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This thesis presents the results of a survey of 113 parents of preschool deaf children living in England and Wales. The sample included 18 deaf parents and 115 hearing parents from all walks of life. All the families had been exposed to parent guidance based on the importance of amplified speech in the education of the deaf. The Lewis Report(1968) recommended that a survey of communication used by parents and very young deaf children should be done. The survey aimed to provide information about modes of communication used by parents and very young deaf children. The data was gathered by systematic interviews, tape recordings and direct observations in people's homes. The results support the hypothesis that a mixture of verbal and nonverbal procedures are typical in such families. 68% of hearing parents used a mixture of pointing, showing objects, esoteric gestures and short simple sentences with the children. Child-parent communication was dominated by physical contact, esoteric gestures and pointing. The data suggest as negative association between speaking and use of natural gestures. 65% of the children did not develop connected speech in spite of much exposure to amplified speech. Parent guidance was evaluated in terms of support for parents, information for parents, direct teaching of parents and child teaching. 57% of mothers and 70% of fathers asked for more child teaching rather than more Information or support for themselves. Hearing parents reported difficulties in holding the child's attention (67%) communication problems (52%) child uncooperative (45%) when attempting to apply guidance principles with their own children. Information from the deaf parents suggests that such problems are greatly reduced by use of a shared sign language in such families. Several ways of improving parent guidance are discussed in the light of these findings.

As a complement to the philosophy of language, the cybernetics of language-is to synthesise a picture of language as a whole; and runs into-(descriptive) difficulties where (at any one time) we can only speak about bounded portions of the world (Wittgenstein). This same difficulty permeates the short history of cybernetics in the concern for wholistic representation, and thus the concern of the cybernetics of language leads to (or arises in) the concern for the language of cybernetics. It becomes resolvable in the context of Second order cybernetics (i.e. the cybernetics of' describing as well as described systems (von Foerster)). The difficulty and the possibility of its resolution are introduced in terms of differences between Russell and Wittgenstein; in terms of the second order cybernetic discussions of the black box (seen as capturing Wittgenstein's silence and, in general, interpretation) and distinctions (G. S. Brown); and in terms of the distinction between natural and artificial languages and the problem of describing description (self-reference). Here the cybernetics of language concerns the nature of inquiry into our descriptive abilities and activities, and determines what we can and what we cannot (objectively) speak about. The notions of 'the function of language' and 'the existence of language' (presupposed in a first order description) are shown to be mutually interdependent, giving rise to a paradox of means (and giving rise to the question of the 'origin of language'). This paradox is resolved where a language is seen as constructed (for a particular purpose), and thus the circularity is unfolded, considering that (i) in terms of a constructive function of language, there is no language (something is in the process of being constructed); (ii) in terms of a communicative function of language, such a construction is in the process of being accepted (something is being negotiated); (iii) in terms of an argumentative-function of language, a language (accepted, eg. having, been negotiated) is used to negotiate things distinct from-this language. Language is seen as comprising the interaction between these activities. The cybernetics of language is developed in terms of the requirements for an observer to construct, communicate and argue: a language is constructed for the description of these processes in terms of the; complementarity between description and interpretation (underlying the process of construction) and the complementarity between saying and doing (enabling an observer to explore, eg. question, test and explain his construction and distinguish another observer; and enabling two or more observers to negotiate and accept relations and argue by distinguishing both a language and the things this is used to describe).

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The original idea underlying this thesis was to show whether and to what extent it is feasible to use a surgical C-arm for volume reconstruction in the field of medical imaging. Surgical C-arms are widely used as intra-operational imaging tools, for instance for navigation or verification. They provide the possibility to easily obtain X-ray images of a patient from an arbitrary viewing angle. These features make them a promising candidate for the use in volume reconstruction, since this requires the recording of a number of images from many directions. In the last years, several approaches with different kinds of C-arms have been undertaken by different research groups or companies. Since the three-dimensional imaging of patients requires an accurate knowledge of the projection geometry, the larger, more rigid, and motorised vascular G-arm systems have been the gantries of choice. These C-arms are usually heavy and ceiling-mounted. They require a large room to be placed and moved in. Surgical C-arms, on the other hand, are less stable, but movable and flexible. The feasibility of using a movable surgical C-arm for volume reconstruction of its linear attenuation coefficients will be evaluated in this thesis. To this end, system calibration techniques, reconstruction methods, visualisation techniques, and possible medical applications have been explored. It will be shown, which extend of calibration is necessary for a surgical C- arm to provide the quality of data needed for volume reconstruction. The reconstructions are performed on the basis of a standard algorithm due to Feldkamp et al. Results are presented as maximum-intensity-projections (MIPs), volume slices as in classic CT, and volume-rendered images of the reconstructed volumes. These images could be used for the diagnosis of bone-related injuries or diseases, the planning and/or verification of the placement of pins supporting the recovery of splintered bones or the modelling of prostheses with a higher precision than today. The work includes the development of new interpolative calibration methods that enable the use of arbitrary system trajectories in future C-arm gantries.

