There are two components of communication that provide a natural index of mutual-understanding in dialogue. The first is Repair; the ways in which people detect and deal with problems with understanding. The second is Ellipsis/Anaphora; the use of expressions that depend directly on the accessibility of the local context for their interpretation. This thesis explores the use of these two phenomena in systematic comparative analyses of human-human dialogue under different task and media conditions. In order to do this it is necessary to a) develop reliable, valid protocols for coding the different Repair and Ellipsis/Anaphora phenomena b) establish their baseline patterns of distribution in conversation and c) model their basic statistical inter-relationships and their predictive value. Two new protocols for coding Repair and Ellipsis/Anaphora phenomena are presented and applied to two dialogue corpora, one of ordinary 'everyday' conversations and one of task-oriented dialogues. These data illustrate that there are significant differences in how understanding is created and negotiated across conditions. Repair is shown to be a ubiquitous feature in all dialogue. The goals of the speaker directly affect the type of Repair used. Giving instructions leads to a higher rate of self-editing; following instructions increases corrections and requests for clarification. Medium and familiarity also influence Repair; when eye contact is not possible there are a greater number of repeats and clarifications. Anaphora are used less frequently in task-oriented dialogue whereas types of Ellipsis increase. The use of Elliptical phrases that check, confirm or acknowledge is higher when there is no eye contact. Familiar pairs use more elliptical expressions, especially endophora and elliptical questions. Following instructions leads to greater use of elliptical (non-sentential) phrases. Medium, task and social norms all have a measureable effect on the components of dialogue that underpin mutual-understanding.

New services are emerging rapidly within the world of telecommunications. Charging strategies that were appropriate for individual transfer capabilities are no longer appropriate for an integrated broadband communications network. There is currently a range of technologies (such as cable television, telephony and narrow band ISDN) for the different services in use and a limited number of charging schemes are applicable for each of the underlying technologies irrespective of the services used over it. Difficulties arise when a wide range of services has to be supported on the same integrated technology such as asynchronous transfer mode (ATM); in such cases the type of service in use and the impact it has on the network becomes much more important. The subject of this thesis, therefore, is the charging strategies for integrated broadband communications networks. That is, the identification of the requirements associated with ATM charging schemes and the proposal of a new approach to charging for ATM called the “quality of service based charging scheme”. Charging for ATM is influenced by three important components: the type and content of a service being offered; the type of customer using the services; and the traffic characteristics belonging to the application supporting the services. The first two issues will largely be dependent on the business and regulatory requirements of the operators. The last item, and an essential one for ATM, is the bridge between technology and business; how are the resources used by a service quantified? Charging that is based on resource usage at the network level was the prime focus of the research reported here. With the proposed charging scheme, a distinction is first made between the four different ATM transfer capabilities that will support various services and the different quality of service requirements that may be applicable to each of them. Then, resources are distributed among buffers set-up to support the combination of these transfer capabilities and quality of services. The buffers are dimensioned according to the M/D/1/K and the ND/D/1 queuing analysis to determine the buffer efficiency and quality of service requirements. This dimensioning provides the basis for fixing the price per unit of resource and time. The actual resource used by a connection is based on the volume of cells transmitted or peak cell rate allocation in combination with traffic shapers if appropriate. Shapers are also dimensioned using the quality of service parameters. Since the buffer 4 efficiency is dependent on the quality of service requirements, users (customers) of ATM networks buy quality of service. The actual price of a connection is further subjected to a number of transformations based on the size of the resource purchased, the time of the day at which a connection is made, and the geographical locality of the destination switch. It is demonstrated that the proposed charging scheme meets all the requirements of customers and of network operators. In addition the result of the comparison of the new scheme with a number of existing, prominent, ATM charging schemes is presented, showing that the performance of the proposed scheme is better in terms of meeting the expectations of both the customers and the network operators.

