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For the research on real-time accurate testing technology for the explosion point spatial coordinate of munitions, its currently commonly used methods such as acoustic–electric detection or high-speed imaging are limited by the field conditions, response rate, cost, and other factors. In this paper, a method of spatial coordinate testing for the explosion point based on a 2D PSD (position-sensitive detector) intersection is proposed, which has the advantages of a faster response, better real-time performance, and a lower cost. Firstly, a mathematical model of the spatial coordinate testing system was constructed, and an error propagation model for structural parameters was developed. The influence of the position of the optical axes' intersection as well as the azimuth angle and pitch angle on the test accuracy of the system was simulated and analyzed, thus obtaining the distribution and variation trend of the overall error propagation coefficient of the system. Finally, experiments were designed to obtain the test error of the system for validation. The results show that the system test accuracy is high when the azimuth angle is 20°–50°, the overall error propagation coefficient does not exceed 48.80, and the average test error is 56.17 mm. When the pitch angle is −2.5°–2.5°, the system has a higher test accuracy, with the overall error propagation coefficient not exceeding 44.82, and the average test error is 41.87 mm. The test accuracy of the system is higher when the position of the optical axes' intersection is chosen to make sure that explosion points fall in the region of the negative half-axis of the Z w -axis of the world coordinate system, with an overall error propagation coefficient of less than 44.78 and an average test error of 73.38 mm. It is shown that a reasonable selection of system structure parameters can significantly improve the system test accuracy and optimize the system deployment mode under the long-distance field conditions so as to improve the deployment efficiency. [ABSTRACT FROM AUTHOR]

The rocket sled, as a ground dynamic test system, combines the characteristics of the wind tunnel test and the flight test. However, some practical factors, such as shock wave interference, ground effect, and high-intensity aerodynamic noise will cause serious interference and even failure of the uniformly distributed sensors during horizontal sliding in a wide speed range. The AGARD HB-2 standard model is employed as the payload to simulate the aerodynamic and aeroacoustic characteristics during the variable acceleration period, aiming to optimize the test sensors layout. It is observed that in the high Mach number flow fields, strong coupling behaviors among complex waves will occur. The peak of wake vortex strength will appear at 1.5 s and gradually diminish over time. In addition, when the vortex between the load and the booster is monitored, its position shifts forward in the subsonic stage, then gradually moves backward and expands in the supersonic stage. Acoustic directivity is pronounced at subsonic and transonic speeds, pointing towards 75° and 135° relative to the sliding speed, respectively. These results can provide technical support for sensor layout and high-precision testing in rocket sled tests. [ABSTRACT FROM AUTHOR]

Ferromagnetic pipes are widely used in the oil and gas industry. They are subject to cracks due to corrosion, pressure, and fatigue. It is significant to detect cracks for the safety of pipes. A residual magnetic field testing (RMFT) system is developed for crack detection in ferromagnetic pipes. Based on this background, a detection probe based on an array of tunneling magneto-resistive (TMR) sensors and permanent magnets is exploited. The probe is able to partially magnetize the pipe wall and collect magnetic signals simultaneously. First, a theoretical analysis of RMFT is presented. The physics principle of RMFT is introduced, and a finite element model is built. In the finite element simulations, the effects of the crack length and depth on the RMFT signal are analyzed, and the signal characteristics are selected to represent the crack size. Next, the validated experiments are conducted to demonstrate the feasibility of the proposed RMFT method in this paper. [ABSTRACT FROM AUTHOR]

The nondestructive spectral analysis of surface waves (SASW) technique determines the shear wave velocities along the wide wavelength range using Rayleigh-type surface waves that propagate along pairs of receivers on the surface. The typical configuration of source-receivers consists of a vertical source and three vertical receivers arranged in a linear array. While this approach allows for effective site characterization, laterally variable sites are often challenging to characterize. In addition, in a traditional SASW test configuration system, where sources are placed in one direction, the data are collected more on one side, which can cause an imbalance in the interpretation of the data. Data interpretation issues can be resolved by moving the source to opposite ends of the original array and relocating receivers to perform a second complete set of tests. Consequently, two different Vs profiles can be provided with only a small amount of additional time at sites where lateral variability exists. Furthermore, the testing procedure can be modified to enhance the site characterization during data collection. The advantages of performing SASW testing in both directions are discussed using a real case study. [ABSTRACT FROM AUTHOR]

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception. [ABSTRACT FROM AUTHOR]

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace. [ABSTRACT FROM AUTHOR]

Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal, the accident can be avoided or not by the driver when reacting to this signal. [ABSTRACT FROM AUTHOR]

