs. The proposed method displayed a wide linear range from 0.8 to 225.0 µM with a correlation coefficient of 0.9992 and a limit of detection as low as 279 nM. It was successfully applied to the determination of MNZ in commercial tablets and rabbit plasma with excellent sensitivity and selectivity, which indicates its potential applications in clinical analysis and biologically related studies. Full article

In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naïve approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space. Full article

In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as “newly built”, “taller”, “demolished”, and “lower” by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm. Full article

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. Full article

In this paper, we compare six known linear distributed average consensus algorithms on a sensor network in terms of convergence time (and therefore, in terms of the number of transmissions required). The selected network topologies for the analysis (comparison) are the cycle and the path. Specifically, in the present paper, we compute closed-form expressions for the convergence time of four known deterministic algorithms and closed-form bounds for the convergence time of two known randomized algorithms on cycles and paths. Moreover, we also compute a closed-form expression for the convergence time of the fastest deterministic algorithm considered on grids. Full article

Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a representative green grape named Centennial Seedless as the research object, daytime and night-time grape images were captured by a custom-designed visual system. Detection was conducted employing the following steps: (1) The RGB (red, green and blue). Color model was determined for night-time green grape detection through analysis of color features of grape images under daytime natural light and night-time artificial lighting. The R component of the RGB color model was rotated and the image resolution was compressed; (2) The improved Chan–Vese (C–V) level set model and morphological processing method were used to remove the background of the image, leaving out the grape fruit; (3) Based on the character of grape vertical suspension, combining the principle of the minimum circumscribed rectangle of fruit and the Hough straight line detection method, straight-line fitting for the fruit stem was conducted and the picking point was calculated using the stem with an angle of fitting line and vertical line less than 15°. The visual detection experiment results showed that the accuracy of grape fruit detection was 91.67% and the average running time of the proposed algorithm was 0.46 s. The picking-point calculation experiment results showed that the highest accuracy for the picking-point calculation was 92.5%, while the lowest was 80%. The results demonstrate that the proposed method of night-time green grape detection and picking-point calculation can provide technical support to the grape-picking robots. Full article

The measurement of static vertical deflections on bridges continues to be a first-level technological challenge. These data are of great interest, especially for the case of long-term bridge monitoring; in fact, they are perhaps more valuable than any other measurable parameter. This is because material degradation processes and changes of the mechanical properties of the structure due to aging (for example creep and shrinkage in concrete bridges) have a direct impact on the exhibited static vertical deflections. This paper introduces and evaluates an approach to monitor displacements and rotations of structures using a novel laser and video-based displacement transducer (LVBDT). The proposed system combines the use of laser beams, LED lights, and a digital video camera, and was especially designed to capture static and slow-varying displacements. Contrary to other video-based approaches, the camera is located on the bridge, hence allowing to capture displacements at one location. Subsequently, the sensing approach and the procedure to estimate displacements and the rotations are described. Additionally, laboratory and in-service field testing carried out to validate the system are presented and discussed. The results demonstrate that the proposed sensing approach is robust, accurate, and reliable, and also inexpensive, which are essential for field implementation. Full article

Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics of STS performance by non-invasive in vitro detection and to nondestructively estimate the muscle contributions to STS training with different loadings, a wearable sensor system was developed with ground reaction force (GRF) platforms, motion capture inertial sensors and electromyography (EMG) sensors. To estimate the internal moments of hip, knee and ankle joints and quantify the contributions of individual muscle and gravity to STS movement, the inverse dynamics analysis on a simplified STS biomechanical model with external loading is proposed. The functional roles of the lower limb individual muscles (rectus femoris (RF), gluteus maximus (GM), vastus lateralis (VL), tibialis anterior (TA) and gastrocnemius (GAST)) during STS motion and the mechanism of the muscles’ synergies to perform STS-specific subtasks were analyzed. The muscle contributions to the biomechanical STS subtasks of vertical propulsion, anteroposterior (AP) braking and propulsion for body balance in the sagittal plane were quantified by experimental studies with EMG, kinematic and kinetic data. Full article

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid. Full article

In this paper, we present a new mobile wireless communication platform for real-time monitoring of an individual’s breathing rate. The platform takes the form of a wearable stretching T-shirt featuring a sensor and a detection base station. The sensor is formed by a spiral-shaped antenna made from a multi-material fiber connected to a compact transmitter. Based on the resonance frequency of the antenna at approximately 2.4 GHz, the breathing sensor relies on its Bluetooth transmitter. The contactless and non-invasive sensor is designed without compromising the user’s comfort. The sensing mechanism of the system is based on the detection of the signal amplitude transmitted wirelessly by the sensor, which is found to be sensitive to strain. We demonstrate the capability of the platform to detect the breathing rates of four male volunteers who are not in movement. The breathing pattern is obtained through the received signal strength indicator (RSSI) which is filtered and analyzed with home-made algorithms in the portable system. Numerical simulations of human breath are performed to support the experimental detection, and both results are in a good agreement. Slow, fast, regular, irregular, and shallow breathing types are successfully recorded within a frequency interval of 0.16–1.2 Hz, leading to a breathing rate varying from 10 to 72 breaths per minute. Full article

The development of information technology has paved the way for faster and more convenient payment process flows and new methodology for the design and implementation of next generation payment systems. The growth of smartphone usage nowadays has fostered a new and popular mobile payment environment. Most of the current generation smartphones support Bluetooth Low Energy (BLE) technology to communicate with nearby BLE-enabled devices. It is plausible to construct an Over-the-Air BLE-based mobile payment system as one of the payment methods for people living in modern societies. In this paper, a secure indoor positioning-based mobile payment authentication protocol with BLE technology and the corresponding mobile payment system design are proposed. The proposed protocol consists of three phases: initialization phase, session key construction phase, and authentication phase. When a customer moves toward the POS counter area, the proposed mobile payment system will automatically detect the position of the customer to confirm whether the customer is ready for the checkout process. Once the system has identified the customer is standing within the payment-enabled area, the payment system will invoke authentication process between POS and the customer’s smartphone through BLE communication channel to generate a secure session key and establish an authenticated communication session to perform the payment transaction accordingly. A prototype is implemented to assess the performance of the proposed design for mobile payment system. In addition, security analysis is conducted to evaluate the security strength of the proposed protocol. Full article

Normally the olive oil quality is assessed by chemical analysis according to international standards. These norms define chemical and organoleptic markers, and depending on the markers, the olive oil can be labelled as lampante, virgin, or extra virgin olive oil (EVOO), the last being an indicator of top quality. The polyphenol content is related to EVOO organoleptic features, and different scientific works have studied the positive influence that these compounds have on human health. The works carried out in this paper are focused on studying relations between the polyphenol content in olive oil samples and its spectral response in the near infrared spectra. In this context, several acquisition parameters have been assessed to optimize the measurement process within the virgin olive oil production process. The best regression model reached a mean error value of 156.14 mg/kg in leave one out cross validation, and the higher regression coefficient was 0.81 through holdout validation. Full article

In the distributed optical fiber sensing (DOFS) domain, simultaneous measurement of vibration and temperature/strain based on Rayleigh scattering and Brillouin scattering in fiber could have wide applications. However, there are certain challenges for the case of ultra-long sensing range, including the interplay of different scattering mechanisms, the interaction of two types of sensing signals, and the competition of pump power. In this paper, a hybrid DOFS system, which can simultaneously measure temperature/strain and vibration over 150 km, is elaborately designed via integrating the Brillouin optical time-domain analyzer (BOTDA) and phase-sensitive optical time-domain reflectometry (Ф-OTDR). Distributed Raman and Brillouin amplifications, frequency division multiplexing (FDM), wavelength division multiplexing (WDM), and time division multiplexing (TDM) are delicately fused to accommodate ultra-long-distance BOTDA and Ф-OTDR. Consequently, the sensing range of the hybrid system is 150.62 km, and the spatial resolution of BOTDA and Ф-OTDR are 9 m and 30 m, respectively. The measurement uncertainty of the BOTDA is ± 0.82 MHz. To the best of our knowledge, this is the first time that such hybrid DOFS is realized with a hundred-kilometer length scale. Full article

A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion gain pixels have emerged, they still suffer from huge RTS noise, which is inherent to the CISs. The proposed method utilizes a multi-aperture (MA) camera that is composed of multiple sets of an SPCL CIS and a moderately fast and compact imaging lens to emulate a very fast single lens. Due to the redundancy of the MA camera, the RTS noise is removed by the maximum likelihood estimation where noise characteristics are modeled by the probability density distribution. In the proposed method, the photon shot noise is also relatively reduced because of the averaging effect, where the pixel values of all the multiple apertures are considered. An extremely low-light condition that the maximum number of electrons per aperture was the only 2 ${\mathrm{e}}^{-}$ was simulated. PSNRs of a test image for simple averaging, selective averaging (our previous method), and the proposed method were 11.92 dB, 11.61 dB, and 13.14 dB, respectively. The selective averaging, which can remove RTS noise, was worse than the simple averaging because it ignores the pixels with RTS noise and photon shot noise was less improved. The simulation results showed that the proposed method provided the best noise reduction performance. Full article

In spaceborne synthetic aperture radar (SAR), moving targets are almost buried in ground clutter due to the wide clutter Doppler spectrum and the restricted pulse repetition frequency (PRF), which increases the difficulty of moving target detection. Clutter suppression is one of the key issues in the spaceborne SAR moving target indicator operation. In this paper, we describe the clutter suppression principle and analyze the influence of amplitude and phase error on clutter suppression. In the following, a novel dual-channel SAR clutter suppression algorithm is proposed, which is suitable for the Gaofen-3(GF-3) SAR sensor. The proposed algorithm consists of three technique steps, namely adaptive two-dimensional (2D) channel calibration, refined amplitude error correction and refined phase error correction. After channel error is corrected by these procedures, the clutter component, especially a strong clutter component, can be well suppressed. The validity of the proposed algorithm is verified by GF-3 SAR real data which demonstrates the ground moving-target indication (GMTI) capability of GF-3 SAR sensor. Full article

