资源列表
waveletthreshold
- threshold method to denoise signals
NewFolder
- feature extraction from speech
realizationofavarietyofwavelettransform
- 小波变换及其应用--时频分析 详细介绍了小变换的实现及其应用。 介绍了各种小波的实现，可供深入学习者参考。-Wavelet Transform and its applications- time-frequency analysis described in detail the realization of transformations and their applications. Introduced a variety of wavelet implementation, f
work
- 小波去噪程序，用于实现对含有噪声的信号进行去噪，还是有点用。-Wavelet de-noising procedures for the realization of the noisy signal de-noising, or a bit to use.
xiaobo_MATALBR2007
- 《小波分析理论与MATALB R2007 实现》该书中各例子的源码，该书是一本描写小波分析及其MATLAB实现比较不错的书籍-" Wavelet Analysis Theory and MATALB R2007 to achieve," the book source code of all examples, the book is a descr iption of wavelet analysis and MATLAB to achieve more good books
wavelet
- 本文在小波变换理论的基础上,总结了小波变换的特点,讨论了在MATLAB语言环境下实现图像压缩的方法,实现了小波变换在 图像中的应用。-Based on the wavelet transform theory, this paper summs up the characteristics of wavelet transform .Tt discusses the methods of achieving image compression in the MATLAB language e
edge1234
- edge detection procedure
xiaobobianhuan
- 在VC环境下的数字图像处理程序，小波变换-xiaobobianhuan
DB4PR0
- c语言编写的db4小波变换程序，包含滤波器函数，小波分析和小波重构，可以直接使用，对写其它小波程序有参考价值。-written in c db4 wavelet transform procedures, including filter functions, wavelet analysis and wavelet reconstruction, can be used directly on to write other wavelet process a valuable referenc
MATLABwavelets
- matlab编写的小波变换程序，有很多实例，是《Matlab小波分析》（张德丰）教程的源程序，很适合学习使用。-matlab wavelet transform procedures for the preparation, there are many examples is " Matlab Wavelet Analysis" (Zhang Defeng) tutorial source code, it is suitable for learning to use.
xiaobo
- 小波分析是当前应用数学和工程学科中一个迅速发展的新领域，经过近10年的探索研究，重要的数学形式化体系已经建立，理论基础更加扎实。与Fourier变换相比，小波变换是空间(时间)和频率的局部变换，因而能有效地从信号中提取信息。-Wavelet analysis is currently applied mathematics and engineering disciplines in a rapidly developing new fields, after almost 10 years o
xiaobo
- 用Visual C++编写的小波变换，小波除噪程序，可以显示信噪比.-Written using Visual C++ wavelet transform, wavelet denoising procedure, you can display signal to noise ratio.