Thesis (M. Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on Sept. 4, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Communications, Department of Electrical and Computer Engineering, University of Alberta." Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 91-100). Also available in electronic version.

Thesis (M.E.)--University of Canterbury, 2006. / Typescript (photocopy). "November 2006." Includes bibliographical references (p. [137]-142). Also available via the World Wide Web.

This research concerns the continuing development of Seaweb underwater networking. In this type of wireless network the radio channel is replaced by an underwater acoustic channel which is strongly dependent on the physical properties of the ocean medium and its boundaries, the link geometry and the ambient noise. Traditional acoustic communications have involved a priori matching of the signaling parameters (e.g., frequency band, source level, modulation type, coding pulse length) to the expected characteristics of the channel. To achieve more robust communications among the nodes of the acoustic network, as well as high quality of service, it is necessary to develop a type of adaptive modulation in the acoustic network. Part of this process involves estimating the channel scattering function in terms of impulse response, the Doppler effects, and the link margin. That is possible with the use of a known probe signal for analyzing the response of the channel. The estimated channel scattering function can indicate the optimum signaling parameters for the link (adaptive modulation). This approach is also effective for time varying channels, including links between mobile nodes, since the channel characteristics can be updated each time we send a probe signal.

The overall objective of this work is to evaluate the ability of homing and docking an unmanned underwater vehicle (Hydroid REMUS 100 UUV) to a moving unmanned surface vehicle (Wave-Adaptive Modular Surface Vehicle USV) using a Hydroid Digital Ultra-Short Baseline (DUSBL) acoustic positioning system (APS), as a primary navigation source. An understanding of how the UUV can rendezvous with a stationary USV first is presented, then followed by a moving USV. Inherently, the DUSBL-APS is susceptible to error due to the physical phenomena of the underwater acoustic channel (e.g. ambient noise, attenuation and ray refraction). The development of an APS model has allowed the authors to forecast the UUV’s position and the estimated track line of the USV as determined by the DUSBL acoustic sensor. In this model, focus is placed on three main elements: 1) the acoustic channel and sound ray refraction when propagating in an in-homogeneous medium; 2) the detection component of an ideal DUSBL-APS using the Neyman-Pearson criterion; 3) the signal-to-noise ratio (SNR) and receiver directivity impact on position estimation. The simulation tool is compared against actual open water homing results in terms of the estimated source position between the simulated and the actual USBL range and bearing information. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

by Hing-cheung So. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 133-141). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Time Delay Estimation (TDE) and its Applications --- p.1 / Chapter 1.2 --- Goal of the Work --- p.6 / Chapter 1.3 --- Thesis Outline --- p.9 / Chapter 2 --- Adaptive Methods for TDE --- p.10 / Chapter 2.1 --- Problem Description --- p.11 / Chapter 2.2 --- The Least Mean Square Time Delay Estimator (LMSTDE) --- p.11 / Chapter 2.2.1 --- Bias and Variance --- p.14 / Chapter 2.2.2 --- Probability of Occurrence of False Peak Weight --- p.16 / Chapter 2.2.3 --- Some Modifications of the LMSTDE --- p.17 / Chapter 2.3 --- The Adaptive Digital Delay-Lock Discriminator (ADDLD) --- p.18 / Chapter 2.4 --- Summary --- p.20 / Chapter 3 --- The Explicit Time Delay Estimator (ETDE) --- p.22 / Chapter 3.1 --- Derivation and Analysis of the ETDE --- p.23 / Chapter 3.1.1 --- The ETDE system --- p.23 / Chapter 3.1.2 --- Performance Surface --- p.26 / Chapter 3.1.3 --- Static Behaviour --- p.28 / Chapter 3.1.4 --- Dynamic Behaviour --- p.30 / Chapter 3.2 --- Performance Comparisons --- p.32 / Chapter 3.2.1 --- With the LMSTDE --- p.32 / Chapter 3.2.2 --- With the CATDE --- p.34 / Chapter 3.2.3 --- With the CRLB --- p.35 / Chapter 3.3 --- Simulation Results --- p.38 / Chapter 3.3.1 --- Corroboration of the ETDE Performance --- p.38 / Chapter 3.3.2 --- Comparative Studies --- p.44 / Chapter 3.4 --- Summary --- p.48 / Chapter 4 --- An Improvement to the ETDE --- p.49 / Chapter 4.1 --- Delay Modeling Error of the ETDE --- p.49 / Chapter 4.2 --- The Explicit Time Delay and Gain Estimator (ETDGE) --- p.52 / Chapter 4.3 --- Performance Analysis --- p.55 / Chapter 4.4 --- Simulation Results --- p.57 / Chapter 4.5 --- Summary --- p.61 / Chapter 5 --- TDE in the Presence of Multipath Propagation --- p.62 / Chapter 5.1 --- The Multipath TDE problem --- p.63 / Chapter 5.2 --- TDE with Multipath Cancellation (MCTDE) --- p.64 / Chapter 5.2.1 --- Structure and Algorithm --- p.64 / Chapter 5.2.2 --- Convergence Dynamics --- p.67 / Chapter 5.2.3 --- The Generalized Multipath Cancellator --- p.70 / Chapter 5.2.4 --- Effects of Additive Noises --- p.73 / Chapter 5.2.5 --- Simulation Results --- p.74 / Chapter 5.3 --- TDE with Multipath Equalization (METDE) --- p.86 / Chapter 5.3.1 --- The Two-Step Algorithm --- p.86 / Chapter 5.3.2 --- Performance of the METDE --- p.89 / Chapter 5.3.3 --- Simulation Results --- p.93 / Chapter 5.4 --- Summary --- p.101 / Chapter 6 --- Conclusions and Suggestions for Future Research --- p.102 / Chapter 6.1 --- Conclusions --- p.102 / Chapter 6.2 --- Suggestions for Future Research --- p.104 / Appendices --- p.106 / Chapter A --- Derivation of (3.20) --- p.106 / Chapter B --- Derivation of (3.29) --- p.110 / Chapter C --- Derivation of (4.14) --- p.111 / Chapter D --- Derivation of (4.15) --- p.113 / Chapter E --- Derivation of (5.21) --- p.115 / Chapter F --- Proof of unstablity of A°(z) --- p.116 / Chapter G --- Derivation of (5.34)-(5.35) --- p.118 / Chapter H --- Derivation of variance of αs11(k) and Δs11(k) --- p.120 / Chapter I --- Derivation of (5.40) --- p.123 / Chapter J --- Derivation of time constant of αΔ11(k) --- p.124 / Chapter K --- Derivation of (5.63)-(5.66) --- p.125 / Chapter L --- Derivation of (5.68)-(5.72) --- p.129 / References --- p.133

Ngan Lai Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 95-100). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Blind Signal Separation (BSS) Methods --- p.4 / Chapter 1.2 --- Objectives of the Thesis --- p.6 / Chapter 1.3 --- Thesis Outline --- p.8 / Chapter 2 --- Blind Adaptive Frequency-Shift (BA-FRESH) Filter --- p.9 / Chapter 2.1 --- Cyclostationarity Properties --- p.10 / Chapter 2.2 --- Frequency-Shift (FRESH) Filter --- p.11 / Chapter 2.3 --- Blind Adaptive FRESH Filter --- p.12 / Chapter 2.4 --- Reduced-Rank BA-FRESH Filter --- p.14 / Chapter 2.4.1 --- CSP Method --- p.14 / Chapter 2.4.2 --- PCA Method --- p.14 / Chapter 2.4.3 --- Appropriate Choice of Rank --- p.14 / Chapter 2.5 --- Signal Extraction of Spectrally Overlapped Signals --- p.16 / Chapter 2.5.1 --- Simulation 1: A Fixed Rank --- p.17 / Chapter 2.5.2 --- Simulation 2: A Variable Rank --- p.18 / Chapter 2.6 --- Signal Separation of Speech Signals --- p.20 / Chapter 2.7 --- Chapter Summary --- p.22 / Chapter 3 --- Reverberant Environment --- p.23 / Chapter 3.1 --- Small Room Acoustics Model --- p.23 / Chapter 3.2 --- Effects of Reverberation to Speech Recognition --- p.27 / Chapter 3.2.1 --- Short Impulse Response --- p.27 / Chapter 3.2.2 --- Small Room Impulse Response Modelled by Image Method --- p.32 / Chapter 3.3 --- Chapter Summary --- p.34 / Chapter 4 --- Information Theoretic Approach for Signal Separation --- p.35 / Chapter 4.1 --- Independent Component Analysis (ICA) --- p.35 / Chapter 4.1.1 --- Kullback-Leibler (K-L) Divergence --- p.37 / Chapter 4.2 --- Information Maximization (Infomax) --- p.39 / Chapter 4.2.1 --- Stochastic Gradient Descent and Stability Problem --- p.41 / Chapter 4.2.2 --- Infomax and ICA --- p.41 / Chapter 4.2.3 --- Infomax and Maximum Likelihood --- p.42 / Chapter 4.3 --- Signal Separation by Infomax --- p.43 / Chapter 4.4 --- Chapter Summary --- p.45 / Chapter 5 --- Blind Signal Separation (BSS) in Frequency Domain --- p.47 / Chapter 5.1 --- Convolutive Mixing System --- p.48 / Chapter 5.2 --- Infomax in Frequency Domain --- p.52 / Chapter 5.3 --- Adaptation Algorithms --- p.54 / Chapter 5.3.1 --- Standard Gradient Method --- p.54 / Chapter 5.3.2 --- Natural Gradient Method --- p.55 / Chapter 5.3.3 --- Convergence Performance --- p.56 / Chapter 5.4 --- Subband Adaptation --- p.57 / Chapter 5.5 --- Energy Weighting --- p.59 / Chapter 5.6 --- The Permutation Problem --- p.61 / Chapter 5.7 --- Performance Evaluation --- p.63 / Chapter 5.7.1 --- De-reverberation Performance Factor --- p.63 / Chapter 5.7.2 --- De-Noise Performance Factor --- p.63 / Chapter 5.7.3 --- Spectral Signal-to-noise Ratio (SNR) --- p.65 / Chapter 5.8 --- Chapter Summary --- p.65 / Chapter 6 --- Simulation Results and Performance Analysis --- p.67 / Chapter 6.1 --- Small Room Acoustics Modelled by Image Method --- p.67 / Chapter 6.2 --- Signal Sources --- p.68 / Chapter 6.2.1 --- Cantonese Speech --- p.69 / Chapter 6.2.2 --- Noise --- p.69 / Chapter 6.3 --- De-Noise and De-Reverberation Performance Analysis --- p.69 / Chapter 6.3.1 --- Speech and White Noise --- p.73 / Chapter 6.3.2 --- Speech and Voice Babble Noise --- p.76 / Chapter 6.3.3 --- Two Female Speeches --- p.79 / Chapter 6.4 --- Recognition Accuracy Performance Analysis --- p.83 / Chapter 6.4.1 --- Speech and White Noise --- p.83 / Chapter 6.4.2 --- Speech and Voice Babble Noise --- p.84 / Chapter 6.4.3 --- Two Cantonese Speeches --- p.85 / Chapter 6.5 --- Chapter Summary --- p.87 / Chapter 7 --- Conclusions and Suggestions for Future Research --- p.88 / Chapter 7.1 --- Conclusions --- p.88 / Chapter 7.2 --- Suggestions for Future Research --- p.91 / Appendices --- p.92 / A The Proof of Stability Conditions for Stochastic Gradient De- scent Algorithm (Ref. (4.15)) --- p.92 / Bibliography --- p.95

Thesis (Ph.D.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 101 pages. Includes vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Central Florida, 2008. / Adviser: Wasfy B. Mikhael. Includes bibliographical references (p. 152-166).

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy