Calculating materials properties from structural models has long been one of the most important problems of Materials Science (cf. [1]). There is still a need for relatively simple methods of assessment of material properties, especially surface properties, based on analysis of experimental data such as microscopic images. Fractal and symbolic methods of image and signal analysis can be very useful for these purposes. Fractal dimension of a surface in 3-dimensional space, D_s , may be assessed based on fractal dimension of an image of this surface on a plane. Fractal dimension is invariant with respect to linear scale transformations and it is simply related to power spectrum exponent β - an image of a fractal Brownian surface with the power spectrum proportional to f ^-β shows power spectrum proportional to f ^2-β, where β/2 = (3 - D_s) (cf. [2]).

Nanotechnologies provide new sensors that enable easy acquisition of biosignals for monitoring of drivers, pilots, etc. and for clinical applications. But before any signal generated by a nanosensor may be used for monitoring or clinical assessment that signal has to be appropriately processed and visualized. Data-processing algorithms based on fractal and symbolic computational methods may be used for extraction, fusion, and visualization of multi-modal information from nanosensors, for representing and managing signal complexity. These methods are computationally effective and may be applied in real-time.

[1] W.Klonowski, Probabilistic Topological Theory of Systems with Discrete Interactions,I,II, Can.J.Phys. 1989, 66, 1051-1067;
[2] W.Klonowski, Signal and Image Analysis Using Chaos Theory and Fractal Geometry, Mach.Graph.Vis. 2000, 9, 403-431.

Acknowledgements:
This work was partially supported by the Polish State Committee for Scientific Research (KBN) grant No. 4 T11F 01 922 and by European Union FP6 Integrated Project SENSATION (IST 507231).

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