Digital Signal Processing (DSP) is the capture, analysis and manipulation of an analogue signal by a digital computer. The integration of DSP software and hardware into products across a wide range of industries has necessitated the understanding and application of DSP by engineers and technicians.

This Professional Certificate of Competency begins with the introduction of DSP from a practical point of view using a minimum of mathematics. The emphasis is on the practical aspects of DSP, implementation issues, tips, tricks, and pitfalls, and practical applications. Intuitive explanations and appropriate examples are used to develop a fundamental understanding of DSP theory. The program participants will gain a clear understanding of DSP technology in a variety of fields from process control to communications.

Some of the DSP techniques covered in the program include:

• Digital filtering for cleaning a signal from noise
• Discrete Fourier Transforms for finding a particular frequency component
• Correlation techniques to find a signal buried in noise
• Industrial control with digital controllers
• Instrumentation and test for better accuracy
• Vibration analysis for identifying frequency signatures
• Image and video processing for enhancing images
• Communications especially for filtering out noise

COURSE OBJECTIVES

• Digital Signal Processing (DSP)
• The benefits and application of DSP technology to improve efficiency
• Frequency analysis of signals and the application of this knowledge
• Information about and actual design of digital filters
• Analysis of the performance of DSP systems
• Identification of the key issues in designing a DSP system
• An understanding of the features and capabilities of commercial DSP applications
• Current DSP technology

Course Outline

MODULE 1: INTRODUCTION

• Terminology and motivation
• Why process digitally
• A typical DSP system
• Some current application areas

MODULE 2: DIGITAL-TO-ANALOGUE (D/A) AND ANALOGUE-TO-DIGITAL (A/D) CONVERSION

• Periodic sampling and aliasing
• Digital to analogue converters
• Analogue reconstruction
• Analogue to digital converters

MODULE 3: DISCRETE SIGNALS AND SYSTEMS

• Notation and representation of discrete-time systems
• Classification of discrete systems
• The concept of impulse response
• The concept of convolution
• Autocorrelation and cross-correlation of signals

MODULE 4: THE DISCRETE-TIME FOURIER ANALYSIS

• The Discrete-Time Fourier Transform (DTFT)
• Properties of the DTFT
• Frequency domain representation of linear, time-invariant (LTI) systems
• Sampling and reconstruction of analogue signals

MODULE 5: THE Z-TRANSFORM

• The bilateral z-Transform
• Important properties of the z-Transform
• Inversion of the z-Transform
• System representation in the z-Domain

MODULE 6: THE DISCRETE FOURIER TRANSFORM

• The discrete Fourier series
• Sampling and reconstruction in the z-domain
• The Discrete Fourier Transform (DFT)
• Properties of the DFT
• The Fast Fourier Transform (FFT)

MODULE 7: DSP APPLICATION EXAMPLES

• Digital waveform generators
• Speech modelling and synthesis
• Noise reduction and signal enhancement
• Image restoration
• Communications system

MODULE 8: IIR DIGITAL FILTER DESIGN

• Review of classical filter approximation techniques
• Characteristics of IIR filters
• Design methods
• Design examples

MODULE 9: FIR DIGITAL FILTER DESIGN

• Characteristics of FIR filters
• Design methods
• Design examples

MODULE 10: DIGITAL FILTER REALISATION

• Direct form
• Hardware realisations
• Quantisation effects

MODULE 11: COMMERCIAL DSP HARDWARE

• DSP chips vs. general purpose microprocessors
• Texas Instrument TMS320 family
• Motorola DSP56000 family
• Analog Devices ADSP-2100 family
• Choosing a DSP architecture
• DSP trends

MODULE 12: PRACTICAL TOOLS FOR DSP

• System Development
• Simulation tools for algorithm development
• Software development tools
• Hardware development tools

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