Lab No.4: Overlap Add/Save Method

The two methods (OAM & OSM) are linear filtering methods. These are block processing techniques and are suitable for real time signal processing.
Both OAM and OSM breakdown the signal into segments , process each segment and then combine the segments into one final output signal. OAM and OSM methods use FFT because high computational speed is achieved using FFT.

Lab No.3: Fast Fourier Transform

FFT method is different than DFT method. In FFT method the input signal is broken into parts according to the Radix-2 algorithm. The input sequence order and the output sequence order in Radix-2 algorithm is in bit reverse manner.
By performing the experiments, we counted the number of steps that were required to perform the FFT. The result was that the steps required for FFT were much less than that of DFT.
Thus, FFT is computationally a fast method.

Lab No.2 : Discrete Fourier Transform

   DFT is a transform method which produces periodic results. Here period is 'N'.

   The spectrum of DFT signal is discrete and is defined in the range from 0 to 2(pi), where 0 is included but 2(pi) is excluded.

   DFT method breaks the continuous signal into parts and takes the values only as particular intervals, therefore this method does not give exact results bt approximate results, hence the spectrum is also approximate.

   The expansion of DFT signal in time domain gives compressed spectrum in frequency domain. And DFT method is computational slow method.

Lab No.1: Convolution and Correlation

Convolution gives the output of the system, whereas Correlation gives the degree of similarity between two signals.

Autocorrelation signal is an Even signal and autocorrelation of delayed signal is same as that of original signal.

Cross correlation of input signal with delayed version of this input signal is same as that of autocorrelation of this original input signal. Correlation is helpful in finding the degree of similarity between two signals.