Lugo-Beauchamp, Wilfredo E.

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2004 - 200912010 - 20161
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    Local alignment on highly unbalanced dna sequence lengths by reducing search space
    (2016) Lugo-Beauchamp, Wilfredo E.; Seguel, Jaime; College of Engineering; Velez, Bienvenido; Rivera Gallego, Wilson; Arzuaga, Emmanuel; Department of Electrical and Computer Engineering; Pastrana, Belinda
    DNA local sequence alignments provide biological insights that can help scientists identify genetic diseases, map newly obtained sequences to known genomes, or identify common genomic patterns on same species. Even when optimal sequence alignment algorithms have been well understood since more than 3 decades ago, the technological advancements of Next Generation Sequencing and the genomic data explosion they produced made them impractical today. Moreover, there is an increasingly necessity of fast comparison of very small sequences (less than 5,000 base pairs) against full genomes (greater than 100M base pairs). This thesis focuses on the local alignment problem for sequences with extreme length disparity and presents an Improved Search for a Local Alignment (ISLA) algorithm which provides an iteration based algorithm that achieves near optimal results by focusing local alignment only on specic areas of interest. ISLA also provides a probabilistic model to understand the chances of achieving a higher score.
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    Parallelization of hyperspectral imaging classification and dimensionality reduction algorithms
    (2004) Lugo-Beauchamp, Wilfredo E.; Rivera-Gallego, Wilson; College of Engineering; Hunt, Shawn; Seguel, Jaime; Department of Electrical and Computer Engineering; Vasquez, Pedro
    Hyperspectral imaging provides the capability to identify and classify materials remotely. The applications of such technology is applied everywhere from medical devices and military targets to environmental sciences. With the ongoing advances in spectrometers (spatial resolution and bits per pixel density) the data gathered is constantly increasing. Some hyperspectral imaging algorithms could easily take days or weeks in analyzing a full single hyperspectral data set. In this thesis we performed a porting and parallelization of four hyperspectral algorithms representative of the type of analysis done in a typical data set. Two of the algorithms are in the area of data classication, one in the area of feature reduction and the other one is a combination of both areas. The parallelized algorithms were benchmarked on the Intel 32 bits Pentium M architecture and the new Intel 64 bits Itanium 2 architecture. For three of the four algorithms we demonstrated that the use of parallel approaches in combination with computational clusters speedup signicantly the executions times and provide great scalability. On the other algorithm, based on linear algebra manipulations using distributed objects, we obtained execution times that took longer than the sequential implementation. A systematic performance analysis is carried out to explain the performance behavior of the algorithms.