Space Transformation Matrix Research Articles

Block-Wise Computation of Cyclic Redundancy Code Using Factored Toeplitz Matricesin Lieu of Look-Up Table

Cyclic Redundancy Check (CRC) or Cyclic Redundancy Code is a Cyclic Error Detection Code used to preserve the integrity of data in storage and transmission applications. CRC of a stream of message bits is usually calculated block-wise in parallel with the help of a Look-Up Table (LUT) in software or State Space transformation matrix in hardware. Presented here is a novel method and architecture of parallel computation of Cyclic Redundancy Codes without any Look-Up Table for an arbitrary generating polynomial which is programmable at runtime. The method reduces computational complexity and storage requirements by implicitly factorizing the transformation matrix needed to compute the remainder into two simpler Toeplitz matrices. The resulting hardware architecture is suitable for embedded applications.

On Subgradient Methods with Polyak’s Step and Space Transformation

Introduction. Minimization of ravine convex functions, both smooth and non-smooth, arises in many problems of planning, control, stability analysis of dynamic systems, artificial intelligence, and machine learning. Therefore, the development of new and improvement of existing methods is an important task, taking into account the fact that more and more frequently the functions to be minimized depend on a large number of variables. Special attention should be paid to the methods using the operation of linear transformation of space, which allow to improve the properties of the objective function and significantly accelerate its minimization. Known methods of this type, in particular, ellipsoid methods and r-algorithms, require recalculation of the transformation matrix at each iteration. Therefore, the development of methods with a one-time transformation of space, which have a lower cost of iteration, is definitely an urgent task. The purpose of the article is to review modifications of subgradient method with Polyak’s step that use a scalar parameter m(1 and one-time space transformation operation. Supplement the justification of the convergence and convergence rate of the modifications described. Give recommendations regarding determination of parameter m and space transformation matrix B. Results. Subgradient method with Polyak’s step in transformed space and parameter m>1 is an effective method for minimizing smooth and non-smooth convex functions that have a ravine structure. Determination of an appropriate value of the parameter m(1 and space transformation matrix B allows to significantly accelerate this method and use it for minimization functions that depend on a large number of variables. Conclusions. The development of fast methods for minimization of non-smooth convex functions of many variables with a ravine structure makes it possible to effectively solve modern problems of artificial intelligence, in particular, the problems of machine learning, image recognition, big data analysis, etc. Keywords: subgradient method, space transformation, ravine functions.

Adaptive Robust Least-Squares Smoothing Algorithm

The Kalman smoothing algorithm is widely use d in offline data processing in GNSS to improve filter calcu lation accuracy. When there are measurement outliers or b iased random models, the adaptive Kalman smoothing algo rithm can reduce their impact on the smoothing results to a certain extent. Nevertheless, the adaptive Kalman smoothi ng algorithm usually has difficulty obtaining optimal estim ation in a complex observation environment due to the inhe rent defects of the algorithm itself. Therefore, we propose a n adaptive robust least-squares smoothing algorithm. The row and column transformation of the least-square smooth noise covariance matrix is realized by constructing an orth ogonal space transformation matrix, and the noise covaria nce estimation problem of the Kalman filter is transformed into the problem of solving the variance covariance compo nent elements under a specific matrix structure. In additio n, by selecting the observation value with the largest stand ardized residual to determine whether it is gross error, we avoid the gross error misjudgment problem caused by the t wo-factor robust estimation method under least-square sm oothing, and further improve the smoothing accuracy. Fina lly, two experiments are studied to compare the performan ce of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better perform ance in terms of robustness and estimation accuracy.

