High-accuracy Algorithm Research Articles

Development of FPGA based NURBS interpolator and motion controller with multiprocessor technique

The high-speed computational performance is gained at the cost of huge hardware resource, which restricts the application of high-accuracy algorithms because of the limited hardware cost in practical use. To solve the problem, a novel method for designing the field programmable gate array(FPGA)-based non-uniform rational B-spline(NURBS) interpolator and motion controller, which adopts the embedded multiprocessor technique, is proposed in this study. The hardware and software design for the multiprocessor, one of which is for NURBS interpolation and the other for position servo control, is presented. Performance analysis and experiments on an X-Y table are carried out, hardware cost as well as consuming time for interpolation and motion control is compared with the existing methods. The experimental and comparing results indicate that, compared with the existing methods, the proposed method can reduce the hardware cost by 97.5% using higher-accuracy interpolation algorithm within the period of 0.5 ms. A method which ensures the real-time performance and interpolation accuracy, and reduces the hardware cost significantly is proposed, and it’s practical in the use of industrial application.

Numerical systems analysis of multicomponent distributed systems

The paper presents the results in the construction of high-accuracy computational algorithms for the classes of partial derivative problems with discontinuous solutions, including ill-posed and eigenvalue problems. The optimal control in complex distributed systems is investigated. On the basis of the optimal control theory, explicit expressions are obtained for gradients of residual functionals to identify different parameters of multicomponent distributed systems. The possibility of using pseudoinverse matrices to solve some linear inverse problems in a finite number of arithmetic operations is considered.

Image Segmentation Algorithm Based on Improved Ant Colony Algorithm

In the process of image segmentation, the basic ant colony algorithm has some disadvantages, such as long searching time, large amounts of calculation, and rough image segmentation results. This paper proposes an improved ant colony algorithm. Applying different transfer rules and pheromone update strategies to different regions of an image, including background, target, edge and noise, we develop a highly adaptive image segmentation method with high edge detection accuracy and high algorithm implementation efficiency. In the initial stage of image segmentation, we apply the idea of fuzzy clustering, which enables ants to gather quickly to the edge in the background and the target area of the image. In the later stage of image segmentation, we introduce an edge search strategy in the edge area. A following experiment shows that this developed image segmentation method can split the target more quickly and accurately.

Crowd Density Estimation Based on ELM Learning Algorithm

Crowd density estimation in public areas with people gathering and waiting is the important content of intelligent crowd surveillance . A real-time and high accuracy algorithm is necessary to be inputted in the classification and regression of crowd density estimation to improve the speed and increase the efficiency. Extreme Learning Machine (ELM) is a neural network architecture in which hidden layer weights are randomly chosen and output layer weights determined analytically. In this paper, we propose a new method which is based on Haralick’s texture vectors, Gray-level Co-occurrence Matrix and ELM . The datasets are based on PETS2009 and UCSD. The performances are compared among SVM, BP and ELM. The experimental results suggest that the ELM learning algorithm has a good performance of accuracy and a very fast speed than other methods.

Application traffic classification at the early stage by characterizing application rounds

This article proposes a machine learning-based high-accuracy algorithm called “APPlication Round method (APPR)” to identify network application traffic at the early stage. For each TCP/UDP flow, discriminators available at the early stage are determined to support high-accuracy and real-time traffic classification. Such discriminators characterize the possible negotiation behaviors of each flow from an application layer perspective. The ability of flow attributes is tested using several machine learning algorithms. By contrast, this study also compares the level of accuracy of the proposed method with those reported by machine learning-based application traffic classification methods that have addressed real-time application traffic classification problems based on identical sample traffic sets. By applying a pruned C4.5 tree machine learning algorithm to real traffic traces, the proposed method offers a maximal 99.21%, with an average overall accuracy of 92.88% for all traffic samples. Compared to other machine learning algorithms, the proposed algorithm not only provides a minimal accuracy improvement of approximately 7–8% for normal ratio data sets and more than 15–30% improvement of overall accuracy for fixed ratio data samples, but is also suitable for on-line identification because of the low-flow test duration. Furthermore, the proposed method is also appropriate for identifying encrypted protocols because it demonstrates high accuracy, classifies encryption-based protocols, and supports real-time classification.