In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced to enable switching of ON/OFF and measurement mode after implantation. A wireless power transmission system with a maximum efficiency of 70% and an access distance of up to 5 cm was developed to allow the sensor node to survive for a duration of several weeks from a few minutes’ remote charge. The results of field tests using laboratory mice and a cow indicated the high accuracy of the collected biological data and bio-compatibility of the package. As a result of extensive application of the above technologies, a fully solid wireless pH sensor and a surgical navigation system using artificial magnetic field and a 3D MEMS magnetic sensor are introduced in this paper, and the preliminary experimental results are presented and discussed. Full article

When using distributed optical fiber sensors (DOFS) on reinforced concrete structures, a compromise must be achieved between the protection requirements and robustness of the sensor deployment and the accuracy of the measurements both in the uncracked and cracked stages and under loading, unloading and reloading processes. With this in mind the authors have carried out an experiment where polyimide-coated DOFS were installed on two concrete beams, both embedded in the rebar elements and also bonded to the concrete surface. The specimens were subjected to a three-point load test where after cracking, they are unloaded and reloaded again to assess the capability of the sensor when applied to a real loading scenarios in concrete structures. Rayleigh Optical Frequency Domain Reflectometry (OFDR) was used as the most suitable technique for crack detection in reinforced concrete elements. To verify the reliability and accuracy of the DOFS measurements, additional strain gauges were also installed at three locations along the rebar. The results show the feasibility of using a thin coated polyimide DOFS directly bonded on the reinforcing bar without the need of indention or mechanization. A proposal for a Spectral Shift Quality (SSQ) threshold is also obtained and proposed for future works when using polyimide-coated DOFS bonded to rebars with cyanoacrylate adhesive. Full article

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system. Full article

Scanning micromirrors have been used in a wide range of areas, but many of them do not have position sensing built in, which significantly limits their application space. This paper reports an integrated optoelectronic position sensor (iOE-PS) that can measure the linear displacement and tilting angle of electrothermal MEMS (Micro-electromechanical Systems) scanning mirrors. The iOE-PS integrates a laser diode and its driving circuits, a quadrant photo-detector (QPD) and its readout circuits, and a band-gap reference all on a single chip, and it has been fabricated in a standard 0.5 μm CMOS (Complementary Metal Oxide Semiconductor) process. The footprint of the iOE-PS chip is 5 mm × 5 mm. Each quadrant of the QPD has a photosensitive area of 500 µm × 500 µm and the spacing between adjacent quadrants is 500 μm. The iOE-PS chip is simply packaged underneath of an electrothermally-actuated MEMS mirror. Experimental results show that the iOE-PS has a linear response when the MEMS mirror plate moves vertically between 2.0 mm and 3.0 mm over the iOE-PS chip or scans from −5 to +5°. Such MEMS scanning mirrors integrated with the iOE-PS can greatly reduce the complexity and cost of the MEMS mirrors-enabled modules and systems. Full article

A micro-projection enabled short-range communication (SRC) approach using red-, green- and blue-based light-emitting diodes (RGB-LEDs) has experimentally demonstrated recently that micro-projection and high-speed data transmission can be performed simultaneously. In this research, a reconfigurable design of a polarization modulated image system based on the use of a Liquid Crystal on Silicon based Spatial Light Modulator (LCoS-based SLM) serving as a portable optical terminal capable of micro-projection and bidirectional multi-wavelength communications is proposed and experimentally demonstrated. For the proof of concept, the system performance was evaluated through a bidirectional communication link at a transmission distance over 0.65 m. In order to make the proposed communication system architecture compatible with the data modulation format of future possible wireless communication system, baseband modulation scheme, i.e., Non-Return-to-Zero On-Off-Keying (NRZ_OOK), M-ary Phase Shift Keying (M-PSK) and M-ary Quadrature Amplitude Modulation (M-QAM) were used to investigate the system transmission performance. The experimental results shown that an acceptable BER (satisfying the limitation of Forward Error Correction, FEC standard) and crosstalk can all be achieved in the bidirectional multi-wavelength communication scenario. Full article

In this paper, an analytical method to estimate the complex dielectric constant of liquids is presented. The method is based on the measurement of the transmission coefficient in an embedded microstrip line loaded with a complementary split ring resonator (CSRR), which is etched in the ground plane. From this response, the dielectric constant and loss tangent of the liquid under test (LUT) can be extracted, provided that the CSRR is surrounded by such LUT, and the liquid level extends beyond the region where the electromagnetic fields generated by the CSRR are present. For that purpose, a liquid container acting as a pool is added to the structure. The main advantage of this method, which is validated from the measurement of the complex dielectric constant of olive and castor oil, is that reference samples for calibration are not required. Full article

