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ELEC50009 Information Processing (shared wi ELEC50013 S&S in Autumn)


Lecturer(s): Prof Patrick Naylor; Dr Christos Bouganis

Aims

The aim of this topic is to take data and transform it or analyse it. This may be time-series data, spatial data, or unstructured data. The unifying idea of the module is that there are certain fundamental ideas such as sampling and transforms that apply throughout, and then there are many different mathematical and applied tools which could be used to implement them in different scenarios.

Learning Outcomes

Upon successful completion of this module, you will be able to:
1. Use the techniques of Laplace Transforms to solve ordinary differential equations and apply them in the context of signal processing.
2. Explain and apply convolution for linear time-variant systems using transfer functions for continuous and discrete time systems.
3. Use the sampling theorem with the discrete Fourier Transform and the z-transform.
4. Model a data filtering problem as a transfer function
5. Classify real-world data into different types of signals and data
6. Design filters to meet given requirements
7. Translate continuous filters into software or hardware implementations using standard tools
8. Identify between supervised and unsupervised learning
9. Apply non-linear regression to model data-series
10. Use a machine learning toolbox to train a model

Syllabus

Fundamentals of signals
- Types of data and signals
- Types of analysis
- Representing signals using Laplace Transforms and Z-Transforms
- Transfer functions and frequency response � stability analysis
- Revision of sampling
- Design of Analogue and Digital filters.

Applied signals and information processing
- Applied signal processing using toolboxes
- Implementation of audio and video filters
- Multivariate and irregular data-sets
- Supervised and unsupervised learning
- Regression
- Prediction
- Learning toolboxes

This module and ELEC50013 Signals and Systems share a common Autumn term with both cohorts sharing lectures and being taught the same material.
Assessment
Exam Duration: N/A
Coursework contribution: 50%

Term: Autumn & Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:
         Laboratory Experiment
         Non-assessed problem sheets

Oral Exam Required (as final assessment): no

Prerequisite module(s): None required

Course Homepage: unavailable

Book List: