DSPP EXPERIMENTS

In this experiment we designed a digital Chebyshev filter from analog filter. We took the input specifications for the following parameters: passband and stopband attenuation, passband and stopband frequency and also the sampling frequency. And then displayed the results in the form of waveforms on Scilab. We designed both the lowpass and the highpass filter and compared the values of passband and stopband attenuation with the given input values.We observed that magnitude spectrum exhibits ripple in paasband and no monotonic in stopband. Also the Chebyshev's filter requires less hardware components for realisation. Chebyshev filters have the property that they minimise the error between the idealised and the actual filter characteristic over the range of filter.

In this experiment we designed a digital filter from analog filter. The input specifications that were given are passband attenuation, stopband attenuation, passband frequency, stopband frequency and sampling frequency. The passband and stopband attenuation values were given in dB and the passband stopband and sampling frequency were given in Hz. Using this data as input from the user we designed the low pass and high pass Butterworth filter. A lowpass filter is a filter that passes signals with a frequency lower than a certain cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. A highpass filter is a filter that passes signals with a frequency higher than a cutoff frequency and attenuates signals with frequency lower than the cutoff frequency. The observed values of the passband and stopband attenuation were noted down and their waveforms were observed on Scilab. Analog LPF poles lie on the lefthand side of the s-plane hence the analog filter is...

OSM and OAM are used to compute DFT when the length of the signal is large. OAM and OSM are thus block processing techniques. They are suitable for real time signal processing. We performed filtering of long data sequence using OAM and OSM. We concluded that both the methods are very efficient in calculating the convolution between long length signal and impulse signal that is of finite length.

A complicated signal can be broken down into simple waves. This breakdown and how much od each wave is neede is the Fast Fourier Transform. Total number of calculations are reduced. We found Fast Fourier Transform and also verified it using mathematical formulations. FFT is computationally Fastest Algorithm. FFT divides the N-point DFT input signal into 2parts- even and odd signal values.   

Discrete Fourier Transform, transforms one function into another which is called the frequency domain repreransentation. In this experiment, we found Discrete Fourier Transform of the input signal and then verified it by mathematical formulae. Then we plotted the magnitude spectrum of the output signal and analysed the results. We found that as N(length of the signal) increases,frequency spacing reduces, approximate error reduces and resolution of the spectra increases. DFT  is used to convert signals from Time Domain to Frequency Domain.

The aim of this experiment was to find Covolution and Correlation of digital signals. We also verified the results using mathematical formulation. In linear convolution causal signal is obtained as the output if both the input signals are causal, circular convolution gives aliased output. Autocorrelation produces an even signal. Autocorrelation of delayed input signals was same as autocorrelation of the original signals. We also did cross correlation of two input signals. Hence, correlation can be used to find the degree of similarity of the signals.