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During this PhD study, several finite-difference time-domain (FDTD) methods were developed to numerically investigate coordinate transformation based metamaterial devices. A novel radially-dependent dispersive FDTD algorithm was proposed and applied to simulate electromagnetic cloaking structures. The proposed method can ac- curately model both lossless and lossy cloaks with ideal or reduced parameters. It was demonstrated that perfect “invisibility” from electromagnetic cloaks is only available for lossless metamaterials and within an extremely narrow frequency band. With a few modifications the method is able to simulate general media, such as concentrators and rotation coatings, which are produced by means of coordinate transformations techniques. The limitations of all these devices were thoroughly studied and explo- red. Finally, more useful cloaking structures were proposed, which can operate over a broad frequency spectrum. Several ways to control and manipulate the loss in the electromagnetic cloak ba- sed on transformation electromagnetics were examined. It was found that, by utili- sing inherent electric and magnetic losses of metamaterials, as well as additional lossy materials, perfect wave absorption can be achieved. These new devices demonstrate super-absorptivity over a moderate wideband range, suitable both for microwave and optical applications. Furthermore, a parallel three-dimensional dispersive FDTD method was introdu- ced to model a plasmonic nanolens. The device has its potential in subwavelength imaging at optical frequencies. The finiteness of such a nano-device and its impact on the system dynamic behaviour was numerically exploited. Lastly, a parallel FDTD method was also used to model another interesting coordinate transformation based device, an optical black hole, which can be characterised as an omnidirectional broad- band absorber.

This thesis focuses on applying adaptive system level cooperative coverage to wireless communication networks and proposes an Adaptive Coverage System (ACS) for flexible and resilient radio coverage to handle time-varying geographic traffic distribution, providing better than conventional radio resource allocation results. The ACS utilises antenna arrays called Semi-Smart Antennas to shape cell coverage to alleviate cell overloading and balance loads as equally as possible among cells. Previously work in this field used a Bubble Oscillation Algorithm (BOA) to solve the load balancing problem in the ACS. Modification is made to the core algorithm to enhance its performance for its applications in WCDMA networks, to handle the impact of complex radio propagation environment. The work presented in this thesis then extends into tackling the problem from an optimisation problem point of view. It firstly examines the dynamics of user assignment in a network level and based on these findings, the possibility and approaches towards finding optimal coverage shapes for a mobile network are then presented, including problem formulation, algorithm design, and simulation results. This thesis investigates a system wide cooperative coverage approach to explore the potentials of adaptive coverage systems, has achieved sub-optimal radio resource allocation results, and provides an insight into general network-level radio resource allocation problems and their solutions.

This dissertion explores the relation between two formalisms and one algebraic framework for concurrency. Session Types and Concurrent Separation Logic are formalisms that support independent reasoning about concurrent processes, and our motivating question is whether their modularity springs from the same source despite the distance between their models. We first translate a small language we call Baby Session Types (BST), into a ‘basic’ version of Concurrent Separation Logic (BCSL), and we show that the translation is sound. We then describe a model for Separation Logic (SL) based on Actions, which exhibits some of the structure of a Concurrent Kleene Algebra, an algebra where operators for parallel and sequential composition are linked by a version of the exchange law from category theory. The model connects the algebraic notions to locality concepts that underlie Separation Logic. We then move on to provide a more general construction of an algebra model of BCSL, which can be built from (Baby) Session Types. Thus, we end up with a model that brings together concepts from all of Session Types, Separation Logic, and Concurrent Kleene Algebra. Thus, the model links diverse models of concurrency. In addition to this it suggests alterations of the algebraic axioms as well as the foundational models underlying Separation Logic. It is hoped that, apart from these specific results, this dissertation can in some modest way contribute to unification in concurrency theory, a theory (or theories) based presently on diverse models.

This thesis presents a study of the radiation characteristics of corner reflector antennas. The influence of the design parameters on the radiation characteristics are assessed using an analytical method and the Finite Difference Time Domain (FDTD) method. The FDTD method for corner reflector antennas which are electrically small to medium sized antennas is developed in detail. The important subject of the Absorbing Boundary Conditions (ABCs) is studied including a study of Mur ABC and Perfectly Matched Layers. It is shown that both methods reduce the reflections from the boundaries sufficient so that the far-field radiation pattern can be computed accurately. An analytical solution to compute the far-field radiation pattern for infinite comer reflector antennas is derived and used to understand the radiation mechanisms. Based on those results, the FDTD method is used to conduct a parametric study on finite sized comer reflector antennas. Experimental antennas have been built and measured in order to verify the computational predictions. Very good agreement is reported The novel idea of a variable beam-width comer reflector antenna is developed and practical designs of such an antenna are presented. The principle is to design the comer reflector antenna such that the beam-width of the antenna can be precisely modified. Data on the gain and beam-width are presented. This has been done both by computational and by an experimental model. The influence on the performance of the comer reflector antenna when substituting the solid reflector plates by rods has been investigated. The computational predictions have been verified by measurements of an experimental antenna. Very good agreement has been achieved. III[ The possibility of modifying the shape of the comer reflector antenna is investigated. It is shown that a modified comer reflector antenna with less depth produces the same far-field pattern as a standard corner reflector antenna. It is also shown that the performance of small aperture size corner reflector antennas is superior to a cylindrical parabolic reflector antenna.

