Reference no: EM132515487

ME606 Digital Signal Processing - Melbourne Institute of Technology

Assessment Title - Study of decimation and interpolation techniques

Unit Learning Outcomes covered in this assessment -

a. Development and implementation of signal processing algorithms in MATLAB or Scilab.

b. In-depth design of digital filters c. Understand the design of multirate signal processing and their applications.

Assignment - Digital signal processing (DSP) is the manipulation of an information signal to extract information or modify it. These include compressing, measuring, and filtering real-world digital signals. Digital signal processing can involve linear or nonlinear operations. Some application of digital signal processing are:

1. Audio signal processing,

2. Audio compression,

3. Digital image processing,

4. Video compression,

5. Speech processing,

6. Speech recognition,

7. Digital communications,

8. RADAR,

9. SONAR,

10. Financial signal processing,

11. Seismology,

12. Biomedicine

In this assignment, we start exploring the world of signal processing by investigating deeper into some areas of its applications. This assignment has two parts.

Part A: Literature review on some of the applications of signal processing and tools

Section 1 - Review one of the following study areas:

Elaborate on the differences between Audio processing and speech processing, application areas, their commonality and differences and type of processing they need.

A detailed review of algorithms for image interpolation using a discrete cosine transform (DCT) and fast Fourier transform (FFT).

A detailed review of polynomial interpolation techniques (including spline) and their applications.

Section 2 -

Introduce software packages, hardware, firmware and procedures that are used in implementations of signal processing algorithms.

In this section, we expect you to cover tools, implementation techniques, and standalone hardware that are widely used in developing DSP applications.

You must demonstrate that you have understood the field and major industries that are dedicated to this field.

Report content and style: Write and submit a report of not less than four (4) A4 pages with a single line spacing length with following sections:

1. Abstract

2. Introduction

3. Project scope and goals

4. A clear statement of the problem to be addressed

5. Expected significance of the results

6. A review of relevant literature. We expect to see five papers, published in the last 5 years for section 1. For section 2, you may refer to web pages of developers or vendors.

7. References (IEEE format)

Part B: Signal decimation Using MATLAB

A signal in MATLAB ".mat" format is given in the Moodle with the name "original_signal.mat" that contains a vector "x" with 200000 samples taken at a rate of 24 Mega samples per second (24MHz sampling frequency) from a modulated baseband signal.

Section 1 - Up/downsampling and spectral estimation with MATLAB:

1. Load the signal "x" into your workspace, and draw the first 2000 samples of this signal. (Do not forget to use the title command title ("MIT19...") to insert your student ID on top of every plot that you include in your report.)

2. Under sample "x" by the rate of L=3 and call it "y"; Follow the following link and use both variations of periodogram spectral estimation shown there to find power spectrum of "x" and "y". Set the FFT size 512. Show your codes and the spectrums they give in your report. Discuss the similarities and differences in the spectrums of "x" and "y".

3. Zero pad (up-sample) the "y" signal sample with 2 zeros in the middle of adjacent samples to increase the sampling frequency by 3 and call the resultant signal "zy". Load a root raised cosine filter impulse response, h, from the given file "rrc_filter.mat" into your workspace. Filter your signal ("zy") using the command "fzy=conv(x,h);" and draw the power spectrum of "fzy" and "zy" in dB on one axis using one of the practiced programs. Report your code, plots and discuss the zero-padding and filtering effects on the signal spectrum.

4. Use the cubic spline interpolation in MATLAB to interpolate the signal "y" using the command "ys= interp1(1: length(y), y,1:1/3:length(y), 'spline');". Draw the spectrum of "ys" and compare it with the result of section 3. Repeat section 3 by changing the method to 'linear' and 'nearest'.

5. Filter the signals "ys" in section 12 with the given filter and find and draw the spectrum and discuss the result.

Section 2 - Discussion and literature review on spectral estimation methods:

Give an overview of the work you have done and discuss the strength and weakness of each used interpolation method in Part 2. In addition, give a brief literature review on spectral estimation methods.

Note - Just need to do part A.

Attachment:- Digital Signal Processing Assignment File.rar