Fast ICA for Blind Source Separation and its Implementation

Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussian. Fast ICA is a common method to identify aircrafts and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement Fast ICA for real-time signal processing. In this thesis, the Fast ICA algorithm is implemented by hand coding HDL code. In addition, in order to increase the number of precision, the floating point (FP) arithmetic units are also implemented by HDL coding.To verify the algorithm, MATLAB simulations are also performed for both off line signal rocessing and real-time signal processing

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Not AvailableAssessing genetic diversity and development of a core set of elite breeding lines is a prerequisite for selective hybridization programes intended to improve the yield potential in rice. In the present study, the genetic diversity of newly developed elite lines derived from indicax tropical japonica and indicax indica crosses were estimated by 38 reported molecular markers. The markers used in the study consist of 24 gene-based and 14 random markers related to grain yield-related QTLs distributed across the rice genome. Genotypic characterization was carried out to determine the genetic similarities between the elite lines. In total, 75 alleles were found using 38 polymorphic markers, with polymorphism information content ranging from 0.10 to 0.51 with an average of 0.35. The genotypes were divided into three groups based on cluster analysis, structure analysis and also dispersed throughout the quadrangle of PCA, but nitrogen responsive lines clustered in one quadrangle. Seven markers (GS3_RGS1, GS3_RGS2, GS5_Indel1, Ghd 7_05SNP, RM 12289, RM 23065 and RM 25457) exhibited PIC values C 0.50 indicating that they were effective in detecting genetic relationships among elite rice. Additionally, a core set of 11 elite lines was made from 96 lines in order to downsize the diversity of the original population into a small set for parental selection. In general, the genetic information collected in this work will aid in the study of grain yield traits at molecular level for other sets of rice genotypes and for selecting diverse elite lines to develop a strong crossing programme in rice.ICA