Sensor Distortion Research Articles

A braided construction of ionogels as a wearable sensor for human motion monitoring

A highly stable flexible sensor from the braided organization of linear ionogel (IG-BPS) was reported in this work to monitor the human physiological signals and daily movements. The compression effect in the normal direction arisen from the longitudinal stretching is prone to sensor distortion and energy dissipation. This braid construction can effectively reduce the interference of stretching on the stress sensing accuracy. The piezoresistive variations in the longitude and latitude directions of the braided structure can be mutually compensated during sensor deformation, simultaneously generating ample conductive pathway to attenuate hysteresis behaviors significantly. The IG-BPS features great flexibility with broad pressure applications (0–30kPa), high sensing behavior (S=0.137 kPa−1, GF=0.029) and stability (>1000 cycles), which can be fabricated in a simple but efficient strategy. This sensor is capable to accurately detect normal movement and subtle epidermal pulse waves at various locations of human body (heart beating and pulse bounding), which is crucial for real-time tracking the changes in overall wellness status especially the cardiovascular health went through an intensive exercise process. It is expected that this braided organization sensor would act as an exciting part in sports-level tracking, healthcare assessment as well as human-machine interactions development.

Distortion Evaluation of EMP Sensors Using Associated-Hermite Functions

Electromagnetic pulse (EMP) is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum, which usually disrupts communications and damages electronic equipment and system. It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum. Therefore, analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results. Waveform fidelity is usually employed to evaluate the distortion of an antenna. However, this metric depends on specific signal waveforms, thus is unsuitable for evaluating and analyzing the distortion of EMP sensors. In this paper, an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed, which is general and independent from individual waveforms. The system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite functions. Distortion of a sensor vs. frequency is then visualized via the system transfer matrix, which is convenient in quantitative analysis of the distortion. Measurement of a current probe, a coaxial pulse voltage probe and a B-field sensor were performed, based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.

Quantification of Uncertainty and Its Applications to Complex Domain for Autonomous Vehicles Perception System

Over the last decades,deep neural networks have been penetrated into all fields of science and the real world. As a result of the lack of quantifiable data and model uncertaint, deep learning is frequently brittle,illogical, and challenging to provide trustworthy assurance for autonomous vehicles(AV) perception.This hole is filled by the suggested approach to uncertainty quantification. Nevertheless, most of the previous studies focused on the methodology and there is still a lack of research on the application of AV. To our knowledge, this paper is the first time to review the application of uncertainty in the field of AV perception and localization. In the first place, this paper analyzes the sources of uncertainty in autonomous perception,including the uncertainty brought on by sensor internal and external factors as well as the sensor distortion caused on by complex scenes. In the second place,we propose an evaluation criterion and use the criterion to carry out a quantitative analysis of perception field of application for autonomous vehicles,and we discuss the mainstream datasets. Thirdly, we put forward a number of open issues and raise some future research directions, which is of guiding significance to readers who are beginning to enter this field.We believe that epistemic uncertainty is currently the dominant research direction and that there is still a long way to go in the study of aleatoric uncertainty.And our paper will be devoted to promoting the development of uncertainty research of AV perception.

Spatio-Temporal Context Based Adaptive Camcorder Recording Watermarking

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.

A Machine Learning Strategy for Race-Tracking Detection During Manufacturing of Composites by Liquid Moulding

This work presents a supervised machine learning (ML) model to detect race-tracking disturbances during the liquid moulding manufacturing of structural composites. Race-tracking is generated by unexpected resin channels at mould edges that may induce dry spots and porosity formation. The ML model uses the pressure signals recorded by a sensor network as input, providing a classification of the race-tracking event from a set of possible scenarios, and a subsequent variable regression for their position, size and strength. Such a model is based on the residual network (ResNet), a well-known artificial intelligence architecture that makes use of convolutional neural networks for image recognition. Training of the ML classifier and regressors was carried out with the aid of a synthetically generated simulation data set obtained throughout computational fluid dynamics simulations. The time evolution of the pressure sensors was used as grey-level images, or footprints, as inputs to the ResNet ML. The trained model was able to recognise the presence of race-tracking channels from the pressure data yielding good accuracy in terms of label prediction as well as position, size and strength. The model correlation was carried out with a set of injection experiments performed with a constant thickness closed mould containing induced race-tracking channels. The ability of ML models to provide an approximation to the inverse problem, relating the pressure sensor distortions to the cause of such events, is analysed and discussed.

