基于梯度滤波器的多尺度高精度微运动测量

微运动测量在微纳制造和生物医学等高技术领域担任了一个重要的角色,本文将梯度滤波器方法和多尺度方法相结合,提出了一种用于微运动测量的基于梯度滤波器的多尺度方法．在该方法中,利用多尺度金字塔迭代方法,对测量图像进行降采样和低通滤波,将较大像素的图像运动转化为多个小像素的图像运动进行估计,从而提高运动估计精度．提出的方法用于测量两个像素附近的MEMS微机械图像运动时,测量偏差达到了0．005个像素．模拟实验结果表明,这种基于梯度滤波器的多尺度方法能够实现高精度的微运动测量．     

RME:基于滑动窗口的实时人脸检测算法

研究了基于滑动窗口的视频实时人脸检测,提出了刚性运动估计(RME)算法.该算法以小尺度人脸瞬时刚性运动为假设,根据几何变换对窗口图像的运动进行描述,以光流代替运动矢量计算运动参数进而识别窗口图像的刚性、非刚性运动类型,通过排除非刚性窗口以提高人脸检测效率.对比实验与分析表明,该算法在准确率与时间效率方面具有优势.     

超声速运动目标被动声定位算法的后置处理

超声速运动目标在空气中习行时产生激波,前文＾「１」利用激波到达时间提出一种基于波前方向矢量的定位算法,本文着重分析该定位算法的理论估计精度。提出一种基于数据融合的后置处理算法,以提高估计精度。     

基于LMedS 估计的快速稳像方法

为了提高数字稳像的快速性和鲁棒性,研究了一种基于LMedS 估计的图像稳定方法.在图像的运动估计中,提出了预判局部模块的算法:为避免误匹配,在计算运动矢量前对模块的梯度信息进行预分析,具有独特纹理特性的模块才被选用,通过减少参与计算的模块数目提高处理速度;采用改进的快速序贯相似性算法(SSDA)进行块匹配,提高运动矢量的计算速度;采用LMedS 估计法去除不精确的运动向量,然后用最小二乘法得到全局运动模型的参数.仿真结果表明该方法在干扰下能保持1/4 像素以内的稳像精度,一次稳像时间小于7 ms .     

基于UKF的简化交互多模型视频图像机动目标跟踪算法

在视频图像运动目标的状态估计与跟踪问题中,常用的扩展卡尔曼(EKF)算法简单、计算量小,但仅适用于弱非线性和弱高斯环境下.本文提出一种基于无迹卡尔曼滤波(UKF)与简化交互多模型(IMM算法相结合的视频图像运动目标跟踪算法,有效地克服了EKF算法在强非线性状态下或对小运动目标跟踪时精度低,容易发散的问题.仿真结果表明,该算法估计和跟踪非线性目标的性能明显优于基于EKF算法,其跟踪精度可达到三阶(泰勒级数展开)精度.     

基于H.264的改进运动估计算法及其性能对比实验研究

为提高视频压缩效率,在传统搜索算法的基础上,结合实际运动图像中的运动向量,以水平方向向量为主要特点,提出一种利用偏水平十字模板搜索与偏向双菱形模板搜索相结合的改进搜索算法。该改进算法可以根据运动向量的特点来减少模板的搜索点数,达到提高视频压缩效率、节省运动估计时间的目的。性能对比实验结果表明:该改进算法适合各种运动类型的视频序列,尤其适用于运动变化剧烈的序列,并且能够在PSNR值和码率值极其接近FS算法的情况下,对QCIF格式图像的运动估计时间(MET)减少约95%,对CIF格式图像的运动估计时间(MET)减小约94.5%,大大减少运动估计时间。     

基于CPU-GPU异构平台的高层结构地震响应分析方法研究

为了解决传统的串行有限元分析方法计算耗时多精度低的问题,基于GPU并行计算能力在CUDA架构下建立了一套兼顾精度和效率的高层结构有限元分析的CPU-CPU的异构平台。基于CPU-GPU异构平台研究了高层结构地震响应算法,将整个时间步积分在GPU中计算完成,每一时间步下利用基于GPU的预处理共轭梯度迭代法求解线性方程组获得该时刻的位移,最终实现了基于GPU的Newmark-β法。通过算例验证了本文所提方法的高精度、高效率。     

基于分块的自适应超分辨率算法

本文主要研究序列图像超分辨率重建技术.本文提出了一种新的基于分块策略的超分辨率算法,由于采用分块策略,矩阵规模极大减小,且其规模与分块大小相关,而与原始序列大小无关,故能有效降低系统内存开销,提高系统处理效率.实验表明,传统多帧算法对运动估计的精度要求很高,而本文算法对此要求不高,在采用低精度运动估计技术的条件下,本文...     

