Resolution Of Sensor Research Articles

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions

Distributed Persistent Coverage Control over Importance-based Environments with Quadrotors⋆

This paper proposes a distributed persistent coverage strategy for environments with important regions. The strategy involves using quadrotors at varying altitudes to acquire high-resolution information from these regions. The information is then transmitted through distributed communication protocols that use local coverage maps to retain periodic and high-quality data. To resolve the trade-off between high-resolution data from low altitudes and wider coverage from high altitudes, cost functions based on distance, coverage, and sensor resolution are used to determine three-dimensional waypoints. Further, Voronoi tessellations are used to aid in collision avoidance and efficient computation. To generate feasible trajectories, the quadrotor's dynamics are considered, along with error functions that use state information and desired waypoints. Simulation results demonstrate the efficacy of the proposed approach for multi-agent systems under quadrotor dynamics.

A doped-polymer based porous silicon photonic crystal sensor for the detection of gamma-ray radiation.

In this research, a theoretical investigation of the one-dimensional defective photonic crystals is considered for the detection of gamma-ray radiation. Each unit cell of the considered one-dimensional photonic crystals (1D PhCs) is composed of two layers designed from porous silicon infiltrated by poly-vinyl alcohol polymer doped with crystal violet (CV) and carbol fuchsine (CF) dyes (doped-polymer) with different porosity. In addition, a single layer of doped-polymer is included in the middle of the designed 1D PhCs to stimulate the localization of a distinct resonant wavelength through the photonic band gap. In particular, the appearance of this resonant mode represents the backbone of our study towards the detection of γ-ray radiation with doses from 0 to 70 Gy. The Bruggeman's effective medium equation, the fitted experimental data to the refractive index of the doped-polymer, and the Transfers Matrix Method (TMM) serve as the mainstay of our theoretical treatment. The numerical findings provide significant contributions to some of the governing parameters such as the thicknesses of the considered materials on the performance of the presented sensor, the effect of incidence angle and the porosity of the considered materials on the resonance wavelength. In this regard, at optimum values of these parameters the sensitivity, quality factor, signal-to-noise ratio, detection limit, sensor resolution, and figure of merit that are obtained are 205.7906 nm RIU-1, 9380.483, 49.315, 2.05 × 10-5 RIU, 3.27 × 10-5, and 2429.31 RIU-1, respectively. Therefore, we believe that the suggested design could be of significant interest in many industrial, medical, and scientific applications.

Speed Measurement Method for Moving Conductors Based on Motion-Induced Eddy Current

Speed measurement plays an important role in many measurement and control systems in industry. A simple and efficient speed measurement method for smooth conductive specimens based on motion-induced eddy currents is studied in this paper. The speed sensor consists of a magnet and a Hall element. When the conductor moves beneath the magnet, eddy currents are generated, which further generate a secondary magnetic field that is dependent on the moving speed of the conductor. The Hall element picks up this field to determine the relative speed between the conductor and the sensor. First of all, a 2D analytical model for the induced eddy currents and corresponding magnetic fields has been proposed by solving the Maxwell’s equations using spatial Fourier transforms. Then, the change in current density and magnetic field with conductor speed is analyzed using both the analytical model and the finite element method. Speed sensing experiments were conducted on a rotating conductive disk, the results showed good agreement with the proposed analytical model. Finally, specifications such as accuracy, repeatability, linearity, and resolution of the prototype sensor have been tested.

An AlN Piezoelectric Micromachined Ultrasonic Transducer-Based Liquid Density Sensor

As an important physical parameter of liquids, the liquid density plays an important role in many applications. As a result, the development of liquid density sensors is critical. However, current liquid density sensors have drawbacks, such as large size of conventional liquid density sensors, low quality factor of microcantilever beam structures, low sensitivity of quartz crystal tuning forks, and high operating frequency requirement of surface-acoustic-wave Love-mode resonators. In this article, we report an aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT)-based liquid density sensor to mitigate the aforementioned drawbacks of traditional liquid density sensors. The PMUT sensor utilizes CMOS-compatible AlN as the piezoelectric material. This sensor operates by measuring the change in resonant frequency of the PMUT to calculate the liquid density. In an experiment, the resonance of the PMUT sensor is measured by a digital holographic microscope (DHM), which allows direct measurement of the PMUT surface displacement, providing intuitive vibration amplitude and mode. Moreover, the DHM-based optical measurement approach has a higher sensitivity than the conventional electrical impedance-based measurement approach, allowing measurement of weak PMUT vibrations in dense liquids. The quality factor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> ) value of the PMUT sensor is measured to be 49.38 in water. A high sensitivity of 12.71 kHz/( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{g}\cdot $ </tex-math></inline-formula> cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{3}}$ </tex-math></inline-formula> ) was determined for this sensor by measuring the resonant frequencies immersed in different glycerol aqueous solutions. We measured 12 PMUTs and the maximum deviation of resonant frequency between different PMUTs is 9.85%. For the first time, the resolution of the PMUT density sensor is determined to be <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times 10^{-{4}}\,\,\text{g}\cdot $ </tex-math></inline-formula> cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{-{3}}$ </tex-math></inline-formula> .

