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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