Calibration-based Method Research Articles

Infrared non-uniformity correction method based on binocular detection system without blindsight

The output image of an infrared detector often suffers from non-uniformity due to the inhomogeneity of the device material and limitations in the manufacturing process, which can reduce the detector's capability to detect and identify targets. It is therefore necessary to adequately correct the non-uniformity to fully utilize the high temperature sensitivity of the detector. Calibration-based non-uniformity correction technology is one of the earliest and most effective methods of correcting non-uniformity. Typically, a relatively uniform shutter is used to mask the field of view for calibration correction. However, this process interrupts the observation due to the need for masking, severely limiting its use in real-world projects. In order to maintain the performance superiority of the calibration-based method and eliminate the defect of target loss caused by masking, this paper takes the occlusion calibration as the theoretical basis, which consists of dual detectors to form a binocular detection system, and uses one of the occluded calibration auxiliary detector to assist the other occlusion-free primary detector, and takes advantage of the fact that the radiation is the same when observing the same target at the same time to make the correct response based on the steepest gradient descent learning algorithm, which removes the non-uniformity while achieving detail fidelity, no ghosting and continuous output.

IMJENSE: Scan-Specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI.

Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5× and 6× accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.

An anti-steganalysis adaptive steganography for HEVC video based on PU partition modes

Calibration-based steganalysis methods are a fatal threat to current HEVC steganography algorithms. The current steganography algorithms based on prediction unit (PU) still lack resistance to this type of steganalysis. The reason is that the improvements in HEVC codec provide more potential steganographic space as well as introduce more detectable distortion. Therefore, a HEVC video steganography method that has resistance to PU shift-based steganalysis is proposed in this paper. First, the PU shift phenomenon that exists in PU-based steganography is introduced. Then the existing calibration-based steganalysis method is enhanced. According to the encoding rules of HEVC, the distortion cost function according to the difference of rate distortion cost (RD cost) during recompression is designed, which aims to obtain better resistance to calibration-based steganalysis. Experimental results demonstrate that the steganography algorithm designed in this paper provides excellent resistance ability for calibration-based steganalysis.

Pre-set estimation-based in-silico silhouette-based methodology for improving the robustness to viewing direction difference for assisting forensic gait analysis.

Forensic gait analysis is used to visually and quantitatively analyze information regarding the appearance and style of walking that can be presented as evidence in the court. The demand for analyzing CCTV pedestrian footage in video surveillance has been increasing. The dependence of the accuracy of semiautomatic silhouette-based analysis, often used in forensic science, on the differences in the viewing directions is a very challenging issue that is yet to be resolved for real case applications. Currently, the different viewing directions used in comparison footage significantly decrease the accuracy of same person analysis when using the silhouette-based method, often used in the Japanese forensic science domain. A calibration-based method was previously prosed to resolve this problem, but it requires performing an elaborate measurement procedure at the camera installation site for an accurate analysis. In this study, we propose a novel in-silico silhouette-based analysis method that significantly expands the number of viewing direction pre-set settings to 900 from the 24 used in the previous method. Several software tools have been developed to ensure that all the procedures can be executed on a computer. The experimental results confirm that the accuracy of the proposed method is comparable to that of the calibration-based method. Furthermore, the practical comparison results from actual consultation confirmed the effectiveness of the proposed method under existing viewing direction differences. We therefore anticipate that the proposed method will be beneficial for improving the analysis accuracy in real cases and therefore serve as a substitute of the previous method.

Omnidirectional Stereo Vision Study from Vertical and Horizontal Stereo Configuration

In stereo vision, an omnidirectional camera has high distortion compared to a standard camera, so the camera calibration is very decisive in its stereo matching. In this study, we will perform stereo matching for an omnidirectional camera with vertical and horizontal configuration so that the result of the image's depth has a 360-degree field of view by transforming the image using a calibration-based method. The result is that by using a vertical camera configuration, the image can be stereo matched directly, but by configuring a horizontal image, it is necessary to carry out a different stereo-matching process in each direction. Stereo matching with the semi-global matching method has better image results than block matching with more image objects detectable by the semi-global block matching method with a maximum disparity value of 32 pixels and with a window size of 21 pixels.

Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors

Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimize environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analyzed and discussed. The smart sensor-based leakage detection methods can be categorized as data-driven methods and model-based methods. Data-driven methods can be further categorized as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorized as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: (1) most of the leakage detection methods are focused on burst detection, and different types of leakage should be considered in future research, (2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed, (3) it is important to consider hydraulic model uncertainties, (4) unrealistic assumptions should be addressed in future research, and (5) spatial relations between sensors could provide more information and should be considered.

A two-step calibration framework for hydrological parameter regionalization based on streamflow and remote sensing evapotranspiration

• We proposed a two-step calibration based parameter regionalization method. • This method combines spatial proximity method and RS ET based parameter calibration method. • The proposed regionalization method achieves the overall best model performance. • The accuracy of RS ET is the main factor affecting the performance of the proposed method. Runoff prediction in ungauged catchments is an important and challenging research topic in the field of hydrology. However, traditional parameter regionalization methods may not be applicable in regions with sparsely distributed hydrological stations. Remote sensing (RS) evapotranspiration (ET) products provide valuable information for model parameter estimations. This study aimed to improve the accuracy of hydrological modeling in ungauged catchments by combining the traditional spatial proximity (SP) method with the RS ET-based parameter calibration method to form a two-step calibration-based regionalization method (SP-bcET method using bias-corrected RS ET data and SP-rawET method using raw RS ET data). The proposed regionalization scheme is demonstrated by using the variable infiltration capacity model in the Yangtze River Basin, China, and its performance was evaluated and compared with the SP method and two RS ET-based parameter calibration schemes (bcET method using bias-corrected RS ET data and rawET method using raw RS ET data). The results indicate that the bcET method performs better than the rawET method, the SP method outperforms the RS ET-based parameter calibration method, and the SP-bcET and SP-rawET methods achieve the best overall performance among the five methods. Taking the simulated streamflow at a daily scale in the calibration period as an example, the average Nash-Sutcliffe coefficient of efficiency for the SP, bcET, rawET, SP-bcET, and SP-rawET methods were 0.69, 0.61, 0.56, 0.71, and 0.71, respectively. Moreover, the SP-bcET and SP-rawET methods improved the simulation of the temporal dynamics and spatial variability of soil moisture anomalies. The accuracy of the RS ET data was one of the most dominant factors affecting the performance of the proposed parameter regionalization scheme. The SP-bcET method is expected to perform better than the SP method in warmer catchments or when gauged catchments are sparely distributed. The findings demonstrate the great potential of combining the traditional parameter regionalization and RS ET-based parameter calibration methods in hydrological simulations in ungauged catchments.

A calibration-based method for interval reliability analysis of the multi-manipulator system

The multiple manipulators can construct a special multi-agent system with the distinction that the type can be serial or parallel according to their cooperative way. We proposed a comprehensive method to handle the problem of reliability estimation. The wide and narrow bound method are applied to calculate the interval reliability respectively when multiple manipulators work as the series system. Aims to decrease the system complexity and enhance the dynamic adjustment capability, the base frame calibration technique is presented to convert the series system to a parallel one, naturally the reliability can be improved significantly. A system composed by three manipulators is utilized as an example to illustrate the feasibility of the proposed method.

Multi-camera stereo-DIC methods and application in full-field deformation analysis of reinforced Coral-SWSSC beams

Multi-camera stereo-digital image correlation (stereo-DIC) methods have gained popularity as non-contact methods for measuring high-spatial-resolution deformation of large engineering structures. Common multi-camera stereo-DIC methods can be classified into two types: target-based and calibration-based methods. Considering the wide application prospects of multi-camera stereo-DIC methods, it is important to study and compare the performances of these two types of multi-camera stereo-DIC methods. A four-point bending test of reinforced seawater sea-sand coral concrete (Coral-SWSSC) beams was conducted in this study to investigate the performance of multi-camera stereo-DIC in deformation measurement of slender components, and the measurement accuracy, robustness, and applicability of the two types of multi-camera stereo-DIC methods were compared and discussed. Furthermore, deformation analysis of reinforced Coral-SWSSC beams based on high-resolution multi-camera stereo-DIC methods was performed. The analysis showed that beams strengthened with basalt fiber-reinforced polymer wrapped steel tube and ultra high performance concrete (UHPC) can fully exploit the properties of Coral-SWSSC; the two strengthening materials demonstrated a higher compatibility, which compensates for the low strength defect of the Coral-SWSSC, making it a viable strengthening method. This study may promote the application of multi-camera stereo-DIC methods to measuring high-resolution deformation of large engineering structures.

