MSc Communications and Signal Processing / Course details

Year of entry: 2024

Course unit details:
Applied Digital Signal Processing

Course unit fact file
Unit code EEEN60472
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Available as a free choice unit? No


Convolution and correlation. Using impulse function to represent discrete signals; description of convolution using linear superposition; Fourier interpretation of convolution; simple filtering using convolution; auto-correlation and cross-correlation; cross-correlation, matched filters and signal-to-noise ratio enhancement; temporal smearing and pseudo random bit sequences.

Fourier analysis. The continuous trigonometric Fourier series for periodic signals; data representation and graphing; the continuous trigonometric Fourier series for aperiodic signals; observations on the continuous Fourier series; exponential representation of the Fourier series; the continuous Fourier transform; discrete Fourier analysis; introduction to the fast Fourier transform.

Discrete Fourier properties and processing. Window functions; spectral leakage; representation of spectral data; considerations of phase; key properties of the discrete Fourier transform; discrete Fourier transform signal processing.

The Laplace transform. Its use in differential equation; the s-plane; circuit analysis; analogue filter design.

The z-transform and digital filter design. Definitions and properties; digital filters, diagrams and the z transfer function; filter deign using pole-zero placement; FIR and IIR filters: merits and disadvantages.

Signal sampling. The process of sampling; signal digitisation; principles of analogue to digital and digital to analogue conversion; ADCs and DACs in system.

Design of FIR filters. The window method; phase linearity; the frequency sampling method; software for arbitrary FIR design; inverse filtering and signal reconstruction.

Design of IIR filters. The bilinear z-transform; the BZT and 2nd order passive systems; digital Butterworth and Chebyshev IIR filters; pole-zero placement revisited; biquad algorithm design strategies; FIR expression of IIR responses.

Adaptive filters. Brief theory of adaptive FIR filters; the least mean square (LMS) adaptive FIR algorithm; use of the adaptive filter in system modelling; delayed (single) input adaptive LMS filters for noise removal; the true (dual input) adaptive LMS filter.

Real time DSP: the ADSPBF706 design. System architecture; assembly code programming; real time system design; peripheral interfacing; FIR, IIR and adaptive filters in real time.

DSP in audio applications: reverberation, equalization and string synthesis.

Basic toolbox operations: linear scaling / gain control; addition (mixing); averaging; white noise; PBRS generators; real-time implementation; sine wave and function generation; function approximation.

Quadrature and multi-rate signal processing: the Hilbert transform; waveform modulation; waveform detection/demodulation; envelope detection and rectification; quadrature frequency translation; decimation; interpolation; base-band sampling; IF and under sampling, frequency shift and recovery.

Audio and musical affects: Spectrogram analysis; musical note synthesis – plucked strings; reverberation, echo, vibrato, chorus, flanging; equalization.

The impact of DS


The course unit aims to:

  • Provide a thorough and complete introduction to the subject of modern digital signal processing;
  • Emphasise the links between the theoretical foundations of the subject and the essentially practical nature of its realisation;
  • Encourage and understanding through the use of algorithms and real world examples;
  • Provide useful skills through detailed practical laboratories, which explore both off-line and real-time DSP software and hardware.
  • Gain experience of teamwork and contributing to group solutions in a constructive and formalised manner.
  • Understanding the importance of ethical behaviour as an engineer, including the impact of technology on both society and the environment.


Learning outcomes

On the successful completion of the course, students will be able to:




Demonstrate an advanced mastery and detailed knowledge of the founding principles of DSP, and understand how the various fundamental equations both operate and are constructed.




Recognise the different classes of problem in digital signal processing, and to decide upon appropriate methodologies in their solution. Identify time-domain and spectrum-based approaches.




Code, with expertise, and test off-line and real-time DSP algorithms, both using PCs and dedicated DSP hardware.




Design, from system level, a complete DSP engineering solution (hardware and software specification) intended for real-time use.




Develop real-time software using an Eclipse-based coding and debugging framework.




Understand and develop DSP solutions in a range of widely encountered problem spaces, including audio, communications and advanced signal recovery.



ILO7 Understand the ethics of this technology, and recognise how it affects society and the environment.



ILO8 Gain experience of working as part of a team during the laboratory work. Students are required to contribute

Teaching and learning methods

Lectures; laboratories; interactive PDF notes that include fully developed software, clickable audio notes and audio examples.


Assessment methods

Method Weight
Other 20%
Written exam 80%

Assessment task


How and when feedback is provided

Weighting within unit (if relevant)



Laboratory in real-time DSP. This comprises six programming tasks (team based) and one design report (individual)


9 hours

During laboratory and mark uploaded to Blackboard 10 days after submission of design report.



Feedback methods


Recommended reading

Digital Signal Processing: A Practical Approach, Emmanuel C. Ifeachor and B Jervis

Foundations of Digital Signal Processing: Theory, algorithms and hardware design, Patrick Gaydecki


Study hours

Scheduled activity hours
Lectures 24
Practical classes & workshops 9
Tutorials 4
Independent study hours
Independent study 113

Teaching staff

Staff member Role
Patrick Gaydecki Unit coordinator

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