Learning outcomes

• Review and consolidate fundamental DSP principles, including sampling theory, quantisation, and frequency relationships between continuous and discrete-time signals.
• Analyse discrete-time signals in the frequency domain using DTFT, DFT, and FFT, with an understanding of spectral resolution and leakage.
• Design and evaluate digital filters, including FIR and IIR types, using concepts such as impulse response, transfer function, and frequency response.
• Implement DSP algorithms in MATLAB, including spectral analysis, filtering, and signal reconstruction.
• Apply key operations such as convolution and correlation, and understand their roles in filtering and signal analysis.
• Model and mitigate noise and distortion in DSP systems, including quantisation noise, harmonic distortion, and spurious components.
• Develop adaptive filtering solutions, such as Wiener and LMS filters, for estimation and system identification tasks.
• Perform multirate signal processing, including sampling rate conversion via decimation, interpolation, and polyphase structures.
• Understand the principles and architectures of ADCs and DACs, and evaluate their impact on signal integrity.
• Model and simulate digital modulation schemes, such as BPSK and QAM, using baseband and bandpass representations.

Who is the course for?

Course Outline

Topics (by week number)
1 Review of DSP Fundamentals
• Sampling theorem, Nyquist frequency and aliasing phenomenon
• Quantisation, signal distortion and noise floor
• Sensitivity and signal dynamic range analysis
• Numerical representation: two’s complement
2 Fourier Transform
• DTFT, DFT and FFT.
• Frequency domain analysis, spectral leakage phenomenon.
• Windowing technique to reduce spectral leakage
• FFT and zero padding.
Convolution, Correlation, and Prediction
3 Frequency domain analysis
• Harmonic Distortion and Spurs:
• Spurious free dynamic range and interleaving spurs
Discrete time Z-transform and difference equations
• Z-transform and its relations to DTFT.
• Difference equation and digital filters
4 Filters and filter Design using Matlab
• FIR and IIR filter (including “all pass” filters) design methods.
• Symmetric and half band filters
• Windowing technique to improve response of FIR filters
5 Adaptive filters
• Wiener filter theory
• Least mean square (LMS) algorithm
• Recursive least squares (RLS)
6-7 Multirate signal analysis and multirate filter design
• Polyphase filters, CIC filters
• Interpolation and decimation theory
• Imaging, aliasing and Nyquist zones
• Integer rate changes
• Classic and polyphase interpolator / decimator architectures
• Direct Digital Up / Downconverter (DUC and DDC) architecture
• Sampling rate conversion by arbitrary factor
• Cascaded integrator-comb (cic) filters
8 Types of ADC/DAC
• flash ADC (parallel ADC), pipelined ADC, dual-slope ADC, sigma-delta (ΔΣ) ADC
• binary-weighted resistor DAC, resistor string DAC (R-2R LADDER DAC), delta-sigma (ΣΔ) DAC
9-10 DSP for communication
• Baseband communication systems
• Bandpass communication systems
• Quivalent complex baseband system: i/q channels
• Spectral representation for digital modulation
11 Audio digital signal processing
• Coordinate Rotation Digital Computer (CORDIC)
• Sub-band coding for decreasing the bit rate of audio signals
• Transform coding (DCT)
12 DSP Implementation
• Benefit and pitfall of MATLAB, implementation cost (QOR)
• Floating point filter example in Matlab
• Fixed point implementation of Matlab
13-14 Review
15 Exam

Trainer Profile

Nam Tran is an Associate Professor at the School of Electrical and Electronic Engineering, University College Dublin, Ireland. Prior to this appointment, he was a Lecturer in the Department of Electronic Engineering at Maynooth University, Ireland. He has also held senior postdoctoral research positions at the Signal Processing Laboratory, KTH Royal Institute of Technology in Sweden, and at the Centre for Wireless Communications and the Department of Communications Engineering, University of Oulu, Finland.

Dr Tran has over a decade of teaching and research experience in the areas of digital signal processing and wireless digital communications at both undergraduate and postgraduate levels. Notably, he supervised a Master’s project that received the IEEE PIMRC 2020 Best Student Experimental Paper Award. He is also the holder of a US patent in the field of signal detection for wireless communications.