The linear stepped frequency and linear frequency shift keying (FSK) signal has been widely used in radar systems. However, such linear modulation signals suffer from the range–Doppler coupling that degrades radar multi-target resolution. Moreover, the fixed frequency-hopping or frequency-coded sequence can be easily predicted by the interception receiver in the electronic countermeasures (ECM) environments, which limits radar anti-jamming performance. In addition, the single FSK modulation reduces the radar low probability of intercept (LPI) performance, for it cannot achieve a large time–bandwidth product. To solve such problems, we propose a novel chaos-based stepped frequency (CSF) synthesized wideband signal in this paper. The signal introduces chaotic frequency hopping between the coherent stepped frequency pulses, and adopts a chaotic frequency shift keying (CFSK) and phase shift keying (PSK) composited coded modulation in a subpulse, called CSF-CFSK/PSK. Correspondingly, the processing method for the signal has been proposed. According to our theoretical analyses and the simulations, the proposed signal and processing method achieve better multi-target resolution and LPI performance. Furthermore, flexible modulation is able to increase the robustness against identification of the interception receiver and improve the anti-jamming performance of the radar. Full article

Mindlin’s two-dimensional theory has been derived and applied to research on quartz resonators for a long time. However, most works have focused on vibrations varying only in two directions, including thickness direction, while the effect of other directions like the length or width direction is normally neglected. Besides, researchers often model quartz resonators as fully electroded plates because of the resulting simplicity. Since a real device is finite in all directions and is only centrally electroded, results obtained in such works cannot offer quantitative information on vibrations with enough accuracy. In this paper, a theoretical analysis of a rectangular trapped-energy resonator of AT-cut quartz is studied using the Ritz method, associated with the variational formulation of Mindlin’s first-order equations. Frequency spectra and mode shapes of a real-scaled trapped-energy resonator, which is finite in all directions, are obtained with the consideration of mode couplings among thickness-shear mode, thickness-twist mode, and flexural mode. Results show the existence of an energy-trapping and coupling phenomenon and are helpful for thorough and accurate understanding of quartz resonator vibrations. Detailed discussions on the effects of structural parameters on mode couplings and energy trapping are provided, and the results can helpfully guide the selection of aspect ratio, length/thickness ratio, and electrode inertia in device design. Full article

In this paper, a novel 2D analytical model based on the Huygens’s principle of wave propagation is proposed in order to predict the directivity patterns of contact type ultrasonic transducers in the generation of guided waves (GWs). The developed model is able to estimate the directivity patterns at any distance, at any excitation frequency and for any configuration and shape of the transducers with prior information of phase dispersive characteristics of the guided wave modes and the behavior of transducer. This, in turn, facilitates to choose the appropriate transducer or arrays of transducers, suitable guided wave modes and excitation frequency for the nondestructive testing (NDT) and structural health monitoring (SHM) applications. The model is demonstrated for P1-type macro-fiber composite (MFC) transducer glued on a 2 mm thick aluminum (Al) alloy plate. The directivity patterns of MFC transducer in the generation of fundamental guided Lamb modes (the S0 and A0) and shear horizontal mode (the SH0) are successfully obtained at 80 kHz, 5-period excitation signal. The results are verified using 3D finite element (FE) modelling and experimental investigation. The results obtained using the proposed model shows the good agreement with those obtained using numerical simulations and experimental analysis. The calculation time using the analytical model was significantly shorter as compared to the time spent in experimental analysis and FE numerical modelling. Full article

All-optical fiber magnetic field sensor based on the Gd₂O₃ nano-particles (NPs)-doped alumino-silicate glass optical fiber was developed, and its temperature and vibration dependence on the Faraday Effect were investigated. Uniformly embedded Gd₂O₃ NPs were identified to form in the core of the fiber, and the measured absorption peaks of the fiber appearing at 377 nm, 443 nm, and 551 nm were attributed to the Gd₂O₃ NPs incorporated in the fiber core. The Faraday rotation angle (FRA) of the linearly polarized light was measured at 650 nm with the induced magnetic field by the solenoid. The Faraday rotation angle was found to increase linearly with the magnetic field, and it was about 18.16° ± 0.048° at 0.142 Tesla (T) at temperatures of 25 °C–120 °C, by which the estimated Verdet constant was 3.19 rad/(T∙m) ± 0.01 rad/(T∙m). The variation of the FRA with time at 0.142 T and 120 °C was negligibly small (−9.78 × 10⁻⁴ °/min). The variation of the FRA under the mechanical vibration with the acceleration below 10 g and the frequency above 50 Hz was within 0.5°. Full article

