DSPP EXPERIMENTS

An analysis of a type of comb filter Several basic arrangements of delay unit, combining unit and feedback unit are shown to have amplitude/frequency response characteristics of the type which has given rise to the name comb filter. Such filters are useful for the selection or rejection of a train of uniformly spaced regular pulses. The response of the most suitable configurations to such a train of pulses and to a Gaussian noise input is investigated with particular reference to the improvement in signal/noise ratio possible with this type of filter. The practical realization of such filters is discussed and the most suitable forms of practical comb filter are indicated, together with a brief discussion of their incorporation into a radar system.

Signal processing system and method for enhanced cascaded integrator-comb interpolation filter stabilization   Patent No.  US 5880973 A A signal processing system and method for stabilizing cascaded integrator-comb (CIC) interpolation filters. The signal processing system includes an overflow detection circuit and reset circuitry. The overflow detection circuit monitors the output of the CIC interpolation filter for an overflow condition which is indicative of filter instability. When an overflow condition is detected, the reset circuitry automatically resets the filter, thereby eliminating the instability.  ransmission filter 102 first interpolates the input samples (31.5 MHz samples on bus 112) by a factor of 4, as well as shapes the spectral response. In general, implementation of transmission filter 102 is well known in the art. For example, a conventional finite impulse response (FIR) filter, with one or two stages, can be used. With respect to a QPSK-based cellu...

We have seen the mathematical analysis and various digital systems with digital signals. However, they are of no use if they can't be used in the real world.  We need to have some physical hardware that will implement these operations. This is where the DSP processor comes in.  We used a custom board of the popular C2000 processor. The coding platform used was Code Composer Studio. Using the implementations of DSP algorithms in C language developed previously, the code was tweaked to work on-chip in embedded C. Using this processor we can carry out  simple mathematical operations such as addition, subtraction, bit manipulation,etc.

A filter is essentially a system or a network that selectively changes the wave shape, amplitude–frequency and/or phase-frequency characteristics of a signal in desired manner. Common filtering objectives are to improve the quality of a signal, to extract information from signal or to separate two or more signals previously combined.  FSM for FIR filter  design is perhaps the simplest and most direct technique when a desired frequency response  has been specified. T he given frequency response is sampled at a set of equally spaced frequencies to obtain N samples. Thus , sampling the continuous frequency response Hd(w) at N points essentially gives us the N-point DFT of Hd(2pnk/N). Thus by using the IDFT formula, the filter co-efficients can be calculated. One way to reduce the error is to increase the number of frequency samples. Thus we FIR filter design using Frequency Sampling Method and noted the output.

In this experiment we designed Linear phase FIR filter using Scilab. These are filters that have a Finite Impulse Response. Broadly, the method for designing the filter is the same as IIR filters. Filter parameters like Attenuation in Stop band (As) and Pass band (Ap) as well as Pass band frequency, Stop band frequency and sampling frequency were given as input. You input the formulae in a Scilab code and run the program. Window function include Hamming window, Bartlet Window, Hanning window, Blackman window etc. We used a Hanning Window as the window function and wrote the code accordingly. The difference between IIR and FIR designing is that much of the calculation is done in time domain rather than the transform domain..

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.