Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of the optimization tools used widely in solving problems based on natural selection and genetics. This paper is intended to cover the study of GA and parallel GA and analyses its usage in CPU and GPU. One of the popular ways to speed up the processing time was by running them as parallel. The idea of parallel GAs may refer to an algorithm that works by dividing large problem into smaller tasks. Broad literature review in this paper includes a categorization of the GA operations that involved with some theories and techniques used in GA, presented with the aid of diagrams. This review attempts to study and analyse the behaviour of GA and parallel GA categories to work in GPU depending on the type of genetic algorithm. Parallel GA for GPU covers the architecture of Compute Unified Device Architecture (CUDA).
Artificial Magnetic Conductor, AMC is introduced into RFID application to overcome the problem of metal object detection. The AMC act as the Perfect Magnetic Conductor, PMC exhibits a reflectivity of +1 (in-phase reflection). In this paper, the stacked wafers AMC structure is designed to operate at 920 MHz frequency. The proposed stacked wafers AMC is an evolution from the basic square patch AMC. By introducing different size of slots into the square patch will help to reduce the frequency hence increase the bandwidth of the reflection phase. Another method to increase the bandwidth is by increasing the thickness of the structure. For the single cell of stacked wafers AMC proposed in this paper, the simulated bandwidth is 3.5% with reduced size of 45.56% than the square AMC. An optimized structure of 3x2 stacked wafers AMC give better return loss = -21.8 dB and gain = 3.04 dB with total efficiency of 82.3%.
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