Wearable electronics are rapidly expanding, especially in applications like health monitoring through medical sensors and body area networks (BANs). Thermoelectric generators (TEGs) have been the main candidate among the different types of energy harvesting methods for body-mounted or even implantable sensors. Introducing new semiconductor materials like organic thermoelectric materials and advancing manufacturing techniques are paving the way to overcome the barriers associated with the bulky and inflexible nature of the common TEGs and are making it possible to fabricate flexible and biocompatible modules. Yet, the lower efficiency of these materials in comparison with bulk-inorganic counterparts as well as applying them mostly in the form of thin layers on flexible substrates limits their applications. This research aims to improve the functionality of thin and flexible organic thermoelectric generators (OTEs) by utilizing a novel design concept inspired by origami. The effects of critical geometric parameters are investigated using COMSOL Multiphysics to further prove the concept of printing and folding as an approach for the system level optimization of printed thin film TEGs. Full article

In this paper, the electronic transduction of DNA hybridization is presented by coupling organic charge-modulated field-effect transistors (OCMFETs) and hairpin-shaped probes. These probes have shown interesting properties in terms of sensitivity and selectivity in other kinds of assays, in the form of molecular beacons (MBs). Their integration with organic-transistor based sensors, never explored before, paves the way to a new class of low-cost, easy-to-use, and portable genetic sensors with enhanced performances. Thanks to the peculiar characteristics of the employed sensor, measurements can be performed at relatively high ionic strengths, thus optimizing the probes’ functionality without affecting the detection ability of the device. A complete electrical characterization of the sensor is reported, including calibration with different target concentrations in the measurement environment and selectivity evaluation. In particular, DNA hybridization detection for target concentration as low as 100 pM is demonstrated. Full article

Resilient position, navigation, and timing (PNT) data is indispensable information in the field of e-navigation. An automatic identification system (AIS) based ranging mode (R-Mode) is put forward to develop a terrestrial backup system in order to overcome the vulnerability of the global navigation satellite system (GNSS). In general, at least three base stations are required in the traditional R-Mode positioning method. However, the geometric distribution of existing base stations is not considered for positioning, as AIS is a communication system. In some cases, a vessel can only receive signals from two base stations. In this paper, an improved position estimation method based on displacement correction is therefore proposed to solve this problem. Compared with the prior displacement correction position estimation (DCPE) method, the proposed method can improve positioning accuracy effectively by adopting a more precise motion model for the vessel, including an accelerated motion and a turning motion model. Moreover, the motion model is employed adaptively to correct the displacement of the vessel. Finally, the proposed method is verified and the performance is analyzed and compared by simulation. This study can extend the application region of AIS R-Mode. Full article

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method. Full article

Mid-infrared (MIR) spectroscopy has received widespread interest as a method to complement traditional soil analysis. Recently available portable MIR spectrometers additionally offer potential for on-site applications, given sufficient spectral data quality. We therefore tested the performance of the Agilent 4300 Handheld FTIR (DRIFT spectra) in comparison to a Bruker Tensor 27 bench-top instrument in terms of (i) spectral quality and measurement noise quantified by wavelet analysis; (ii) accuracy of partial least squares (PLS) calibrations for soil organic carbon (SOC), total nitrogen (N), pH, clay and sand content with a repeated cross-validation analysis; and (iii) key spectral regions for these soil properties identified with a Monte Carlo spectral variable selection approach. Measurements and multivariate calibrations with the handheld device were as good as or slightly better than Bruker equipped with a DRIFT accessory, but not as accurate as with directional hemispherical reflectance (DHR) data collected with an integrating sphere. Variations in noise did not markedly affect the accuracy of multivariate PLS calibrations. Identified key spectral regions for PLS calibrations provided a good match between Agilent and Bruker DHR data, especially for SOC and N. Our findings suggest that portable FTIR instruments are a viable alternative for MIR measurements in the laboratory and offer great potential for on-site applications. Full article

Condition monitoring in polymer composites and structures based on continuous carbon fibers show overwhelming advantages over other potentially competitive sensing technologies in long-gauge measurements due to their great electromechanical behavior and excellent reinforcement property. Although carbon fibers have been developed as strain- or stress-sensing agents in composite structures through electrical resistance measurements, the electromechanical behavior under flexural loads in terms of different loading positions still lacks adequate research, which is the most common situation in practical applications. This study establishes the relationship between the fractional change in electrical resistance of carbon fibers and the external loads at different loading positions along the fibers’ longitudinal direction. An approach for real-time monitoring of flexural loads at different loading positions was presented simultaneously based on this relationship. The effectiveness and feasibility of the approach were verified by experiments on carbon fiber-embedded three-dimensional (3D) printed thermoplastic polymer beam. The error in using the provided approach to monitor the external loads at different loading positions was less than 1.28%. The study fully taps the potential of continuous carbon fibers as long-gauge sensory agents and reinforcement in the 3D-printed polymer structures. Full article

A real-time gas monitoring system based on optical absorption spectroscopy is proposed for localized carbon dioxide (CO₂) measurement in respiratory tracts. In this system, a small gas cell is attached to the end of a hollow optical fiber that delivers mid-infrared light with small transmission loss. The diameters of the fiber and the gas cell are smaller than 1.2 mm so that the probe can be inserted into a working channel of common bronchoscopes. The dimensions of the gas cell are designed based on absorption spectra of CO₂ standard gases in the 4.2 μm wavelength region, which are measured using a Fourier-transform infrared spectrometer. A miniature gas cell that is comprised of a stainless-steel tube with slots for gas inlet and a micro-mirror is fabricated. A compact probing system with a quantum cascade laser (QCL) light source is built using a gas cell with a hollow optical fiber for monitoring CO₂ concentration. Experimental results using human breaths show the feasibility of the system for in-situ measurement of localized CO₂ concentration in human airways. Full article

This paper proposes the automatic coast mode tracking of centroid trackers for infrared images to overcome the target occlusion status. The centroid tracking method, using only the brightness information of an image, is still widely used in infrared imaging tracking systems because it is difficult to extract meaningful features from infrared images. However, centroid trackers are likely to lose the track because they are highly vulnerable to screened status by the clutter or background. Coast mode, one of the tracking modes, maintains the servo slew rate with the tracking rate right before the loss of track. The proposed automatic coast mode tracking method makes decisions regarding entering coast mode by the prediction of target occlusion and tries to re-lock the target and resume the tracking after blind time. This algorithm comprises three steps. The first step is the prediction process of the occlusion by checking both matters which have target-likelihood brightness and which may screen the target despite different brightness. The second step is the process making inertial tracking commands to the servo. The last step is the process of re-locking a target based on the target modeling of histogram ratio. The effectiveness of the proposed algorithm is addressed by presenting experimental results based on computer simulation with various test imagery sequences compared to published tracking algorithms. The proposed algorithm is tested under a real environment with a naval electro-optical tracking system (EOTS) and airborne EO/IR system. Full article

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate. Full article

Smart communication protocols are becoming a key mechanism for improving communication performance in networks such as wireless sensor networks. However, the literature lacks mechanisms for simulating smart communication protocols in precision agriculture for decreasing production costs. In this context, the current work presents an agent-based simulator of smart communication protocols for efficiently managing pesticides. The simulator considers the needs of electric power, crop health, percentage of alive bugs and pesticide consumption. The current approach is illustrated with three different communication protocols respectively called (a) broadcast, (b) neighbor and (c) low-cost neighbor. The low-cost neighbor protocol obtained a statistically-significant reduction in the need of electric power over the neighbor protocol, with a very large difference according to the common interpretations about the Cohen’s d effect size. The presented simulator is called ABS-SmartComAgri and is freely distributed as open-source from a public research data repository. It ensures the reproducibility of experiments and allows other researchers to extend the current approach. Full article

Indium oxide octahedral nanopowders were obtained from an ionic precursor compound after an oxidation process conducted under a low-oxygen atmosphere. This method was found to produce contamination-free indium oxide nanomaterial with very similar morphological and crystalline properties to the one produced by vapor-phase transport, but at significantly lower temperatures and higher yield. The as-synthesized indium oxide was mixed to an organic vehicle and microdrop deposited to form a film bridging the interdigitated silver electrodes patterned on top of a flexible, polyimide (Kapton^®), substrate. The gas sensing properties of the flexible chemoresistors towards ammonia vapors, hydrogen, and nitrogen dioxide were investigated. It was found that these sensors were remarkably sensitive to nitrogen dioxide at a low operating temperature of 150 °C. These results are consistent with the performance of vapor-phase transport synthesized indium oxide octahedra sensors on rigid, ceramic substrates. Therefore, the results presented here pave the way for the mass production of inexpensive gas sensors onto flexible substrates via additive manufacturing. Full article

Self-loosening of bolts caused by repetitive loads and vibrations is one of the common defects that can weaken the structural integrity of bolted steel joints in civil structures. Many existing approaches for detecting loosening bolts are based on physical sensors and, hence, require extensive sensor deployment, which limit their abilities to cost-effectively detect loosened bolts in a large number of steel joints. Recently, computer vision-based structural health monitoring (SHM) technologies have demonstrated great potential for damage detection due to the benefits of being low cost, easy to deploy, and contactless. In this study, we propose a vision-based non-contact bolt loosening detection method that uses a consumer-grade digital camera. Two images of the monitored steel joint are first collected during different inspection periods and then aligned through two image registration processes. If the bolt experiences rotation between inspections, it will introduce differential features in the registration errors, serving as a good indicator for bolt loosening detection. The performance and robustness of this approach have been validated through a series of experimental investigations using three laboratory setups including a gusset plate on a cross frame, a column flange, and a girder web. The bolt loosening detection results are presented for easy interpretation such that informed decisions can be made about the detected loosened bolts. Full article

We demonstrate the successful fabrication of highly sensitive capillary pressure sensors using an innovative 3D printing method. Unlike conventional capacitive pressure sensors where the capacitance changes were due to the pressure-induced interspace variations between the parallel plate electrodes, in our capillary sensors the capacitance was determined by the extrusion and extraction of liquid medium and consequent changes of dielectric constants. Significant pressure sensitivity advances up to 547.9 KPa⁻¹ were achieved. Moreover, we suggest that our innovative capillary pressure sensors can adopt a wide range of liquid mediums, such as ethanol, deionized water, and their mixtures. The devices also showed stable performances upon repeated pressing cycles. The direct and versatile printing method combined with the significant performance advances are expected to find important applications in future stretchable and wearable electronics. Full article

