Sensors

This study aimed to improve the sensitivity of ferrous particle sensors used in various mechanical systems such as engines to detect abnormalities by measuring the number of ferrous wear particles generated by metal-to-metal contact. Existing sensors collect ferrous particles using a permanent magnet. However, their ability to detect abnormalities is limited because they only measure the number of ferrous particles collected on the top of the sensor. This study provides a design strategy to boost the sensitivity of an existing sensor using a multi-physics analysis method, and a practical numerical method was recommended to assess the sensitivity of the enhanced sensor. The sensor’s maximum magnetic flux density was increased by around 210% compared to the original sensor by changing the core’s form. In addition, in the numerical evaluation of the sensitivity of the sensor, the suggested sensor model has improved sensitivity. This study is important because it offers a numerical model and verification technique that may be used to enhance the functionality of a ferrous particle sensor that uses a permanent magnet. Full article

Achieving carbon neutrality is important to solve environmental problems and thus requires decarbonizing manufacturing processes to reduce greenhouse gas emissions. The firing of ceramics, including calcination and sintering, is a typical fossil fuels-driven manufacturing process that requires large power consumption. Although the firing process in manufacturing ceramics cannot be eliminated, an effective firing strategy to reduce processing steps can be a choice to lower power consumption. Herein, we suggest a one-step solid solution reaction (SSR) route to manufacture (Ni, Co, and Mn)O₄ (NMC) electroceramics for their application in temperature sensors with negative temperature coefficient (NTC). Additionally, the effect of the one-step SSR route on the electrical properties of the NMC is investigated. Similar to the NMC prepared using the two-step SSR route, spinel structures with dense microstructure are observed in the NMC prepared via the one-step SSR route. Based on the experimental results, the one-step SSR route can be considered as one of the effective processing techniques with less power consumption to manufacture electroceramics. Full article

Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor’s algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The NIST has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two PQC algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing. Full article

Trajectory data has gained increasing attention in the transportation industry due to its capability of providing valuable spatiotemporal information. Recent advancements have introduced a new type of multi-model all-traffic trajectory data which provides high-frequency trajectories of various road users, including vehicles, pedestrians, and bicyclists. This data offers enhanced accuracy, higher frequency, and full detection penetration, making it ideal for microscopic traffic analysis. In this study, we compare and evaluate trajectory data collected from two prevalent roadside sensors: LiDAR and camera (computer vision). The comparison is conducted at the same intersection and over the same time period. Our findings reveal that current LiDAR-based trajectory data exhibits a broader detection range and is less affected by poor lighting conditions compared to computer vision-based data. Both sensors demonstrate acceptable performance for volume counting during daylight hours, but LiDAR-based data maintains more consistent accuracy at night, particularly in pedestrian counting. Furthermore, our analysis demonstrates that, after applying smoothing techniques, both LiDAR and computer vision systems accurately measure vehicle speeds, while vision-based data show greater fluctuations in pedestrian speed measurements. Overall, this study provides insights into the advantages and disadvantages of LiDAR-based and computer vision-based trajectory data, serving as a valuable reference for researchers, engineers, and other trajectory data users in selecting the most appropriate sensor for their specific needs. Full article

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection. Full article

Underwater vehicles can operate independently in the exploitation of marine resources. However, water flow disturbance is one of the challenges underwater vehicles must face. The underwater flow direction sensing method is a feasible way to overcome the challenges but faces difficulties such as integrating the existing sensors with underwater vehicles and high-cost maintenance fees. In this research, an underwater flow direction sensing method based on the thermal tactility of the micro thermoelectric generator (MTEG) is proposed, with the theoretical model established. To verify the model, a flow direction sensing prototype is fabricated to carry out experiments under three typical working conditions. The three typical flow direction conditions are: condition No. 1, in which the flow direction is parallel to the x-axis; condition No. 2, in which the flow direction is at an angle of 45° to the x-axis; and condition No. 3, which is a variable flow direction condition based on condition No. 1 and condition No. 2. According to the experimental data, the variations and orders of the prototype output voltages under three conditions fit the theoretical model, which means the prototype can identify the flow direction of three conditions. Besides, experimental data show that in the flow velocity range of 0~5 m/s and the flow direction variation range of 0~90°, the prototype can accurately identify the flow direction in 0~2 s. The first time utilizing MTEG on underwater flow direction perception, the underwater flow direction sensing method proposed in this research is cheaper and easier to be applied on the underwater vehicles than traditional underwater flow direction sensing methods, which means it has great application prospects in underwater vehicles. Besides, the MTEG can utilize the waste heat of the underwater vehicle battery as the energy source to achieve self-powered work, which greatly enhances its practical value. Full article

This study aimed to evaluate 2D magnetic flux leakage (MFL) signals (B_x, B_y) in D19-size reinforcing steel with several defect conditions. The magnetic flux leakage data were collected from the defected and new specimens using an economically designed test setup incorporating permanent magnets. A two-dimensional finite element model was numerically simulated using COMSOL Multiphysics to validate the experimental tests. Based on the MFL signals (B_x, B_y), this study also intended to improve the ability to analyze defect features such as width, depth, and area. Both the numerical and experimental results indicated a high cross-correlation with a median coefficient of 0.920 and a mean coefficient of 0.860. Using signal information to evaluate defect width, the x-component (B_x) bandwidth was found to increase with increasing defect width and the y-component (B_y) amplitude rise with increasing depth. In this two-dimensional MFL signal study, both parameters of the two-dimensional defects (width and depth) affected each other and could not be evaluated individually. The defect area was estimated from the overall variation in the signal amplitude of the magnetic flux leakage signals with the x-component (B_x). The defect areas showed a higher regression coefficient (R² = 0.9079) for the x-component (B_x) amplitude from the 3-axis sensor signal. It was determined that defect features are positively correlated with sensor signals. Full article

Lane-level self-localization is essential for autonomous driving. Point cloud maps are typically used for self-localization but are known to be redundant. Deep features produced by neural networks can be used as a map, but their simple utilization could lead to corruption in large environments. This paper proposes a practical map format using deep features. We propose voxelized deep feature maps for self-localization, consisting of deep features defined in small regions. The self-localization algorithm proposed in this paper considers per-voxel residual and reassignment of scan points in each optimization iteration, which could result in accurate results. Our experiments compared point cloud maps, feature maps, and the proposed map from the self-localization accuracy and efficiency perspective. As a result, more accurate and lane-level self-localization was achieved with the proposed voxelized deep feature map, even with a smaller storage requirement compared with the other map formats. Full article

This study involved channel modeling and characteristics analysis of unmanned aerial vehicles (UAVs) according to different operating trajectories. Based on the idea of standardized channel modeling, air-to-ground (AG) channel modeling of a UAV was carried out, taking into consideration that both the receiver (Rx) and the transmitter (Tx) ran along different types of trajectories. In addition, based on Markov chains and a smooth-turn (ST) mobility model, the influences of different operation trajectories on typical channel characteristics—including time-variant power delay profile (PDP), stationary interval, temporal autocorrelation function (ACF), root mean square (RMS) delay spread (DS), and spatial cross-correlation function (CCF)—were studied. The multi-mobility multi-trajectory UAV channel model matched well with actual operation scenarios, and the characteristics of the UAV AG channel could be analyzed more accurately, thus providing a reference for future system design and sensor network deployment of sixth-generation (6G) UAV-assisted emergency communications. Full article

Technological development in terms of the power requirement for data acquisition and processing opens new perspectives in the field of environmental monitoring. Near real-time data flow about the sea condition and a possible direct interface with applications and services devoted to marine weather networks would have a significant impact on several aspects, such as, for example, safety and efficiency. In this scenario, the needs of buoy networks have been analyzed, and the estimation of directional wave spectra from buoys’ data has been deeply investigated. Two methods have been implemented, namely the truncated Fourier series and the weighted truncated Fourier series, and they have been tested by both simulated and real experimental data, representative of typical Mediterranean Sea conditions. From simulation, the second method proved to be more efficient. From the application to real case studies, it emerged that it works effectively in real conditions, as confirmed by parallel meteorological observations. The estimation of the main propagation direction was possible with a small uncertainty of a few degrees, yet the method exhibits a limited directional resolution, which suggests the need for undertaking further studies, briefly addressed in the conclusions. Full article