Fast 3D PIV with direct sparse cross-correlations

The extension of the well-assessed high-accuracy algorithms for two-dimensional-two components particle image velocimetry (PIV) to the case of three-dimensional (3D) data involves a considerable increase of the computational cost. Tomographic PIV is strongly affected by this issue, relying on 3D cross-correlation to estimate the velocity field. In this study, a number of solutions are presented, enabling a more efficient calculation of the velocity field without any significant loss of accuracy. A quick estimation of the predictor displacement field is proposed, based on voxels binning in the first steps of the process. The corrector displacement field is efficiently computed by restricting the search area of the correlation peak. In the initial part of the process, the calculation of a reduced cross-correlation map by using Fast Fourier Transform on blocks is suggested, in order to accelerate the processing by avoiding redundant calculations in case of overlapping interrogations windows. Eventually, direct cross-correlations with a search radius of only 1 pixel in the neighborhood of the estimated peak are employed; the final iterations are consistently faster, since direct correlations can better enjoy the sparsity of the distributions, reducing the number of operations to be performed. Furthermore, three different approaches to reduce the number of redundant calculations for overlapping windows are presented, based on pre-calculations of the contributions to the cross-correlations coefficients along segments, planes or blocks. The algorithms are tested both on synthetic and real images, showing that a potential speed-up of up to 800 times can be obtained, depending on the complexity of the flow field to be analyzed. The challenging application on a real swirling jet results in a speed-up of an order of magnitude.

Study of interpolation methods for high-accuracy computations on overlapping grids

Overset strategy can be an efficient way to keep high-accuracy discretization by decomposing a complex geometry in topologically simple subdomains. Apart from the grid assembly algorithm, the key point of overset technique lies in the interpolation processes which ensure the communications between the overlapping grids. The family of explicit Lagrange and optimized interpolation schemes is studied. The a priori interpolation error is analyzed in the Fourier space, and combined with the error of the chosen discretization to highlight the modification of the numerical error. When high-accuracy algorithms are used an optimization of the interpolation coefficients can enhance the resolvality, which can be useful when high-frequency waves or small turbulent scales need to be supported by a grid. For general curvilinear grids in more than one space dimension, a mapping in a computational space followed by a tensorization of 1-D interpolations is preferred to a direct evaluation of the coefficient in the physical domain. A high-order extension of the isoparametric mapping is accurate and robust since it avoids the inversion of a matrix which may be ill-conditioned. A posteriori error analyses indicate that the interpolation stencil size must be tailored to the accuracy of the discretization scheme. For well discretized wavelengthes, the results show that the choice of a stencil smaller than the stencil of the corresponding finite-difference scheme can be acceptable. Besides the gain of optimization to capture high-frequency phenomena is also underlined. Adding order constraints to the optimization allows an interesting trade-off when a large range of scales is considered. Finally, the ability of the present overset strategy to preserve accuracy is illustrated by the diffraction of an acoustic source by two cylinders, and the generation of acoustic tones in a rotor–stator interaction. Some recommandations are formulated in the closing section.

The On-Line Calibration Research of the Picking Robot Binocular Vision System

In the picking robot binocular vision systems research, the camera calibration is often an indispensable step and these basements to locate the target of the object and rebuild the three-dimensional construction based on the robot stereo vision for the follow-up study. So, searching for a high accuracy and simple camera calibration algorithm is of great significance and necessary. However, For most of these camera calibration algorithms, it is necessary to establish a reference object, namely the target, in front of the camera at present, but posing the target is very not convenient or almost impossible in some cases. Therefore, a picking robot online calibration algorithm based on the vision scene was proposed by studying the work environment characteristics of the picking robot binocular vision system and the invariant projective geometry. The experimental results showed that this algorithm’s calibration accuracy and precision good meets to the requirement of the robot binocular vision system camera calibration in the complex environment.