On-farm milk flow meter technology facilitates real-time assessment of individual cow milking observations and could be used to detect milking liner slips during machine milking of dairy cows. Here, we compared the accuracy of on-farm milk flow meters for detecting milking liner slips with that of audible detection and that of a portable vacuum recording system. Compared to audible detection methods, the on-farm milk flow meter facilitated the detection of milking liner slips with moderate accuracy. Using the vacuum recording system as the gold standard, the milk flow meter system failed to detect most of the liner slips, leading to poor agreement between the two devices. We conclude that the on-farm milk flow meter system tested here compared well with audible detection; however, when vacuum recordings were considered, we found significant levels of under-detection. Taken together, dairy operators may use the on-farm milk flow meter system to inform adjustments of the milking machine settings and monitor milking routine performance. However, the system is not suitable for monitoring short-duration vacuum fluctuations. Future research is warranted to optimize the sensor-based detection of milking liner slips. [ABSTRACT FROM AUTHOR]

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including avgE, minE, maxE, ssE, and Stdev, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This potency of the method is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications. [ABSTRACT FROM AUTHOR]

This paper proposed a real-time fault diagnostic method for hydraulic systems using data collected from multiple sensors. The method is based on a proposed multi-sensor convolutional neural network (MS-CNN) that incorporates feature extraction, sensor selection, and fault diagnosis into an end-to-end model. Both the sensor selection process and fault diagnosis process are based on abstract fault-related features learned by a CNN deep learning model. Therefore, compared with the traditional sensor-and-feature selection method, the proposed MS-CNN can find the sensor channels containing higher-level fault-related features, which provides two advantages for diagnosis. First, the sensor selection can reduce the redundant information and improve the diagnostic performance of the model. Secondly, the reduced number of sensors simplifies the model, reducing communication burden and computational complexity. These two advantages make the MS-CNN suitable for real-time hydraulic system fault diagnosis, in which the multi-sensor feature extraction and the computation speed are both significant. The proposed MS-CNN approach is evaluated experimentally on an electric-hydraulic subsea control system test rig and an open-source dataset. The proposed method shows obvious superiority in terms of both diagnosis accuracy and computational speed when compared with traditional CNN models and other state-of-the-art multi-sensor diagnostic methods. [ABSTRACT FROM AUTHOR]

Tool frame calibration has been widely used in robot-assisted printing, welding, and grinding, but it is not suitable for ultrasonic testing because the robot is submerged in water. The purpose of this paper is to present a tool frame calibration method, which is suitable for improving the precision of ultrasonic testing. In uniform mediums, sound travels along a straight line like ray. A reflector is fixed in water to reflect ultrasound, which makes it possible to measure distances between incidence points on a reflector and tool center point (TCP) on an ultrasound transducer. In addition, the positions and poses of the end flange are recorded through a robot controller. Finally, an optimization method is applied to calculate the position and pose errors of the tool frame relative to the end flange according to such records. The presented method was implemented in an ultrasonic testing system. We selected 100 incidence points on the reflector to calculate the assembly errors of the transducer. The pulse amplitude rose obviously after calibration, which verifies that this is an effective method. Considering that ultrasonic transducers can be used as a measuring tool, this paper proposes a tool frame calibration method for ultrasonic testing robots without introducing other measuring devices, which draws the conclusion that tool frame can be calibrated through ultrasound. [ABSTRACT FROM AUTHOR]

This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human–computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system's utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive system for testing and verifying shunt active power filter control methods. The aim of this experimental platform is to provide tools to a user to objectively compare the individual control methods. The functionality of the system was verified on a hardware platform using least mean squares and recursive least squares algorithms. In the experiments, an average relative suppression of the total harmonic distortion of 22% was achieved. This article describes the principle of the shunt active power filter, the used experimental platform of the controlled current injection source, its control system based on virtual instrumentation and control software and ends with experimental verification. The discussion of the paper outlines the extension of the experimental platform with the cRIO RTOS control system to reduce the latency of reference current generation and further planned research including motivation. [ABSTRACT FROM AUTHOR]

Traditionally, freight wagon technology has lacked digitalization and advanced monitoring capabilities. This article presents recent advancements in freight wagon digitalization, covering the system's definition, development, and field tests on a commercial line in Sweden. A number of components and systems were installed on board on the freight wagon, leading to the intelligent freight wagon. The digitalization includes the integration of sensors for different functions such as train composition, train integrity, asset monitoring and continuous wagon positioning. Communication capabilities enable data exchange between components, securely stored and transferred to a remote server for access and visualization. Three digitalized freight wagons operated on the Nässjo–Falköping line, equipped with strategically placed monitoring sensors to collect valuable data on wagon performance and railway infrastructure. The field tests showcase the system's potential for detecting faults and anomalies, signifying a significant advancement in freight wagon technology, and contributing to an improvement in freight wagon digitalization and monitoring. The gathered insights demonstrate the system's effectiveness, setting the stage for a comprehensive monitoring solution for railway infrastructures. These advancements promise real-time analysis, anomaly detection, and proactive maintenance, fostering improved efficiency and safety in the domain of freight transportation, while contributing to the enhancement of freight wagon digitalization and supervision. [ABSTRACT FROM AUTHOR]

