Blind equalization

An equalizer is an adaptive filter that compensates for the non-ideal characteristics of a communication channel by processing the received signal. The adaptive algorithm searches for the inverse impulse response of the channel, and it requires knowledge of a training sequence, in order to generate an error signal necessary for the adaptive process. There are practical situations where it would be highly desirable to achieve complete adaptation without the use of a training sequence, hence the the term blind . Examples of these situations are multipoint data networks, high-capacity line-of-sight digital radio, and reflection seismology. A blind adaptive algorithm has been developed, based on simplified equalization criteria. These criteria are that the second- and fourth-order moments of the input and output sequences are equalized. The algorithm is entirely driven by statistics, only requiring knowledge of the variance of the input signal. Because of the insensitivity of higher-order statistics to Gaussian processes, the algorithm performs well when additive white Gaussian noise is present in the channel. Simulations are presented in which the new blind equalizer developed is compared to other equalization algorithms

Parametric estimation in noisy blind deconvolution model: a new estimation procedure

In a parametric framework, the paper is devoted to the study of a new estimation procedure for the inverse filter and the level noise in a complex noisy blind discrete deconvolution model. Our estimation method is a consequence of the sharp exploitation of the specifical properties of the Hankel forms. The distribution of the input signal is also estimated. The strong consistency and the asymptotic distribution of all estimates are established. A consistent simulation study is added in order to demonstrate empirically the computational performance of our estimation procedures.Comment: Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Ultra-fast Imaging of Two-Phase Flow in Structured Monolith Reactors; Techniques and Data Analysis

This thesis will address the use of nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques to probe the “monolith reactor”, which consists of a structured catalyst over which reactions may occur. This reactor has emerged as a potential alternative to more traditional chemical engineering systems such as trickle bed and slurry reactors. However, being a relatively new design, its associated flow phenomena and design procedures are not rigorously understood, which is retarding its acceptance in industry. Traditional observations are unable to provide the necessary information for design since the systems are opaque and dynamic. Therefore, NMR is proposed as an ideal tool to probe these systems in detail. The theory of NMR is summarised and the development of novel NMR techniques is presented. Novel techniques are validated in simple systems, and tested in more complex systems to ascertain their quantitative nature, and to find their limitations. These techniques are improvements over existing techniques in that they either decrease the acquisition time (allowing the observation of dynamically-changing systems) or allow us to probe systems in different ways to extract useful information. The goal of this research is to better understand the flow phenomena present in such systems, and to use this information to design better, more efficient, more controllable industrial reactors. The analysis of the NMR data acquired is discussed in detail, and several novel image-processing techniques have been developed to aid in the quantification of features within the images, and also to measure quantities such as holdup and velocity. These novel techniques are validated, and then applied to the systems of interest. Various configurations of monolith reactor, ranging from low flow rate systems to more challenging (and more industrially relevant) turbulent systems, are probed using these methods, and the contrasting flow phenomena and performance of these systems are discussed, with a view to optimisation of the choice of design parameters

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Blind Deconvolution of Anisoplanatic Images Collected by a Partially Coherent Imaging System

Coherent imaging systems offer unique benefits to system operators in terms of resolving power, range gating, selective illumination and utility for applications where passively illuminated targets have limited emissivity or reflectivity. This research proposes a novel blind deconvolution algorithm that is based on a maximum a posteriori Bayesian estimator constructed upon a physically based statistical model for the intensity of the partially coherent light at the imaging detector. The estimator is initially constructed using a shift-invariant system model, and is later extended to the case of a shift-variant optical system by the addition of a transfer function term that quantifies optical blur for wide fields-of-view and atmospheric conditions. The estimators are evaluated using both synthetically generated imagery, as well as experimentally collected image data from an outdoor optical range. The research is extended to consider the effects of weighted frame averaging for the individual short-exposure frames collected by the imaging system. It was found that binary weighting of ensemble frames significantly increases spatial resolution