Our interdisciplinary research focuses on the application of connectionist modelling techniques to the study of language disorders. In recent years, artificial neural network models of aphasia have enabled cognitive neuropsychologists to explore contemporary theories of language processing. Such work may, in the future, lead to the development of innovative strategies for the rehabilitation of brain-damaged patients. The aim of our work has been to analyse the modelling techniques employed in existing connectionist accounts of language disorders, and, on the basis of our findings, to propose novel and computationally well-grounded architectures which may be used to explore cognitive neuropsychological theories. The majority of connectionist language disorder models reported in the literature may be categorised as network-level models, consisting of a single homogeneous structure built from identical processing elements. We believe that in order to simulate more fully the complexity of human language processing, it may be necessary to move away from this approach, in favour of nervous system-level models, in which a number of network-level models are interconnected to form a modular connectionist architecture. The suitability of these architectures for language disorder simulation has been assessed through the construction of LISA: a Language Impairment Simulation Architecture. LISA comprises a number of linked connectionist networks which have been collectively trained to simulate object naming and word repetition. By lesioning one or more components of our modular system, it is possible to simulate the impaired language production of an aphasic patient. We present our attempts to simulate an acquired disorder of repetition, deep dysphasia, and a progressive disorder, semantic dementia, using LISA. The results of our experiments are encouraging, and lead us to conclude that the cognitive neuropsychology community may indeed benefit from the use of modular connectionist architectures in the simulation of both progressive and acquired language disorders.

This thesis covers some of the most advanced theories in the computer analysis of skeletal structures. Linear and nonlinear finite element formulations for skeletal structures are presented. Solution algorithms for linear and nonlinear finite element equilibrium equations are described. A general direct/iterative procedure is proposed for structural analysis. In this new direct/iterative procedure, displacements are divided into a number of groups. The total equilibrium equations are split into the same number of subsystems. These subsystems are solved directly or iteratively. These solutions are further included in a main iterative procedure. A variety of available iterative (indirect) methods in the structural analysis can be related to this general procedure. New direct/iterative procedures are proposed for the linear and nonlinear analysis of frameworks by choosing different displacement groups and corresponding approximate stiffness matrices. Using these techniques, the storage requirement for the analysis of space frames can be reduced to 1/4 for linear analysis and 1/2 for nonlinear analysis. Processor time savings can be achieved for large frameworks, especially when accelerations are used.

Neural network applications can generally be divided into two categories. The first involves function approximation, where the neural network is trained to perform intelligent interpolation and curve fitting from the training data. The second category involves classification, where specific exemplar classes are used to train the neural network. This thesis directs its investigations towards the latter, i.e. classification. Most existing neural network models are developments that arise directly from human cognition research. It is felt that while neural network research should head towards the development of models that resemble the cognitive system of the brain, researchers should not abandon the search for useful task oriented neural networks. These may not possess the intricacies of human cognition, but are efficient in solving industrial classification tasks. It is the objective of this thesis to develop a neural network that is fast learning, able to generalise and achieve good capacity to discern different patterns even though some patterns may be similar in structure. This eventual neural network will be used in the pattern classification environment. The first model developed, was the result of studying and modifying the basic ART I model. The "Fast Learning Artificial Neural Network I" (FLANN I) maintains good generalisation properties and is progressive in learning. Although this neural network achieves fast learning speeds of one epoch, it was limited only to binary inputs and was unable to operate on continuous values. This posed a real problem because industrial applications usually require the manipulation of continuous values. The second model, FLANN II, was designed based on the principles of FLANN I. It was built on the nearest neighbour recall principle, which allowed the network to operate On continuous values. Experiments were conducted on the two models designed and the results were favourable. FLANN II was able to learn the points in a single epoch and obtain exceptional accuracy. This is a significant improvement to other researcher's results. A further study was conducted on the FLANN models in the parallel processing environment. The parallel investigations led to the development of a new paradigm; Parallel Distributed Neural Networks (PDNNs), which allows several neural networks to operate concurrently to solve a single classification problem. This paradigm is powerful because it is able to reduce the overall memory requirements for some classification problems.

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