Ouyang, Hua. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 81-89). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Biometric Authentication --- p.1 / Chapter 1.2 --- Multimodal Biometric Authentication --- p.2 / Chapter 1.2.1 --- Combination of Different Biometric Traits --- p.3 / Chapter 1.2.2 --- Multimodal Fusion --- p.5 / Chapter 1.3 --- Audio-Visual Bi-modal Authentication --- p.6 / Chapter 1.4 --- Focus of This Research --- p.7 / Chapter 1.5 --- Organization of This Thesis --- p.8 / Chapter 2 --- Audio-Visual Bi-modal Authentication --- p.10 / Chapter 2.1 --- Audio-visual Authentication System --- p.10 / Chapter 2.1.1 --- Why Audio and Mouth? --- p.10 / Chapter 2.1.2 --- System Overview --- p.11 / Chapter 2.2 --- XM2VTS Database --- p.12 / Chapter 2.3 --- Visual Feature Extraction --- p.14 / Chapter 2.3.1 --- Locating the Mouth --- p.14 / Chapter 2.3.2 --- Averaged Mouth Images --- p.17 / Chapter 2.3.3 --- Averaged Optical Flow Images --- p.21 / Chapter 2.4 --- Audio Features --- p.23 / Chapter 2.5 --- Video Stream Classification --- p.23 / Chapter 2.6 --- Audio Stream Classification --- p.25 / Chapter 2.7 --- Simple Fusion --- p.26 / Chapter 3 --- Weighted Sum Rules for Multi-modal Fusion --- p.27 / Chapter 3.1 --- Measurement-Level Fusion --- p.27 / Chapter 3.2 --- Product Rule and Sum Rule --- p.28 / Chapter 3.2.1 --- Product Rule --- p.28 / Chapter 3.2.2 --- Naive Sum Rule (NS) --- p.29 / Chapter 3.2.3 --- Linear Weighted Sum Rule (WS) --- p.30 / Chapter 3.3 --- Optimal Weights Selection for WS --- p.31 / Chapter 3.3.1 --- Independent Case --- p.31 / Chapter 3.3.2 --- Identical Case --- p.33 / Chapter 3.4 --- Confidence Measure Based Fusion Weights --- p.35 / Chapter 4 --- Regularized k-Nearest Neighbor Classifier --- p.39 / Chapter 4.1 --- Motivations --- p.39 / Chapter 4.1.1 --- Conventional k-NN Classifier --- p.39 / Chapter 4.1.2 --- Bayesian Formulation of kNN --- p.40 / Chapter 4.1.3 --- Pitfalls and Drawbacks of kNN Classifiers --- p.41 / Chapter 4.1.4 --- Metric Learning Methods --- p.43 / Chapter 4.2 --- Regularized k-Nearest Neighbor Classifier --- p.46 / Chapter 4.2.1 --- Metric or Not Metric? --- p.46 / Chapter 4.2.2 --- Proposed Classifier: RkNN --- p.47 / Chapter 4.2.3 --- Hyperkernels and Hyper-RKHS --- p.49 / Chapter 4.2.4 --- Convex Optimization of RkNN --- p.52 / Chapter 4.2.5 --- Hyper kernel Construction --- p.53 / Chapter 4.2.6 --- Speeding up RkNN --- p.56 / Chapter 4.3 --- Experimental Evaluation --- p.57 / Chapter 4.3.1 --- Synthetic Data Sets --- p.57 / Chapter 4.3.2 --- Benchmark Data Sets --- p.64 / Chapter 5 --- Audio-Visual Authentication Experiments --- p.68 / Chapter 5.1 --- Effectiveness of Visual Features --- p.68 / Chapter 5.2 --- Performance of Simple Sum Rule --- p.71 / Chapter 5.3 --- Performances of Individual Modalities --- p.73 / Chapter 5.4 --- Identification Tasks Using Confidence-based Weighted Sum Rule --- p.74 / Chapter 5.4.1 --- Effectiveness of WS_M_C Rule --- p.75 / Chapter 5.4.2 --- WS_M_C v.s. WS_M --- p.76 / Chapter 5.5 --- Speaker Identification Using RkNN --- p.77 / Chapter 6 --- Conclusions and Future Work --- p.78 / Chapter 6.1 --- Conclusions --- p.78 / Chapter 6.2 --- Important Follow-up Works --- p.80 / Bibliography --- p.81 / Chapter A --- Proof of Proposition 3.1 --- p.90 / Chapter B --- Proof of Proposition 3.2 --- p.93

No description available.

In order to perform star-identification with lower processing requirements, multi-dimensional techniques are implemented in this research as a database search as well as to create star pattern parameters. New star pattern parameters are presented which produce a well-distributed database, required by the database search algorithm to achieve the fastest performance. To mitigate problems introduced by the star pattern selection, incorrect entries are added to the database, which reduces the number of iterations of the run-time algorithm. The associated algorithms, star pattern parameters, and database preparation are collectively referred to as Multi-dimensional Star-Identification (Star-ND). The star pattern parameters developed may also be extended to star patterns with an arbitrarily large number of stars, while retaining the well-distributed property. The algorithm is contrasted with the current state-of-the-art star-ID algorithm, Pyramid. The database is found to grow linearly with the size of the star catalog, while Pyramid's database grows quadratically. The running time of Star-ND is found to be on average a factor of 25 times faster than the time for Pyramid.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Yun, Xiaoping. Second Reader: Julian, Alex. "December 2009." Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: System identification, state-space, pole placement, full state feedback, observer. Includes bibliographical references (p. 91). Also available in print.

published_or_final_version / Microbiology / Master / Master of Philosophy

published_or_final_version / Ecology and Biodiversity / Doctoral / Doctor of Philosophy

Thesis (MTech. degree in Policing) -- Tshwane University of Technology, 2010. / Abstract to follow

Unmanned Aerial Vehicles (UAVs) are used in many fields such as construction surveying and farming. These aircraft are typically less than 75kg and can be as light as 1kg. Because of the small size of the aircraft, and the increasing interest in this field, research is conducted to both accurately simulate these aircraft and identify their aerodynamic parameters by means of a low-cost and easy to implement methods. The research presented in this thesis describes the development of a comprehensive UAV simulation. This simulation is aimed at developing and testing different system identification techniques on the longitudinal mode of the aircraft. Two identification methods are tested on the simulation. These identification techniques are then examined on flight test data of an actual aircraft. The methods work well on the simulation results; however, flight test were performed in unfavorable environmental conditions and did produced any useful results.

The goal of this thesis is to identify airplanes’ reduced order transfer functions, and aerodynamic derivatives in the longitudinal channel. The outcome of the research will benefit aircraft systems’ controller design, modeling and simulation. To identify the system transfer functions and aerodynamic derivatives, direct and indirect frequency domain identification methods are applied. For the direct method, the Equation Error (EE) method is adopted to process the Cropcam’s input-output data pairs and identify the aerodynamic derivatives from the flight data directly. The indirect approach is called the Transfer Function (TF) method. The derivatives identified by the EE method and transfer function method are compared with the ones computed from a Vortex Lattice based program called AVL. The identification results are further verified by comparing computer simulation outputs with flight test responses.

Addendum pasted onto verso of back end paper. / Copy of author's previously published article inserted. / Bibliography: leaves 220-236. / xiv, 256, [35] leaves of plates : ill., maps ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This study focused on phylogenetic relationships among genera of Baeini and taxonomy and relationships of Ceratobaeus Ashmead, the largest genus in Australasia. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied and Molecular Ecology, 1999