| Paper: | MLSP-P2.3 | ||
| Session: | Bioinformatics and Biomedical Applications | ||
| Time: | Wednesday, May 19, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Bioinformatics Applications | ||
| Title: | DNA-BASED MATCHING OF DIGITAL SIGNALS | ||
| Authors: | Sotirios Tsaftaris; Northwestern University | ||
| Aggelos Katsaggelos; Northwestern University | |||
| Thrasyvoulos Pappas; Northwestern University | |||
| Eleftherios Papoutsakis; Northwestern University | |||
| Abstract: | Adleman with his pioneering work set the stage for the new field of bio-computing research [1]. His main idea was to use actual chemistry to solve problems that are either unsolvable by conventional computers, or require an enormous amount of computation. The main focus of our research is to consider the application of molecular computing to the domain of digital signal processing (DSP). In this paper we consider matching problems that arise in signal processing applications and are amenable to a DNA-based solution. Digital data are encoded in DNA sequences using a sophisticated codeword set that satisfies the Noise Tolerance Constraint (NTC) that we introduce. NTC, one of the main contributions of our work, takes into account the presence of noise in digital signals by exploiting the annealing between non-perfect complementary sequences. We propose an algorithm to map binary values into DNA codewords by satisfying a number of constraints, including the NTC. Using that algorithm we retrieved 128 codewords that enables us to use a DNA based approach to digital signal matching. | ||
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