speech_recognition_with_matlab

function [x, hist] = secant(x, f, tola, tolr) SECANT This a secant code with no line search. This code terminates on small relative-absolute errors function [X, HIST] = SECANT(X, F, TOLA, TOLR) Inputs: X = initial iterate F = function TOLA = absolute error tolerance TOLR = relative error tolerance Output: X = approximate result

说明：  S变换的MATLAB程序，可用于地震勘探去噪中，性能优于小波变换(S transform MATLAB program can be used for seismic exploration in the de-noising performance is better than wavelet transform)

说明：  这是求一个最大Lyapunov指数(largest Lyapunov exponent)的matlab预案程序代码。(This is for one of the largest Lyapunov index (largest Lyapunov exponent) of the plan matlab program code.)

matlab矩阵用合同变换的方式进行对角化(matlab matrix transformations performed by way of contract diagonalization)

Face recognition using ICA normalized

一个利用matlab画圆球的程序。圆球有立体，高光效果。(Draw a sphere using matlab program. Three-dimensional sphere, high-light effect.)

filtering with kalman

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

de boor 算法在turbo C2.0开发环境下的程序代码(de boor algorithm development environment in the turbo C2.0 program code)

Quadrature amplitude modulation (QAM) is a modulation scheme in which two sinusoidal carriers, one exactly 90 degrees out of phase with respect to the other, are used to transmit data over a given physical channel. One signal is called the I signal, and the other is called the Q signal. Because the orthogonal carriers occupy the same frequency band and differ by a 90 degree phase shift, each can be modulated independently, transmitted over the same frequency band, and separated by demodulation at the receiver. For a given available bandwidth, QAM enables data transmission at twice the rate of standard pulse amplitude modulation (PAM) without any degradation in the bit error ratio (BER).