This paper addresses two aspects of the metrology of spherical, petal polymer reflectors which are part of an effort by the European Space Agency (ESA) to develop actively controlled foldable reflectors, enabling larger apertures on CubeSats and small satellites. The first problem is that of measuring the surface figure error of the spherical reflector alone during the development phase, and to assess the quality before assembling the telescope (large stroke, low accuracy). The SCOTS (Software Configurable Optical Testing System) appears to provide a fast and satisfactory solution to this problem. The second problem is the wavefront error reconstruction when the petal reflector is mounted on the telescope, because parts of the petals are obscured by the secondary mirror, in such a way that the petals appear completely disconnected, making the gradient-based metrology impossible. Using the fact that the petals have common mechanical boundary conditions at the central support ring, the problem is solved by using a set of orthogonal modes satisfying the same boundary conditions. The vibration modes are used for this purpose; the modal amplitudes are reconstructed from slope data outside the obstruction, allowing for wavefront error reconstruction over the entire surface. [ABSTRACT FROM AUTHOR]

This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning. [ABSTRACT FROM AUTHOR]

Glass Fiber reinforced polymers (GFRPs) are widely used and play an important role in modern society. The multilayer structure of GFRPs can lead to delamination defects during production and service, which can have a significant impact on the integrity and safety of the equipment. Therefore, it is important to monitor these delamination defects during equipment service in order to evaluate their effects on equipment performance and lifespan. Microwave imaging testing, with its high sensitivity and noncontact nature, shows promise as a potential method for detecting delamination defects in GFRPs. However, there is currently limited research on the quantitative characterization of defect images in this field. In order to achieve visual quantitative nondestructive testing (NDT), we propose a 2D-imaging visualization and quantitative characterization method for delamination defects in GFRP, and realize the combination of visual detection and quantitative detection. We built a microwave testing experimental system to verify the effectiveness of the proposed method. The results of the experiment indicate the effectiveness and innovation of the method, which can effectively detect all delamination defects of 0.5 mm thickness inside GFRP with high accuracy, the signal-to-background ratio (SBR) of 2D imaging can reach 4.41 dB, the quantitative error of position is within 0.5 mm, and the relative error of area is within 11%. [ABSTRACT FROM AUTHOR]

Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields. [ABSTRACT FROM AUTHOR]

This article is about the design, development and validation of a new monitoring architecture for individual cells and stacks to facilitate the study of proton exchange fuel cells. The system consists of four main elements: input signals, signal processing boards, analogue-to-digital converters (ADCs) and a master terminal unit (MTU). The latter integrates a high-level graphic user interface (GUI) software developed by National Instruments LABVIEW, while the ADCs are based on three digital acquisition units (DAQs). Graphs showing the temperature, currents and voltages in individual cells as well as stacks are integrated for ease of reference. The system validation was carried out both in static and dynamic modes of operation using a Ballard Nexa 1.2 kW fuel cell fed by a hydrogen cylinder, with a Prodigit 32612 electronic load at the output. The system was able to measure the voltage distributions of individual cells, and temperatures at different equidistant points of the stack both with and without an external load, validating its use as an indispensable tool for the study and characterization of these systems. [ABSTRACT FROM AUTHOR]

E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry. [ABSTRACT FROM AUTHOR]

The proliferation of devices for the Internet of Things (IoT) and their implication in many activities of our lives have led to a considerable increase in concern about the security of these devices, posing a double challenge for designers and developers of products. On the one hand, the design of new security primitives, suitable for resource-limited devices, can facilitate the inclusion of mechanisms and protocols to ensure the integrity and privacy of the data exchanged over the Internet. On the other hand, the development of techniques and tools to evaluate the quality of the proposed solutions as a step prior to their deployment, as well as to monitor their behavior once in operation against possible changes in operating conditions arising naturally or as a consequence of a stress situation forced by an attacker. To address these challenges, this paper first describes the design of a security primitive that plays an important role as a component of a hardware-based root of trust, as it can act as a source of entropy for True Random Number Generation (TRNG) or as a Physical Unclonable Function (PUF) to facilitate the generation of identifiers linked to the device on which it is implemented. The work also illustrates different software components that allow carrying out a self-assessment strategy to characterize and validate the performance of this primitive in its dual functionality, as well as to monitor possible changes in security levels that may occur during operation as a result of device aging and variations in power supply or operating temperature. The designed PUF/TRNG is provided as a configurable IP module, which takes advantage of the internal architecture of the Xilinx Series-7 and Zynq-7000 programmable devices and incorporates an AXI4-based standard interface to facilitate its interaction with soft- and hard-core processing systems. Several test systems that contain different instances of the IP have been implemented and subjected to an exhaustive set of on-line tests to obtain the metrics that determine its quality in terms of uniqueness, reliability, and entropy characteristics. The results obtained prove that the proposed module is a suitable candidate for various security applications. As an example, an implementation that uses less than 5% of the resources of a low-cost programmable device is capable of obfuscating and recovering 512-bit cryptographic keys with virtually zero error rate. [ABSTRACT FROM AUTHOR]