Afferent signals recorded from the dorsal root ganglion can be used to extract sensory information to provide feedback signals in a functional electrical stimulation (FES) system. The goal of this study was to propose an efficient feature projection method for detecting sensory events from multiunit activity-based feature vectors of tactile afferent activity. Tactile afferent signals were recorded from the L4 dorsal root ganglion using a multichannel microelectrode for three types of sensory events generated by mechanical stimulation on the rat hind paw. The multiunit spikes (MUSs) were extracted as multiunit activity-based feature vectors and projected using a linear feature projection method which consisted of projection pursuit and negentropy maximization (PP/NEM). Finally, a multilayer perceptron classifier was used to detect sensory events. The proposed method showed a detection accuracy superior to those of other linear and nonlinear feature projection methods and all processes were completed within real-time constraints. Results suggest that the proposed method could be useful to detect sensory events in real time. We have demonstrated the methodology for an efficient feature projection method to detect real-time sensory events from the multiunit activity of dorsal root ganglion recordings. The proposed method could be applied to provide real-time sensory feedback signals in closed-loop FES systems. Full article

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. Full article

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand. Full article

The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects. Full article

With an aim to reduce the cost of prototype development, this paper establishes a PSPICE hybrid model for the simulation of capacitive microelectromechanical systems (MEMS) gyroscopes. This is achieved by modeling gyroscopes in different modules, then connecting them in accordance with the corresponding principle diagram. Systematic simulations of this model are implemented along with a consideration of details of MEMS gyroscopes, including a capacitance model without approximation, mechanical thermal noise, and the effect of ambient temperature. The temperature compensation scheme and optimization of interface circuits are achieved based on the hybrid closed-loop simulation of MEMS gyroscopes. The simulation results show that the final output voltage is proportional to the angular rate input, which verifies the validity of this model. Full article

All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created an experimental system that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication—an equal error rate (EER) of 4.62% in the simulated environment and an EER of 7.86% in the real-traffic environment—confirm the feasibility of this approach. Full article

This paper proposes an autonomous algorithm to determine the relative pose between the chaser spacecraft and the uncooperative space target, which is essential in advanced space applications, e.g., on-orbit serving missions. The proposed method, named Congruent Tetrahedron Align (CTA) algorithm, uses the very sparse unorganized 3D point cloud acquired by a LIDAR sensor, and does not require any prior pose information. The core of the method is to determine the relative pose by looking for the congruent tetrahedron in scanning point cloud and model point cloud on the basis of its known model. The two-level index hash table is built for speeding up the search speed. In addition, the Iterative Closest Point (ICP) algorithm is used for pose tracking after CTA. In order to evaluate the method in arbitrary initial attitude, a simulated system is presented. Specifically, the performance of the proposed method to provide the initial pose needed for the tracking algorithm is demonstrated, as well as their robustness against noise. Finally, a field experiment is conducted and the results demonstrated the effectiveness of the proposed method. Full article

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn. Full article

A diaphragm-free fiber-optic Fabry-Perot (FP) interferometric gas pressure sensor is designed and experimentally verified in this paper. The FP cavity was fabricated by inserting a well-cut fiber Bragg grating (FBG) and hollow silica tube (HST) from both sides into a silica casing. The FP cavity length between the ends of the SMF and HST changes with the gas density. Using temperature decoupling method to improve the accuracy of the pressure sensor in high temperature environments. An experimental system for measuring the pressure under different temperatures was established to verify the performance of the sensor. The pressure sensitivity of the FP gas pressure sensor is 4.28 nm/MPa with a high linear pressure response over the range of 0.1–0.7 MPa, and the temperature sensitivity is 14.8 pm/°C under the range of 20–800 °C. The sensor has less than 1.5% non-linearity at different temperatures by using temperature decoupling method. The simple fabrication and low-cost will help sensor to maintain the excellent features required by pressure measurement in high temperature applications. Full article

A proper antibody immobilization on a biosensor is a crucial step in order to obtain a high sensitivity to be able to detect low target analyte concentrations. In this paper, we present an experimental study of the immobilization process of antibodies as bioreceptors on a photonic ring resonator sensor. A protein A intermediate layer was created on the sensor surface in order to obtain an oriented immobilization of the antibodies, which enhances the interaction with the target antigens to be detected. The anti-bovine serum albumin (antiBSA)-bovine serum albumin (BSA) pair was used as a model for our study. An opto-fluidic setup was developed in order to flow the different reagents and, simultaneously, to monitor in real-time the spectral response of the photonic sensing structure. The antiBSA immobilization and the BSA detection, their repeatability, and specificity were studied in different conditions of the sensor surface. Finally, an experimental limit of detection for BSA recognition of only 1 ng/mL was obtained. Full article

