Thesis (Ph.D.)-- University of Adelaide, Dept. of Electrical and Electronic Engineering, 2000. / Bibliography: p. 235-243.

by Chi-Chiu Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 124-131). / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Blind Signal Separation Problem --- p.1 / Chapter 1.2 --- Contributions of this Thesis --- p.3 / Chapter 1.3 --- Applications of the Problem --- p.4 / Chapter 1.4 --- Organization of the Thesis --- p.5 / Chapter 2 --- The Blind Signal Separation Problem --- p.7 / Chapter 2.1 --- The General Blind Signal Separation Problem --- p.7 / Chapter 2.2 --- Convolutive Linear Mixing Process --- p.8 / Chapter 2.3 --- Instantaneous Linear Mixing Process --- p.9 / Chapter 2.4 --- Problem Definition and Assumptions in this Thesis --- p.9 / Chapter 3 --- Literature Review --- p.13 / Chapter 3.1 --- Previous Works on Blind Signal Separation with Instantaneous Mixture --- p.13 / Chapter 3.1.1 --- Algebraic Approaches --- p.14 / Chapter 3.1.2 --- Neural approaches --- p.15 / Chapter 3.2 --- Previous Works on Blind Signal Separation with Convolutive Mixture --- p.20 / Chapter 4 --- The Information-theoretic ICA Scheme --- p.22 / Chapter 4.1 --- The Bayesian YING-YANG Learning Scheme --- p.22 / Chapter 4.2 --- The Information-theoretic ICA Scheme --- p.25 / Chapter 4.2.1 --- Derivation of the cost function from YING-YANG Machine --- p.25 / Chapter 4.2.2 --- Connections to previous information-theoretic approaches --- p.26 / Chapter 4.2.3 --- Derivation of the Algorithms --- p.27 / Chapter 4.2.4 --- Roles and Constraints on the Nonlinearities --- p.30 / Chapter 4.3 --- Direction and Motivation for the Analysis of the Nonlinearity --- p.30 / Chapter 5 --- Properties of the Cost Function and the Algorithms --- p.32 / Chapter 5.1 --- Lemmas and Corollaries --- p.32 / Chapter 5.1.1 --- Singularity of J(V) --- p.33 / Chapter 5.1.2 --- Continuity of J(V) --- p.34 / Chapter 5.1.3 --- Behavior of J(V) along a radially outward line --- p.35 / Chapter 5.1.4 --- Impossibility of divergence of the information-theoretic ICA al- gorithms with a large class of nonlinearities --- p.36 / Chapter 5.1.5 --- Number and stability of correct solutions in the 2-channel case --- p.37 / Chapter 5.1.6 --- Scale for the equilibrium points --- p.39 / Chapter 5.1.7 --- Absence of local maximum of J(V) --- p.43 / Chapter 6 --- The Algorithms with Cubic Nonlinearity --- p.44 / Chapter 6.1 --- The Cubic Nonlinearity --- p.44 / Chapter 6.2 --- Theoretical Results on the 2-Channel Case --- p.46 / Chapter 6.2.1 --- Equilibrium points --- p.46 / Chapter 6.2.2 --- Stability of the equilibrium points --- p.49 / Chapter 6.2.3 --- An alternative proof for the stability of the equilibrium points --- p.50 / Chapter 6.2.4 --- Convergence Analysis --- p.52 / Chapter 6.3 --- Experiments on the 2-Channel Case --- p.53 / Chapter 6.3.1 --- Experiments on two sub-Gaussian sources --- p.54 / Chapter 6.3.2 --- Experiments on two super-Gaussian sources --- p.55 / Chapter 6.3.3 --- Experiments on one super-Gaussian source and one sub-Gaussian source which are globally sub-Gaussian --- p.57 / Chapter 6.3.4 --- Experiments on one super-Gaussian source and one sub-Gaussian source which are globally super-Gaussian --- p.59 / Chapter 6.3.5 --- Experiments on asymmetric exponentially distributed signals .。 --- p.60 / Chapter 6.3.6 --- Demonstration on exactly and nearly singular initial points --- p.61 / Chapter 6.4 --- Theoretical Results on the 3-Channel Case --- p.63 / Chapter 6.4.1 --- Equilibrium points --- p.63 / Chapter 6.4.2 --- Stability --- p.66 / Chapter 6.5 --- Experiments on the 3-Channel Case --- p.66 / Chapter 6.5.1 --- Experiments on three pairwise globally sub-Gaussian sources --- p.67 / Chapter 6.5.2 --- Experiments on three sources consisting of globally sub-Gaussian and globally super-Gaussian pairs --- p.67 / Chapter 6.5.3 --- Experiments on three pairwise globally super-Gaussian sources --- p.69 / Chapter 7 --- Nonlinearity and Separation Capability --- p.71 / Chapter 7.1 --- Theoretical Argument --- p.71 / Chapter 7.1.1 --- Nonlinearities that strictly match the source distribution --- p.72 / Chapter 7.1.2 --- Nonlinearities that loosely match the source distribution --- p.72 / Chapter 7.2 --- Experiment Verification --- p.76 / Chapter 7.2.1 --- Experiments on reversed sigmoid --- p.76 / Chapter 7.2.2 --- Experiments on the cubic root nonlinearity --- p.77 / Chapter 7.2.3 --- Experimental verification of Theorem 2 --- p.77 / Chapter 7.2.4 --- Experiments on the MMI algorithm --- p.78 / Chapter 8 --- Implementation with Mixture of Densities --- p.80 / Chapter 8.1 --- Implementation of the Information-theoretic ICA scheme with Mixture of Densities --- p.80 / Chapter 8.1.1 --- The mixture of densities --- p.81 / Chapter 8.1.2 --- Derivation of the algorithms --- p.82 / Chapter 8.2 --- Experimental Verification on the Nonlinearity Adaptation --- p.84 / Chapter 8.2.1 --- Experiment 1: Two channels of sub-Gaussian sources --- p.84 / Chapter 8.2.2 --- Experiment 2: Two channels of super-Gaussian sources --- p.85 / Chapter 8.2.3 --- Experiment 3: Three channels of different signals --- p.89 / Chapter 8.3 --- Seeking the Simplest Workable Mixtures of Densities ......... .。 --- p.91 / Chapter 8.3.1 --- Number of components --- p.91 / Chapter 8.3.2 --- Mixture of two densities with only biases changeable --- p.93 / Chapter 9 --- ICA with Non-Kullback Cost Function --- p.97 / Chapter 9.1 --- Derivation of ICA Algorithms from Non-Kullback Separation Functionals --- p.97 / Chapter 9.1.1 --- Positive Convex Divergence --- p.97 / Chapter 9.1.2 --- Lp Divergence --- p.100 / Chapter 9.1.3 --- De-correlation Index --- p.102 / Chapter 9.2 --- Experiments on the ICA Algorithm Based on Positive Convex Divergence --- p.103 / Chapter 9.2.1 --- Experiments on the algorithm with fixed nonlinearities --- p.103 / Chapter 9.2.2 --- Experiments on the algorithm with mixture of densities --- p.106 / Chapter 10 --- Conclusions --- p.107 / Chapter A --- Proof for Stability of the Equilibrium Points of the Algorithm with Cubic Nonlinearity on Two Channels of Signals --- p.110 / Chapter A.1 --- Stability of Solution Group A --- p.110 / Chapter A.2 --- Stability of Solution Group B --- p.111 / Chapter B --- Proof for Stability of the Equilibrium Points of the Algorithm with Cubic Nonlinearity on Three Channels of Signals --- p.119 / Chapter C --- Proof for Theorem2 --- p.122 / Bibliography --- p.124

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically.

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations.

The intense requirements for high processing rates of multidimensional Digital Signal Processing systems in practical applications justify the Application Specific Integrated Circuits designs and parallel processing implementations. In this dissertation, we propose novel theories, methodologies and architectures in designing high-performance VLSI implementations for general multidimensional multirate Digital Signal Processing systems by exploiting the parallelism within those applications. To systematically exploit the parallelism within the multidimensional multirate DSP algorithms, we develop novel transformations including (1) nonlinear I/O data space transforms, (2) intercalation transforms, and (3) multidimensional multirate unfolding transforms. These transformations are applied to the algorithms leading to systematic methodologies in high-performance architectural designs. With the novel design methodologies, we develop several architectures with parallel and distributed processing features for implementing multidimensional multirate applications. Experimental results have shown that those architectures are much more efficient in terms of execution time and/or hardware cost compared with existing hardware implementations.

Thesis (Ph.D.)--University of Wollongong, 2005. / Typescript. Includes bibliographical references: leaf 159-168.

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

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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / Our telemetry department has an application for a data categorization/compression of a high speed transient signal in a short period of time. Categorization of the signal reveals important system performance and compression is required because of the terminal nature of our telemetry testing. Until recently, the hardware for the system of this type did not exist. A new exploratory device from Intel has the capability to meet these extreme requirements. This integrated circuit is an analog neural network capable of performing 2 billion connections per second. The two main advantages of this chip over traditional hardware are the obvious computation speed of the device and the ability to compute a three layer feed-forward neural network classifier. The initial investigative development work using the Intel chip has been completed. The results from this proof of concept will show data categorization/compression performed on the neural network integrated circuit in real time. We will propose a preliminary design for a transient measurement system employing the Intel integrated circuit.

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