filter design for signal processing using matlab and mathematica

A fast Fourier transform can be used to solve various types of equations, or show various types of frequency activity in useful ways.It may be useful in reading things like sound waves, or for any image-processing technologies. A fast Fourier transform can be used in various types of signal processing.

A fast Fourier transform (FFT) is an algorithm that calculates the discrete Fourier transform (DFT) of some sequence and transforms the structure of the cycle of a waveform into sine components.This is where FFT comes in to save the day! Fast Fourier Transform (FFT): This could just take hours and hours which would simply consume a lot of power and more importantly, your precious time. Hold on! What if you have a signal with 10^6 samples? How long will you take to compute the frequency components? Doing simple math will tell you that it would be no less than 10^12. If you know the formula for calculating DFT, you'd understand that the time it takes to do that computation is of order ( 512 x 512). Imagine you have a voice signal consisting of 512 samples, in order to know its frequency response one would need to compute DFT. Wondering why we humans were in dire need of FFT even though DFT existed to do the same job? I've mentioned a few important characteristics of FFT below for you to understand, keep reading! Before the uprise of FFT, the Discrete Fourier Transform (DFT) which does the same job as that of FFT existed, and it has not lost its charm even today.

Those of us aware of what Digital Signal Processing (DSP) is and does, will undoubtedly have studied about FFT, its uses and applications.

The only motto of this project is to make one understand how Fast Fourier Transform can be used in the simplest of ways to remove unwanted frequencies in a given signal.

Filter design for signal processing using matlab and mathematica