用聚类—矩阵和法评价不同产地丹参提取物相关活性

目的:对不同产地丹参有效部位进行药效学比较并进行评价。方法:对8个不同产地丹参含丹参素及含丹参酮成分进行了ADP诱导的血小板聚集实验、凝血酶原时间的测定及MDA的测定,对实验结果进行聚类分析和综合药效评价。结果:8种丹参的两种提取物均具有抗血小板聚集,延长凝血酶原时间及降低MDA的作用。结论:就上述活性而言,山西产丹参野生及栽培药效近似且较优于其他产地,四川中江栽培、山东野生、河南栽培药效近似,优劣居中;陕西安康野生和河南栽培相对较差。

RFD-Rao and RFD-Wald tests for distributed targets with range walking effect

Two novel adaptive distributed target detectors, the range frequency domain-Rao （RFD-Rao） and range frequency domain-Wald （RFD-Wald） tests are proposed in this work. The application methods for these tests consider a partially homogeneous disturbance environment and a target range walking effect in a coherent processing interval （CPI）. The asymptotic performance of the detectors is analyzed, and the constant false alarm rate （CFAR） properties with respect to the clutter covariance matrix and power level are shown. The performances of the proposed adaptive detectors are assessed through Monte-Carlo simulations, and the results are presented to demonstrate the effectiveness of the proposed detection algorithms compared to those of similar existing detectors.

1-Bit compressive sensing: Reformulation and RRSP-based sign recovery theory

Recently, the 1-bit compressive sensing （1-bit CS） has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. We first show that a necessary assumption （that has been overlooked in the literature） should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an t0-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property （RRSP） of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a k-sparse signal can be exactly recovered with 1-bit basis pursuit.

A stereo matching algorithm based on SIFT feature and homography matrix

Aiming at the low speed of traditional scale-invariant feature transform(SIFT) matching algorithm, an improved matching algorithm is proposed in this paper. Firstly, feature points are detected and the speed of feature points matching is improved by adding epipolar constraint; then according to the matching feature points, the homography matrix is obtained by the least square method; finally, according to the homography matrix, the points in the left image can be mapped into the right image, and if the distance between the mapping point and the matching point in the right image is smaller than the threshold value, the pair of matching points is retained, otherwise discarded. Experimental results show that with the improved matching algorithm, the matching time is reduced by 73.3% and the matching points are entirely correct. In addition, the improved method is robust to rotation and translation.