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

No description available.

Bibliography: p. 235-243. / xxvi, 243 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Concerned with the mathematical characterisation and adaptive processing of narrowband high frequency signals received by a very wide aperture antenna array after reflection from the ionosphere. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 2000

"November 2004" / Includes bibliographical references (leaves 283-293) / xxix, 293 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, School of Electrical and Electronic Engineering, 2004

University of Macau / Faculty of Science and Technology / Department of Mathematics

No description available.

University of Macau / Faculty of Science and Technology / Department of Mathematics

Ocean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods that automatically detect and classify vocalization patterns of marine mammals. The first work performed is the classification of bottlenose dolphin calls by type. The extraction of salient and distinguishing features from recordings is a major part of this endeavor. To this end, two strategies are evaluated with real datasets provided by Woods Hole Oceanographic Institution: The first strategy is to use contour-based features such as Time-Frequency Parameters and Fourier Descriptors and the second is to employ texture-based features such as Local Binary Patterns (LBP) and Gabor Wavelets. Once dolphin whistle features are extracted for spectrograms, selection of classification procedures is crucial to the success of the process. For this purpose, the performances of classifiers such as K-Nearest Neighbor, Support Vector Machine, and Sparse Representation Classifier (SRC) are assessed thoroughly, together with those of the underlined feature extractors. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

In the first part of this thesis, the information capacity of time-varying fading channels is analysed using finite-state Markov channel (FSMC) models. Both fading channel amplitude and fading channel phase are modelled as finite-state Markov processes. The effect of the number of fading channel gain partitions on the capacity is studied (from 2 to 128 partitions). It is observed that the FSMC capacity is saturated when the number of fading channel gain partitions is larger than 4 to 8 times the number of channel input levels. The rapid FSMC capacity saturation with a small number of fading channel gain partitions can be used for the design of computationally simple receivers, with a negligible loss in the capacity. Furthermore, the effect of fading channel memory order on the capacity is studied (from first- to fourth-order). It is observed that low-order FSMC models can provide higher capacity estimates for fading channels than high-order FSMC models, especially when channel states are poorly observable in the presence of channel noise. To explain the effect of memory order on the FSMC capacity, the capacities of high-order and low-order FSMC models are analytically compared. It is shown that the capacity difference is caused by two factors: 1) the channel entropy difference, and 2) the channel observability difference between the high-order and low-order FSMC models. Due to the existence of the second factor, the capacity of high-order FSMC models can be lower than the capacity of low-order FSMC models. Two sufficient conditions are proven to predict when the low-order FSMC capacity is higher or lower than the high-order FSMC capacity. In the second part of this thesis, a new implicit (blind) channel estimation method in time- varying fading channels is proposed. The information source emits bits ???0??? and ???1??? with unequal probabilities. The unbalanced source distribution is used as a priori known signal structure at the receiver for channel estimation. Compared to pilot-symbol-assisted channel estimation, the proposed channel estimation technique can achieve a superior receiver bit error rate performance, especially at low signal to noise ratio conditions.

In the first part of this thesis, the information capacity of time-varying fading channels is analysed using finite-state Markov channel (FSMC) models. Both fading channel amplitude and fading channel phase are modelled as finite-state Markov processes. The effect of the number of fading channel gain partitions on the capacity is studied (from 2 to 128 partitions). It is observed that the FSMC capacity is saturated when the number of fading channel gain partitions is larger than 4 to 8 times the number of channel input levels. The rapid FSMC capacity saturation with a small number of fading channel gain partitions can be used for the design of computationally simple receivers, with a negligible loss in the capacity. Furthermore, the effect of fading channel memory order on the capacity is studied (from first- to fourth-order). It is observed that low-order FSMC models can provide higher capacity estimates for fading channels than high-order FSMC models, especially when channel states are poorly observable in the presence of channel noise. To explain the effect of memory order on the FSMC capacity, the capacities of high-order and low-order FSMC models are analytically compared. It is shown that the capacity difference is caused by two factors: 1) the channel entropy difference, and 2) the channel observability difference between the high-order and low-order FSMC models. Due to the existence of the second factor, the capacity of high-order FSMC models can be lower than the capacity of low-order FSMC models. Two sufficient conditions are proven to predict when the low-order FSMC capacity is higher or lower than the high-order FSMC capacity. In the second part of this thesis, a new implicit (blind) channel estimation method in time- varying fading channels is proposed. The information source emits bits ???0??? and ???1??? with unequal probabilities. The unbalanced source distribution is used as a priori known signal structure at the receiver for channel estimation. Compared to pilot-symbol-assisted channel estimation, the proposed channel estimation technique can achieve a superior receiver bit error rate performance, especially at low signal to noise ratio conditions.