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小波分解，得到低频系数并求特征熵，可用来对信号进行分选识别(Wavelet decomposition, low coefficient and entropy, which can be used for signal sorting and recognition)

自己编写离散小波变换与离散小波反变换程序对一幅图像做2级小波分解（离散小波变换）与合成（离散小波反变换）(Write your own discrete wavelet transform and discrete wavelet inverse transform procedure on an image to do two wavelet transform (DWT) and synthesis (discrete wavelet inverse transform))

小波分解在图像处理和电能质量分析中应用广泛，本文应用小波分解的方法将高频的暂态信号提取出来，获取暂态信号的特征量，就能够有效地计算暂态信号。(Wavelet decomposition in image processing and power quality analysis is widely used, this application of wavelet decomposition method to extract high-frequency transient signals, to obtain the amount of transient characteristics of the signal, we can effectively calculate the transient signal.)

实现了基于小波变化的图像融合,并将融合结果显示出来(It realize the Image fusion based on wavelet transform and show the tesult)

小波神经网络实现短期电力负荷预测，神经网络模型及其BP学习算法。 (Wavelet neural network short term load forecasting, neural network model and BP learning algorithm.)

wavelet transforms in advanced forms

用matlab编写找到向量维度的波峰波谷的值.(Prepared using matlab to find vector value of the dimension of the peaks and valleys.)

根据采集的时域数据，做fft得到其频谱，并计算其功率谱密度(According to the collected time-domain data, the frequency spectrum is obtained by FFT and the power spectral density is calculated.)

本文研究的是利用奇异性指数实现对设备状态监视和故障预警功能。首先用LabVIEW 采集传感器电信号，然后对采集到的电信 号进行小波变换得到信号包络奇异点的奇异性指数，即设备运行状态的特征值(In this research project, we use LabVIEW to do wavelet-transformation to the signal collected by sensors in order to get singularity index from the singular points of the particular signal’s envelope. Through comparing this eigenvalue to the figures in a standard database, we can know the functional status of the instruments. Not only the diagnoses are continuously displayed, but alarms about signs of dysfunction will also be raised in a proper way. In other words, both the supervision of the instrument and warning of dysfunction can be achieved)

AR过程的线性建模的Burg算法参数估计m文件(Burg method for AR processing)