Digital Signal Processing Design and Applications

LocationCavendish, Central London
FacultyScience and Technology

Digital Signal Processing (DSP) is a vital element in most modern systems for electronics and instrumentation. While many advances in DSP implementation take place at a blistering pace, understanding the fundamentals is paramount for practitioners in long-term involvement with this dynamic field.

This short course is a blend of mathematics, system and communication theory, signal and system analysis as well as probability concepts. We will reinforce topics by selected practical applications and numerous computer-based demonstrations. The course fee includes all course notes and many MATLAB files used during the week.

Who is this course for?

You should have a background in differential and integral calculus, and be functional in the use of complex variables. If you are a new graduates who needs training and refreshing in DSP concepts as well as MATLAB, you can also attend.

This course is based on MATLAB and Simulink, and takes place in a laboratory environment. Some knowledge of MATLAB would be advantageous, but it is not crucial.

Please note that this short course is delivered alongside MSc students.

Dates Duration Price  
We run this course on request for a number of people. Please register your interest. 5 days


Register your interest

Course content

This course will cover:

Introduction to DSP concepts

  • Time and frequency representations
  • Sampling, FST, DFT, FFT
  • Familiarity with spectra

Group delay, pole-zero patterns and more transforms

  • Understanding how group delay affects signals
  • PZPs, system stability and relationship to filtering
  • Z-transform and how it is used in analysing system behaviour

Convolution, more z-transforms and tone detection

  • Input/output relationships
  • DTMF tone detection using Simulink, extraction of information from sub-bands
  • Hardware structures
  • System identification
  • Compensation

The DSP landscape, non-optimal designs, windowing

  • Differences in IIR and FIR filters
  • Design exercises using MATLAB
  • Comparisons of different techniques for linear-phase filter design
  • Limitations of windowing
  • Filter quality metrics

Stochastic signal processing

  • Probability and random variables
  • PDFs and measures of concentration
  • Structural and probabilistic components of random signals
  • Time correlations and energy spectral density
  • Statistical correlations and power spectra
  • Ergodicity
  • Effects of LTIV systems on random signals
  • Noise colouration
  • Whitening and inverse filters
  • Signal-to-noise improvement for noisy sinusoids

By the end of the course you will be able to:

  • understand the relationships between continuous-time and discrete-time signal representation
  • perform transformations using the FST, the z-transform and the DFT
  • use pole-zero patterns in gaining understanding of discrete-time systems and how to stabilise and compensate them
  • recognise and design elementary linear-phase FIR digital filters
  • estimate energy and power spectra for deterministic and stochastic signals, and calculate the effect of filtering
  • view spectral analysis as filterbank processing and configure such elements to extract information in bands of interest

Contact us

Faculty of Science and Technology

Verena Bogner
Programme Administrator

+44 (0)20 7911 5000 ext 69529