Thu.3 13:30–14:45 | H 3004 | APP
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Other Applications

Chair: Jean-Paul Arnaout
13:30

Sugandima Mihirani Vidanagamachchi

joint work with Shirly Devapriya Dewasurendra

A Reconfigurable Architecture for Commentz-Walter Algorithm

This work describes the hardware implementation of Commentz-Walter algorithm to match multiple patterns in protein sequences. Multiple pattern matching for different applications with the most widely used Aho-Corasick algorithm has been carried out over the years by PCs as well as GPUs and FPGAs. However, due to the complexity of Commentz-Walter algorithm, it has not been implemented in any hardware platform except in PCs. In this work, a specific architecture for this task using Commentz-Walter algorithm has been developed, tested and implemented on an FPGA to efficiently match proteins.

13:55

Yidiat Omolade Aderinto

joint work with Issa Temitope Issa

On Mathematical Model of Biological Pest Control of Sorghum Production

In this paper, mathematical model of Sorghum production from seed/planting to harvesting stage was presented with respect to its pests(prey) and the corresponding natural prey enemy (predators) at every stage. The model was characterized, the existence and uniqueness of the model solution was established. And finally numerical applications was carried out using Differential Transform method, and it was found that pests at different stages of sorghum production can be minimized below injury level using biological control which in turn leads to maximization of the sorghum production.

14:20

Jean-Paul Arnaout

Worm Optimization Algorithm for the Euclidean Location-Allocation Problem

Facility location is a critical aspect of strategic planning for a broad spectrum of public and private firms. This study addresses the Euclidean location-allocation problem with an unknown number of facilities, and an objective of minimizing the fixed and transportation costs. This is a NP-hard problem and in this paper, a worm optimization (WO) algorithm is introduced and its performance is evaluated by comparing it to Genetic Algorithms (GA) and Ant Colony Optimization (ACO). The results show that WO outperformed ACO and GA and reached better solutions in a faster computational time.