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Digital Signal Processing (DSP) Techniques with VHDL


Digital signal processing (DSP) is the backbone of numerous technological fields, such as wired and wireless communication, military and intelligence gathering, industrial process control, medical science, cryptography and transportation, just to name a few. DSP algorithms such as spectral analysis and Kalman filtering -- traditionally executed with specialised digital signal processors -- are now increasingly carried out with FPGAs for their greater processing capabilities, increased chip-level integration and lower power consumption. The move towards FPGAs also mean DSP algorithms are now "coded" in hardware description languages rather than assembly, C, or C++.

This course gives an introduction to DSP and how its applications is being implemented using hardware-based field-programmable gate arrays (FPGAs).

Course highlights

Participants will have introductory knowledge on tools and processes used to model and build practical DSP applications.

What You Will Learn

This short course concentrates on the theoretical and practical knowledge to allow participants to achieve the following learning outcomes. Upon completing the course, participants would be able to:

Who Should Attend

This course is particularly suitable as an introductory-level course in digital signal processing (DSP) for engineers and scientists. This course is also suitable for advanced DSP engineers and scientists who would like to find out what other industrial methods are being employed to implement DSP solutions.


Participants should have a diploma/degree in electronics (and related) engineering with an understanding of digital signal processing. Working knowledge on digital systems is encouraged, but not required.

Course Methodology

This course is presented in a workshop style with example-led lectures interlaced with demonstrations for maximum understanding.

Course Duration

Two (2) days, 0900 to 1700.

Course Structure

  1. Introduction
    • Overview of digital signal processing.
    • Discrete-time vs. continuous-time: Analogies and similarities.
  2. Applications of DSP
    • Digital filtering and equalisation.
    • Source coding: data compression, encryption, scrambling.
    • Image/video processing, audio/speech processing.
    • Digital communications systems.
    • Digital control systems.
  3. Development phases in DSP designs
    • The modelling phase.
    • The implementation phase.
  4. The modelling phase of DSP development
    • Software modelling tools.
    • An example with Sage Math.
  5. The implementation phase of DSP development
    • Implementation options: Hardware or Software?
    • Advantages and disadvantages of hardware vs. software development of DSP systems.
    • An example hardware DSP design using FPGAs: a digital FIR filter.
    • Demo: Digital FIR filter - An DSP FPGA hardware design example.
    • The FPGA / ASIC DSP design and implementation flow.
  6. An overview of some DSP concepts
    • Discrete-time (DT) vs. continuous-time (CT) systems and notations.
    • Difference equations.
    • Convolution sum: A widely-used concept for DSP designs.
      • The unit impulse excitation and response for determining system characteristics.
      • Concept of convolution.
      • Similarities between the DT convolution sum and the CT convolution integral.
      • Responses of systems to other standard impulse excitations.
    • The Fourier series.
    • Time-to-frequency transformations: the Laplace, Fourier, and z transforms.
    • The Laplace transform.
    • The Fourier transform.
      • The discrete Fourier transform (DFT).
      • The fast Fourier transform (FFT): a popular DFT algorithm.
    • The z-transform.
    • Similarities between the DT z-transform, and the CT transforms such as Laplace (s-domain) and Fourier transforms.

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