A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China

Shale gas reservoir evaluation and production optimization both require geological models. However, currently, shale gas modeling remains relatively conventional and does not reflect the unique characteristics of shale gas reservoirs. Based on a case study of the Fuling shale gas reservoir in China, an integrated geological modeling workflow for shale gas reservoirs is proposed to facilitate its popularization and application and well improved quality and comparability. This workflow involves four types of models: a structure-stratigraphic model, reservoir (matrix) parameter model, natural fracture (NF) model, and hydraulic fracture (HF) model. The modeling strategies used for the four types of models vary due to the uniqueness of shale gas reservoirs. A horizontal-well lithofacies sublayer calibration-based method is employed to build the structure-stratigraphic model. The key to building the reservoir parameter model lies in the joint characterization of shale gas “sweet spots.” The NF models are built at various scales using various methods. Based on the NF models, the HF models are built by extended simulation and microseismic inversion. In the entire workflow, various types of models are built in a certain sequence and mutually constrain one another. In addition, the workflow contains and effectively integrates multisource data. Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. The workflow allows the complex process of geological modeling of shale gas reservoirs to be more systematic. It is of great significance for a dynamic analysis of reservoir development, from individual wells to the entire gas field, and for optimizing both development schemes and production systems.

Uncalibrated stereo vision with deep learning for 6-DOF pose estimation for a robot arm system

This paper proposes a novel method for six degrees of freedom pose estimation of objects for the application of robot arm pick and place. It is based on the use of a stereo vision system, which does not require calibration. Using both cameras, four corner points of the object are detected. A deep-neural-network (DNN) is trained for the prediction of the 6 DOF pose of the object from the four detected corner points’ coordinates in each image of both cameras. The stereo vision used is a low-end vision system placed in a custom-made setup. Before the training phase of the DNN, the robot is set to auto collect data in a predefined workspace. This workspace is defined dependently on the spatial feasibility of the robot arm and the shared field of view of the stereo vision system. The collected data represent images of a 2D marker attached to the robot arm gripper. The 2D marker is used for data collection to ease the detection of the four corner points. The proposed method succeeds in estimating the six degrees of freedom pose of the object, without the need for the determination of neither the intrinsic nor the extrinsic parameters of the stereo vision system. The optimum design of the proposed DNN is obtained after comparing different activation functions and optimizers associated with the DNN.The proposed uncalibrated DNN-based method performance is compared to that of the traditional calibration-based method. In the calibration-based method, the rotational matrix relating the robot coordinates to the stereo vision coordinates is computed using two approaches. The first approach uses Singular Value Decomposition (SVD) while the second approach uses a novel proposed modification of particle swarm optimization (PSO) called Hyper particle Scouts optimization (HPSO). HPSO outperforms other metaheuristic optimization algorithms such as PSO and genetic algorithm (GA).Exhaustive tests are performed, and the proposed DNN-based method is shown to outperform all tested alternatives.

High dynamic range infrared image acquisition based on an improved multi-exposure fusion algorithm

In this paper, a novel scheme for acquiring high dynamic range infrared images is proposed. We firstly apply non-uniformity correction and contrast enhancement to the raw infrared images captured at different integration times, and then obtain the high dynamic range infrared images by fusing the enhanced images via the proposed improved multi-exposure fusion algorithm. Compared with the calibration-based methods, the fussy calibration process is not required to estimate the camera response function of the infrared camera. The proposed multi-exposure fusion algorithm is based on the multi-scale strategy, and the multi-scale details of the fused pyramid and the weighted pyramid are compensated to reduce the loss of details during the pyramid fusion process. Thus, details of the original images are better preserved in the fused image. The experiments are conducted on the grayscale version of the multi-exposure fused image dataset given by Ma et al. [1] and Cai et al. [2]. The proposed method shows superior performance over the other five state-of-the-art methods in terms of the visual quality and the evaluation index MEF-SSIM. The intermediate and final images during the actual infrared image processing are also given to illustrate the robustness and effectiveness of the algorithm.