Combined Model Color-Correction Method Utilizing External Low-Frequency Reference Signals for Large-Scale Optical Satellite Image Mosaics

Optical satellites are affected by factors such as seasonal and atmospheric variation, illumination, and sensor distortion. Thus, satellite images covering large-scale area often show conspicuous color differences, resulting in poor color continuity of the mosaicked satellite image. This study proposes a novel combined model color correction (CMCC) method for high-resolution optical satellite images, which constructively combines a defogging model with a radiation correction model. First, this study analyzed the feasibility of using easily available low-resolution satellite images as external references to correct the color of high-resolution images and describes the selection criteria for external references. Second, considering the negative effects of atmosphere on the color and clarity of remote sensing images, we proposed an optical satellite image enhancement method, which is based on the content characteristics of remote sensing images and the dark channel prior defogging method. Finally, we designed a two-stage color correction process: 1) correcting the color of downsampled images via low-frequency modeling and replacement and 2) mapping the color of downsampled images to original images through local modeling and super-resolution color correction. Furthermore, this study proposes an indicator of quality considered mean absolute error (QCMAE) for quantitative evaluation of the color correction result. We selected 328 Gaofen-1 (GF-1) high-resolution images for the experiments. Visual effects and statistical results of images after being processed by the proposed CMCC are both superior to the three state-of-the-art methods, which verifies the effectiveness and reliability of the proposed method.

HIGH-RESOLUTION OPTICAL SATELLITE IMAGES COLOR CONSISTENCY METHOD BASED ON EXTERNAL COLOR REFERENCES

Abstract. Affected by factors such as season, illumination, atmospheric and sensor distortion, different satellite images often show obvious color difference, resulting in “stitching seams” at the edge of adjacent images, which seriously affects the application of satellite images. This study proposes a novel color consistency method for optical satellite images utilizing external color reference. Firstly, we improved the dark channel defogging method combining with the atmospheric distribution characteristics of satellite images, and used it to perform atmospheric correction on satellite images; Secondly, we corrected the color of atmospheric corrected satellite images through low-frequency signal replacement. Finally, we use a linear model to establish the relationship between high and low frequency signals, and stretching the high-frequency signal of images through local modelling. We selected two sets of representative experimental data for experiments, both the visual and quantitative obtained excellent results.

Sensor Distortion Effects in Photon Monte Carlo Simulations

Abstract We present a detailed method to simulating sensor distortions using a photon and electron Monte Carlo method. We use three-dimensional electrostatic simulations to parameterize the perturbed electric field profile for nonideal sensor details. We follow the conversion of simulated photons, and the subsequent response of the converted electrons to the electric field pattern. These nonideal sensor details can be implemented efficiently in a Monte Carlo approach. We demonstrate that the nonideal sensor distortions have a variety of observable consequence including the modification of the astrometric pattern; the distortion of the electron diffusion size and shape; and the distortion of flats. We show analytic validation of the diffusion physics, reproduce two kinds of edge distortion, and show qualitative validation of field-free regions, lithography errors, and fringing. We also demonstrate that there are two related effects of doping variation having different observable consequences. We show that field distortions from accumulated electrons lead to intensity-dependent point-spread functions and the sublinear variance in flats. The method is implemented in the Photon Simulator and the code is publicly available.

Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

Reconstruction Technique of Distorted Sensor Signals with Low-Pass NGD Function

This paper develops a new reconstruction technique of undesirable signal distortion generated by sensor electronic circuits. The introduced reconstruction technique is originally realized with unfamiliar low-pass negative group delay (NGD) function. The feasibility condition of the proposed reconstruction technique in function of the sensor signal spectrum bandwidth under consideration is elaborated. The reconstruction technique principle is theoretically introduced by means of identification of low-pass NGD function parameters and the appropriated circuit topology. The unfamiliar low-pass NGD function analysis and synthesis equations are established. As an example, for the feasibility study, an RC-network based low-pass active cell is considered to implement the low-pass NGD function. A design method of NGD circuit in function of the sensor distortion transfer function is described in different successive steps. The developed NGD reconstruction technique is validated by different proofs of concept. First, transient simulations are carried out with Gaussian and sinc analytical signals. Then, experimental feasibility study is also performed with arbitrary waveform signal. As expected, the NGD reconstruction technique efficiency is confirmed with improvement of distorted signal integrity parameters and cross correlation better than 97%.

