Bibliography: leaves 197-205. / xiv, 205 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis is concerned with methods for protecting speech coding parameters transmitted over noisy channels. A linear prediction (LP) coder is employed to remove the short term correlations of speech. Protection of two sets of parameters are investigated. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1995?

Thesis (Ph.D.)--University of Melbourne, Dept. of Electrical and Electronic Engineering, 2008. / Typescript. Includes bibliographical references (leaves 149-158).

Thesis (M. App. Sc.)--Carleton University, 2001. / Includes bibliographical references (p. 126-131). Also available in electronic format on the Internet.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2009. / Thesis Advisor(s): Robertson, R. Clark. "September 2009." Description based on title screen as viewed on 6 November 2009. Author(s) subject terms: Link-16/JTIDS, (31, 15) Reed-Solomon (RS) coding, 32-ary Orthogonal signaling, Additive White Gaussian Noise (AWGN), Pulse-Noise Interference (PNI), Perfect Side Information (PSI). Includes bibliographical references (p. 49-51). Also available in print.

Thesis (M.S. in Electronic Warfare Systems Engineering)--Naval Postgraduate School, September 2009. / Thesis Advisor(s): Robertson, Ralph C. "September 2009." Description based on title screen as viewed on 5 November 2009. Author(s) subject terms: Link-16/JTIDS, Reed-Solomon (RS) coding, Cyclic Code-Shift Keying (CCSK), Minimum-Shift Keying (MSK), convolutional codes, concatenated codes, perfect side information (PSI), Pulsed-Noise Interference (PNI), Additive White Gaussian Noise (AWGN), coherent detection, noncoherent detection. Includes bibliographical references (p. 79). Also available in print.

Thesis (M.S.)--Oregon State University, 2011. / Printout. Includes bibliographical references (leaves 115-116). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

A large literature has documented a pattern of stochastic, or random, choice in individual decision making. In games, in which payoffs depend on beliefs over opponents’ behavior, another potentially important source of stochasticity is in the beliefs themselves. Hence, there may be both “noisy actions” and “noisy beliefs”. This dissertation explores the equilibrium implications of both types of noise in normal form games. Theory is developed to understand the effects of noisy beliefs, and the model is compared to the canonical model of noisy actions. Predictions—and assumptions—are tested using existing and novel experimental data. Chapter 1 introduces noisy belief equilibrium (NBE) for normal form games, a model that injects “noisy beliefs” into an otherwise standard equilibrium framework. Axioms restrict the belief distributions to be unbiased with respect to and responsive to changes in the opponents’ behavior. We compare NBE to an axiomatic form of quantal response equilibrium (QRE) in which players have correct beliefs over their opponents’ behavior, but take “noisy actions”. We show that NBE generates similar predictions as QRE such as the “own-payoff effect”, and yet is more consistent with the empirically documented effects of changes in payoff magnitude. Unlike QRE, NBE is a refinement of rationalizability and invariant to affine transformations of payoffs. Chapter 2, joint with Jeremy Ward, studies an equilibrium model in which there is both “noisy actions” and “noisy beliefs”. The model primitives are an action-map, which determines a distribution of actions given beliefs, and a belief-map, which determines a distribution of beliefs given opponents’ behavior. These are restricted to satisfy the axioms of QRE and NBE, respectively, which are simply stochastic generalizations of “best response” and “correct beliefs”. In our laboratory experiment, we collect actions data and elicit beliefs for each game within a family of asymmetric 2-player games. These games have systematically varied payoffs, allowing us to “trace out” both the action- and belief-maps. We find that, while both sources of noise are important in explaining observed behaviors, there are systematic violations of the axioms. In particular, although all subjects observe and play the same games, subjects in different roles have qualitatively different belief biases. To explain this, we argue that the player role itself induces a higher degree of strategic sophistication in the player who faces more asymmetric payoffs. This is confirmed by structural estimates. Chapter 3 considers logit QRE (LQRE), the common parametric form of QRE; and we endogenize its precision parameter "lambda", which controls the degree of “noisy actions”. In the first stage of an endogenous quantal response equilibrium (EQRE), each player chooses her precision optimally subject to costs, taking as given other players’ (second-stage) behavior. In the second stage, the distribution of players’ actions is a heterogenous LQRE given the profile of first-stage precision choices. EQRE satisfies a modified version of the regularity axioms, nests LQRE as a limiting case for a sequence of cost functions, and admits analogues of classic results for LQRE such as those for equilibrium selection. We show how EQRE differs from LQRE using the family of generalized matching pennies games.

As a result of our heavy reliance on internet usage and running online transactions, authentication has become a routine part of our daily lives. So, what happens when we lose or cannot use our digital credentials? Can we securely recover our accounts? How do we ensure it is the genuine user that is attempting a recovery while at the same time not introducing too much friction for the user? In this dissertation, we present research results demonstrating that account recovery is a growing need for users as they increase their online activity and use different authentication factors. We highlight that the account recovery process is the weakest link in the authentication domain because it is vulnerable to account takeover attacks because of the less secure fallback authentication mechanisms usually used. To close this gap, we study user behavior-based machine learning (ML) modeling as a critical part of the account recovery process. The primary threat model for ML implementation in the context of authentication is poisoning and evasion attacks. Towards that end, we research randomized modeling techniques and present the most effective randomization strategy in the context of user behavioral biometrics modeling for account recovery authentication. We found that a randomization strategy that exclusively relied on the user’s data, such as stochastically varying the features used to generate an ensemble of models, outperformed a design that incorporated external data, such as adding gaussian noise to outputs. This dissertation asserts that account recovery process security posture can be vastly improved by incorporating user behavior modeling to add resiliency against account takeover attacks and nudging users towards voluntary adoption of more robust authentication factors.

Approved for public release; distribution in unlimited. / PC and MNF transforms are two widely used methods that are utilized for various applications such as dimensionality reduction, data compression and noise reduction. In this thesis, an in-depth study of these two methods is conducted in order to estimate their performance in hyperspectral imagery. First the PCA and MNF methods are examined for their effectiveness in image enhancement. Also, the various methods are studied to evaluate their ability to determine the intrinsic dimension of the data. Results indicate that, in most cases, the scree test gives the best measure of the number of retained components, as compared to the cumulative variance, the Kaiser, and the CSD methods. Then, the applicability of PCA and MNF for image restoration are considered using two types of noise, Gaussian and periodic. Hyperspectral images are corrupted by noise using a combination of ENVI and MATLAB software, while the performance metrics used for evaluation of the retrieval algorithms are visual interpretation, rms correlation coefficient spectral comparison, and classification. In Gaussian noise, the retrieved images using inverse transforms indicate that the basic PC and MNF transform perform comparably. In periodic noise, the MNF transform shows less sensitivity to variations in the number of lines and the gain factor. / Lieutenant, Hellenic Navy