Hole position quick modification method for automatic drilling and riveting system considering workpiece pose deviation

Workpiece pose deviation is inevitable during automatic drilling and riveting in aircraft assembly, and it either leads to increased time cost or decreased position accuracy with the present methods. This study aims to propose a quick modification method to modify the coordinates of assembly holes based on online detection of the position of pre-assembly holes considering the effect of abnormal data caused by workpiece pose deviation. In the case of hole-making in the double curvature skin region of three pre-assembly holes, the space transformation matrix is determined using the unit quaternion method in accordance with the on-line detection of the hole position coordinates. In the case of more than three assembly holes with abnormal data, a new modification method using a random sampling consistency RANSAC algorithm is used to optimize the spatial transformation matrix and eliminate the abnormal data effectively. This method has been integrated into automatic drilling and riveting systems. Numerical simulation and experiment tests were conducted, and the result comparison showed that the method has obvious positive effect dealing with the abnormal data considering component pose deviation, which proves the effectiveness of the method.

Robotic microassembly for meso-scale application

Purpose – The purpose of this paper is to design and develop 14-degree of freedom (DOF) robotic micromanipulator with which LIGA devices and axle hole part can be both manipulated and assembled. Design/methodology/approach – The in-house robotic microassembly system is composed of a 6-DOF large motion serial robot with microgrippers, a hexapod six-DOF precision alignment worktable and a vision system whose optical axis of the microscope is parallel with the horizontal plane. A prism with special coating is fixed in front of the objective lens, thus, two-part figures can be acquired simultaneously by the microscope with 1.67 to 9.26 micron optical resolution. The relative discrepancy between the two parts can be calculated from image plane coordinate instead of calculating the space transformation matrix. A modified microgripper was designed to clamp meso-scale parts and its effectiveness was confirmed experimentally. Through the use of the other vision system, the insert action can be successfully manipulated. A laser ranger finder was integrated in this micro-assembly system to measure the assembly result. Findings – A new 14-DOF robotic micromanipulator, including eight axes automatically and six axes manually, has been developed for the assembly of LIGA meso-scale flat parts and axle hole parts. The microassembly system with coaxial alignment function (MSCA) system is able to concurrently manipulate all eight axes automatically and six axes manually. Originality/value – The robotic microassembly is applied in the assembly of meso-scale parts. The new capabilities of the MSCA will allow for the assembly of microsystems more efficiently and more precisely.

十亿像素瞬态成像系统实时图像拼接

In order to meet the requirement of the engineering application about the real-time image processing,according to the one billion pixel transient cloud imaging system which has been designed based on the combined structure of a concentric spherical lens and micro camera mosaic array,an adaptive image mosaic algorithm of parallel acceleration based on the compute unified device architecture( CUDA) and prior information has been proposed. First,the imaging overlap region of the adjacent micro camera has been calibrated with high-precision four-axe calibration table,and the speed-up robust features( SURF) method has been used to extract the candidate feature points of the overlap region. Then,the approximate nearest neighbor( ANN)search algorithm based on random K-D tree which has been accelerated by the CUDA basic linear algebra subroutines( CUBLAS) is used to obtain the initial matching points. Finally,the improved parallel progressive sample consensus( IPROSAC) algorithm is used to eliminate the false matching points and estimate the parameters of the space transformation matrix,and the spatial geometry transformation relationship has been obtainedabout mosaic images. Experimental results indicate that the image mosaic time is 287 ms and the speed is improved about 30 times compared with serial algorithm using CPU.

An improved vision calibration method for coaxial alignment microassembly

Purpose – The purpose of this paper is to propose a sub-pixel calibration method for a microassembly system with coaxial alignment function (MSCA) because traditional sub-pixel calibration approaches cannot be used in this system. Design/methodology/approach – The in-house microassembly system comprises a six degrees of freedom (6-DOF) large motion serial robot with microgrippers, a hexapod 6-DOF precision alignment worktable and a vision system whose optical axis of the microscope is parallel with the horizontal plane. A prism with special coating is fixed in front of the objective lens; thus, two parts’ Figures, namely the images of target and base part, can be acquired simultaneously. The relative discrepancy between the two parts can be calculated from image plane coordinate instead of calculating space transformation matrix. Therefore, the traditional calibration method cannot be applied in this microassembly system. An improved calibration method including the check corner detection solves the distortion coefficient conversely. This new way can detect the corner at sub-pixel accuracy. The experiment proves that the assembly accuracy of the coaxial microassembly system which has been calibrated by the new method can reach micrometer level. Findings – The calibration results indicate that solving the distortion conversely could improve the assembly accuracy of MSCA. Originality/value – The paper provides certain calibration methodological guidelines for devices with 2 dimensions or 2.5 dimensions, such as microelectromechanical systems devices, using MSCA.