Image sensors for precision astronomy: an introduction

The fourth workshop on Image Sensors for Precision Astronomy addresses the challenges of making state-of-the-art measurements with imperfect sensors. This article is associated with the opening talk at this workshop. The article includes a few key examples of sensor distortion with their possible impacts on today’s science goals. It also presents a few recent developments to mitigate their effects.

A Low-Power Compression-Based CMOS Image Sensor With Microshift-Guided SAR ADC

This brief presents a low-power compression-based CMOS image sensor for wireless vision applications. The sensor implements low-bit-depth imaging with planned sensor distortion (PSD) to effectively compress both data bandwidth and processing power while maintaining high reconstruction image quality. Accordingly, a column-parallel microshift-guided successive-approximation-register (SAR) ADC is proposed to enable 3-bit PSD imaging based on a ${3\times 3}$ pattern. To support normal imaging with low area overhead, the circuit is reconfigurable as an 8-bit SAR/single-slope ADC. The data bandwidth is further compressed by a customized lossless encoder based on predictive coding and run length coding. A ${256\times 216}$ prototype imaging system composed of the compression-based image sensor and the lossless encoder is fabricated in a 0.18- ${\mu }\text{m}$ standard CMOS process. Measurement results show that the image sensor achieves 3 bit/pixel (bpp) with 34.9-dB reconstruction peak signal-to-noise ratio (PSNR) and 0.91 structural similarity index (SSIM). With 1/4 spatial downsampling and lossless encoding, 0.31 bpp is obtained with 29.2-dB PSNR and 0.83 SSIM. The sensor consumes $14.8~{\mu }\text{W}$ (full resolution) and $4.3~{\mu }\text{W}$ (downsampling) at 15 fps, achieving state-of-the-art FoMs of 17.8 and 5.2 pJ/pixel $\cdot $ frame, respectively. Including the encoder, the overall system dissipates as low as $1.2~{\mu }\text{J}$ /frame, making it an attractive solution for wireless sensor networks.

Laboratory calibration of star sensors using a global refining method.

Laboratory calibration is critical to ensure the precise attitude determination of star sensors. Existing laboratory star sensor calibration methods exhibit disadvantages for large-field-of-view star sensors and large amounts of calibration data. Inspired by the least-squares method and Li's method, a global refining method is proposed to overcome the inherent disadvantages by simultaneously obtaining all of the star sensor's parameters. It first employs the maximum likelihood estimation method to optimize the initial estimation of the principal point and focal length. Next, a linear least-squares solution is used to initially estimate the star sensor distortion. Taking the installation error into account, we conduct a maximum likelihood estimation to estimate the installation angles from the estimated parameters of the first two steps. Finally, we determine a globally optimal solution to refine the star sensor parameters. Compared with the traditional method and Li's method under the same conditions, both the simulation and real data results demonstrate that the proposed method is more robust and can achieve high precision. In addition, the experimental results show that the calibration method can satisfy the precision requirements for large-field-of-view star sensors.

A micro S-shaped optical fiber temperature sensor based on dislocation fiber splice

We fabricated a simple, compact, and stable temperature sensor based on an S-shaped dislocated optical fiber. The dislocation optical fiber has two splice points, and we obtained the optimal parameters based on the theory and our experiment, such as the dislocation amount and length of the dislocation optical fiber. According to the relationship between the temperature and the peak wavelength shift, the temperature of the environment can be obtained. Then, we made this fiber a micro bending as S-shape between the two dislocation points, and the S-shaped micro bending part could release stress with the change in temperature and reduce the effect of stress on the temperature measurement. This structure could solve the problem of sensor distortion caused by the cross response of temperature and stress. We measured the S-shaped dislocation fiber sensor and the dislocation fiber without S-shape under the same environment and conditions, and the S-shaped dislocation fiber had the advantages of the stable reliability and good linearity.

