error_diff

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)

Abstract The study of digital signal processing normally concentrates on the design, realization, and application of single-input, single-output digital filters. There are applications, as in the case of spectrum analyzer, where it is desired to separate a signal into a set of sub-band signals occupying, usually nonoverlapping, portions of the original frequency band. In other applications, it may be desired to combine many such sub-band

基于fht的匹配追踪算法，算法速度要比fft快很多，提高了一个数量级(Fht based matching pursuit algorithm, the algorithm much faster speed than fft, an order of magnitude increase)

LDA的matlab源码,主要用于人脸识别方面的研究(code for LDA)

本文介绍kalman滤波的基本原理及发展现状，主要从模型的建立和非线性模型的线性化两方面进行介绍(In this paper, the basic principle of kalman filter and the development present situation, mainly from the establishment of model and the linearization of the nonlinear model is presented in two aspects)

this is a simple Genetic Algorithm (GA) testing the optimal solution for a simple function.

Some paintings lyapunov chaotic attractor and the related terms, while the sub-critical and supercritical classification

programm for newton methode

1. 图像二值化 2. 边界跟踪，去除离散点 3. 边界提取 4. 删除短边 5. 区分河流与道路 先找到桥梁，利用桥梁上的种子点 对道路进行跟踪，从而提取整条主干道。 (1 image binarization 2boundary tracking, removal of discrete points 3 boundary extraction 4 Remove the short side 5 distinguish rivers and roads the curvature is too large) First find the bridge, the bridge on the use of seed points on the road to follow, thereby extracting the whole trunk.)

compressive sensing theory