UKF和KF两级滤波的雷达与红外配准算法

针对雷达与红外传感器的时间和空间偏差配准问题,给出时空偏差配准模型,提出了一种时空偏差实时估计算法.该算法将目标的运动状态和传感器偏差组合在同一状态方程中,构建扩维状态的系统动态方程和量测方程,并通过对量测方程的非线性分析,采用UKF和KF两级滤波的方法进行目标状态和配准偏差的联合估计.仿真结果表明,与采用UKF滤波的方法相比,该算法具有更高的估计精度,而且减小了计算量.     

基于2D-3D双目运动估计的立体视觉定位算法

运动估计算法是影响立体视觉定位精度的重要因素,传统的3D-3D运动估计算法受噪声影响很大,计算精度不高.本文提出了一种基于2D-3D双目运动估计的立体视觉定位算法.算法不使用运动后的特征点3D坐标,而直接利用其2D图像投影坐标.首先,利用EPnP运动估计算法确定匹配内点和初始运动参数.接着,利用双目相机之间的2D投影几...     

Cyclic optimization algorithms for simultaneous structure and motion recovery in computer vision

With few exceptions, most previous approaches to the structure from motion (SFM) problem in computer vision have been based on a decoupling between motion and depth recovery, usually via the epipolar constraint. This article offers closed-form cyclic optimization algorithms for the simultaneous recovery of motion and depth in the discrete SFM problem. Cyclic coordinate descent (CCD) algorithms in which each stage admits closed-form solutions are developed for two widely used fitting criteria: the geometric error in one image, and the reprojection error criterion. As a by-product, analytic gradients that can be used in descent-based optimization methods are also obtained. The computational efficiency, statistical consistency, noise robustness, and accuracy of the algorithms are assessed via experiments with synthetic image data.     

基于位移传感器的包装机械手运动跟踪方法设计

目的为提高包装机械手末端执行器轨迹跟踪精度,提出一种三维运动轨迹跟踪方法。方法获取拉绳式位移传感器在预先建立的三维坐标系中坐标数据和拉线长度。利用坐标和拉线长度计算运动物体的三维坐标数据,进而形成所述运动物体的三维运动轨迹。同时,给出基于ARM的轨迹跟踪控制器结构以及软件实现方法。结果实验结果表明,与传统示教盒相比,该方法可将定位精度提高1倍,相关误差可控制在0.3 mm以内。该方法在执行效率方面大约能够提升33%,提高了包装机械手的执行速度和分拣效率。结论所述轨迹跟踪方法能够提高包装机械手末端执行器的定位精度和定位速度,符合包装、食品、化工等行业的工艺要求。     

Rapid tracking of small displacements with ultrasound

Time-delay estimators, such as normalized cross correlation and phase-shift estimation, form the computational basis for elastography, blood flow measurements, and acoustic radiation force impulse (ARFI) imaging. This paper examines the performance of these algorithms for small displacements (less than half the ultrasound pulse wavelength). The effects of noise, bandwidth, stationary echoes, kernel size, downsampling, interpolation, and quadrature demodulation on the accuracy of the time delay estimates are measured in terms of bias and jitter. Particular attention is given to the accuracy and resolution of the displacement measurements and to the computational efficiency of the algorithms. In most cases, Loupas' two-dimensional (2-D) autocorrelator performs as well as the gold standard, normalized cross correlation. However, Loupas' algorithm's calculation time is significantly faster, and it is particularly suited to operate on the signal data format most commonly used in ultrasound scanners. These results are used to implement a real-time ARFI imaging system using a commercial ultrasound scanner and a computer cluster. Images processed with the algorithms are examined in an ex vivo liver ablation study.     

