Research into energy harvesting from ambient vibration sources has attracted great interest over the last few years, largely due to the rapid development in the areas of wireless technology and low power electronics. One of the mechanisms for converting mechanical vibration to electrical energy is the use of piezoelectric materials, typically operating as a cantilever in a bending mode, which generate a voltage across the electrodes when they are stressed. Traditionally, the piezoelectric materials are deposited on a non-electro-active substrate and are physically clamped at one end to a rigid base, which serves as a mechanical supporting platform. In this research, a three dimensional thick-film structure in the form of a free-standing cantilever incorporated with piezoelectric materials is proposed. The advantages of this structure include minimising the movement constraints on the piezoelectric, thereby maximising the electrical output and offering the ability for integration with other microelectronic devices. A series of free-standing composite cantilevers in the form of unimorphs were fabricated and characterised for their mechanical and electric properties. The unimorph structure consists of a pair of silver/palladium (Ag/Pd) electrodes sandwiching a laminar layer of lead zirconate titanate (PZT). An extended version of this unimorph, in the form of multimorph was fabricated to improve the electrical output performance, by increasing the distance of the piezoelectric layer from the neutral axis of the structure. This research also discusses the possibility of using an array of free-standing cantilevers in harvesting vibration energy in a broader bandwidth from an unpredictable ambient environment.

This research introduces a conceptual framework for the design of serious games and uses the Technology Acceptance Model (TAM) for its validation. An initial study identified 12 attributes of educational serious games that can be used to support effective learning. These attributes are used in the conceptual framework to support learning and pedagogy in combination with the games. The Serious-Games-TAM was used to confirm that serious games, based on the proposed framework, would both be accepted by the learner and be useful for learning. Validation involved the collection and analysis of data from learners using a specially developed serious game that introduced international students to public transport in Southampton. After completing the game, participants completed a short questionnaire and the data was analysed using structural equation modelling (SEM). The results identified the attributes and combinations of attributes that led the learners to accept and to use the serious game for learning. These findings significantly contribute to helping game designers and educational practitioners design serious games for effective learning.

The design and implementation of high purity, high speed and power efficient clock generation Integrated Circuits continue to be one the greatest challenges facing IC designers today. In order to address this challenge, this thesis considers the modeling and design of two fundamental clock generation circuits – the VCO and PLL. An improved ring oscillator topology is proposed which has the advantage of an ultra wide tuning range. A novel noise aware ring oscillator model is also proposed which links the noise performance of the oscillator to its transistor dimensions giving insight to the design procedure. The use of this VCO model in a noise-aware PLL model allows the trade-off between noise performance and the loop bandwidth to be quantified accurately. From further analysis of the proposed PLL model, a novel PLL structure has been designed which is extremely successful at reference spur suppression. Simulation results based on the proposed model and foundry BSIM3v3 models are provided for all the VCO and PLL designs. To validate the proposed VCO topology and VCO model, two prototype chips have been fabricated and measured results show close agreement with theoretical analysis and simulation

Image feature extraction is a fundamental area of image processing and computer vision.There are many ways that techniques can be created that extract features and particularly novel techniques can be developed by taking influence from the physical world.This thesis presents the Image Ray Transform (IRT), a technique based upon an analogy to light, using the mechanisms that define how light travels through different media and analogy to optical fibres to extract structural features within an image. Through analogising the image as a transparent medium we can use refraction and reflection to cast many rays inside the image and guide them towards features, transforming the image in order to emphasise tubular and circular structures. The power of the transform for structural feature detection is shown empirically in a number of applications, especially through its ability to highlight curvilinear structures. The IRT is used to enhance the accuracy of circle detection through use as a preprocessor, highlighting circles to a greater extent than conventional edge detection methods. The transform is also shown to be well suited to enrolment for ear biometrics, providing a high detection and recognition rate with PCA, comparable to manual enrolment. Vascular features such as those found in medical images are also shown to be emphasised by the transform, and the IRT is used for detection of the vasculature in retinal fundus images. Extensions to the basic image ray transform allow higher level features to be detected. A method is shown for expressing rays in an invariant form to describe the structures of an object and hence the object itself with a bag-of-visual words model. These ray features provide a complementary description of objects to other patch-based descriptors and have been tested on a number of object categorisation databases. Finally a different analysis of rays is provided that can produce information on both bilateral (reflectional) and rotational symmetry within the image, allowing a deeper understanding of image structure. The IRT is a flexible technique, capable of detecting a range of high and low level image features, and open to further use and extension across a range of applications.

