The optimum linear data fusion of independent observation data

Data Fusion is one of the attractive topic in sonar signal processing. Decision level data fusion of multi-sensor (multi-array) system is described in this paper. The optimum linear data fusion algorithm for N independent observation data is derived. It is proved that the estimation error of optimum data fusion is not greater than that of individual components. The expression of estimation error and weight coefficients are presented. The results of numerical calculation and some examples are illustrated. The effect of input signal to noise ratio for the data fusion is described.

High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated highdimensional data describing non-independent observations to identify SNPs related to a phenotype.We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection and population structure adjustment in highdimensional prediction models.Specifically,the model simultaneously selects variables and estimates their effects,taking into account correlations between individuals.Single nucleotide polymorphisms(SNPs)are a type of genetic variation and each SNP represents a difference in a single DNA building block,namely a nucleotide.Previous research has shown that SNPs can be used to identify the correct source population of an individual and can act in isolation or simultaneously to impact a phenotype.In this regard,the study of the contribution of genetics in infectious disease phenotypes is of great importance.In this study,we used uncorrelated variables from the construction of blocks of correlated variables done in a previous work to describe the most related observations of the dataset.The model was trained with 90%of the observations and tested with the remaining 10%.The best model obtained with the generalized information criterion(GIC)identified the SNP named rs2493311 located on the first chromosome of the gene called PRDM16((PR/SET domain 16))as the most decisive factor in malaria attacks.