Multichannel Consistent Sampling and Reconstruction Associated With Linear Canonical Transform

Multichannel sampling is fundamental in the theory of multichannel parallel analog-to-digital converters and multiplexing wireless communication. This letter investigates the multichannel consistent sampling theory associated with the linear canonical transform, which can combine the reconstruction of the signal and the correction of sensor distortions. The consistency requests that a resampling of the reconstruction by the original sampling operation yields the same measurements as before. The consistent sampling is applicable to an arbitrary finite energy signal and without the constraint such as band-limiting. Under this circumstance, two effective reconstruction methods based on the shift-invariant reconstruction space and the linear canonical basis functions space are developed. Finally, the reconstruction experiment of the radar echo signal is presented.

Event-triggered reliable [formula omitted] filter design for networked systems with multiple sensor distortions: A probabilistic partition approach

In this paper, the problem of event-triggered reliable H∞ filtering for networked systems with multiple sensor distortions is investigated. The interval of sensor distortion in each channel is partitioned several segments. By introducing a set of rand variables, the model of multiple sensor distortions with their information of probability distribution in each segment is established, which is more general than the one in open results. Furthermore, an event-triggered communication scheme is proposed to mitigate the utility of limited network bandwidth. Then a unified model with consideration of the event-triggered communication scheme and the sensor distortion is put forward. Based on this model, sufficient conditions of the mean square stability of the filtering error system and H∞ filter parameters are achieved. Finally, a simulation example is exploited to demonstrate the effectiveness of the presented method.

Mathematical Model and Calibration Procedure of a PSD Sensor Used in Local Positioning Systems.

Here, we propose a mathematical model and a calibration procedure for a PSD (position sensitive device) sensor equipped with an optical system, to enable accurate measurement of the angle of arrival of one or more beams of light emitted by infrared (IR) transmitters located at distances of between 4 and 6 m. To achieve this objective, it was necessary to characterize the intrinsic parameters that model the system and obtain their values. This first approach was based on a pin-hole model, to which system nonlinearities were added, and this was used to model the points obtained with the nA currents provided by the PSD. In addition, we analyzed the main sources of error, including PSD sensor signal noise, gain factor imbalances and PSD sensor distortion. The results indicated that the proposed model and method provided satisfactory calibration and yielded precise parameter values, enabling accurate measurement of the angle of arrival with a low degree of error, as evidenced by the experimental results.

Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods

In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discussed.

Eliminating and Modelling Non-metric Camera Sensor Distortions Caused by Sidewise and Forward Motion of the UAV

Abstract. This paper explains the critical importance of modelling and eliminating the effect of motion of an Unmanned Aerial Vehicle (UAV) as a result of air turbulence and unstable flight conditions on a camera sensor and the image geometry. A new method for improving the geometrical distortions caused by this motion is introduced. We have developed a hardware and software tool to minimize and model the geometric distortion of the image from commercial off-the-shelf (COTS) cameras which are commonly being used in aerial mapping UAVs. Due to the rolling shutter mechanism of the most common SLR cameras, sideway and forward motions of the UAV during image capture will have a strong effect on the image geometry and final product accuracies. As the amount of this random distortion varies from one photo to the next, a unique and robust camera calibration model cannot be established for interior orientation and image processing using photogrammetric methods, even by self-calibration. To achieve the highest possible accuracy, we also consider temperature effects on the camera calibration parameters. In this paper we show the results, accuracies and benefits of using this method compared with a typical UAV mapping system. To the best of our knowledge this is the first time that this method has been implemented in a UAV mapping system.

자기장 지도를 이용한 위치 추정

Geomagnetic is refracted by building's wall and pillar. Therefore refracted geomagnetic is able to be used as feature point. In a specific space, a mobile device that is equipped with magnetic sensor array measures 3-axis magnetic field for each point. Magnetic field map is acquired by collecting the every sample point in the magnetic field. The measured magnetic field must be calibrated, because each magnetic sensor has a distortion. For this reason, sensor distortion model and sensor calibration method are proposed in this paper. Magnetic field that is measured by mobile device matches magnetic field map. Result of the matching is used for position estimation. This paper implements hardware system for position estimation method using magnetic field map.