Continuous delay estimation with polynomial splines

Delay estimation is used in ultrasonic imaging to estimate blood flow, determine phase aberration corrections, and to calculate elastographic images. Several algorithms have been developed to determine these delays. The accuracy of these methods depends in differing ways on noise, bandwidth, and delay range. In most cases relevant to delay estimation in ultrasonics, a subsample estimate of the delay is required. We introduce two new delay algorithms that use cubic polynomial splines to continuously represent the delay. These algorithms are compared to conventional delay estimators, such as normalized cross correlation and autocorrelation, and to another spline-based method. We present simulations that compare the algorithms' performance for varying amounts of noise, delay, and bandwidth. The proposed algorithms have better performance, in terms of bias and jitter, in a realistic ultrasonic imaging environment. The computational requirements of the new algorithms also are considered.     

High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.     

A spline-based algorithm for continuous time-delay estimation using sampled data

Time delay estimation (TDE) lies at the heart of signal processing algorithms in a broad range of application areas, including communications, coherent imaging, speech processing, and acoustics. In medical ultrasound for example, TDE is used in blood flow estimation, tissue motion measurement, tissue elasticity estimation, phase aberration correction, and a number of other algorithms. Because of its central significance, TDE accuracy, precision, and computational cost are of critical importance. Furthermore, because TDE is typically performed on sampled signals-and delay estimates are usually desired over a continuous domain-time delay estimator performance should be considered in conjunction with associated interpolation. In this paper we present a new time-delay estimator that directly determines continuous time-delay estimates from sampled data. The technique forms a spline-based, piecewise continuous representation of the reference signal then solves for the minimum of the sum squared error between the reference and the delayed signals to determine their relative time delay. Computer simulation results clearly show that the proposed algorithm significantly outperforms other algorithms in terms of jitter and bias over a broad range of conditions. We also describe a modified version of the algorithm that includes companding with only a minor increase in computational cost.     

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

Error sources and algorithms for white-light fringe estimation at low light levels

We address the problem of highly accurate phase estimation at low light levels, as required by the Space Interferometry Mission (SIM). The most stringent SIM requirement in this regard is that the average phase error over a 30-s integration time correspond to a path-length error of approximately 30 pm. Most conventional phase-estimation algorithms exhibit significant enough bias at the signal levels at which the SIM will be operating so that some correction is necessary. Several algorithms are analyzed, and methods of compensating for their bias are developed. Another source of error in phase estimation occurs because the phase is not constant over the integration period. Errors that are due to spacecraft motion, the motion of compensating optical elements, and modulation errors are analyzed and simulated. A Kalman smoothing approach for compensating for these errors is introduced.     

机床直线轴重复定位精度三维评价方法研究

重复定位精度是机床的一个重要性能指标，会直接影响加工产品的质量一致性。现有以直线轴运动方向的位置偏差作为评价指标的一维评价方法已经不适用于精密机床重复定位精度的评价。为了更全面地评价机床直线轴重复定位精度，提出了机床直线轴重复定位精度的三维评价方法，以球概率误差半径作为评价指标，将一维定位评价扩展到三维空间评价。首先，以卧式加工机床为例，通过齐次坐标变换建立了运动件单轴和机床的重复定位精度的数学模型，对现有一维评价方法的局限性进行分析。其次，基于卡方分布的性质，对球概率误差半径的计算过程进行简化。最后，通过简化的球概率误差半径计算方法对机床直线轴重复定位精度进行评价。以精密卧式机床的工作台为研究对象，通过实验对一维评价方法和三维评价方法进行了对比，结果表明，以球概率误差半径为评价指标的三维评价结果与定位点空间分布的离散程度基本一致。采用三维评价方法可以使机床重复定位精度的评价更加全面，可为机床整体性能的提升提供更可靠的理论指导。     

基于三次相关改进的广义互相关时延估计法在局部放电超声波定位中的研究

在现有的开关柜等电气设备局部放电超声波定位技术中,到达时间差定位法(Time Difference of Arrival,TDOA)在定位精度与技术实现等方面有着一定的优势,得到了广泛的使用,是目前常用的方法,其中的时延估计算法对整个系统起着关键作用。文章首先对目前现有的基本相关、广义相关、二次相关等时延估计算法进行了分析。其次,在二次相关基础上再进行一次相关,并设计了新型的加权函数,将三次相关与广义互相关结合在一起,成为一种新的方法,即广义三次相关时延估计法。最后,搭建了相应的开关柜实验平台并对以上方法进行了实验及对比,分析了各算法的性能。结果表明,广义三次相关时延估计法在相对强噪声环境中较其他算法抗噪性能更强,具有更好的优越性。