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The main objective of this thesis is to demonstrate the application of the accelerated gradient techniques to various fields of adaptive signal processing. A variety of adaptive algorithms based on the accelerated gradient techniques are developed and analysed in terms of the convergence speed, computational complexity and numerical stability. Extensive simulation results are presented to demonstrate the performance of the proposed algorithms when applied to the fields of adaptive noise cancelling, broad band adaptive array processing and narrow band adaptive spectral estimation. These results are very encouraging in terms of convergence speed and numerical stability of the developed algorithms. The proposed algorithms appear to be attractive alternatives to the conventional recursive least squares algorithms. In addition, the thesis includes a review chapter in which the conventional approaches (ranging from the least mean squares algorithm to the computationally demanding recursive least squares algorithm) to three types of minimization problems (namely unconstrained, linearly constrained and quadratically constrained) are discussed.

Graduate

Graduate

Graduate

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The past two decades have witnessed a rapid expansion of the telecommunications industry. This growth has been primarily fuelled by the proliferation of the digital communication systems and services which have become easily available through wired and wireless networks. Current research trends involving integration and packetisation of voice, video and data channels into true multimedia communications, promise a similar technological revolution in the next decade. The available bandwidth in wire based terrestrial network is a relatively cheap and expandable resource. However in satellite and cellular radio systems the bandwidth is inherently limited and an expensive resource. In order to accommodate ever growing numbers of subscribers whilst maintaining high quality and low operational costs, it is essential to maximise the spectral efficiency. The research presented in this thesis has focused on the development of new source compression algorithms, tailored for human speech in order to improve the spectral efficiency of digital transmission systems. Recently there is an increasing interest on speech coding algorithms which combine various existing technologies in order to improve the speech quality .whilst maintaining the low transmission rate of the existing coding techniques. The aim of the research presented in this thesis was to develop a complete hybrid coding algorithm which combines harmonic and waveform approximating coding techniques. In order to integrate the two coding paradigms novel phase synchronisation and classification techniques were developed. The perceptual quality of the speech synthesised using the unquantised hybrid model achieves nearly transparent quality. The hybrid model was used to develop variable bit rate coders, which are particularly advantageous for voice storage, Code Division Multiple Access (CDMA) wireless networks, packet switched networks, and statistical multiplexing of speech for multi channel communications.

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