Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Social and epistemological bases of technology transfer: The case of artificial intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis addresses a problem in the literature on technology transfer of understanding the local appropriation of knowledge. Based on interpretive and analytic traditions developed in Science and Technology Studies (STS) and ethnomethodology, I conceptualise technology transfer as involving communication between discursive communities. I develop the idea of 'performance of community' to argue that explanations of research and technology, and readings of those explanations, are sites for the elaboration of the identity of a discursive community. I explore this approach through a case study in the field of artificial intelligence (AI). I focus on what I call 'explanatory practices', that is practices of describing, identifying and explaining Al, and trace the differences in these practices, according to location, context and audience. The novelty of my thesis is to show the pervasiveness of performance of community within these explanatory practices, through showing the differences in the claimed identity and significance of Al, associated with different locations, contexts and audiences. I draw out some of the implications of my approach by counterposing it to a theory of technology transfer as the passing of neutral units of information, which I argue is implicit in a complaint made by Al vendors that the Al marketplace had been damaged by overselling or hype. In particular, I show that disclaimers of hype (more than the perpetration of it) had always been associated with the marketing of Al. More generally, my claim is that it is politically important to understand that neutral information is not available even as an ultimate standard, and that the local appropriation of knowledge is not an aberration to be controlled, but a component of both successful and unsuccessful communication between discursive communities

A digital neural network approach to speech recognition

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents two novel methods for isolated word speech recognition based on sub-word components. A digital neural network is the fundamental processing strategy in both methods. The first design is based on the 'Separate Segmentation & Labelling' (SS&L) approach. The spectral data of the input utterance is first segmented into phoneme-like units which are then time normalised by linear time normalisation. The neural network labels the time-normalised phoneme-like segments 78.36% recognition accuracy is achieved for the phoneme-like unit. In the second design, no time normalisation is required. After segmentation, recognition is performed by classifying the data in a window as it is slid one frame at a time, from the start to the end of of each phoneme-like segment in the utterance. 73.97% recognition accuracy for the phoneme-like unit is achieved in this application. The parameters of the neural net have been optimised for maximum recognition performance. A segmentation strategy using the sum of the difference in filterbank channel energy over successive spectra produced 80.27% correct segmentation of isolated utterances into phoneme-like units. A linguistic processor based on that of Kashyap & Mittal [84] enables 93.11% and 93.49% word recognition accuracy to be achieved for the SS&L and 'Sliding Window' recognisers respectively. The linguistic processor has been redesigned to make it portable so that it can be easily applied to any phoneme based isolated word speech recogniser.This work is funded by the Ministry of Science & Technology, Government of Pakistan