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview's timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts. [ABSTRACT FROM AUTHOR]

Displacement is critical when it comes to the evaluation of civil structures. Large displacement can be dangerous. There are many methods that can be used to monitor structural displacements, but every method has its benefits and limitations. Lucas–Kanade (LK) optical flow is recognized as a superior computer vision displacement tracking method, but it only applies to small displacement monitoring. An upgraded LK optical flow method is developed in this study and used to detect large displacement motions. One motion controlled by a multiple purpose testing system (MTS) and a free-falling experiment were designed to verify the developed method. The results provided by the upgraded LK optical flow method showed 97 percent accuracy when compared with the movement of the MTS piston. In order to capture the free-falling large displacement, the pyramid and warp optical flow methods are included in the upgraded LK optical flow method and compared with the results of template matching. The warping algorithm with the second derivative Sobel operator provides accurate displacements with 96% average accuracy. [ABSTRACT FROM AUTHOR]

Using distributed MEMS pressure sensors to measure small flow rates in high resistance fluidic channels is fraught with challenges far beyond the performance of the pressure sensing element. In a typical core-flood experiment, which may last several months, flow-induced pressure gradients are generated in porous rock core samples wrapped in a polymer sheath. Measuring these pressure gradients along the flow path requires high resolution pressure measurement while contending with difficult test conditions such as large bias pressures (up to 20 bar) and temperatures (up to 125 °C), as well as the presence of corrosive fluids. This work is directed at a system for using passive wireless inductive-capacitive (LC) pressure sensors that are distributed along the flow path to measure the pressure gradient. The sensors are wirelessly interrogated with readout electronics placed exterior to the polymer sheath for continuous monitoring of experiments. Using microfabricated pressure sensors that are smaller than ø15 × 3.0 mm3, an LC sensor design model for minimizing pressure resolution, accounting for sensor packaging and environmental artifacts is investigated and experimentally validated. A test setup, built to provide fluid-flow pressure differentials to LC sensors with conditions that mimic placement of the sensors within the wall of the sheath, is used to test the system. Experimental results show the microsystem operating over full-scale pressure range of 20,700 mbar and temperatures up to 125 °C, while achieving pressure resolution of <1 mbar, and resolving gradients of 10–30 mL/min, which are typical in core-flood experiments. [ABSTRACT FROM AUTHOR]

The safety assessment of cyber-physical systems (CPSs) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPSs is fault injection (FI). The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost. In this study, we propose a reinforcement learning (RL)-based method to automatically configure faults in the system under test and to find catastrophic faults in the early stage of system development at the model level. The proposed method provides a guideline to utilize high-level domain knowledge about a system model for constructing the reinforcement learning agent and fault injection setup. In this study, we used the system (safety) specification to shape the reward function in the reinforcement learning agent. The reinforcement learning agent dynamically interacted with the model under test to identify catastrophic faults. We compared the proposed method with random-based fault injection in two case studies using MATLAB/Simulink. Our proposed method outperformed random-based fault injection in terms of the severity and number of faults found. [ABSTRACT FROM AUTHOR]

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes. [ABSTRACT FROM AUTHOR]

The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into the measurement data while bypassing the detection of the security system, ultimately causing the results of state estimation to deviate from secure values. Since FDIAs tampering with the measurement data of some buses will lead to error offset, this paper proposes an attack-detection algorithm based on statistical learning according to the different characteristic parameters of measurement error before and after tampering. In order to detect and classify false data from the measurement data, in this paper, we report the model establishment and estimation of error parameters for the tampered measurement data by combining the the k-means++ algorithm with the expectation maximization (EM) algorithm. At the same time, we located and recorded the bus that the attacker attempted to tamper with. In order to verify the feasibility of the algorithm proposed in this paper, the IEEE 5-bus standard test system and the IEEE 14-bus standard test system were used for simulation analysis. Numerical examples demonstrate that the combined use of the two algorithms can decrease the detection time to less than 0.011883 s and correctly locate the false data with a probability of more than 95%. [ABSTRACT FROM AUTHOR]