Sparse tensor embedding based multispectral face recognition

Face recognition using different imaging modalities has become an area of growing interest. A large number of multispectral face recognition algorithms/systems have been proposed in last decade. How to fuse features of different spectrum has still been a crucial problem for face recognition. To address this problem, we propose a sparse tensor embedding (STE) algorithm which represents a multispectral image as a third-order tensor. STE constructs sparse neighborhoods and the corresponding weights of the tensor. One advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. STE iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, STE can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on multispectral face databases, Equinox and PolyU-HSFD face databases, show that the performance of the proposed method outperform that of the state-of-the-art algorithms.

Robust Tensor Preserving Projection for Multispectral Face Recognition

Multiple imaging modalities based face recognition has become a hot research topic. A great number of multispectral face recognition algorithms/systems have been designed in the last decade. How to extract features of different spectrum has still been an important issue for face recognition. To address this problem, we propose a robust tensor preserving projection (RTPP) algorithm which represents a multispectral image as a third-order tensor. RTPP constructs sparse neighborhoods and then computes weights of the tensor. RTPP iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, RTPP can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on both Equinox and DHUFO face databases show that the performance of the proposed method is better than those of related algorithms.

Image Mosaic Based on Improved SIFT

Feature matching used in SIFT methods to extracting feature points, is the key of image mosaic. The advantages of SIFT is one of the most robust and the widely used image matching algorithms based on local features, which ensure the good mosaic image and reliable result. In this paper, we proposed a new image mosaic technology based on simple, which can solve massive calculation and long processing time produced by SIFT. The first step from this method is that 32 vectors, from the feature points, reduce to 16 vectors, and the feature points with 16 vectors construction a pot set, which is the space transformation matrix. And space transformation, the transformation is the mapping of the two images. The processed image is edge processed to eliminate image edge ambiguity and the right results is also rests on cluster .The stitched image is achieved thorough it. Experimental results show that the proposed algorithm is effective and comparing favorable with existing techniques, Practical application shows that this method also proposes a reliable parameter estimation method, and the result is reliable to stitching a large image. At the same time, the processing time is faster than SIFT.

Sparse tensor discriminant color space for face verification.

As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem, we propose a sparse tensor discriminant color space (STDCS) model that represents a color image as a third-order tensor in this paper. The model cannot only keep the underlying spatial structure of color images but also enhance robustness and give intuitionistic or semantic interpretation. STDCS transforms the eigenvalue problem to a series of regression problems. Then one spare color space transformation matrix and two sparse discriminant projection matrices are obtained by applying lasso or elastic net on the regression problems. The experiments on three color face databases, AR, Georgia Tech, and Labeled Faces in the Wild face databases, show that both the performance and the robustness of the proposed method outperform those of the state-of-the-art TDCS model.

Tensor Discriminant Color Space for Face Recognition

Recent research efforts reveal that color may provide useful information for face recognition. For different visual tasks, the choice of a color space is generally different. How can a color space be sought for the specific face recognition problem? To address this problem, this paper represents a color image as a third-order tensor and presents the tensor discriminant color space (TDCS) model. The model can keep the underlying spatial structure of color images. With the definition of n-mode between-class scatter matrices and within-class scatter matrices, TDCS constructs an iterative procedure to obtain one color space transformation matrix and two discriminant projection matrices by maximizing the ratio of these two scatter matrices. The experiments are conducted on two color face databases, AR and Georgia Tech face databases, and the results show that both the performance and the efficiency of the proposed method are better than those of the state-of-the-art color image discriminant model, which involve one color space transformation matrix and one discriminant projection matrix, specifically in a complicated face database with various pose variations.