The study of music perception lies at the intersection of several disciplines: perceptual psychology and cognitive science, musicology, psychoacoustics, and acoustical signal processing amongst others. Developments in perceptual theory over the last fifty years have emphasised an approach based on Shannon’s information theory and its basis in probabilistic systems, and in particular, the idea that perceptual systems in animals develop through a process of unsupervised learning in response to natural sensory stimulation, whereby the emerging computational structures are well adapted to the statistical structure of natural scenes. In turn, these ideas are being applied to problems in music perception. This thesis is an investigation of the principle of redundancy reduction through unsupervised learning, as applied to representations of sound and music. In the first part, previous work is reviewed, drawing on literature from some of the fields mentioned above, and an argument presented in support of the idea that perception in general and music perception in particular can indeed be accommodated within a framework of unsupervised learning in probabilistic models. In the second part, two related methods are applied to two different low-level representations. Firstly, linear redundancy reduction (Independent Component Analysis) is applied to acoustic waveforms of speech and music. Secondly, the related method of sparse coding is applied to a spectral representation of polyphonic music, which proves to be enough both to recognise that the individual notes are the important structural elements, and to recover a rough transcription of the music. Finally, the concepts of distance and similarity are considered, drawing in ideas about noise, phase invariance, and topological maps. Some ecologically and information theoretically motivated distance measures are suggested, and put in to practice in a novel method, using multidimensional scaling (MDS), for visualising geometrically the dependency structure in a distributed representation.

Recent years have seen an explosion of user-generated, untagged multimedia data, generating a need for efficient search and retrieval of this data. The predominant method for content-based tagging is through manual annotation. Consequently, automatic tagging is currently the subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users' responses to the multimedia content are analysed in order to generate descriptive tags. We approach this problem through the modalities of facial expressions and EEG signals. We investigate tag validation and affective tagging using EEG signals. The former relies on the detection of event-related potentials triggered in response to the presentation of invalid tags alongside multimedia material. We demonstrate significant differences in users' EEG responses for valid versus invalid tags, and present results towards single-trial classification. For affective tagging, we propose methodologies to map EEG signals onto the valence-arousal space and perform both binary classification as well as regression into this space. We apply these methods in a real-time affective recommendation system. We also investigate the analysis of facial expressions for implicit tagging. This relies on a dynamic texture representation using non-rigid registration that we first evaluate on the problem of facial action unit recognition. We present results on well-known datasets (with both posed and spontaneous expressions) comparable to the state of the art in the field. Finally, we present a multi-modal approach that fuses both modalities for affective tagging. We perform classification in the valence-arousal space based on these modalities and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.

The significance of sparse representations has been highlighted in numerous signal processing applications ranging from denoising to source separation and the emerging field of compressed sensing has provided new theoretical insights into the problem of inverse systems with sparsity constraints. In this thesis, these advances are exploited in order to tackle the problem of direction-of-arrival (DOA) estimation in sensor arrays. Assuming spatial sparsity e.g. few sources impinging on the array, the problem of DOA estimation is formulated as a sparse representation problem in an overcomplete basis. The resulting inverse problem can be solved using typical sparse recovery methods based on convex optimization i.e. `1 minimization. However, in this work a suite of novel sparse recovery algorithms is initially developed, which reduce the computational cost and yield approximate solutions. Moreover, the proposed algorithms of Polytope Faces Pursuits (PFP) allow for the induction of structured sparsity models on the signal of interest, which can be quite beneficial when dealing with multi-channel data acquired by sensor arrays, as it further reduces the complexity and provides performance gain under certain conditions. Regarding the DOA estimation problem, experimental results demonstrate that the proposed methods outperform popular subspace based methods such as the multiple signal classification (MUSIC) algorithm in the case of rank-deficient data (e.g. presence of highly correlated sources or limited amount of data) for both narrowband and wideband sources. In the wideband scenario, they can also suppress the undesirable effects of spatial aliasing. However, DOA estimation with sparsity constraints has its limitations. The compressed sensing requirement of incoherent dictionaries for robust recovery sets limits to the resolution capabilities of the proposed method. On the other hand, the unknown parameters are continuous and therefore if the true DOAs do not belong to the predefined discrete set of potential locations the algorithms' performance will degrade due to errors caused by mismatches. To overcome this limitation, an iterative alternating descent algorithm for the problem of off-grid DOA estimation is proposed that alternates between sparse recovery and dictionary update estimates. Simulations clearly illustrate the performance gain of the algorithm over the conventional sparsity approach and other existing off-grid DOA estimation algorithms.