Many parameters can be used to express a machine's condition and to track its evolution through time, such as modal parameters extracted from vibration signals. Operational Modal Analysis (OMA), commonly used to extract modal parameters from systems under operating conditions, was successfully employed in many monitoring systems, but its application in rotating machinery is still in development due to the distinct characteristics of this system. To implement efficient monitoring systems based on OMA, it is essential to automatically extract the modal parameters, which several studies have proposed in the literature. However, these algorithms are usually developed to deal with structures that have different characteristics when compared to rotating machinery, and, therefore, work poorly or do not work with this kind of system. Thus, this paper proposes, and has as its main novelty in, a new automated algorithm to carry out modal parameter identification on rotating machinery through OMA. The proposed technique was applied in two different datasets to enable the evaluation of the robustness to different systems and test conditions. It is revealed that the proposed algorithm is suitable for the accurate extraction of frequencies and damping ratios from the stabilization diagram, for both the rotor and the foundation, and only one user defined parameter is required. [ABSTRACT FROM AUTHOR]

Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate traffic in order to evade the rules applied, several machine learning-based IDSs have been developed. In this study, we focused on one such model involving several algorithms and used the NSL-KDD dataset as a benchmark to train and evaluate its performance. We demonstrate a way to create adversarial instances of network traffic that can be used to evade detection by a machine learning-based IDS. Moreover, this traffic can be used for training in order to improve performance in the case of new attacks. Thus, a generative adversarial network (GAN)—i.e., an architecture based on a deep-learning algorithm capable of creating generative models—was implemented. Furthermore, we tested the IDS performance using the generated adversarial traffic. The results showed that, even in the case of the GAN-generated traffic (which could successfully evade IDS detection), by using the adversarial traffic in the testing process, we could improve the machine learning-based IDS performance. [ABSTRACT FROM AUTHOR]

To achieve a wide range and high accuracy detection of the vacuum level, for example, in an encapsulated vacuum microcavity, a composite-type MEMS Pirani gauge has been designed and fabricated. The Pirani gauge consists of two gauges of different sizes connected in series, with one gauge having a larger heat-sensitive area and a larger air gap for extending the lower measurable limit of pressure (i.e., the high vacuum end) and the other gauge having a smaller heat-sensitive area and a smaller air gap for extending the upper measurable limit. The high-resistivity titanium metal was chosen as the thermistor; SiNx was chosen as the dielectric layer, considering the factors relevant to simulation and manufacturing. By simulation using COMSOL Multiphysics and NI Multisim, a range of measurement of 2 × 10−2 to 2 × 105 Pa and a sensitivity of 52.4 mV/lgPa were obtained in an N2 environment. The performance of the fabricated Pirani gauge was evaluated by using an in-house made vacuum test system. In the test, the actual points of measurement range from 6.6 × 10−2 to 1.12 × 105 Pa, and the highest sensitivity is up to 457.6 mV/lgPa. The experimental results are better in the range of measurement, sensitivity, and accuracy than the simulation results. The Pirani gauge proposed in this study is simple in structure, easy to manufacture, and suitable for integration with other MEMS devices in a microcavity to monitor the vacuum level therein. [ABSTRACT FROM AUTHOR]

In this paper, we present a wireless sensor system (WSS) that integrated an inductive wireless power transfer (I-WPT) module for battery-free real-time monitoring of the status of rotating shaft in ships. Firstly, an optimized I-WPT module for seamless power supply was implemented using multiple Tx and Rx coils, and its power capability of 1.75 W with an efficiency of 75% was achieved. Secondly, as a result of the high-power transfer performance of the implemented I-WPT module, an entire WSS that integrated four sensors was designed on the rotary shaft. Finally, the designed WSS was installed on a small-scale test bench system with a shaft diameter of 200 mm; it was demonstrated that the status of a propulsion shaft could be monitored in real time without a battery. [ABSTRACT FROM AUTHOR]

In this paper, a ratiometric approach to sensing temperature variations is shown using specialty fiber optic devices. We analyzed the transmission response of cascaded segments of multicore fibers (MCFs), and dissimilar lengths were found to generate an adequate scheme for ratiometric operation. The perturbation of optical parameters in the MCFs translates to a rich spectral behavior in which some peaks increase their intensity while others decrease their intensity. Thus, by selecting opposite-behavior peaks, highly sensitive ratiometric measurements that provide robustness against spurious fluctuations can be performed. We implemented this approach using seven-core fiber (SCF) segments of 5.8 cm and 9.9 cm. To test the system's response under controlled perturbations, we heated one of the segments from ambient temperature up to 150 °C. We observed defined peaks with opposite behavior as a function of temperature. Two pairs of peaks within the interrogation window were selected to perform ratiometric calculations. Ratiometric measurements exhibited sensitivities 6–14 times higher than single-wavelength measurements. A similar trend with enhanced sensitivity in both peak pairs was obtained. In contrast to conventional interferometric schemes, the proposed approach does not require expensive facilities or micrometric-resolution equipment. Moreover, our approach has the potential to be realized using commercial splicers, detectors, and filters. [ABSTRACT FROM AUTHOR]