Enhanced hybrid method for low complexity block motion estimation

The modified one-bit transform (M1BT) motion-estimation method efficiently combines the low-complexity 1BT algorithm and the high-accuracy full search algorithm. As a result, it provides a good tradeoff between performance and computational complexity. The search range scheme in the M1BT method, however, is somewhat inefficient. A proposal was made for a new search range scheme that efficiently increases the search range without increasing the computational complexity. The proposed method also makes use of statistical data to reduce the number of search points. By using both the new search range scheme and the new search strategy, the proposed method not only reduces the complexity, but also improves the performance. The proposed method can be easily extended to similar methods, such as the modified two-bit transform (M2BT) and modified constrained 1BT (MC1BT) methods.

A reduced-complexity scheme using message passing for location tracking

Abstract This article presents a low-complexity and high-accuracy algorithm using message-passing approach to reduce the computational load of the traditional tracking algorithm for location estimation. In the proposed tracking scheme, a state space model for the location-estimation problem can be divided into many mutual-interaction local constraints based on the inherent message-passing features of factor graphs. During each iteration cycle, the message with reliability information is passed efficiently with an adaptive weighted technique and the error propagation law, and then the message-passing approach based on prediction-correction recursion is to simplify the implementation of the Bayesian filtering approach for location-estimation and tracking systems. As compared with a traditional tracking scheme based on Kalman filtering (KF) algorithms derived from Bayesian dynamic model, the analytic result and the numerical simulations show that the proposed forward and one-step backward tracking approach not only can achieve an accurate location very close to the traditional KF tracking scheme, but also has a lower computational complexity.

서포트 벡터 머신과 퍼지 클러스터링 기법을 이용한 오디오 분할 및 분류

최근 멀티미디어 정보가 급증함에 따라 콘텐츠 관리에 대한 요구도 함께 증가되고 있다. 이에 오디오 분할 및 분류는 멀티미디어 콘텐츠를 효과적으로 관리할 수 있는 대안이 될 수 있다. 따라서 본 논문에서는 동영상에서 취득한 오디오 신호를 분할하고, 분할된 오디오 신호를 음악, 음성, 배경 음악이 포함된 음성, 잡음이 포함된 음성, 묵음(silence)으로 분류하는 정확도가 높은 오디오 분할 및 분류 알고리즘을 제안한다. 제안하는 알고리즘은 오디오 분할을 위해 서포트 벡터 머신(support vector machine, SVM)을 이용하였다. 오디오 신호의 분류를 위해서는 분할된 오디오 신호의 특징을 추출하고 이를 퍼지 클러스터링 알고리즘(fuzzy c-means, FCM)의 입력으로 사용하여 각 계층으로 오디오 신호를 분류하였다. 제안하는 알고리즘의 평가는 분할과 분류에 대해 각각 그 성능을 평가하였으며, 분할 성능 평가는 정확도율(precesion rate)과 오차율(recall rate)을 이용하였으며, 분류 성능 평가는 정확성(classification accuracy)을 사용하였다. 또한 오디오 분할의 경우는 이진 분류기와 퍼지 클러스터링을 이용한 기존의 알고리즘과 그 성능을 비교하였다. 모의 실험 결과, 제안한 알고리즘의 분류 성능이 기존 알고리즘 보다 정확도율과 오차율 면에서 모두 우수하였다. The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

An Improved High-Accuracy CORDIC Algorithm for DSC in Endoscopic Ultrasonography System

An Improved High-Accuracy CORDIC (COordinate Rotation Digital Computer) algorithm for digital scan conversion is presented in this paper to enhance the accuracy and speed of coordinate conversion for Endoscopic Ultrasonography. Several optimization methods are carried out to make coordinate conversion implemented more exactly with fewer resources of FPGA. In the paper, the Cartesian coordinates are re-demarcated to save LE (Logic Element) resources of FPGA. The bit width of data, the scale factor correction and the convergence range are all optimized to improve the accuracy of the algorithm. Further more, a special processing for the near-field data is carried out to reduce the errors of digital scan conversion. With a full pipeline structure implemented on FPGA, the Improved High-Accuracy CORDIC algorithm is validated by both simulation and real-time ultrasound imaging experiment, making the accuracy enhanced and the image quality improved.