This paper presents a concept for soft field tomographic scan of all the projections of electromagnetic waves emanating from an array of electrodes. Instead of the sequential excitation of all pairs of electrodes in the list of all projections, the new method present here consists of a single and continuous excitation. This excitation signal is the linear combination of the excitation signals in the projection set at different AC frequencies. The response to a given projection is discriminated by selecting the corresponding AC frequency component in the signal spectra of the digitally demodulated signals. The main advantage of this method is the suppression of transients after each projection, which is particularly problematic in electrical impedance tomography due to contact impedance phenomena and skin effect. The second benefit over the sequential scan method is the increased number of samples for each measurement for reduced noise sensitivity with digital demodulation. The third benefit is the increased temporal resolution in high-speed applications. The main drawback is the increased number of signal sources required (one per electrode). This paper focuses on electrical impedance tomography, based on earlier work by the authors. An experimental proof-of-concept using a simple 4-electrodes electrical impedance tomographic system is presented using simulations and laboratory data. The method presented here may be extended to other modalities (ultrasonic, microwave, optical, etc.). Full article

Although some researchers have proposed the Field Programmable Gate Array (FPGA) architectures of Feature From Accelerated Segment Test (FAST) and Binary Robust Independent Elementary Features (BRIEF) algorithm, there is no consideration of image data storage in these traditional architectures that will result in no image data that can be reused by the follow-up algorithms. This paper proposes a new FPGA architecture that considers the reuse of sub-image data. In the proposed architecture, a remainder-based method is firstly designed for reading the sub-image, a FAST detector and a BRIEF descriptor are combined for corner detection and matching. Six pairs of satellite images with different textures, which are located in the Mentougou district, Beijing, China, are used to evaluate the performance of the proposed architecture. The Modelsim simulation results found that: (i) the proposed architecture is effective for sub-image reading from DDR3 at a minimum cost; (ii) the FPGA implementation is corrected and efficient for corner detection and matching, such as the average value of matching rate of natural areas and artificial areas are approximately 67% and 83%, respectively, which are close to PC’s and the processing speed by FPGA is approximately 31 and 2.5 times faster than those by PC processing and by GPU processing, respectively. Full article

The diagnosis method for a weak-fault model with only normal behaviors of each component has evolved over decades. However, many systems now demand a strong-fault models, the fault modes of which have specific behaviors as well. It is difficult to diagnose a strong-fault model due to its non-monotonicity. Currently, diagnosis methods usually employ conflicts to isolate possible fault and the process can be expedited when some observed output is consistent with the model’s prediction where the consistency indicates probably normal components. This paper solves the problem of efficiently diagnosing a strong-fault model by proposing a novel Logic-based Truth Maintenance System (LTMS) with two search approaches based on conflict and consistency. At the beginning, the original a strong-fault model is encoded by Boolean variables and converted into Conjunctive Normal Form (CNF). Then the proposed LTMS is employed to reason over CNF and find multiple minimal conflicts and maximal consistencies when there exists fault. The search approaches offer the best candidate efficiency based on the reasoning result until the diagnosis results are obtained. The completeness, coverage, correctness and complexity of the proposals are analyzed theoretically to show their strength and weakness. Finally, the proposed approaches are demonstrated by applying them to a real-world domain—the heat control unit of a spacecraft—where the proposed methods are significantly better than best first and conflict directly with A* search methods. Full article

This study proposes two models for precise time transfer using the BeiDou Navigation Satellite System triple-frequency signals: ionosphere-free (IF) combined precise point positioning (PPP) model with two dual-frequency combinations (IF-PPP1) and ionosphere-free combined PPP model with a single triple-frequency combination (IF-PPP2). A dataset with a short baseline (with a common external time frequency) and a long baseline are used for performance assessments. The results show that IF-PPP1 and IF-PPP2 models can both be used for precise time transfer using BeiDou Navigation Satellite System (BDS) triple-frequency signals, and the accuracy and stability of time transfer is the same in both cases, except for a constant system bias caused by the hardware delay of different frequencies, which can be removed by the parameter estimation and prediction with long time datasets or by a priori calibration. Full article

Structural Health Monitoring (SHM) has moved to data-dense systems, utilizing numerous sensor types to monitor infrastructure, such as bridges and dams, more regularly. One of the issues faced in this endeavour is the scale of the inspected structures and the time it takes to carry out testing. Installing automated systems that can provide measurements in a timely manner is one way of overcoming these obstacles. This study proposes an Artificial Neural Network (ANN) application that determines intact and damaged locations from a small training sample of impact-echo data, using air-coupled microphones from a reinforced concrete beam in lab conditions and data collected from a field experiment in a parking garage. The impact-echo testing in the field is carried out in a semi-autonomous manner to expedite the front end of the in situ damage detection testing. The use of an ANN removes the need for a user-defined cutoff value for the classification of intact and damaged locations when a least-square distance approach is used. It is postulated that this may contribute significantly to testing time reduction when monitoring large-scale civil Reinforced Concrete (RC) structures. Full article