Thesis (M. Eng.)--Carleton University, 1989. / Also available in electronic format on the Internet.

Thesis (Ph.D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 147-152)

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 57-61)

No description available.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

A wide range of modern large-scale systems relies on recovering a signal from noisy linear measurements. In many applications, the useful signal has inherent properties, such as sparsity, low-rankness, or boundedness, and making use of these properties and structures allow a more efficient recovery. Hence, a significant amount of work has been dedicated to developing and analyzing algorithms that can take advantage of the signal structure. Especially, since the advent of Compressed Sensing (CS) there has been significant progress towards this direction. Generally speaking, the signal structure can be harnessed by solving an appropriate regularized or constrained M-estimator. In modern Multi-input Multi-output (MIMO) communication systems, all transmitted signals are drawn from finite constellations and are thus bounded. Besides, most recent modulation schemes such as Generalized Space Shift Keying (GSSK) or Generalized Spatial Modulation (GSM) yield signals that are inherently sparse. In the recovery procedure, boundedness and sparsity can be promoted by using the ℓ1 norm regularization and by imposing an ℓ∞ norm constraint respectively. In this thesis, we propose novel optimization algorithms to recover certain classes of structured signals with emphasis on MIMO communication systems. The exact analysis permits a clear characterization of how well these systems perform. Also, it allows an automatic tuning of the parameters. In each context, we define the appropriate performance metrics and we analyze them exactly in the High Dimentional Regime (HDR). The framework we use for the analysis is based on Gaussian process inequalities; in particular, on a new strong and tight version of a classical comparison inequality (due to Gordon, 1988) in the presence of additional convexity assumptions. The new framework that emerged from this inequality is coined as Convex Gaussian Min-max Theorem (CGMT).

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 76-77).

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 59-61). Also available in print.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 59-60). Also available in electronic version. Access restricted to campus users.

Στη διπλωματική εργασία μελετήθηκε ο σχεδιασμός και η βελτιστοποίηση αρχιτεκτονικών αποκωδικοποιητων LDPC. Μελετήθηκε η δυνατότητα απλούστευσης του πολύπλοκου δικτύου διασύνδεσης των επεξεργαστικών στοιχείων της αρχιτεκτονικής που αποτελεί καθοριστικό παράγοντα της πολυπλοκότητας της υλοποίησης σε υλικό των αποκωδικοποιητών. Αναπτύχθηκε συστηματικός τρόπος αντιμετώπισης του προβλήματος αυτού, ανεξαρτήτως συγκεκριμένου κώδικα LDPC. / The design and optimization of LDPC decoders architecture was studied. An architecture with simplified interconnections and a systematic algorithm to derive it, regardless of the LDPC code, was proposed.