paper

A toolbox to handle 2D curve and multiresolution representation of curve(A toolbox to handle 2D curve and multiresol ution representation of curve)

inverter with RL load and given as a pulse width modulus (PWM) at all gates of the circuit.

说明：  这是一个维纳滤波器设计的程序，用matlab语言实现。(This is a Wiener filter design program, complete with matlab language.)

digital signal processing

基于受控自回归积分滑动平均 (CARIMA)模型控对象的预测控制程序(Points based on the controlled auto-regressive moving average (CARIMA) Model Predictive Control control procedures)

说明：  自动提取曲线的峰值的matlab程序，比较有用 (Automatic extraction of the peak curve matlab procedures, more useful)

说明：  LMS 算法MATLAB代码,可以经常LMS的计算.这个是.M文件.希望大家多交流(LMS algorithm MATLAB code, LMS can often calculated. the yes. M documents. we hope that more exchanges)

说明：  此程序给出信号的多分辨率分析（Multiresolution Analysis），即信号的多分辨率分解（Multiresolution Decomposition）方法及应用。该程序是一个多抽样率系统（Multirate System）举例，用M个的滤波器，将信号的频谱等分成M部分，使所得抽样频率降为1/M，然后再依据它们的“重要性”给一不同的bit数，那么所传输的bit流必然会进一步下降，起到压缩带宽的作用。(This program gives the signal of the multi-resolution analysis (Multiresolution Analysis), the multi-resolution signal decomposition (Multiresolution Decomposition) method and its application. The program is a multirate rate system (Multirate System) example, with M a filter, the signal' s frequency spectrum is divided into M parts, the proceeds of sampling frequency reduced to 1/M, then according to their &quot importance&quot to the bit of a different number, then the transmitted bit stream is bound to decline further, to play the role of bandwidth compression.)

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.

matlab中用打靶法求解常微分方程组的两点边值问题(shooting method for bvps)