This work aimed to develop a real-time test platform for systems associated with the tactile internet area. The proposal comprises a master device, a communication channel and a slave device. The master device is a tactile glove (wearable technology) that works as a tactile interface based on vibratory feedback. The master device can interact with virtual elements (local or remote). The Matlab/Simulink environment and a robotics toolbox form the communication channel and the slave device. The communication channel introduces a bidirectional connection of variable latency, and the slave device is defined as a robotic phantom omni manipulator emulated in Matlab/Simulink. The virtual robotic manipulator, the slave device, can generate different types of tactile sensations in the tactile glove, that is, in the master device. The platform can model tactile sensations such as coarse roughness, fine roughness, smoothness, dripping and softness. The proposed platform presented adequate results and can be used to test various algorithms and methods correlated to the tactile internet. [ABSTRACT FROM AUTHOR]

Assessment of the composition of meat-containing products is the task in demand due to their frequent deviations from declared recipes. The paper presents the developed test system for immunochromatographic determination of total meat content. The assay is based on the simultaneous use of monoclonal antibodies, which specifically interacts with mammalian skeletal troponin I, and polyclonal antibodies, which specifically detect bird immunoglobulin Y. To integrate the detection of both types of meat by the same test strip, the antibodies are mixed in the analytical zone of the test strip and in complex with a gold nanoparticle label. The chosen ratios of the antibodies for both mixtures provide the same contribution of different types of mammalian and bird raw materials of muscle tissues to the label binding. The test system demonstrates suitability for products containing beef, pork, rabbit, lamb, chicken, and turkey meat. The minimal detectable content of meat in samples is 0.1%. The samples for the testing are diluted 100 times, thus eliminating matrix effects, and providing high reproducibility of the color intensity for extracts of different compositions. The obtained results allow the recommendation of the developed test system for rapid on-site control of meat products. [ABSTRACT FROM AUTHOR]

Cooking at home is a critical survival skill. We propose a new cooking assistance system in which a user only needs to wear an all-in-one augmented reality (AR) headset without having to install any external sensors or devices in the kitchen. Utilizing the built-in camera and cutting-edge computer vision (CV) technology, the user can direct the AR headset to recognize available food ingredients by simply looking at them. Based on the types of the recognized food ingredients, suitable recipes are suggested accordingly. A step-by-step video tutorial providing details of the selected recipe is then displayed with the AR glasses. The user can conveniently interact with the proposed system using eight kinds of natural hand gestures without needing to touch any devices throughout the entire cooking process. Compared with the deep learning models ResNet and ResNeXt, experimental results show that the YOLOv5 achieves lower accuracy for ingredient recognition, but it can locate and classify multiple ingredients in one shot and make the scanning process easier for users. Twenty participants test the prototype system and provide feedback via two questionnaires. Based on the analysis results, 19 of the 20 participants would recommend others to use the proposed system, and all participants are overall satisfied with the prototype system. [ABSTRACT FROM AUTHOR]

Recent acoustic telemetry positioning systems are able to reconstruct the positions and trajectories of organisms at a scale of a few centimeters to a few meters. However, they present several logistical constraints including receiver maintenance, calibration procedures and limited access to real-time data. We present here a novel, easy-to-deploy, energy self-sufficient underwater positioning system based on the time difference of arrival (TDOA) algorithm and the Global System for Mobile (GSM) communication technology, capable of locating tagged marine organisms in real time. We provide an illustration of the application of this system with empirical examples using continuous and coded tags in fish and benthic invertebrates. In situ experimental tests of the operational system demonstrated similar performances to currently available acoustic positioning systems, with a global positioning error of 7.13 ± 5.80 m (mean ± SD) and one-third of the pings can be localized within 278 m of the farthest buoy. Despite some required improvements, this prototype is designed to be autonomous and can be deployed from the surface in various environments (rivers, lakes, and oceans). It was proven to be useful to monitor a wide variety of species (benthic and pelagic) in real time. Its real-time property can be used to rapidly detect system failure, optimize deployment design, or for ecological or conservation applications. [ABSTRACT FROM AUTHOR]

