Professional and short courses
Digital Signal Processing Design and Applications
|Location||Cavendish, Central London|
|Faculty||Science 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.
|We run this course on request for a number of people. Please register your interest.||5 days||
|Register your interest|
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
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
- 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