Visualization of Quick sort

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It demonstrates two comparison sorting algorithms: Bubble sort and Quick sort.
Comparison sorting algorithms are only allowed to ‘see’ the data through a sequence of pair-wise comparisons, therefore they are applicable to any type of comparable objects: numbers, strings, colored balls, etc

Bubble sort is very simple but has poor performance. A comparison sorting algorithm’s performance is usually measured by the number of comparisons it makes. Bubble sort performs on the order of n^2 comparisons to sort n elements.

Quick sort is only slightly more complicated but usually performs much better (as demonstrated in the video). It performs on average an order of n log(n) comparisons to sort n elements. This is much lower than n^2 for large values of n. However, if the algorithm makes some ‘unlucky’ choices it might require n^2 comparisons after all.

Other algorithms exist that guarantee the number of comparisons will not exceed n log(n), however, in practice Quick sort usually out-performs all other comparison sorting algorithms due to its simplicity.

If other operations other than pair-wise comparisons are allowed, then a broader range of algorithms can be used. Some of them can perform much faster than Quick sort, but they are limited to a particular type of elements, e.g., numbers is a certain range.

Duration : 0:2:56

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Illustrative Visualization and Exploration of MR/fMRI Data

Full Title: Illustrative Hybrid Visualization and Exploration of
Anatomical and Functional Brain Data

Common practice in brain research and brain surgery involves the multi-modal acquisition of brain anatomy and brain activation data. These highly complex three-dimensional data have to be displayed simultaneously in order to convey spatial relationships. Unique challenges in information and interaction design have to be solved in order to keep the visualization sufficiently complete and uncluttered at the same time. The visualization method presented in this paper addresses these issues by using a hybrid combination of polygonal rendering of brain structures and direct volume rendering of activation data. Advanced rendering techniques including illustrative display styles and ambient occlusion calculations enhance the clarity of the visual output. The presented rendering pipeline produces real-time frame rates and offers a high degree of configurability. Newly designed interaction and measurement tools are provided, which enable the user to explore the data at large, but also to inspect specific features closely. We demonstrate the system in the context of a cognitive neurosciences dataset. An initial informal evaluation shows that our visualization method is deemed useful for clinical research.

Link to thesis:

http://www.jainek.de/projects/iBrain/

Link to publication:

http://www.janfischer.com/pub_pages/pub-jainek08-eurovis.html

Reference:
W. Jainek and S. Born and D. Bartz and W. Straßer and J. Fischer, Illustrative Hybrid Visualization and Exploration of Anatomical and Functional Brain Data, Computer Graphics Forum (Special Issue on Eurographics Symposium on Visualization), September 2008 (Vol. 27, Issue 3)

Duration : 0:2:48

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