Nonuniformity Correction Design and Implementation for Infrared Image Based on FPGA and Artificial Neural Networks

Infrared imaging technology is widely used in military and civilian applications. However, due to the materials and manufacturing technology, the response of each unit in the infrared detector is not completely consistent, which leads to the nonuniformity of the infrared focal plane array. In this article, we propose a way to use artificial neural network by self-adaptively updating the correction coefficients based on FPGA. Compared with traditional calibration-based method, our method is more flexible and can avoid the effects of temperature drift efficiently.

Bias Reduction Methods for Propensity Scores Estimated from Error-Prone EHR-Derived Covariates.

As the use of electronic health records (EHR) to estimate treatment effects has become widespread, concern about bias introduced by error in EHR-derived covariates has also grown. While methods exist to address measurement error in individual covariates, little prior research has investigated the implications of using propensity scores for confounder control when the propensity scores are constructed from a combination of accurate and error-prone covariates. We reviewed approaches to account for error in propensity scores and used simulation studies to compare their performance. These comparisons were conducted across a range of scenarios featuring variation in outcome type, validation sample size, main sample size, strength of confounding, and structure of the error in the mismeasured covariate. We then applied these approaches to a real-world EHR-based comparative effectiveness study of alternative treatments for metastatic bladder cancer. This head-to-head comparison of measurement error correction methods in the context of a propensity score-adjusted analysis demonstrated that multiple imputation for propensity scores performs best when the outcome is continuous and regression calibration-based methods perform best when the outcome is binary.

Calibration-Based Phase Coherence of Incoherent and Quasi-Coherent 160-GHz MIMO Radars

Imaging radars are usually realized fully coherently. However, the distribution of one common radio frequency signal to all transmit and receive paths requires a high degree of hardware complexity. In order to reduce the hardware effort significantly, a novel phase synchronization method for incoherent and quasi-coherent frequency-modulated continuous-wave (FMCW) imaging radars with individual signal synthesis per channel is presented. The quasi-coherent setup uses one common oscillator for all frequency synthesizers. It is shown that in the case of the quasi-coherent system, only a phase difference between the calibration and the measurement has to be corrected to achieve coherence. In comparison, an incoherent system causes additional time, frequency, and FMCW ramp slope errors due to the different behavior of the oscillators. In order to achieve phase coherence and to correct the error sources, a calibration-based method using a defined signal path as part of the radar system is proposed. The imaging radar used for verification of the theory consists of individual single-channel radar monolithic microwave integrated circuits (MMICs) at 160 GHz; each MMIC fed by an individual frequency synthesizer. As shown by measurements, it is possible to achieve phase coherence for both system approaches and to perform angle estimation.

GraphIPS: Calibration-Free and Map-Free Indoor Positioning Using Smartphone Crowdsourced Data

Indoor positioning plays an important role in a variety of applications under Internet of Things (IoT). Conventional WiFi fingerprinting-based indoor positioning systems (IPSs) usually require extensive manual calibrations to construct radio maps. This process severely limits the system scalability and adaptiveness. Pedestrian dead reckoning (PDR) is a popular method that can avoid the calibration process. However, PDR-based IPSs typically suffer from accumulated errors. To tackle this problem, many refinement methods require map information or floorplans which may not be available or up-to-date in practice. With the development of IoT, various types of crowdsourced data become available. In this work, we propose GraphIPS, a calibration-free and map-free IPS which dynamically generates accurate radio maps by utilizing smartphone crowdsourced WiFi and inertial measurement unit (IMU) data. GraphIPS fuses the crowdsourced data into a graph-based formulation and applies the multidimensional scaling (MDS) algorithm to compute the positions of the user's steps. The experimental results show that GraphIPS achieves comparable accuracy to the calibration-based method in a significantly shorter run time than optimization-based methods. In addition to smartphones, GraphIPS is also potentially applicable for the smart wearables with embedded WiFi modules and IMUs.

End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.

