vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff5f662b000000ea91040001000200
In the recent times, Finite Impulse Response (FIR) filters are deemed to be the most suitable model for almost all the adaptive system applications because of its simplicity and assured stability. But, designing linear-phase Finite Impulse Response filters with fewer hardware resources is a challenging task. To address this issue, this paper presents an efficient and novel technique to design one dimensional and two-dimensional linear-phase Finite Impulse Response filters. The primary focus of this paper is the implementation of crossover bacterial foraging and Cuckoo Search techniques for effectively designing one-dimensional and two-dimensional linear phase Finite Impulse Response filters. The superiority of the design is affirmed by its ingenuity to obtain the solution through the convergence of a biased random search using crossover bacterial foraging optimization. Also, the solution is obtained in a quicker fashion through the convergence of a metaheuristic search technique called the Cuckoo Search technique. For better benchmarking and evaluation of the results, the outcomes of the proposed techniques are compared with the well-known genetic algorithm and bacterial foraging optimization techniques. The experimental analysis is carried out using three one-dimensional and two two-dimensional Finite Impulse Response filters.