Despite the widespread agreement on the need for the regular repositioning of at-risk individuals for pressure injury prevention and management, adherence to repositioning schedules remains poor in the clinical environment. The situation in the home environment is likely even worse. Our team has developed a non-contact system that can determine an individual's position in bed (left-side lying, supine, or right-side lying) using data from a set of inexpensive load cells placed under the bed. This system was able to detect whether healthy participants were left-side lying, supine, or right-side lying with 94.2% accuracy in the lab environment. The objective of the present work was to deploy and test our system in the home environment for use with individuals who were sleeping in their own beds. Our system was able to detect the position of our nine participants with an F1 score of 0.982. Future work will include improving generalizability by training our classifier on more participants as well as using this system to evaluate adherence to two-hour repositioning schedules for pressure injury prevention or management. We plan to deploy this technology as part of a prompting system to alert a caregiver when a patient requires repositioning. [ABSTRACT FROM AUTHOR]

Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm's data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system. [ABSTRACT FROM AUTHOR]

In this paper, the interactive application of data gloves based on emotion recognition and judgment system is investigated. A system of emotion recognition and judgment is established based on the set of optimal features of physiological signals, and then a data glove with multi-channel data transmission based on the recognition of hand posture and emotion is constructed. Finally, the system of virtual hand control and a manipulator driven by emotion is built. Five subjects were selected for the test of the above systems. The test results show that the virtual hand and manipulator can be simultaneously controlled by the data glove. In the case that the subjects do not make any hand gesture change, the system can directly control the gesture of the virtual hand by reading the physiological signal of the subject, at which point the gesture control and emotion control can be carried out at the same time. In the test of the manipulator driven by emotion, only the results driven by two emotional trends achieve the desired purpose. [ABSTRACT FROM AUTHOR]

For solving the problem of polar performance of the inertial navigation system (INS) at mid-low latitudes, the simulation test system constructed by the "attitude and velocity invariant method of trajectory transfer rule based on the transverse coordinate system (T-AVIM)" of the Earth sphere model is used. The test system structure, especially the IMU conversion formula from mid-low latitudes to polar region simulation test, is introduced, and it is proved that the IMU conversion error can be equivalently superimposed on the bias error of the polar simulated IMU. According to the marine estimation formula for the effect of the reference error on the IMU conversion error, the specific influence of the constant error component and the random error component of the reference system on the simulated IMU is analyzed. The calculation method of the simulated IMU error is given with examples and intuitively explained, and the correctness of the theory is verified through simulation experiments. [ABSTRACT FROM AUTHOR]

The prognostics and health management disciplines provide an efficient solution to improve a system's durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem's remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications. [ABSTRACT FROM AUTHOR]

At present, most cone penetration test experiments use cables to transmit data. The cables not only make the exploration operation very complicated, but also hinder the realization of automatic exploration. An excessively long cable can also bring about additional attenuation and noise during the transmission of probe signal. In order to simplify the procedures of exploration operation and to improve the detection accuracy, a cableless cone penetration test system is proposed in this study. It improved the system by using magnetic communication and converts the electrical signal into a magnetic signal at the connection of two adjacent probe rods for relay transmission. Exploratory experiments were carried out to evaluate the feasibility and accuracy of the new system. The experimental results show that the experimental data collected by the new system is more accurate than that collected by traditional CPT equipment with cable. The new system simplifies exploration operations and enables real-time data transmission to detect abnormalities in time. This anomaly usually means that the probe is pressed against hard rock. It is more convenient and accurate to use the new system for exploration. [ABSTRACT FROM AUTHOR]

A single-beam radar system cannot adopt a side-looking installation scheme, which is completely perpendicular to the moving direction of the target in an intelligent transportation system (ITS), because of its own limitations. In this paper, a side-looking radar velocity measurement system that utilizes a new signal processing method and multi-channel radar scheme is proposed. Constant false alarm rate (CFAR) and generalized likelihood ratio test (GLRT) detectors are used to detect the data processing results in different stages in order to reduce the false alarm rate of targets. At the same time, a deconvolution-based clutter map algorithm is proposed to solve the problem of clutter interference in the test environment, and its theoretical performance is verified by simulation. Finally, 77 G commercial radar is used to test the system, and the results show that this algorithm can effectively detect and accurately estimate the speed of tangential low-speed targets under clutter interference. [ABSTRACT FROM AUTHOR]

The pressure change rate (PCR) of the brake chamber is the key control parameter and evaluation index in the pneumatic braking system for intelligent braking. The PCR threshold value of commercial vehicle brake chambers for braking comfort is analyzed. The PCR measurement method based on a laminar flow resistance tube is proposed, and the PCR test system is designed. The simulation model of a PCR test system for commercial vehicle brake chambers is presented. By analyzing the simulation and experimental results, it is validated that the PCR test system of commercial vehicle brake chambers has the function of measuring PCR in real time. Finally, according to the MSA (Measurement System Analysis) evaluation method, the performance of the PCR test system for commercial vehicle brake chambers is analyzed, and the correctness and applicability of the test system are verified. [ABSTRACT FROM AUTHOR]