Blood pressure (BP) is a vital sign that provides fundamental health information regarding patients. Continuous BP monitoring is important for patients with hypertension. Various studies have proposed cuff-less BP monitoring methods using pulse transit time. We propose an end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism. The proposed model consisted of a convolutional neural network, a bidirectional gated recurrent unit, and an attention mechanism. The model was trained by a calibration-based method, using the data of each subject. The performance of the model was compared to the model that used each combination of the three signals, and the model with the attention mechanism showed better performance than other state-of-the-art methods, including conventional linear regression method using pulse transit time (PTT). A total of 15 subjects were recruited, and electrocardiogram, ballistocardiogram, and photoplethysmogram levels were measured. The 95% confidence interval of the reference BP was [86.34, 143.74] and [51.28, 88.74] for systolic BP (SBP) and diastolic BP (DBP), respectively. The values were 0.52 and 0.49, and the mean-absolute-error values were 4.06 ± 4.04 and 3.33 ± 3.42 for SBP and DBP, respectively. In addition, the results complied with global standards. The results show the applicability of the proposed model as an analytical metric for BP estimation.

Calibration-Free Indoor Positioning Using Crowdsourced Data and Multidimensional Scaling

Indoor positioning plays an important role in various location-based services (LBSs). In conventional systems, the process of constructing radio maps for positioning usually involves labor-intensive manual calibrations, which seriously limits the system’s scalability and adaptiveness. In this paper, we propose an efficient calibration-free method by leveraging on crowdsourced WiFi signal data that are captured passively through a WiFi sensing testbed. Since the ground truths of the crowdsourced data are unavailable, the radio maps cannot be directly constructed. In the proposed method, we adopt the multidimensional scaling (MDS) technique to compute the positions of the unlabeled data thereby generating radio maps. In order to enable MDS, we estimate the pairwise distances among the unlabeled data by using an improved trilateration method and a law of cosine (LoC)-based geometrical algorithm without online pairwise measurements. Experimental results show that the accuracy of the proposed method is higher than trilateration-based method and reasonably lower than that of calibration-based method. Meanwhile, the run time of the proposed method is shorter than previous optimization-based methods. The short run time allows the radio maps to be dynamically updated against the environmental variations.

Efficient and Scalable Calibration-Free Indoor Positioning Using Crowdsourced Data

With the development of Internet of Things (IoT), a variety of crowdsourced data becomes available. In this article, we propose to utilize the crowdsourced WiFi received signal strength (RSS) data to perform indoor positioning. In many instances, the size of crowdsourced WiFi RSS data is potentially large, and the ground truths of the corresponding RSS fingerprints are unavailable. Therefore, it is challenging to construct the associated radio map. In the proposed method, a heuristic geometrical algorithm is developed to convert the RSS data into pairwise distances among the fingerprints. Based on these pairwise distances, multidimensional scaling (MDS) is then applied to compute the positions of all the fingerprints, thereby building a radio map. We show that the accuracy of the proposed method is only reasonably lower than a state-of-the-art calibration-based method. Further, we present a ${k}$ -means clustering-based region partitioning method that partitions the large crowdsourced data set effectively. The parallel processing on the partitioned data and computational simplicity of MDS result in significantly shorter runtime for the proposed method than the previous optimization-based methods.

On-Site Calibration-Based Estimation Method of Forward Seabed Elevation Using Forward Scan Sonar

This paper addresses a novel calibration-based estimation method to measure the forward seabed elevation (FSE) where an area illuminated by forward scan sonar (FSS) for unmanned underwater vehicles (UUVs). The conventional method measures the FSE using one or two down-facing single-beam sonars; this method can, however, cause errors. In order to accurately measure the FSE, a sensor capable of measuring the 2D space of the seabed is needed, such as FSS. The method proposed in this work consists in estimating the FSE by analyzing the acoustic beam distribution characteristics of FSS. A novel FSS beam distribution model is proposed, which simplifies beam distribution characteristics. Then, on-site calibration is conducted to estimate the parameters of this model. Based on the estimated model, templates can be generated and compared with FSS data acquired at the experiment in order to estimate geometrical information at the FSE. The FSE of the best matched template is the estimated FSE of the FSS data acquired in the experiment. In order to verify the performance of the proposed method, experiments using an UUV were carried out in an indoor water tank. We also verified that the proposed method performed well even when there were objects on the seabed.