## DSP Concepts, Programming and Prototyping using MATLAB

Duration: 5 Days

### Intended Audience

The course is aimed at engineers having interest in mathematical analysis and modeling who have some experience working with MATLAB, and, who require an introduction to basic and advanced DSP concepts and who wish to learn the techniques and theory of mathematical modeling of DSP systems via the use of MATLAB software and associated toolboxes such as the MATLAB image processing toolbox, signal processing toolbox, filter design toolbox and wavelet toolbox.

### Synopsis

Digital Signal Processing (DSP) is concerned with the digital representation of signals and the use of
digital hardware to analyze, modify, or extract information from these signals.
Advances in digital signal processing technology have considerably reduced the cost of the implementation of sophisticated DSP algorithms.Matlab and associated DSP toolboxes provide an interactive software development environment for prototyping, testing and realising DSP software. In particular, this, facilitates the design of applications that make use of DSP filters and DSP transformsuch without having to work through a lot of mathematics. Once designed, much of the corresponding DSP code can be generated and evaluated.

This course provides a comprehensive introduction to both basic and more advanced DSP algorithms and their underlying concepts. In this course the principles and concepts underlying the mathematical analysis and modeling of signal processing systems are carefully covered.

The main topics covered in this course include filter design methods as well as adaptive filtering. The course also introduces multirate systems, image processing systems and wavelet analysis of signals.

### Contents:

- Review of Analog Signals and Systems
- Signal Sampling and Quantization
- Sampling Theorem for Bandlimited signals
- Aliasing and Anti-Aliasing Filters
- Sampling Theorem for Bandpass Signals
- Analog-to-Digital and Digital-to-Analog conversion
- Source Coding and Huffman Coding
- Discrete Signals and Systems in Time and Frequency domains
- Discrete Signals and Systems in Time and Frequency domains
- Auto-Correlation and Cross-Correlation of Signals
- Fourier Transform of Discrete Signals
- Linear and Time-Invariant Systems
- Stability Criterion for an LTI System
- Impulse Response and Frequency Response of a System
- Magnitude and Phase Responses of System
- Linear Constant Coefficient Difference Equation
- Recursive and Non-Recursive Systems
- Discrete Fourier Transform and Signal Spectrum
- Discrete Fourier Series Coefficients
- Discrete Fourier Transform
- Fast Fourier Transforms (Decimation in Time and Decimation in Frequency)
- Complex Domain Representation of Digital Signals
- Z-Transform and Properties of the Z-Transform
- Region of Convergence in Z plane
- Inverse Z-Transform
- Solution of Difference Equations using Z Transform
- Digital Processing Systems and Digital Filter Realizations
- Difference Equations and Transfer Function
- System Function and Pole-Zero Diagram and Stability Criterion.
- Realization of Digital Filters
- Tranformation of Analog Systems to Digital Systems
- Finite Impulse Response Systems
- FIR Filter Design
- Realizations of FIR Systems
- Coefficient Accuracy Effects on FIR Filters
- Infinite Impulse Response Systems
- IIR System: Definition and Difference Equation
- Digital Butterworth and Chebyshev Filter Design
- Higher order Infinite Impulse Response Filter Design using Cascade Method
- Pole-Zero Placement Method for IIR Filters
- Adaptive Filters
- Least Mean Square Adaptive Finite Impulse Response Filters
- Basic Wiener Filter Theory
- Applications of Adaptive Filtering
- Noise Cancellation
- Waveform Quantization and Compression
- Waveform Coding
- Differential Pulse Code Modulation
- Delta Modulation
- Adaptive Pulse Code Modulation
- Discrete Cosine Transform
- Multirate Digital Signal Processing
- Discrete Discrete Cosine Transform
- Multirate Digital Signal Processing Basics
- Decimation and Interpolation
- Polyphase Filter Structure and Implementation
- Image Processing Basics
- Image Data Formats
- Image Histogram and Equalization
- Image Level Adjustment and Contrast
- Image Filtering Enhancement
- Image Pseudo Color Generation
- Image Spectra
- Image Compression by DCT
- Video Signal Basics
- Motion Estimation in Video