Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the author. Logarithmic CMOS image sensors capture high dynamic range scenes without saturation or loss of perceptible detail but problems exist with image quality. This thesis develops and applies methods of modelling and calibration to understand and improve the fixed pattern noise (FPN) and colour rendition of logarithmic imagers. Chapter 1 compares CCD and CMOS image sensors and, within the latter category, compares linear and logarithmic pixel designs. Chapter 2 reviews the literature on multilinear algebra, unifying and extending approaches for analytic and numeric manipulation of multi-index arrays, which are the generalisation of scalars, vectors and matrices. Chapter 3 defines and solves the problem of multilinear regression with linear constraints for the calibration of a sensor array, permitting models with linear relationships of parameters

Abstract. The sparse Cholesky factorization of some large matrices can require a two dimensional partitioning of the matrix. The sparse hypermatrix storage scheme produces a recursive 2D partitioning of a sparse matrix. The subblocks are stored as dense matrices so BLAS3 routines can be used. However, since we are dealing with sparse matrices some zeros may be stored in those dense blocks. The overhead introduced by the operations on zeros can become large and considerably degrade performance. In this paper we present an improvement to our sequential in-core implementation of a sparse Cholesky factorization based on a hypermatrix storage structure. We compare its performance with several codes and analyze the results. 1

Efficient execution of numerical algorithms requires adapting the code to the underlying execution platform. In this paper we show the process of fine tuning our sparse Hypermatrix Cholesky factorization in order to exploit efficiently two important machine resources: processor and memory. Using the techniques we presented in previous papers we tune our code on a different platform. Then, we extend our work in two directions: first, we experiment with a variation of the ordering algorithm, and second, we reduce the data submatrix storage to be able to use larger submatrix sizes.

Prof. Daniel P. Bovet“Ladies and gentleman, this is your captain speaking. I have some good news and I have some bad news. The good news is, that we have a very strong tail wind, and we are doing one thousand four hundred miles per hour over land. The bad news is, that all of our navigation instruments are out, and we don’t know where we are, and we don’t know where we are going.”