Showcase 2019- Registered Projects
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SHOWCASE 2019-PROJECTS
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Project NameProject ProgramTeam Lead NameTeam Lead E-mailBriefly describe your project
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Scale Dependent Dynamic AlignmentSpace SciencesKarly lorenziniKlorenzini2016@my.fit.eduInvestigate the dependence of solar wind velocity with respect to local mean magnetic fields.
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Implementation of Immersed Boundary Method to solve swimmer/fluid interactions common in Biological SystemsMathematical SciencesBindi Mahesh Nagdabnagda2015@my.fit.eduThis is a math research poster presentation that involves implementing numerical methods in order to solve the complex fluid dynamics equations involved during swimmer (or microbe) interaction with a Low Reynold's number two-phase fluid. The immersed boundary framework is applied as the numerical scheme and implemented using GMRES in PetsC software. Results are presented in the form of graphs to show the effect of changing fluid elasticity and swimming stroke (i.e. kicker or burrower motion) on the swimming speed of the microorganism.
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Identification of Style-Markers and Composer Attribution in Classical MusicMathematical SciencesShelley Mitchellsmitchell2015@my.fit.eduStylometry is the quantitative study of literary style. It is based on the
observation that authors tend to write in relatively consistent, recognizable and unique ways. In this project we investigate the current approaches in stylometric analysis and apply machine learning methodology to identify style-markers and composer attribution in classical music.
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An Efficient Genetic Algorithm and User-Friendly Software to Solve the Traveling Salesman ProblemMathematical SciencesPaul Arbicparbic2014@my.fit.eduThe Traveling Salesman Problem (TSP) introduced in the 18th century by Hamilton is one of the most intensively studied NP-complete problems. TSP can be applied to various real-world problems such as routing, drilling of printed circuit boards, overhauling gas turbine engines, transportation and telecommunication networks. Since the problem is of practical importance and computationally challenging several research efforts are channeled to the area of developing efficient and effective techniques for its solution. These methods vary from most efficient but less effective greedy-type heuristics to more sophisticated algorithmic approaches including genetic algorithms inspired by Darwin’s theory of natural evolution.

In this project we developed a genetic algorithm for solving the TSP. Our algorithm is hardcoded in PHP, JavaScript, and HTML. As such, the program runs primarily in a standard internet browser. The proposed algorithm uses sixteen weighted variables, including present position, average distance of other points, number of selected and unselected points, and a bias. We tested our algorithm on several large-scale datasets. We analyze the benefits and shortfalls of genetic/evolutionary algorithms and demonstrate how an effective solution can be found to most, if not all, optimization problems using limited data and information. We then applied our algorithm to printed circuit board drilling, where a major challenge is to increase the productivity and quality while minimizing the assembly time and per unit cost of production. We compare our proposed genetic algorithm with other heuristic methods for solving printed circuit board drilling problem and perform an in-depth analysis of the computational results.
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Mapping Genetic Sequences – Theory & ApplicationMathematical SciencesGyorgy Haloghalo2014@my.fit.eduA reliable and traceable method based on linear algebra to compare and contrast genomic sequences was developed by Randić et al. (2000). His mapping technique stores exons of the genomic map in a matrix to represent the nucleic acid bases by their totality. In this research we adopt Randić et al.'s approach and apply it to full mitochondrial genome of the Human Neuroblastoma Cell Line 751-NA to compare a cancerous genotype sequence with a noncancerous genotype sequence. The mapping technique proposed by Randić et al. (2000) is a valuable tool to compare large sets of DNA sequences together. Genetic analysis techniques could be made easily with a superimposed DNA graph so any mismatches between any two DNA sequences could immediately be recognized. Either comparing different species genomic sequences with same function, or comparing a disease causing gene sequence to a healthy genotype could be inspected without any degree of degeneracy on the graph. Applying such a computational tool in clinical research is a valuable technique that could save time and if time matters then it could eventually save life as well.
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Design and Analysis of Travelling Salesman ProblemMathematical SciencesPaul Arbicparbic2014@my.fit.eduWe are using genetic learning algorithms to find a semi-optimal solution to the travelling salesman problem, and want to build a board of lights to demonstrate the solutions found to various sets of data-points in a fun and interactive way.
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