JON McCORMACK::HONOURS PROJECTS::2003

Here are my honours projects for 2003. You need to come and see a supervisor (me) regarding any project you intend doing before selecting that project.

Kit: Multi-dimensional data visualization
  Interactive Adaptive Learning Systems
  Evolution of minimal generative grammars
  Non-linear geometric transformations for Virtual Reality
  Functional Music (co-supervised with Lloyd Allison)
See also: Past honours projects (CEMA research group)

 

Multi-dimensional data visualization (12/20pt project)

Many scientific applications require visualization of multi-dimensional data. For dimensions of 2 or 3 there are a number of established techniques to examine the relationships between dimensions (graphs, 3D plots, animated plots). For higher orders things become much more difficult, particularly when we are limited to static, flat display devices.

The aim of this project is to experiment with ways in which higher order data can be visualized using both static and animated displays. Essentially this is a mapping process – looking for intuitive ways in which data can be mapped from higher to lower dimensions. Some graphics experience would be useful for this project (e.g. 3rd year graphics course).

Preliminary Reading:
Tufte, E.R. (1983), The Visual Display of Quantitative Information, Graphics Press, Cheshire, Conn. (Box 430, Cheshire 06410).

Tufte, E.R. (1991), Envisioning Information, (2nd printing with revisions.), Graphics Press, Cheshire, Conn. (P.O. Box 430, Cheshire 06410).

Tufte, E.R. (1997), Visual Explanations : Images and Quantities, Evidence and Narrative, Graphics Press, Cheshire, Conn.


Interactive Adaptive Learning Systems (12/20pt project)

This project will investigate the use of adaptive learning systems for a real-time interactive application. Collections of virtual creatures are required to develop a symbiotic relationship with a human audience – responding to movement and gesture. Students should be prepared to investigate a number of adaptive learning techniques including classifier systems and evolutionary ANN (Artificial Neural Network) approaches, with the goal of creating a novel system that evolves and adapts to its real and virtual environments. The key emphasis for this system must be flexibility and real-time behaviour as the dynamic environment is actually changing in real-time.

References:
General Introductions to Adaptive and Evolutionary Systems (all available from the Hargrave library):

Flake, G.W. (1998), The Computational Beauty of Nature : Computer Explorations of Fractals, Chaos, Complex Systems, and Adaptation, MIT Press, Cambridge, Mass.

Pfeifer, R. and C. Scheier (1999), Understanding Intelligence, MIT Press, Cambridge, Mass.

Holland, J.H. (1995), Hidden Order : How Adaptation Builds Complexity, Helix Books, Addison-Wesley, Reading, Mass.


More specific papers:

McCormack, J. (2002), Evolving for the Audience, International Journal of Design Computing 4 (Special Issue on Designing Virtual Worlds). pdf version.

McCormack, J. (2003), Evolving Sonic Ecosystems, Kybernetes 32(1/2). pdf version.


Evolution of minimal generative grammars (12/20pt project)

Generative grammars, such as L-systems, are a good compression system for generating complex hierarchical data. Such data can be used for a variety of purposes, such as the modelling of plants, visual morphogenesis, and neural network encoding.

The aim of this project is to combine generative grammar systems with genetic algorithms to find the minimal (or smallest) grammar that can successfully generate a supplied pattern or string. This is known in the literature as the “inference problem” — to infer a grammar from what it generates. To do this, populations of generators compete to generate sequences of strings. Generators are credited for being correct and having minimal use of resources (the fitness measure). At each generation, the fittest individuals are subject to the genetic operations of crossover and mutation with the goal of evolving good encoding grammars. The project can be extended using more complex generative systems that have developmental and temporal components, their generation also influenced by environmental factors. A successful system could be used to infer grammars given the metrics of a particular species of plant, for example. Such a grammar could then go and generate a developmental model of that plant.

Preliminary Reading:

Prusinkiewicz, P. and A. Lindenmayer (1990), The Algorithmic Beauty of Plants, The Virtual Laboratory, Springer-Verlag, New York.

Mitchell, M. (1996), Introduction to Genetic Algorithms, Complex Adaptive Systems, MIT Press, Cambridge, MA.

Jacob, C. (1996), Evolving Evolution Programs: Genetic Programming and L-Systems, in Koza, J.R., et al. (eds), Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Cambridge, MA. pp. 28-31.


Non-linear geometric transformations for Virtual Reality (12/20 pt project)

This project investigates techniques for non-linear “perspective-like” transformations for a large-scale virtual reality (VR) system currently under development and to be exhibited at the Melbourne Museum before the end of 2003. In conventional VR, two separate perspective views are presented, one to each eye of the viewer. These views normally use traditional perspective projection to achieve realism. For this project, we would like to investigate other types of projections for use in stereoscopic systems, for example, a variance of focal length over the image area, or the simulation of the vision systems of animals other than humans. Ideally, such as system should be able to pre-transform geometry before it is rendered by a conventional computer graphics system.

Students wishing to do this project must have successfully completed CSE3313 Computer Graphics (or the equivalent of this course).


Functional Music (12/20pt project, co supervised with Lloyd Allison)

The project is to create methods to discover pattern and structure in music and to answer questions such as: What are the themes and variations in a musical score? Given two musical scores are they related? Given a midi file, to what extent can the original score be recovered? Can we infer a model that will create music in the style of X, or can we "cross-fade" or "mix" models of X and Y to get a new composition?

Musical ability is not strictly necessary but an interest in music is important and a (basic) knowledge of musical notation will be useful. The project will use the Haskore [ http://haskell.cs.yale.edu/haskore/ ] notation and system but an open mind is much more important than experience in functional programming. Read this: http://cogprints.ecs.soton.ac.uk/archive/00001771/00/Perception73.pdf   Potential candidates must talk to the supervisor(s) first.