Dear Samer

Wavelet transform is a  combination of low pass and high pass filter, ie it is a  multi spectral   analysis. Hardware component can be implemented according to the  requirements. for more details you can see this link

www.ijaiem.org/volume2issue10/IJAIEM-2013-10-03-001.pdf

Regards

Thanks A lot, Dr Muzhir

Very Helpful 

Dear Mr Samer

WT is integral transform. It decomposes signal into different frequency subbands with central frequency which depends on type of wavelet. The decomposition algorithm can be defined broadly in redundant and non redundant. The decomposition algorithm is decided by application mainly computational time and available memory. Form implementation point, Lifting technique is best for real time application.    

Thanks a lot Dr Gupta 

thanks for your help 

Hi, there is no relation between  wavelet transform and EMG sensors. EMG sensor is the one which is used to sense the electrical activity of the skeletal muscles. After sensing, u will have a signal at hand that could be transformed to suitable domain to process. so wavelet transform is a tool to convert signal domain to Scale-space domain. After taking your signal to wavelet domain , u will get a set of wavelet coefficients according to decomposition level.. Since WT has many important unique characteristics like time & frequency localization,  multiresolution, sparse representation   etc. , the processing of most of the signal gets easier in WT domain. Hope it would be useful.

In fact, the EMG signals has small range and small derivation from (10-500) and from (0-8) mV , Because of this the Wavelet transform can deal with this, the truth is I don't want to process these signals in a computer not matlab, I want to simplify the EMG signal by using HARDWARE component.

Thanks a lot :)

really happy for your concern    

Dear Mr Samer

As i have seen your requirement to implement in hardware, you can apply Lifting algo and decompose your signal in different level (depends on sampling time and no of sample to use result in real time). For exoskeleton system, find out the required signature in different frequency bands.

Dear Mr. Samer , Hello , In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. Nowadays, wavelet transformation is one of the most popular candidates of the time-frequency-transformations.

First a wavelet transform is applied. This produces as many coefficients as there are pixels in the image (i.e., there is no compression yet since it is only a transform). These coefficients can then be compressed more easily because the information is statistically concentrated in just a few coefficients. This principle is called transform coding. After that, the coefficients are quantized and the quantized values are entropy encoded and/or run length encoded.

A few 1D and 2D applications of wavelet compression use a technique called "wavelet footprints . The wavelet transform can provide us with the frequency of the signals and the time associated to those frequencies, making it very convenient for its application in numerous fields. For instance, signal processing of accelerations for gait analysis, for fault detection, for design of low power pacemakers and also in ultra-wideband (UWB) wireless communications .

and as already mentioned in this context, the wavelet-transformation corresponds to a convolution of a function y(t) and a wavelet-function. A convolution can be implemented as a multiplication in the frequency domain. With this the following approach of implementation results into:

Fourier-transformation of signal y(k) with the FFT

Selection of a discrete scaling factor

Scaling of the wavelet-basis-function by this factor and subsequent FFT of this function

Multiplication with the transformed signal YFFT of the first step

Inverse transformation of the product into the time domain results in YW for different discrete values of τ and a discrete value Cn .