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