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 timeseries 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 timevariant systems using transfer functions for continuous and discrete time systems. 3. Use the sampling theorem with the discrete Fourier Transform and the ztransform. 4. Model a data filtering problem as a transfer function 5. Classify realworld 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 nonlinear regression to model dataseries 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 ZTransforms  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 datasets  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. Exam Duration: N/A Coursework contribution: 50% Term: Autumn & Spring Closed or Open Book (end of year exam): N/A Coursework Requirement: Laboratory Experiment Nonassessed problem sheets Oral Exam Required (as final assessment): no Prerequisite module(s): None required Course Homepage: unavailable Book List:
