@article {17930,
	title = {Parallel, stochastic measurement of molecular surface area},
	journal = {Journal of Molecular Graphics and Modelling},
	volume = {27},
	year = {2008},
	month = {2008/08//},
	pages = {82 - 87},
	abstract = {Biochemists often wish to compute surface areas of proteins. A variety of algorithms have been developed for this task, but they are designed for traditional single-processor architectures. The current trend in computer hardware is towards increasingly parallel architectures for which these algorithms are not well suited.We describe a parallel, stochastic algorithm for molecular surface area computation that maps well to the emerging multi-core architectures. Our algorithm is also progressive, providing a rough estimate of surface area immediately and refining this estimate as time goes on. Furthermore, the algorithm generates points on the molecular surface which can be used for point-based rendering.
We demonstrate a GPU implementation of our algorithm and show that it compares favorably with several existing molecular surface computation programs, giving fast estimates of the molecular surface area with good accuracy.
},
	keywords = {gpu, Molecular surface, Parallel, Progressive, Quasi-random, Stochastic},
	isbn = {1093-3263},
	doi = {10.1016/j.jmgm.2008.03.001},
	url = {http://www.sciencedirect.com/science/article/pii/S1093326308000387},
	author = {Juba,Derek and Varshney, Amitabh}
}
@conference {15052,
	title = {Web based progressive transmission for browsing remotely sensed imagery},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	volume = {2},
	year = {2000},
	month = {2000///},
	pages = {591 -593 vol.2 - 591 -593 vol.2},
	abstract = {This paper describes an image representation technique that entails progressive refinement of user specified regions of interest (ROI) of large images. Progressive refinement to original quality can be accomplished in theory. However, due to heavy burden on storage resources for the authors{\textquoteright} applications, they restrict the refinement to about 25\% of the original data resolution. A wavelet decomposition of the data combined with scalar and vector quantization (VQ) of the high frequency components and JPEG/DCT compression of low frequency component is used as representation framework. Their software will reconstruct the region selected by the user from its wavelet decomposition such that it fills up the preview window with the appropriate subimages at the desired resolution, including full resolution stored for preview. Further refinement from the first preview can be obtained progressively by transmitting high frequency coefficients from low resolution to high resolution which are compressed by variant of vector quantization called model-based VQ (MVQ). The user will have an option for progressive build up of the ROIs until full resolution stored or terminate the transmission at any time during the progressive refinement},
	keywords = {based, decomposition;geophysical, image;model-based, interest;remote, interest;vector, mapping;user, mapping;vector, measurement, of, processing;geophysical, processing;image, Progressive, quantisation;wavelet, quantization;wavelet, refinement;region, regions, representation;land, representation;remote, sensing;scalar;terrain, sensing;terrain, signal, specified, specified;user, surface;large, technique;image, techniques;image, transforms;, transmission;browsing;geophysical, VQ;progressive, Web},
	doi = {10.1109/IGARSS.2000.861640},
	author = {Mareboyana,M. and Srivastava,S. and JaJa, Joseph F.}
}