Principle and Application of Flexible Pressure Sensors

Flexible pressure sensors are widely used in many ways, including health care and machine sensors. Compared with the traditional flexible pressure sensor, flexible pressure sensor has quality is light, easy to carry and deformation degree higher advantages are modern science and technology advanced has broad prospects for the development of technology products. In recent years, Remarkable progress has been made in the field of flexible pressure sensors. However, it is still a big challenge to realize the high resolution, high sensitivity, fast response, low-cost manufacturing and complex signal detection of flexible pressure sensors. This paper will introduce the mechanism of the flexible pressure sensor and improve the sensitivity by using the microstructure and the practical application. The research in this paper will have a very important value for the research and application of the flexible pressure sensor.

A GLRT-Based Multi-Pixel Target Detector in Hyperspectral Imagery

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Hyperspectral target detection is a task of primary importance in remote sensing since it allows for location and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with an analogous counterpart by showing the effectiveness of the proposed procedure.

Abundance Matrix Correlation Analysis Network Based on Hierarchical Multihead Self-Cross-Hybrid Attention for Hyperspectral Change Detection

Hyperspectral image (HSI) change detection is a technique for detecting the changes between the multitemporal HSIs of the same scene. Many existing change detection methods have achieved good results, but there still exist problems as follows: 1) mixed pixels exist in HSI due to the low spatial resolution of hyperspectral sensor and other external interference and 2) many existing deep learning-based networks cannot make full use of the correlation difference information between the bitemporal images. These problems are not conducive to further improving the accuracy of change detection. In this article, we propose an abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention (AMCAN-HMSchA) for HSI change detection, which hierarchically highlights the correlation difference information at the subpixel level to detect the subtle changes. The endmember sharing-based abundance matrix learning module (AMLM) maps the changed information between bitemporal HSIs to the corresponding abundance matrices. The hierarchical MSchA extracts the enhanced difference features by constantly comparing the self-correlation with cross correlation between the abundance matrices of the HSIs. Then, the difference features are concatenated and fed into the fully connected layers to obtain the change map. Experiments on three widely used datasets show that the proposed method has superior performance compared with other state-of-the-art methods.

Robust Symmetry Prediction with Multi-Modal Feature Fusion for Partial Shapes

In geometry processing, symmetry research benefits from global geometric features of complete shapes, but the shape of an object captured in real-world applications is often incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Different from the existing works predicting symmetry from the complete shape, we propose a learning approach for symmetry prediction based on a single RGB-D image. Instead of directly predicting the symmetry from incomplete shapes, our method consists of two modules, i.e., the multi-modal feature fusion module and the detection-by-reconstruction module. Firstly, we build a channel-transformer network (CTN) to extract cross-fusion features from the RGB-D as the multi-modal feature fusion module, which helps us aggregate features from the color and the depth separately. Then, our self-reconstruction network based on a 3D variational auto-encoder (3D-VAE) takes the global geometric features as input, followed by a prediction symmetry network to detect the symmetry. Our experiments are conducted on three public datasets: ShapeNet, YCB, and ScanNet, we demonstrate that our method can produce reliable and accurate results.

Collaborative representation with hole filling techniques for Kinect RGB-D face recognition

We present in this paper, three different filtering techniques based on kernel function to fill missing information in depth map images obtained from low resolution sensor such as Kinect to improve the performance accuracy of RGB-D face recognition systems. We propose in this study, an RGB-D face recognition scheme that combines depth map and colour image using wavelet average fusion followed by collaborative representation classifier (CRC) for comparison of reference and probe images. We present the evaluation results based on our GU-RGBD face database and IIIT-D face database to present the significance of our three different filters employed in hole filling. Further, our investigation presents an extensive experimental analysis using eight different feature extraction techniques independently across three different filters to demonstrate the potential of our proposed approach. The proposed approach of hole filling, improves the performance accuracy of RGB-D face recognition as compared to without employing filtering operations.

Physics-Based Fusion of Sentinel-2 and Sentinel-3 for Higher Resolution Vegetation Monitoring

Monitoring vegetation growth, phenology and health in agriculture requires very high spatial and spectral resolution sensors. Sentinel-2 is among the spaceborne sensors that tried to meet these requirements. However, due to physical constraints, few visible bands are present with relatively large spectral responses which limits the leaf pigment content estimation. In this work, we propose to fuse Sentinel-2 bands with a Sentinel-3 image which has a lower spatial resolution but contains several spectral narrower bands in both the visible and near-infrared domains. The fusion procedure consists in sharpening the Sentinel-3 bands to match the Sentinel-2 spatial resolution leading to a higher spatial resolution Sentinel-3 image. The proposed fusion technique follows a physics-based approach based on the use of a radiative transfer model for establishing a correspondence between Sentinel-2 and Sentinel-3 images. In greater details, the main spectrally pure constituent of each high resolution Sentinel-2 pixel is identified for each pixel and it is then related to the corresponding low resolution Sentinel-3 pixel. Pure materials are the barycenters of clusters obtained by an unsupervised classification of the Sentinel-2 image. Sentinel-3 signatures are obtained using coarse resolution pixel matching with the Sentinel-2 image. The latter result is then corrected using radiative transfer modeling allowing the production of more realistic signatures. Validation was done using real Sentinel-2/Sentinel-3 images taken with a delay of only one day or less and by comparing the sharpened Sentinel-3 bands Oa04, Oa06, Oa08, and Oa17 with the corresponding Sentinel-2 bands B2, B3, B4, and B8A, as they are respectively spectrally close. For all the bands, the RMS is lower than 0.007, and compared to the state-of-the-art techniques it shows competitive results and robustness against scene heterogeneity. Besides, our findings prove that the retrieved Sentinel-3 signatures at full resolution are physically consistent and in good agreement with the Sentinel-2 data.

Spatial response resampling (SR2): Accounting for the spatial point spread function in hyperspectral image resampling

With the increased availability of hyperspectral imaging (HSI) data at various scales (0.03–30 m), the role of simulation is becoming increasingly important in data analysis and applications. There are few commercially available tools to spatially degrade imagery based on the spatial response of a coarser resolution sensor. Instead, HSI data are typically spatially degraded using nearest neighbor, pixel aggregate or cubic convolution approaches. Without accounting for the spatial response of the simulated sensor, these approaches yield unrealistically sharp images. This article describes the spatial response resampling (SR2) workflow, a novel approach to degrade georeferenced raster HSI data based on the spatial response of a coarser resolution sensor. The workflow is open source and widely available for personal, academic or commercial use with no restrictions. The importance of the SR2 workflow is shown with three practical applications (data cross-validation, flight planning and data fusion of separate VNIR and SWIR images).•The SR2 workflow derives the point spread function of a specified HSI sensor based on nominal data acquisition parameters (e.g., integration time, altitude, speed), convolving it with a finer resolution HSI dataset for data simulation.•To make the workflow approachable for end users, we provide a MATLAB function that implements the SR2 methodology.

Bond Graph Modeling, Simulation, and Control of Permanent Magnet Linear Synchronous Motor

The high-performance feature of the Permanent Magnet Linear Synchronous Motor (PMLSM) makes it a reliable and valuable motor for use in the automotive industry, especially for electric vehicle (EVs) applications. This research proposes a bond graph approach in modeling the PMLSM as a multi-domain dynamical system. However, A time-based simulation was performed using 20-sim software to simulate the dynamical behavior of the motor. An equivalent model of the motor was first obtained and then modeled and simulated using 20-sim software. The model of the PMLSM drive system was modeled separately and incorporated with PMLSM Motor equivalent model to form a global model. Moreover, the motor drive system response was studied based on the sensor resolutions and the inverter switching frequency. The block diagram and the transfer function methods validated the bond graph model obtained. Two classical PIs such as continuous and discrete were implemented on the motor response to control the velocity of the motor.

A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations

Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (&lt;50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.