Herein, we propose a system for a snapshot video hyperspectral imaging method based on a uniformly distributed-slit array (UDA) coding plate that not only effectively improves the scanning speed of spectrometers but also achieves a high spectral fidelity of snapshot videos. A mathematical model and optical link simulation of the new system are established. The analysis results show that the proposed method can more efficiently collect information and restore the spectral data cube, and the spectral smile of the system is less than 4.86 μm. The results of the spectral performance and external imaging tests of the system show that the system has the ability to collect spatial spectrum video information with a frame rate of 10 Hz and identify dynamic targets, laying a foundation for the design of a system with a higher frame rate and resolution. [ABSTRACT FROM AUTHOR]

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances. [ABSTRACT FROM AUTHOR]

This paper presents the LC-type passive wireless sensing system for the simultaneous and independent detection of triple parameters, featuring three different capacitive sensors controlled by two mechanical switches. The sensor coil was connected with three different capacitors in parallel and two mechanical switches were in series between every two capacitors, which made the whole system have three resonant frequencies. The readout coil was magnetically coupled with the sensor coil to interrogate the sensor wirelessly. The circuit was simulated advanced design system (ADS) software, and the LC sensor system was mathematically analyzed by MATLAB. Results showed that the proposed LC sensing system could test three different capacitive sensors by detecting three different resonant frequencies. The sensitivity of sensors could be determined by the capacitance calculated from the detected resonant frequencies, and the resolution of capacitance was 0.1 PF and 0.2 PF when using the proposed sensor system in practical applications. To validate the proposed scheme, a PCB inductor and three variable capacitors were constructed with two mechanical switches to realize the desired system. Experimental results closely verified the simulation outputs. [ABSTRACT FROM AUTHOR]

A large-dynamic-range and high-stability phase demodulation technology for fiber-optic Michelson interferometric sensors is proposed. This technology utilizes two output signals from a 2 × 2 fiber-optic coupler, the interferometric phase difference of which is π. A linear-fitting trigonometric-identity-transformation differential cross-multiplication (LF-TIT-DCM) algorithm is used to interrogate the phase signal from the two output signals from the coupler. The interferometric phase differences from the two output signals from the 2 × 2 fiber-optic couplers with different coupling ratios are all equal to π, which ensures that the LF-TIT-DCM algorithm can be applied perfectly. A fiber-optic Michelson interferometric acoustic sensor is fabricated, and an acoustic signal testing system is built to prove the proposed phase demodulation technology. Experimental results show that excellent linearity is observed from 0.033 rad to 3.2 rad. Moreover, the influence of laser wavelength and optical power is researched, and variation below 0.47 dB is observed at different sound pressure levels (SPLs). Long-term stability over thirty minutes is tested, and fluctuation is less than 0.36 dB. The proposed phase demodulation technology obtains large dynamic range and high stability at rather low cost. [ABSTRACT FROM AUTHOR]

Presently, in the event of a failure in Automated Driving Systems, control architectures rely on hardware redundancies over software solutions to assure reliability or wait for human interaction in takeover requests to achieve a minimal risk condition. As user confidence and final acceptance of this novel technology are strongly related to enabling safe states, automated fall-back strategies must be assured as a response to failures while the system is performing a dynamic driving task. In this work, a fail-operational control architecture approach and dead-reckoning strategy in case of positioning failures are developed and presented. A fail-operational system is capable of detecting failures in the last available positioning source, warning the decision stage to set up a fall-back strategy and planning a new trajectory in real time. The surrounding objects and road borders are considered during the vehicle motion control after failure, to avoid collisions and lane-keeping purposes. A case study based on a realistic urban scenario is simulated for testing and system verification. It shows that the proposed approach always bears in mind both the passenger’s safety and comfort during the fall-back maneuvering execution. [ABSTRACT FROM AUTHOR]

Although microprisms have become an important medical means of strabismus treatment, related research concerning the design, fabrication, and testing of microprismatic glasses for preventing eyestrain has rarely been reported. In this study, the structure of microprismatic glasses for preventing eyestrain related to using electronic monitors, including computers and mobile phones, is introduced. A designing theory of anti-fatigue glasses with microprisms is developed. The fabrication technique and the process are described, and the performances of the fabricated microprisms are characterized. Finally, a compact testing system for the measurement of prismatic diopter is designed and constructed. This measuring system can be used not only for Fresnel microprisms, but also for other types of prisms. The measured results agree with our calculations. Although this study is focused on optimizing the objective prismatic diopter for anti-fatigue microprismatic glasses, 2.0–3.0 prismatic diopters (Δ) for each eye in the anti-fatigue glasses are suggested according to our experience on strabismus treatments. The clinical research for patients using the developed anti-fatigue glasses will be fully implemented in our further research to confirm the optimal subjective prismatic value. [ABSTRACT FROM AUTHOR]