DIGITAL SIGNAL PROCESSING APPLICATION

              The experiment entitled above was a group activity in which we were asked to select one 1-D DSP appplication and shortlist few Patents and Technical papers based on the same topic respectively based on the same topic .
GROUP MEMBERS:   Kapil Rawal, Harshit Shukla, Chinmay Upadhye, Prerana Sarode, Vaishnav Dandge

APPLICATION TOPIC: NOISE CANCELLATION USING ADAPTIVE FILTERING METHODS

PATENT REVIEW:"Adaptive noise cancellation system "
Patent No: EP19980304043
Publication date: Jan 5, 2000
Inventors: WOODSON DALE WYNN

Summary: In this patent abstract it was observed that when a power line adaptive noise cancellation system receives noise signals from house power line it adaptively cancels the received noise from a power line input signal. The input and the output signals are bandpassed by the filter and  demodulated by the local oscillator. In short, the input to this system is a signal which contains main data signal as well as noise from which the noise is adaptively eliminated so as to obtain the authentic signal.

IEEE Paper Review: 

"Adaptive Noise Cancelling: Principles and applications"

Publisher:  B. Widrow
Date of Publication: Dec, 1975

 Review : In this research paper we studied that when the reference input is free of signal and certain other conditions are met, then noise in the primary input can be essentiany eliminated without signal distortion. It is further shown that in treating periodic interference the adaptive noise canceller acts as a notch filter.
*Notch Filter:  The advantages of this  form of notch filter  are that  it offers easy control of bandwidth, an infinite null,  and the capability of adaptively  tracking  the  exact  frequency of the  interference.

Applications: 

1. Cancelling 60-Hz Interference in Electrocardiography 

2. Cancelling the  Donor ECG  in  Heart-Transplant Electrocardiography 

3.  Cancelling  the  Maternal  ECG in Fetal  Electrocardiography 

4. Cancelling  Antenna  Sidelobe  Interference

 PATENT

IEEE paper

PROOF OF UNIQUENESS

OPERATIONS USING DSP PROCESSOR

         This experiment was actually a demonstration of the project of our senior, Jayganesh Rajaraman. The processor used was TMS320F28375 and the code execution was done in the software called "Code Composer Studio" which is a software provided by टेक्सस इंस्ट्रुमेंट्स (TI).
We were then asked to perform basic arithmetic operations as well as logical operations based on the Addressing modes of the DSP kit used in the project. While performing arithmetic operations like Addition, Subtraction and Multiplication,the value of the registers before and after execution was noted. The logical operations such as - LSL, LSR, ROR, ROL were also performed and the change in the value of the registers was observed.

FIR filter design using WINDOW FUNCTION

      This experiment was to design a linear phase FIR filter using window function. The filter parameters such as Passband frequency and Stopband frequency, attenuation in Passband and Stop band was mentioned by the user. Now depending on the value of As the window function is automatically selected in the program and executed. As we had kept the Stopband attenuation as 50dB the Hamming window function was used for execution. With an increase in the order, the number of lobes in the stop band also increases.

WINDOW FUNCTION

FIR filter design using FREQUENCY SAMPLING METHOD

            Our next experiment was to perform Linear phase FIR filter design using Window function. The parameters such as- attenuation in Pass Band, Stop Band, the Passband and Stopband frequencies were mentioned by the user. The desired freq response obtained was Hd(w) which was then sampled using DFT to obtain H(k) and further executed using IDFT to obtain h(n). It was observed that the order increases, the number of lobes in the stop band increases.

FSM

DESIGN OF CHEBYSHEV IIR FILTER

          The analog chebyshev filter was converted into digital chebyshev filter using BLT method. The magnitude response for both LPF and HPF were plotted. Ripples in the pass band were observed and monotonic in stop band. The number of the peaks in the ripple defined the order of the filter.

For same input specifications for butterworth and chebyshev filter, order of chebyshev filter was smaller. Ultimately, this leads to lesser cost for the hardware required. It is better for the real time application.

CHEBYSHEV

DESIGN OF BUTTERWORTH FILTER

                   In this experiment we were supposed to find the output of digital Butterworth Filter. First the order of the filter and cut off frequency is calculated. After this normalized transfer function is calculated from which denormalised transfer function is calculated by replacing 's'. Then z transform is obtained by BLT and the output graph of magnitude v/s frequency is observed.

Butterworth

(Filtering of long data sequence) OVERLAP ADD METHOD

               In this experiment of the course was based on Linear FIR Filtering Methods - Overlap Add Method and Overlap Save Method. We performed filtering of long data signal using Overlap Add Method using Scilab. A long data signal was taken as the input with length equal to 24. Impulse response was taken as {1,2,3}. The input signal was decomposed into smaller subsequences with length equal to 6. In order to use the radix 2 FFT algorithm we had to use zero padding to make the length of each subsquence equal to 8. Linear Convolution by Circular Convolution, was performed using zero padded h(n) and the individual subsequences. Output y(n) was computed and verified manually. Thus, we can say that OAM is better for real time filtering of long signals.

OVERLAP ADD METHOD

FAST FOURIER TRANSFORM

                After learning DFT,our next experiment was Fast Fourier Transform(FFT). We were asked to take a N input sequence. I took 8 input sequence on which FFT was performed. The code was written in c++ programming language.FFT performs 12 complex multiplications, 24 complex additions, 48 real multiplications and 72 real additions. After calculating FFT we were asked to calculate inverse FFT(IFFT) to check whether we get the same input sequence again. The speed of operation in FFT increases as compared to that of DFT. The Operations are carried out parallely in FFT.

FFT-1

FFT-2

IFFT-1

IFFT-2

DISCRETE FOURIER TRANSFORM

                  In this second experiment of DSPP we were suposed to take a N point signal x(n) as the input and find Discrete Fourier Tramsform of the signal. Also we had to observe the effect of zero padding on the magnitude spectrum of the signal. We used C to write the code for the experiment. After obtaing the output with and without zero padding we learnt that with zero padding, the frequemcy spacing decreases, error in representation decreases and also there is an increase in the resolution and quality of the spectrum. 

DISCRETE FOURIER TRANSFORM

DISCRETE CONVOLUTION AND CORRELATION

              With this experiment we came back to our basics, the C language. In this experiment we performed Linear convolution, Auto-correlation and Cross-correlation of two signals. while performing correlation we observed the following statuses of the signal.

              As we performed auto-correlation, we found that the signal is symmetric with respect to its center point and the value of the center point is the greatest.The cross-correlation of the time delayed  signal causes the output signal to be delayed by the same amount as that of the time shift applied to the original input signal.               The cross-correlation of a time delayed and a zero-padded signal also gives a similar result as that in the case above, but the output signal will be delayed by the total amount as the time shift and the zero padding given.

Circular convolution

Linear convolution

Correlation