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

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


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. Model a data filtering problem as a transfer function
2. Classify real-world data into different types of signals
3. Design a continuous audio filter using LCR components
4. Create a digital video filter using the FFT
5. Describe the circumstances when it is appropriate to use Fourier, FIR, or IIR solutions
6. Apply non-linear regression to model data-series
7. Use a machine learning toolbox to train a model


Signals and Data
Types of data and signals
Types of analysis
Transfer functions
LTI systems Filtering FIR IIR
Fourier methods
Image filtering
Audio filtering
Data analysis
Prediction Learning toolboxes
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: