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Lecturer: Dr R.J. Gibbens
(rg31@cl.cam.ac.uk)
No. of lectures: 4
This course is a prerequisite for Computer Graphics and Image
Processing, Computer Vision (Part II and Diploma),
Information Theory and Coding (Part II) and Neural
Computing (Part II).
Aims
The aims of this course are to review some key concepts and operations
defined in continuous mathematics involving real and complex-valued
functions. Focus is on the use and implementation of these notions as
encountered in computing.
Lectures
- Review of analysis.
Limits, continuity and differentiability. Power series
and transcendental functions. Taylor series. Complex
variables.
- Fourier series.
Introduction. General properties. Uses and
applications.
- Linear vector spaces and decompositions.
Expansions and basis functions. Orthogonality, inner
products and completeness. Useful expansion bases for
functions.
- Signals and systems.
Fourier transforms and their inverses: introduction
and general properties. Uses and applications. Brief
introduction to wavelet analysis and its comparison
with Fourier analysis.
Objectives
At the end of the course students should
- understand how data or functions can be
represented in terms of their projections onto basis
functions
- be fluent in the use and properties of
complex variables
- grasp key properties and uses of Fourier
analysis, transforms, and wavelets
Reference books
Kaplan, W. (1992). Advanced Calculus.
Addison-Wesley (4th ed.).
Oppenheim, A.V. & Willsky, A.S. (1984). Signals
and Systems. Prentice-Hall.
Next: Data Structures and Algorithms
Up: Michaelmas Term 2001: Part
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Christine Northeast
Tue Sep 4 09:34:31 BST 2001