Correlation Matching Research Articles

A Target Identification Method for the Millimeter Wave Seeker via Correlation Matching and Beam Pointing.

Target identification is a challenging task under land backgrounds for the millimeter wave (MMW) seeker, especially under complex backgrounds. Focusing on the problem, an effective method combining correlation matching and beam pointing is proposed in this paper. In the beginning, seeker scanning for target detection is conducted in two rounds, and target information of the detected targets is stored for correlation matching. Point or body feature judgment is implemented by using high resolution range profile (HRRP). Then, the error distribution zone is constructed with the beam pointing as the origin. In the end, we identify the target by searching the one which lies in the closest error distribution from the beam pointing center. The effectiveness of the proposed method is verified by using mooring test-fly and real flight data.

Large-Scale Outdoor SLAM Based on 2D Lidar

For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques.

Evaluating the influence of turbulence models used in computational fluid dynamics for the prediction of airflows inside poultry houses

There are various turbulence models in the computational fluid dynamics (CFD) literature but none has so far proven to be universally applicable. Accurate simulations require the proper choice of model appropriate for each particular situation. In this study, the performance of three types of k-ɛ turbulence model, the standard k-ɛ, renormalisation group (RNG) k-ɛ, and realisable k-ɛ, were evaluated for their ability to accurately simulate the internal turbulent flow of a poultry house. Each model's accuracy was analysed by comparing predicted and experimental results, and its performance was assessed using the coefficient of determination (r2), the root mean square error to the standard deviation ratio (RSR), and a Taylor diagram, which provides a concise statistical summary of how well the correlation (r) and standard deviation (SD) patterns match. The RSR values obtained for air temperature and airspeed were 0.57 and 0.19, 0.30 and 0.16, and 0.64 and 0.23 for the standard k-ɛ, RNG k-ɛ, and Realizable k-ɛ models, respectively, and showed that the RNG k-ɛ model predicted the airspeed and air temperature best. Other models also provided good results, particularly in predicting airspeed; however, their air temperature predictions were not as accurate as those of the RNG k-ɛ model. The results showed that RNG k-ɛ presented the best results overall, whilst realisable k-ɛ did not meet with our expectations.

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

This paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer–Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p=2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their applicability. A general solution for any scenario is simply missing for p=2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behavior of the KF with respect to the channel state (Doppler frequency and signal to noise ratio). Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state. Analytic and numerical comparisons with first- and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p≥15.

A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing.

There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses.

Autonomous motion planning and task execution in geometrically adaptive robotized construction work

Abstract This research explores a means by which a construction robot can leverage its sensors and a Building Information Model (BIM) to perceive and model the actual geometry of its workpieces, adapt its work plan, and execute work in a construction environment. The adaptive framework uses the Generalized Resolution Correlative Scan Matching (GRCSM) search algorithm for model registration, a new formulation for fill plan adaptation, and new hardware for robotic material dispensing. Joint filling is used as a case study to demonstrate the formulation of an adaptive plan and evaluate a robot's ability to perform adaptive work in an environment where the actual location and geometry of its workpieces may deviate from their designed counterparts. Averaged across five experiments, the robot was found capable of identifying the true position and orientation of the joint's center with a mean norm positioning error of 0.11 mm and orientation error of 1.1°. The adaptive framework offers significant promise for a range of construction activities, including those involving objects of complex geometry and detailed work.

Convolutional Neural Networks-Based Locating Relevant Buggy Code Files for Bug Reports Affected by Data Imbalance

Software bug localization is very important in software engineering, but it is also complicated and time consuming. To improve the efficiency of developers, researchers have developed various traditional bug localization and machine learning bug localization methods. In this paper, we propose a novel method that improves bug localization performance. First, surface lexical correlation matching between bug reports and source code files is used to obtain features by deep neural network. Second, to solve the lexical gap between bug reports and source code files, semantic correlation matching between them is used to obtain features based on word embedding and sentence embedding by deep neural network. Then, the joint features obtained by the surface lexical and semantic correlation matching are fused into a unified feature representation for bug reports and source code files. In addition, since our experimental datasets are imbalanced data, we use a focal loss function to solve the impact of data imbalance. Finally, our method obtains the relatively high bug localization performance compared to other classic methods.

A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT.

In smart environments based on the Internet of Things (IoT), almost all of the object information that is collected by various sensors is time series data, which records the behavior of the objects. Analyzing the correlation between different time series data, other than those in the same time series, is more helpful to discovering their behavioral relations. This has become one of the important current issues in the IoT. To analyze the correlation, a clustering algorithm named the CPCCM (clustering algorithm based on precise correlation coefficient matching) is presented. First, each initial sequence is split into a set of subsequences by adopting a preset sliding window. Then, the correlation coefficients between any pair of subsequence sets from two sequences are resolved. Those pairs that pass some preset Pearson correlation coefficient threshold are clustered. In the CPCCM, a cross-traversal strategy is introduced to improve the search efficiency. The cross-traversal strategy alternatively searches the subsequences in two subsequence sets. To improve the clustering efficiency, in each initial sequence, adjacent subsequences are merged into longer subsequences and replaced by it if they appear in the same subsequence set. Finally, by analyzing practical electric power consumption data, the CPCCM is shown to be promising and able to be applied in similar scenarios. By comparison with the agglomerative hierarchical clustering algorithm, the major contributions of this work is that the clustering quality is improved by using the strategy of precise matching and cross-traversal, and complexity of the algorithm is reduced by merging adjacent subsequences. Therefore, CPCCM can be applied to analyze behavior between different objects in smart environments.

Template Matching-Based Method for Intelligent Invoice Information Identification

The increasing use of invoicing has created an unnecessary burden on labor and material resources in the financial sector. This paper proposes a method to intelligently identify invoice information based on template matching, which retrieves the required information by image preprocessing, template matching, an optical character recognizing, and information exporting. The origin invoice image is preprocessed first to remove the useless background information by secondary rotation and edge cutting. Then, the region of the required information in the obtained regular image is extracted by template matching, which is the core of the intelligent invoice information identification. The optical character recognizing is utilized to convert the image information into text so that the extracted information can be directly used. The text information is exported for backup and subsequent use in the last step. The experimental results indicate that the method using normalized correlation coefficient matching is the best choice, demonstrating a high accuracy of 95%, and the average running time of 14 milliseconds.

Two-stage deep learning for supervised cross-modal retrieval

This paper deals with the problem of modeling internet images and associated texts for cross-modal retrieval such as text-to-image retrieval and image-to-text retrieval. Recently, supervised cross-modal retrieval has attracted increasing attention. Inspired by a typical two-stage method, i.e., semantic correlation matching(SCM), we propose a novel two-stage deep learning method for supervised cross-modal retrieval. Limited by the fact that traditional canonical correlation analysis (CCA) is a 2-view method, the supervised semantic information is only considered in the second stage of SCM. To maximize the value of semantics, we expand CCA from 2-view to 3-view and conduct supervised learning in both stages. In the first learning stage, we embed 3-view CCA into a deep architecture to learn non-linear correlation between image, text and semantics. To overcome over-fitting, we add the reconstruct loss of each view into the loss function, which includes the correlation loss of every two views and regularization of parameters. In the second stage, we build a novel fully-convolutional network (FCN), which is trained by joint supervision of contrastive loss and center loss to learn better features. The proposed method is evaluated on two publicly available data sets, and the experimental results show that our method is competitive with state-of-the-art methods.

3D Robot Vision System through 2D Shape Based Matching Using Gaussian Smoothing for Gluing Application

This investigation is solely on the adaptation of a vision system algorithm to classify the processes to regulate the decision making related to the tasks and defect’s recognition. These idea stresses on the new method on vision algorithm which is focusing on the shape matching properties to classify defects occur on the product. The problem faced before that the system required to process broad data acquired from the object caused the time and efficiency slightly decrease. The propose defect detection approach combine with Region of Interest, Gaussian smoothing, Correlation and Template Matching are introduced. This application provides high computational savings and results in better recognition rate about 95.14%. The defects occur provides with information of the height which corresponds by the z-coordinate, length which corresponds by the y-coordinate and width which corresponds by the x-coordinate. This data gathered from the proposed system using dual camera for executing the three dimensional transformation.

Application of Particle Swarm Optimization Algorithm in Geomagnetic Matching Navigation

In order to improve the positioning accuracy of the traditional geomagnetic matching algorithm, a new geomagnetic matching algorithm based on particle swarm optimization is proposed. The algorithm plans the search range of the real position centering on the measurement position of the reference navigation system. Extracting the corresponding geomagnetic data from the reference geomagnetic map, the particle swarm optimization algorithm is then introduced into the matching region search, and the obtained geomagnetic profile values are taken as particles in the particle population. On this basis, the normalized product correlation function is used as the particle fitness function, comparing the similarity between the reference sub graph and the real-time graph profile by the maximum fitness measure, Finally, through simulation analysis, the matching precision and matching time of matching algorithm based on ICP and PSO are compared. The simulation results show that the matching effect based on PSO algorithm is better than the traditional ICP algorithm. Although the matching time is slightly longer than the ICP algorithm, it meets the real-time requirements. Geomagnetic correlation matching algorithm is an extremely important type of algorithm in geomagnetic aided navigation algorithm. The current research focuses on the improvement of the traditional Terrain Contour Matching (TERCOM) algorithm metric function and the introduction of new metric functions and various ICP geomagnetic matching algorithms [1-4].The geomagnetic correlation matching algorithm of the aircraft not only requires high precision and high reliability, but also the execution efficiency of the algorithm is one of the important contents of the research. Based on the research of traditional geomagnetic correlation matching algorithm, this paper introduces particle swarm optimization algorithm into geomagnetic correlation matching algorithm. From the aspects of similarity measure function, geomagnetic matching feature quantity and search strategy, a new method of geomagnetic correlation matching for aircraft is proposed.

Probabilistic power flow computation considering correlated wind speeds

In this paper, the kappa distribution and Archimedean copula are employed to construct the joint probability distribution of correlated wind speeds. A percentile matching method is proposed to parameterize the kappa distribution, such that marginal distributions of wind speeds can be well represented. A correlation coefficient matching method is adopted to determine the parameters of Archimedean copulas, and a Laplace transform based algorithm is used for sample generation. Furthermore, an efficient simplex quadrature rule is introduced to calculate the statistical moments of probabilistic power flow outputs. The case studies show that the kappa distribution fits wind speed distributions better than the Weibull distribution, the Archimedean copula can well characterize the dependence structure of wind speeds, and the simplex rule yields results as accurate as point estimate method of (2m+1) scheme, while reducing the computational burden by half.

Extended Line Map-Based Precise Vehicle Localization Using 3D LIDAR.

An Extended Line Map (ELM)-based precise vehicle localization method is proposed in this paper, and is implemented using 3D Light Detection and Ranging (LIDAR). A binary occupancy grid map in which grids for road marking or vertical structures have a value of 1 and the rest have a value of 0 was created using the reflectivity and distance data of the 3D LIDAR. From the map, lines were detected using a Hough transform. After the detected lines were converted into the node and link forms, they were stored as a map. This map is called an extended line map, of which data size is extremely small (134 KB/km). The ELM-based localization is performed through correlation matching. The ELM is converted back into an occupancy grid map and matched to the map generated using the current 3D LIDAR. In this instance, a Fast Fourier Transform (FFT) was applied as the correlation matching method, and the matching time was approximately 78 ms (based on MATLAB). The experiment was carried out in the Gangnam area of Seoul, South Korea. The traveling distance was approximately 4.2 km, and the maximum traveling speed was approximately 80 km/h. As a result of localization, the root mean square (RMS) position errors for the lateral and longitudinal directions were 0.136 m and 0.223 m, respectively.

Karakter Perempuan Jawa Dalam Leksikon Jawa

This study aims to identify the Java lexicon. The Javanese lexicon which has a relation to the character of Javanese women. The Java Lexicon which is the focus of the study is vocabulary based on the meaning of the components of the character: behavior, habits and character. Location of research in the northern coast of Central Java. Data collection methods are observation, interview (structured interview and in-depth interview), and focus grop discussion (FGD). The analytical method is the equivalent method. Using three analytical techniques: reconstruction, correlation, and context matching. The theoretical basis used is adhesion: the word-concept-referent and Sapir-Worf hypothesis. Research findings are 1) Javanese female characters are reflected in the lexicon; 2) Lexicon of Javanese female characters can be traced internally (word structure) and external (context and philosophy); 3) internal studies are analyzed diachronically and synchronously; 4) External studies are analyzed involving the components of context and cultural order that have philosophical values (micro and macro); 5) The order of word-concept-referent is a formula for reconstructing forms and meanings.

Doppler Passive Fathometry.

Passive fathometry is a technique whereby broadband ambient ocean noise received on an array of hydrophones is averaged and cross-correlated to produce a sub-bottom profile [Siderius, Harrison, and Porter (2006). J. Acoust. Soc. Am. 120, 1315-1323]. Here this technique is extended to determine the vertical velocity of the array, and compensate for it, without any prior knowledge, i.e., Doppler Passive Fathometry. For Fourier transform lengths beyond a certain limit, the differing Doppler between the direct and bottom reflected paths spoils the correlation match, however it is shown by using some experimental data, where the array was known to suffer from arbitrary but near periodic motion, that compensation is possible, enabling continuing time integration. In the process, the vertical velocity becomes known. Velocity, with peak value ∼±1 m/s, is plotted against time and shown to be 90° out of phase with depth, as expected for periodic motion. Since stationary targets have already been detected by noise correlation [Harrison (2008). J. Acoust. Soc. Am. 123, 1834-1837], it is implied that the range of moving targets can also be determined.

Radargrammetric DSM generation in mountainous areas through adaptive-window least squares matching constrained by enhanced epipolar geometry

Radargrammetry is a powerful tool to construct digital surface models (DSMs) especially in heavily vegetated and mountainous areas where SAR interferometry (InSAR) technology suffers from decorrelation problems. In radargrammetry, the most challenging step is to produce an accurate disparity map through massive image matching, from which terrain height information can be derived using a rigorous sensor orientation model. However, precise stereoscopic SAR (StereoSAR) image matching is a very difficult task in mountainous areas due to the presence of speckle noise and dissimilar geometric/radiometric distortions. In this article, an adaptive-window least squares matching (AW-LSM) approach with an enhanced epipolar geometric constraint is proposed to robustly identify homologous points after compensation for radiometric discrepancies and geometric distortions. The matching procedure consists of two stages. In the first stage, the right image is re-projected into the left image space to generate epipolar images using rigorous imaging geometries enhanced with elevation information extracted from the prior DEM data e.g. SRTM DEM instead of the mean height of the mapped area. Consequently, the dissimilarities in geometric distortions between the left and right images are largely reduced, and the residual disparity corresponds to the height difference between true ground surface and the prior DEM. In the second stage, massive per-pixel matching between StereoSAR epipolar images identifies the residual disparity. To ensure the reliability and accuracy of the matching results, we develop an iterative matching scheme in which the classic cross correlation matching is used to obtain initial results, followed by the least squares matching (LSM) to refine the matching results. An adaptively resizing search window strategy is adopted during the dense matching step to help find right matching points. The feasibility and effectiveness of the proposed approach is demonstrated using Stripmap and Spotlight mode TerraSAR-X stereo data pairs covering Mount Song in central China. Experimental results show that the proposed method can provide a robust and effective matching tool for radargrammetry in mountainous areas.

Optical 3-D surface reconstruction with color binary speckle pattern encoding.

This paper proposes a novel 3-D surface profile measurement scheme by only a single-shot color binary speckle pattern (CBSP) and a temporal-spatial correlation matching algorithm, which can be applied to measurements of dynamic and static objects. R/G/B channels of CBSP are coupled with three carefully designed black and white binary speckle patterns (BWBSPs), whose physical features are associated with the system configuration parameters. We mathematically deduce the concrete details of how to design such a pattern and its relationship with the system parameters selected in the experiment. During 3-D reconstruction, we develop an extended temporal-spatial correlation framework to determine the correspondence between two stereo images sequences that are composed of R/G/B images separated from a captured color stereo image pair. Comparative experiments and analysis are implemented to assess the measurement accuracy using standard workpieces (dumbbell and optical flat). The results indicate that the proposed approach enjoys better performance than the conventional BWBSP-based method in terms of spatial resolution, accuracy, and efficient reconstructed points. An experiment of applying CBSP to measuring a moving A4 paper is also presented, demonstrating the success of our computational framework. Finally discussions concerning the limitations of this method are implemented.

Object Tracking Test Automation Using a Robotic Arm

Touch-to-track is a software feature that is used to track objects in embedded systems, such as mobile phones. The research question that is discussed in the paper is as follows: how can one design an automated test method for accurately testing object tracking algorithms? The challenge for the verification team was to design a method of test automation for testing this object tracking feature. The solution to the problem was to develop test automation algorithms using a robotic arm to accurately test this software feature. The robotic arm is used for executing test cases for tracking single or multiple objects in motion. To use the touch-to-track feature, the user selects an area in the camera preview by drawing a bounding box. Hough transformation and color segmentation are applied to accurately detect the bounding box drawn on every frame in which the object is detected. The area inside the bounding box is considered as the template. Template matching algorithms based on normalized cross-correlation, phase correlation, and speeded up robust features (SURF)-based feature extraction and matching are then applied to compare the template and original images. The tracking efficiency is calculated by dividing the total number of frames that contain the object being tracked by the total number of frames being tested. The tracking efficiencies achieved with the Hough-based bounding box detection algorithm followed by template matching algorithms based on normalized cross-correlation, phase correlation, and SURF-based feature extraction and matching are compared. These tracking efficiencies are calculated by comparing the ground truth to the tracking results for each frame. The tracking efficiency calculated for three objects is 87.7% with no colored background. The proposed object tracking solution with hardware acceleration is found to perform better than the third-party solution with respect to the latency in re-establishing tracking and the hand jitter scenario.

Underwater Acoustic Image Encoding Based on Interest Region and Correlation Coefficient

It is difficult for the conventional image compression method to achieve good compression effect in the underwater acoustic image (UWAI), because the UWAI has large amount of noise and low correlation between pixel points. In this paper, fractal coding is introduced into UWAI compression, and a fractal coding algorithm based on interest region is proposed according to the importance of different regions in the image. The application problems of traditional quadtree segmentation in UWAIs was solved by the range block segmentation method in the coding process which segmented the interest region into small size and the noninterest region into large size and balanced the compression ratio and the decoded image quality. This paper applies the classification, reduction codebook, and correlation coefficient matching strategy to narrow the search range of the range block in order to solve the problem of the long encoding time and the calculation amount of encoding process is greatly reduced. The experimental results show that the proposed algorithm improves the compression ratio and encoding speed while ensuring the image quality of important regions in the UWAI.