Hocine Merabti

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Genetic algorithms (GAs) have been successfully applied to resolve adaptive filtering problems. The main advantage of using such algorithms over conventional adaptive filtering techniques, is their ability to deal with nonlinear systems. However, intensive computations are needed to achieve proper performances, which can be very critical when(More)
In this paper, an adaptive edges detection method based on ant colony algorithm is presented. Ant colony algorithm is a swarm-based metaheuristic inspired by the self-organizing properties of ant colony in nature. Artificial ants in movement create a pheromone graph, which denotes data of edge image. Further behaviors were added to each ant in response to(More)
Genetic algorithms are increasingly being used to address adaptive filtering problems. The interest lies in their ability to find the global solutions for linear and nonlinear problems. However, all the work available in the literature use software implementations running on sequential processors. This work proposes a hardware architecture of a real-time(More)
In this paper, we propose an FPGA implementation of a genetic algorithm (GA) for linear and nonlinear auto regressive moving average (ARMA) model parameters identification. The GA features specifically designed genetic operators for adaptive filtering applications. The design was implemented using very low bit-wordlength fixed-point representation, where(More)
Nonlinearities in audio systems are caused by different nonlinear components like amplifiers and speakers. The nature of these impairments makes acoustic echo cancellation (AEC) harder to achieve, requiring the use of advanced and complex algorithms to offer satisfying performance. Adaptive filters in combination with nonlinear mapping schemes like Volterra(More)
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