High Accuracy Algorithm of Airborne Interferometric Synthetic Aperture Radar

Interferometric Synthetic Aperture Radar is one of the most important technologies for topographic mapping. The DEM quality of airborne InSAR system depends on both system hardware performance and data processing methods. To derive large scale topographic and thematic maps up to scale 1:50000 and 1:10000, the whole data processing methods were presented. The methods included SAR imaging, interferometric processing and cartographic processing. Special methods were induced to resolve the problems encountered in project applications. Results using X-band airborne InSAR system data showed validity of the algorithm.

Research of a Driving Equipment for Aero-Engine Rotor Based on FPGA

The positioning accuracy can bring a great impact on inspecting defect for Aero-engine blade. The high or low pressure rotor of engine is generally driven by manual when blade’s defect is inspected. Frequently，False detection and missed happens due to match failure between two persons. Adopting a high-resolution, high-accuracy algorithm based on FPGA to design automotive equipment for high or low rotor . The Frequency range to step motor is 1Hz / s to 65536Hz / s, resolution is 1Hz,and positioning accuracy for blade can be up to 1mm.Resolving the questions of missing detectable rates made by manual during inspection in the paper, and increasing efficiency for inspection and the reliability of the experimental result. Experiment results show that this equipment can meet the challenge of the high-accuracy regulating speed for blade’s inspection.

Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles

The accurate estimation of internal parameters and state-of-charge (SoC) of battery, which greatly depends on proper models and corresponding high-efficiency, high-accuracy algorithms, is one of the critical issues for the battery management system. A model-based online estimation method of a LiFePO4 battery is presented for application in electric vehicles (EVs) by using an adaptive extended Kalman filter (AEKF) algorithm. The Thevenin equivalent circuit model is selected to model the LiFePO4 battery and its mathematics equations are deduced to some extent. Additionally, an implementation of the AEKF algorithm is elaborated and employed for the online parameters’ estimation of the LiFePO4 battery model. To illustrate advantages of the online parameters’ estimation, a comparison analysis is performed on the terminal voltages between the online estimation and the offline calculation under the Hybrid pulse power characteristic (HPPC) test and the Urban Dynamometer Driving Schedule (UDDS) test. Furthermore, an efficient online SoC estimation approach based on the online estimation result of open-circuit voltage (OCV) is proposed. The experimental results show that the online SoC estimation based on OCV–SoC can efficiently limit the error below 0.041.

A New and High-Accuracy People Counting Algorithm

The people counting algorithms are widely applied in many areas. The algorithms may have lower counting accuracy or higher time complexity. To improve that, a new and high-accuracy people counting algorithm is proposed in this paper. It analyzes a background image and series population original images to calculate an average proportion for one person and create a two-dimensional table. Then it uses the average proportion and the two-dimensional table to count the number of people in test images. And two thresholds are adopted to regulate accuracy in counting.

高精度高次非球面最接近球面计算方法

高精度高次非球面最接近球面计算方法

A high accuracy algorithm for 3D periodic electromagnetic scattering

We develop a highly accurate numerical method for scattering of 3D electromagnetic waves by doubly periodic structures. We approximate scattered fields using the Müller boundary integral formulation of Maxwell’s equations. The accuracy is achieved as singularities are isolated through the use of partitions of unity, leaving smooth, periodic integrands that can be evaluated with high accuracy using trapezoid sums. The removed singularities are resolved through a transformation to polar coordinates. The method relies on the ideas used in the free space scattering algorithm of Bruno and Kunyansky.

一种快速高精度的相位恢复迭代法

一种快速高精度的相位恢复迭代法

A High-Accuracy Contour Extraction Algorithm for MR Images Based on Mumford-Shah Model

This paper proposes a high-accuracy contour extraction algorithm based on curve evolution model.Face to the blurred edges and high noise in MR image,Mumford-shah model is improved here and fuzzy clustering is used to establish active contour model to lower sensitivity to irregular details and noises.The derivation of the level set and the semi-implicit implementation based on the additive-multiplicative operator splitting is performed in order to improve the computing efficiency and accuracy.Experimental results are given to demonstrate the feasibility of the proposed method in extracting contour from the blurred edge and high-noise images.