Acknowledgements I would like to thank my supervisor Jennifer Bruton for her guidance, enthusiasm and commitment to this project. Declaration I hereby declare that, except where otherwise indicated, this document is entirely my own work and has not been submitted in whole or in part to any other university. Abstract Identification is the process of modelling of a system based on its inputs and ...

Parametric estimation of signals, based on quantized data, is often carried out by means of least squares (LS) or averaging techniques. Such an approach often leads to optimal performance, resulting in almost unbiased estimators when the quantization error can approximately be modeled as an additive white Gaussian noise, or when other additive white Gaussian noise sources are larger than the qu...

We describe two regularization techniques based on optimal control for solving two types of ill-posed problems. We include convergence proofs of the regularization method and error estimates. We illustrate our method through problems in signal processing and parameter identification using an efficient Riccati solver. Our numerical results are compared to the same examples solved using Tikhonov ...

Formal evaluations conducted by NIST in 1996 demonstrated that systems that used parallel banks of tokenizer-dependent language models produced the best language identification performance. Since that time, other approaches to language identification have been developed that match or surpass the performance of phone-based systems. This paper describes and evaluates three techniques that have be...

Modeling and identification for high dimensional (i.e. signals with many components) data sets poses severe challenges to off-the-shelf techniques for system identification. This is particularly so when relatively small data sets, as compared to the number signal components, have to be used. It is often the case that each component of the measured signal can be described in terms of few other m...