Prosiectau Ffiseg ar gyfer myfyrwyr bl.3 a bl.4

Disgrifiad prosiect

Feature Spatial Analysis of Data from Atomic Force Microscopy

(supervisor: Tom Knight)

Nature of project: data analysis, software

Available to students on full-time physics degree schemes or joint students.

Project description and methodology

AFM (Atomic Force Microscopy) is a powerful tool in materials physics. As physicists, we require equally powerful analysis tools to describe and quantify our observations.

The objective of this project is to develop software that can, in an automated fashion, analyse images taken through AFM and quantify the results in terms of the spatial correlation of particular observed features. Initially the data utilised will be artificially constructed such that the software's efficacy can be confirmed.

A successful project will develop beyond the above in one/some of the following directions:
Application to real-world AFM data (which will be provided, if necessary).

How can we reduce the number of input parameters required to ensure maximum automation? What impact does that have on the efficacy of the analysis?

Can the software automatically identify remarkable features in the data?

When considering where to take your project, please bear in mind the time available. It is preferable to do fewer things well than to try many and not get conclusive results on any of them. However, sometimes it is useful to have a couple of strands of investigation in parallel to work on in case delays occur.

Additional scope or challenge if taken as a Year-4 project: The software must be applied to real-world data, and at least some of the questions covered in additional scope must be tackled.

Please speak to Tom Knight if you consider doing this project.

Initial literature for students:

  1. John C. Russ 2011, The image processing handbook, 6th ed.
  2. PySAL: A Python Library of Spatial Analytical Methods, Rey S.J., Anselin L., The Review of Regional Studies, Vol. 37, No. 1, 2007, pp. 5-27
  3. http://scikit-image.org

Novelty, degree of difficulty and amount of assistance required

Programming heavy project. Previous python (or other object orientated language) experience would be useful though not necessary.

Difficulty is dependent on the students pace though the project.

Project milestones and deliverables (including timescale)

milestoneto be completed by
Familiarity with python and external librariesend of October
Simple application of library to simplified example imageend of November
Application to semi-randomly-generated example applicationend of February
Analysis of performance and/or Refinement of extractionEaster