The backpropagation method of image reconstruction has been known for some time with the advantage of fast processing due to the use of Fast Fourier Transform. But its applicability to underwater imaging has been limited. At present the shift-and-add method is the more widely used method in underwater imaging. This is due to the fact that backpropagation has been derived for plane wave insonification, with the scattered waves detected in transmission-mode, or synthetic aperture set-up. One of the methods being used for underwater imaging is to use a point source for the insonification of the target and the scattered waves detected in reflection-mode by a receiver array. An advantage of this scanning method is only one transmission of the source is required to capture an image, instead of multiple transmissions. Therefore motion artifacts are kept to minimum. To be able to exploit the processing speed of the backpropagation method, it must be adapted for point source insonification. The coverage of this configuration in the literature has been scant, methods for spherical sources have been proposed for transmission mode and arbitrary surfaces in geophysical applications. These methods are complex and difficult to use. A novel point source compensation method is proposed in this thesis so that the backpropagation image formation method can be used for the point source insonification set-up. The method of investigation undertaken to derive this new backpropagation method was through theoretical analysis, numerical simulation and experimental verification. The effect of various compensation factors on the image quality was studied in simulation. In the experimental verification, practical issues relating to the application of the new method was addressed. The final proof of concept of our method was undertaken with our experimental verification. The quality of images formed with the point source compensation methods has also been compared with that with the shiftand- add method. Experimental and simulation results show that the point source compensated backpropagation algorithm can produce images of comparable quality with those formed with shift-and-add method for the set-up of wideband point-source insonification with detection in reflection-mode, with the advantage of faster image formation.

Thesis (Ph. D.)--University of New South Wales, 2003. / Also available online.

Thesis (M.E.) -- University of Adelaide, Department of Mechanical Engineering, 1977.

Thesis--Lund Institute of Technology, 1993.

Thesis (M.S.)--University of Wisconsin--Madison, 1969. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis--Lund Institute of Technology, 1993.

Thesis (M.S. in Physical Oceanography) Naval Postgraduate School, September 1999. / "September 1999". Thesis advisor(s): Robert H. Bourke, James H. Wilson. Includes bibliographical references (p. 107-110). Also Available online.

Thesis--University of Florida. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 150-155).

Thesis--Wisconsin. / Includes bibliographical references (leaves 197-198).

The work presented in this thesis forms part of a research project which attempts to generate a visualisation of a speaker's mouth from purely acoustic speech signals. The aim is to provide an aid for partially hearing impaired people in which visual information is presented alongside limited acoustic signals, facilitating easier use of the telephone. The system is essentially a low-level speech recogniser in which phonemic information is extracted from the speech waveform and mapped onto visemes generated on a synthetic facial image. This thesis presents a description of a major part of this project, that is, the development of an accurate phoneme discriminator which is capable of speaker independent operation, on continuous speech. The recognition process is realised in three stages: a pre-processor to convert the speech into a suitable parametric form; a pattern recogniser to identify the possible phoneme classes and a post-processor to produce the viseme information. The pattern recognition stage uses a self-organising Kohonen network, followed by a Learning Vector Quantiser (LVQ) to further improve the recognition accuracy. The performance of this stage is highly dependent on the choice of pre-processor used at the input to the network and it is the design of the pre-processor stage that forms a significant part of this work. A novel technique known as the pseudo-cepstrum forms the basis of this pre-processor. Extensive investigations have been conducted into the dependence of performance on a range of parameters, both at the pre-processor stage and within the Kohonen classifier. In particular, a performance comparison of several preprocessor techniques, including the pseudo-cepstrum, has been carried out. Factors affecting both the training and operation of the classifier are also described here, with the sensitivity of recognition performance to the input data, being a major issue. Overall recognition accuracies of 80% have been achieved.