Digital Signal Processing Education Kit

Teach the theory and practice of managing digital signals that come in from a variety of sources, which is crucial, given the explosion of digital data in today’s world.  While many DSP courses use software simulation packages, or expensive development kits, this course is based on low-cost, Arm-based hardware boards and Arm software licenses, allowing students to practice theory with advanced hardware.

Kit specification:

  • A full set of lecture slides, ready for use in a typical 10-12-week undergraduate course (full syllabus below)

  • Lab manual with solutions for faculty. Labs are based on low-cost hardware platforms (donated by partners and subject to availability) powered by Arm Cortex-M-based microcontrollers that enable high performance yet energy-efficient digital signal processing, and use the industry-standard Keil MDK-Arm application development tool.
  • Prerequisites: Basic C programming, elementary mathematics.

Course Aim

To produce students who can design DSP systems and create commercially-viable audio applications using high-performance and energy-efficient Arm processors.

Learning Outcomes

  • Knowledge and understanding of
    • DSP basic concepts such as sampling, reconstruction and aliasing
    • Fundamental filtering algorithms such as FIR, IIR, FFT
    • Arm-based microcontrollers as low-power DSP computing platforms
    • Software programming basics and principles
  • Intellectual
    • Ability to choose between different DSP algorithms for different applications
    • Ability to use different design methods to achieve better results
    • Ability to evaluate experimental results (e.g. quality, speed, power) and correlate them with the corresponding designing and programming techniques
  • Practical
    • Ability to implement DSP algorithms and design methods on Arm-based microcontrollers
    • Ability to use commercial hardware and software tools to develop real-time DSP application


1 Discrete Time Signals and Systems: Convolution and Correlation
2 Sampling, Reconstruction and Aliasing: Review of Complex Exponentials and Fourier Analysis
3 Sampling, Reconstruction and Aliasing: Time and Frequency Domains
4 Z-Transform: Time and Frequency Domains
5 FIR Filters: Moving Average Filters
6 FIR Filters: Window Method of Design
7 IIR Filters: Impulse Invariant and Bilinear Transform Methods of Design
8 IIR Filters: Simple Design Example
9 Fast Fourier Transform: Review of Fourier Transformation
10 Fast Fourier Transform: Derivation of Radix-2 FFT
11 Adaptive Filters: Prediction and System Identification
12 Adaptive Filters: Equalization and Noise Cancellation
13 Adaptive Filters: Adaptive FIR Filter
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