In computational photography, high dynamic range (HDR) imaging refers to the family of techniques used to recover a wider range of intensity values compared to the limited range provided by standard sensors. Classical techniques consist of acquiring a scene-varying exposure to compensate for saturated and underexposed regions, followed by a non-linear compression of intensity values called tone mapping. Recently, there has been a growing interest in estimating HDR images from a single exposure. Some methods exploit data-driven models trained to estimate values outside the camera’s visible intensity levels. Others make use of polarimetric cameras to reconstruct HDR information without exposure bracketing. In this paper, we present a novel HDR reconstruction method that employs a single PFA (polarimetric filter array) camera with an additional external polarizer to increase the scene’s dynamic range across the acquired channels and to mimic different exposures. Our contribution consists of a pipeline that effectively combines standard HDR algorithms based on bracketing and data-driven solutions designed to work with polarimetric images. In this regard, we present a novel CNN (convolutional neural network) model that exploits the underlying mosaiced pattern of the PFA in combination with the external polarizer to estimate the original scene properties, and a second model designed to further improve the final tone mapping step. The combination of such techniques enables us to take advantage of the light attenuation given by the filters while producing an accurate reconstruction. We present an extensive experimental section in which we validate the proposed method on both synthetic and real-world datasets specifically acquired for the task. Quantitative and qualitative results show the effectiveness of the approach when compared to state-of-the-art methods. In particular, our technique exhibits a PSNR (peak signal-to-noise ratio) on the whole test set equal to 23 dB, which is $18\%$ better with respect to the second-best alternative. Full article

Conventional designs of an avalanche photodiode (APD) have been based on a planar p–n junction since the 1960s. APD developments have been driven by the necessity to provide a uniform electric field over the active junction area and to prevent edge breakdown by special measures. Most modern silicon photomultipliers (SiPM) are designed as an array of Geiger-mode APD cells based on planar p–n junctions. However, the planar design faces an inherent trade-off between photon detection efficiency and dynamic range due to loss of an active area at the cell edges. Non-planar designs of APDs and SiPMs have also been known since the development of spherical APDs (1968), metal-resistor-semiconductor APDs (1989), and micro-well APDs (2005). The recent development of tip avalanche photodiodes (2020) based on the spherical p–n junction eliminates the trade-off, outperforms the planar SiPMs in the photon detection efficiency, and opens new opportunities for SiPM improvements. Furthermore, the latest developments in APDs based on electric field-line crowding and charge-focusing topology with quasi-spherical p–n junctions (2019–2023) show promising functionality in linear and Geiger operating modes. This paper presents an overview of designs and performances of non-planar APDs and SiPMs. Full article

Precision object handling and manipulation require the accurate positioning of industrial robots. A common practice for performing end effector positioning is to read joint angles and use industrial robot forward kinematics (FKs). However, industrial robot FKs rely on the robot Denavit–Hartenberg (DH) parameter values, which include uncertainties. Sources of uncertainty associated with industrial robot FKs include mechanical wear, manufacturing and assembly tolerances, and robot calibration errors. It is therefore necessary to increase the accuracy of DH parameter values to reduce the impact of uncertainties on industrial robot FKs. In this paper, we use differential evolution, particle swarm optimization, an artificial bee colony, and a gravitational search algorithm to calibrate industrial robot DH parameters. A laser tracker system, Leica AT960-MR, is utilized to register accurate positional measurements. The nominal accuracy of this non-contact metrology equipment is less than 3 μm/m. Metaheuristic optimization approaches such as differential evolution, particle swarm optimization, an artificial bee colony and a gravitational search algorithm are used as optimization methods to perform the calibration using laser tracker position data. It is observed that, using the proposed approach with an artificial bee colony optimization algorithm, the accuracy of industrial robot FKs in terms of mean absolute errors of static and near-static motion over all three dimensions for the test data decreases from its measured value of 75.4 μm to 60.1 μm (a 20.3% improvement). Full article

Studying the nonlinear photoresponse of different materials, including III-V semiconductors, two-dimensional materials and many others, is attracting burgeoning interest in the terahertz (THz) field. Especially, developing field-effect transistor (FET)-based THz detectors with preferred nonlinear plasma-wave mechanisms in terms of high sensitivity, compactness and low cost is a high priority for advancing performance imaging or communication systems in daily life. However, as THz detectors continue to shrink in size, the impact of the hot-electron effect on device performance is impossible to ignore, and the physical process of THz conversion remains elusive. To reveal the underlying microscopic mechanisms, we have implemented drift-diffusion/hydrodynamic models via a self-consistent finite-element solution to understand the dynamics of carriers at the channel and the device structure dependence. By considering the hot-electron effect and doping dependence in our model, the competitive behavior between the nonlinear rectification and hot electron-induced photothermoelectric effect is clearly presented, and it is found that the optimized source doping concentrations can be utilized to reduce the hot-electron effect on the devices. Our results not only provide guidance for further device optimization but can also be extended to other novel electronic systems for studying THz nonlinear rectification. Full article

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection. Full article

The results of magnetoresistance (MR) and resistance relaxation of nanostructured La_1−xSr_xMn_yO₃ (LSMO) films with different film thicknesses (60–480 nm) grown on Si/SiO₂ substrate by the pulsed-injection MOCVD technique are presented and compared with the reference manganite LSMO/Al₂O₃ films of the same thickness. The MR was investigated in permanent (up to 0.7 T) and pulsed (up to 10 T) magnetic fields in the temperature range of 80–300 K, and the resistance-relaxation processes were studied after the switch-off of the magnetic pulse with an amplitude of 10 T and a duration of 200 μs. It was found that the high-field MR values were comparable for all investigated films (~−40% at 10 T), whereas the memory effects differed depending on the film thickness and substrate used for the deposition. It was demonstrated that resistance relaxation to the initial state after removal of the magnetic field occurred in two time scales: fast’ (~300 μs) and slow (longer than 10 ms). The observed fast relaxation process was analyzed using the Kolmogorov–Avrami–Fatuzzo model, taking into account the reorientation of magnetic domains into their equilibrium state. The smallest remnant resistivity values were found for the LSMO films grown on SiO₂/Si substrate in comparison to the LSMO/Al₂O₃ films. The testing of the LSMO/SiO₂/Si-based magnetic sensors in an alternating magnetic field with a half-period of 22 μs demonstrated that these films could be used for the development of fast magnetic sensors operating at room temperature. For operation at cryogenic temperature, the LSMO/SiO₂/Si films could be employed only for single-pulse measurements due to magnetic-memory effects. Full article

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics. Full article

We propose an algorithm for generating a panoramic image of a pipe’s inner surface based on inverse perspective mapping (IPM). The objective of this study is to generate a panoramic image of the entire inner surface of a pipe for efficient crack detection, without relying on high-performance capturing equipment. Frontal images taken while passing through the pipe were converted to images of the inner surface of the pipe using IPM. We derived a generalized IPM formula that considers the slope of the image plane to correct the image distortion caused by the tilt of the plane; this IPM formula was derived based on the vanishing point of the perspective image, which was detected using optical flow techniques. Finally, the multiple transformed images with overlapping areas were combined via image stitching to create a panoramic image of the inner pipe surface. To validate our proposed algorithm, we restored images of pipe inner surfaces using a 3D pipe model and used these images for crack detection. The resulting panoramic image of the internal pipe surface accurately demonstrated the positions and shapes of cracks, highlighting its potential utility for crack detection using visual inspection or image-processing techniques. Full article