DSPP processor

    This was a demo experiment perfomed by demonstrated by our senior Jayganesh from BE EXTC.During demonstration of this experiment he used DSP kit name was TMS320F28375.

     For performing this experiment he written a code in code composer studio software tool.In this he create a new project in which he written various logical and ariyhmetic instructions which explained by him later.
      During operation of experiment,initially registers holds some value which will be changed later at the output of programing and also this value are useful in real time  debugging process.

Design of FIR using window function

     It is a filter whose impulse response if of finite duration. We have implemented this filter for linear phase using both C programming and Scilab.
     We used C programming to find impulse response h(n). Attenuation in passband and stopband (As,AP), passband and stopband frequencies(fS,fP) and sampling frequency(FS) were taken as inputs. From these values desired impulse response function was calculated and was then multiplied by the window function. The window function is decided from the value of As. The As of window function should be greater than that of the input.

     Scilab was used to plot the magnitude spectrum and phase spectrum of the FIR filter.

     As we take window function with higher As value, the side lobe width goes on decreasing with increasing main lobe width. The phase response of the filter is linearly varying with frequency and no distortion is observed in the output. The output is same as input delayed by some constant.

 code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOMkJqeUtlb1NBczA

Design of FIR filter using frequency sampling method

In this experiment the linear phase FIR filter was designed in scilab by taking the input specifications from the user along with the order of the filter.

The input specifications for two different cases were taken one for LPF and other for HPF. The magnitude and phase response for both the filters were plotted in scilab.

It was observed that there were lobes present in the magnitude spectrum in stop band for both cases. The phase spectrum was observed to be linear, and discontinous. The point of discontinuity corresponded to the frequency where the magnitude of the lobes was equal to zero and when the phase angle changed from pi to -pi.

code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOVEllVktxZHl6d1U

Chebyshev Filter Design

Chebyshev filters have steeper roll-off and more passband/stopband ripples than Butterworth filters. Chebyshev filters minimize the error between idealized and actual filter characteristic over the range but with ripples in passband. There is a defnite ZERO at -1, number of poles gives the ordef of filter. Magnitude spectrum is equiripple in passband and monotonic in stop band.

code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOQ2xZQ3BJZHhscDA

Butterworth filter design

The aim which has to be achieved and the contents of this experiments are as follows :
Design of Analog and digital Butterworth filter Design of analog filter is done by calculating normalized and denormalized filter and then using BLT to find digital filter transfer function. magnitude response of Digital low pass filter and digital high pass filter was plotted. On a comparision of therotical values and observed values and observing th pole zero plots we concluded that observed values approach therotical values from response for LPF and HPF

code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOaG80THNoUGc0S0k

Overlap add and save method

This method is suitable to find linear convolution of long input signals using FFT algorithm.

The long signal is first broken down into smaller parts. These smaller signals are then convoluted with the given h[n]. The result obtained from these convolutions is then added in such a way so as to get the linear convolution of the original signal. 

In Overlap Save method the smaller signals are circularly convoluted with the given h[n] and concatenated in such a way to obtain the final result.

Code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOUHZwU2F0bDIwZGs

Fast Fourier Trasform and IFFT

An algorithm for fast computation of DFT is FFT(Fast Fourier Transform).The algorithm which we have implemented can only calculate FFT for signal length of 4 of Radix-2. The FFT algorithm divides the N-point DFT input signal into 2 parts-even and odd signal values of the input. The even signal values can be directly found out whereas the odd signal values is just multiplied by an appropriate twiddle factor which overall reduces the complex multiplications for FFT. This happens because the signal is divided into 2 parts, so the computation is carried out only for N/2 signal values. This increases the speed of computations and makes FFT faster than other algorithms.  

code reference :- https://drive.google.com/open?id=0B-8Do0Av35gOVTc5dGVXZ1ZKS3M