Course Highlights:

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. The rapid advancement in digital technology in recent years has created the implementation of sophisticated DSP algorithms that make real-time tasks feasible. A great deal of research has been conducted to develop DSP algorithms and applications. Since DSP applications are algorithms that are implemented either on a processor or in software, a fair amount of programming is required. Using interactive software such as SCILAB it is now possible to place more emphasis on learning new and difficult signal processing concepts. SCILAB is a computational and programming toolbox containing several tools that can be used to understand and test the complex DSP algorithms. This course provides comprehensive understanding of the DSP algorithms by including basic and advanced DSP concepts and also provides a large number of lab experiments on SCILAB to get insight into how the DSP algorithms work and to evaluate the performance of both simple and advanced DSP algorithms.

In this course more emphasis will be given on mathematical analysis and modeling of signal processing systems. Hence the DSP study in this course encompasses temporal and spatial analysis of signals and systems. For better grasp of the characteristics of signals and behaviour of systems, complex domain representation and analysis of signals and systems is done. Main topics covered in this course include filter designing methods, adaptive filtering, multirate systems, image processing systems and wavelet analysis of signals. Review of analog signals and systems is also done at the beginning of the course.

Objectives of the course:

Upon the completion of the course, the participants will gain a comprehensive understanding of profound DSP algorithms and concepts. Also the participants will gain ability to use SCILAB software as an efficient tool to design DSP systems and subsystems and then test and analyze them in a very efficient manner. Apart from reviewing the analog signals and systems theory and providing understanding of basic concepts of DSP, this course aims at introducing complex DSP topics like wavelet analysis, multirate systems, image processing and etc,. The participants will gain dexterity in the usage of several built-in functions of several SCILAB toolboxes and thus enhancing the efficiency level of designing and implementation of advanced DSP modules that can be used in several applications.

Who Should Attend:

Engineers having interest for mathematical analysis and modeling and would like to  

• get introduced with basic and advanced DSP concepts,
• learn the mathematical modeling of the DSP systems,
• use SCILAB software for getting insight into the DSP concepts and design and test their own DSP systems and subsystems.
• get introduced with a large number of built-in functions provided in the several toolboxes of SCILAB software image processing toolbox, signal processing toolbox, filter design toolbox and wavelet toolbox.


Course Outline:



DAY 1:

• Review of Analog Signals and Systems
• Signal Sampling and Quantization
• Sampling Theorem for Bandlimited signals
• Aliasing & Anti-Aliasing Filter
• Sampling Theorem for Bandpass Signals
• Analog –to-Digital and Digital-to-Analog conversion
• Source Coding &  Huffman Coding
• 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 & 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)

DAY 2:

• 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

DAY 3:

• Adaptive Filters
• Least Mean Square Adaptive Finite Impulse Response Filters
• Basic Wiener Filter Theory
• Applications of Adaptive Filtering
• Noise Cancellation
• Waveform Quantization and Compression
• Wavefoem 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