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.

 

Now Available:

  • The Digital Signal Processing Education Kit now supports the Cortex-M7 based STM32F7 Discovery board.
  • The Cortex-M7 delivers greater processing performance along with a 10x acceleration of single-precision floating-point operations with the built in floating-point unit.
  • Our Education Kit has been redesigned to deliver a more interactive hands-on experience with the Cortex-M7 based microcontroller, on-board DAC, and LCD display of the STM32F7 Discovery board.
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Course Aim

To develop the ability to implement DSP systems and create commercially viable audio applications using high performance and energy-efficient Arm processors.

 

Learning Outcomes

  • Knowledge and understanding of 
    • The properties of discrete-time linear time-invariant system, including how convolution and correlation works.
    • The properties of a Finite Impulse Filter (FIR) and identify its relationship with a moving average filter.
    • The properties of an Infinite Impulse Response (IIR) filter, and compare the bilinear transform and impulse invariant methods for IIR filter design.
    • How to design the mathematical elements of a simple FIR filter using the impulse invariant and bilinear transform methods.
    • The various types of Fourier analyses and their application.
    • The operations of a Finite Impulse Filter (FIR) filter as a basis of a linear adaptive filter, including cost functions, Steepest Descent, and Least Means Squares (LMS) algorithms.
  • Intellectual
    • Describe the Fourier Transform properties of various types of signals.
    • Explain how sampled and reconstructed signals in a basic signal processing system are represented by using Fourier Transform and Nyquist theorem. 
    • Describe the definition, properties, and usage of the z-transform for signal analyses. 
    • Explain how to design basic low-pass and high-pass FIR filters using the window method.
    • Explain how the Radix-2 decimation algorithm works for calculating Fast Fourier Transform (FFT).
    • Describe the operation of adaptive systems including prediction and system identification configurations.
    • Describe the operation of the equalization and noise cancellation configuration involving adaptive filters.
  • Practical
    • Demonstrate the successful set up of hardware and software requirements for digital signal processing concepts and projects.
    • Examine the effects of signal sampling, reconstruction, and aliasing using an audio codec and Digital-to-Analog Converter (DAC) on a development board. 
    • Design and implement a Finite Impulse Response (FIR) and an Infinite Impulse Response (IIR) filter for digital signal processing. 
    • Use an Arm-based development board and Integrated Development Environment (IDE) in example projects related to basic DSP concepts.
    • Use and assess Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) for real-time signal processing.
    • Implement and use adaptive filters using the Least Mean Squares (LMS) algorithm for digital signal processing applications. 

 

Syllabus

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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