A stability and spatial-resolution enhanced laser absorption spectroscopy tomographic sensor for complex combustion flame diagnosis

A novel stable laser absorption spectroscopy (LAS) tomographic sensor with enhanced stability and spatial resolution is developed and applied to complex combustion flame diagnosis. The sensor reduces the need for laser collimation and alignment even in extremely harsh environments and improves the stability of the received laser signal. Furthermore, a new miniaturized laser emission module was designed to achieve multi-degree of freedom adjustment. The full optical paths can be sampled by 8 receivers, with such arrangement, the equipment cost can be greatly reduced, at the same time, the spatial resolution is improved. In fact, 100 emitted laser paths are realized in a limited space of 200mm×200 mm with the highest spatial resolution of 1.67mm×1.67 mm. The stability and penetrating spatial resolution of the LAS tomographic sensor were validated by both simulation and field experiments on the afterburner flames. Tests under two representative experiment states, i.e., the main combustion and the afterburner operation states, were conducted. Results show that the error under the main combustion state was about 4.32% and, 5.38% at the afterburner operation state. It has been proven that this proposed sensor can provide better tomographic measurements for combustion diagnosis, as an effective tool for improving performances of afterburners.

Enhancing UAV images to improve the observation of submerged whales using a water column correction method

Enhancing <scp>UAV</scp> images to improve the observation of submerged whales using a water column correction method

Quad core gold coated photonic crystal fiber temperature sensor based on surface plasmon resonance

This paper shows a simple quad core Photonic Crystal Fiber (PCF) temperature sensor based on Surface Plasmon Resonance (SPR) which has very high sensitivity and sensor resolution. For evaluating the sensing performance Finite Element Method (FEM) is used. In this design, gold is used as plasmonic material and the outer layer of the designed PCF has to be filled by it for making the fabrication easier than any other model. Besides, the Chloroform is used as the temperature sensitive liquid (analyte). The designed temperature sensor has the maximum wavelength sensitivities which are 7.5 nm/°C and 10.5 nm/°C for x and y-polarization respectively. The sensor shows the resolution of 13.33 × 10−3 RIU for x-polarization and 9.52 × 10−3 RIU for y-polarization. Core diameter, radius of air hole, thickness of gold and pitch are the structural parameters which are adjusted to their optimized value for getting higher sensitivity. Easy fabrication and simplicity are the major aspects of this proposed model which makes it applicable to various applications such as medical science, power plant, engineering sector, environment monitoring etc.

The research on improving the spatial resolution of radiant optical fiber temperature sensor

A method to improve the spatial resolution of the radiant optical fiber temperature sensor is proposed in this paper. The front lens of the sensor is designed to limit the diameter of the thermometry target. In order to avoid limiting the influence of the thermometry target diameter on the sensor radiation-light coupling ability, a mathematical model of the radiation-light coupling of the sensor is established, and the relationship between the radiation-light coupling ability of the sensor and the diameter of the thermometry target is analyzed. Furthermore, an aspheric lens is applied to reduce the influence of aberration on the diameter of the thermometry target. The experimental results show that the deviation between the actual temperature measuring target diameter and the theoretical design value is less than 0.2 mm. The radiation received by the sensor outside the temperature measuring target is less than 10% of its total received radiation. Moreover, when there is radiated light interference outside the temperature measurement target, the temperature measurement error of the sensor is only 0.21%–2.61%, indicating that the proposed method has practical significance in improving the spatial resolution of the temperature measurement.

Analysis and Correction of Measurement Error of Spherical Capacitive Sensor Caused by Assembly Error of the Inner Frame in the Aeronautical Optoelectronic Pod.

The ball joint is a multi-degree-of-freedom transmission pair, if it can replace the inner frame in the aviation photoelectric pod to carry the optical load, which will greatly simplify the system structure of the photoelectric pod and reduce the space occupied by the inner frame. However, installation errors in ball joint siting introduce nonlinear errors that are difficult to correct and two degree of freedom angular displacement of the ball joint is difficult to detect, which limits application in the precision control of two degrees of freedom systems. Studies of spherical capacitive sensors to date have not tested sensors for use in an inner frame stabilisation mechanism nor have they analysed the influence of installation error on sensor output. A two-axis angular experimental device was designed to measure the performance of a ball joint capacitive sensor in a frame stabilisation mechanism in an aeronautical optoelectronic pod, and a mathematical model to compensate for ball joint capacitive sensor installation error was created and tested. The experimental results show that the resolution of the capacitive sensor was 0.02° in the operating range ±4°, the repeatability factor was 0.86%, and the pulse response time was 39 μs. The designed capacitive sensor has a simple structure, high measurement accuracy, and strong robustness, and it can be integrated into ball joint applications in the frames of aeronautical photoelectric pods.