The purpose of this work is the development of a collection of satisfiability based algorithms that can be used to solve particular instances of integer programming problems. Satisfiability based algorithms have recently obtained a strong standing within the industrial community and, although for all but a few special cases the problem is NP-complete, research has shown that other problems in this class can often be transformed into a corresponding satisfiability problem and solved more effectively using the best SAT-solvers. One of the most important uses of satisfiability based algorithms is within chip design testing, such as the floating point failure of Pentium processors which require the need of efficient satisfiability based tools to aid in the verification process. We start with the case of Pure 0-1 Integer Programming problems and show how they can be transformed into a general satisfiability problem and solved using an algorithm developed by Davis, Putnam and Loveland. The thesis then concerns itself with making the process as efficient as possible by adopting a number of approaches that include the implementation of polynomial time algorithms including 'Incremental Satisfiability', allowing flexibility to add new branching strategies and the conversion of constraints to logical clauses. The programs are compiled to interact with each other fully and are tested on the 'truss design problem' found in structural engineering.

In magnetic materials, domains of magnetic orientation in opposite direction are often alternated. The transition in orientation between these domains, the domain wall, is not abrupt but spatially extended. When a current is passed through the magnetic material, the resistance of the material is dependent on whether such domain wall exists, which in its turn depends on the external magnetic field. This magneto-resistance is larger for smaller domain walls. Although the domain wall width is in principle a materials parameter, by patterning the magnetic sample into certain nanostructures, it is possible to constrain the domain wall such that its width is smaller than its natural unconstrained width. We have measured domain wall magnetoresistance in a single lithographically constrained domain wall. An H-shaped Ni nanobridge was fabricated by either e-beam lithography or a combination of e-beam lithography and helium ion milling. The two sides of the device are both single magnetic domains showing independent magnetic switching. The connection between the sides constrains the domain wall when the sides line up antiparallel. The magnetoresistance curve clearly identifies the magnetic configurations that are expected from a spin-valve like structure. The room temperature domain wall measurements give a magneto-resistance ratio of 0.1 % for the 94 nm and 0.2 % for the 32 nm constriction. Although these values are in itself small, they are the first results on lithographically prepared single domain spin valves. The single-layered device might allow for easier fabrication and space savings for high dense storage applications as compared to giant magneto-resistance and tunneling magneto-resistance. Also, the research of spin-based logic devices will benefit from the physical understanding that follows from measurements of magneto-resistance devices without material interface.

The increased interest in volumetric images in recent years requires new feature extraction methods for 3D image interpretation. The aim of this study is to provide algorithms that aid the process of detecting and segmenting geometrical objects from volumetric images. Due to high computational expense, such methods have yet to be established in the volumetric space. Only few have tackled this problem using shape descriptors and key-points of a specific shape; those techniques can detect complex shapes rather than simple geometric shapes due to the well defined key-points. Simplifying the data in the volumetric image using a surface detector and surface curvature estimation preserves the important information about the shapes at the same time reducing the computational expense. Whilst the literature describes only the template of the three-dimensional Sobel operator and not its basis, we present an extended version of the Sobel operator, which considers the gradients of all directions to extract an object’s surface, and with clear basis that allows for development of larger operators. Surface curvature descriptors are usually based on geometrical properties of a segmented object rather than on the change in image intensity. In this work, a new approach is described to estimate the surface curvature of objects using local changes of image intensity. The new methods have shown reliable results on both synthetic and on real volumetric images. The curvature and edge data are then processed in two new techniques for evidence gathering to extract a geometrical shape’s main axis or centre point. The accumulated data are taken directly from voxels’ geometrical locations rather than the surface normals as proposed in literature. The new approaches have been applied to detect a cylinder’s axis and spherical shapes. A new 3D line detection based on origin shifting has also been introduced. Accumulating, at every voxel, the angles resulting from a coordinate transform of a Cartesian to spherical system successfully indicates the existence of a 3D line in the volumetric image. A novel method based on using an analogy to pressure is introduced to allow analysis/ visualisation of objects as though they have been separated, when they were actually touching in the original volumetric images. The approach provides a new domain highlighting the connected areas between multiple touching objects. A mask is formed to detach the interconnected objects and remarkable results are achieved. This is applied successfully to isolate coins within an image of a Roman hoard of coins, and other objects. The approach can fail to isolate objects when the space between them appears to be of similar density to the objects themselves. This motivated development of an operator extended by high-pass filtering and morphological operations. This led to more accurate extraction of coins within the Roman hoard, and to successful isolation of femurs in a database of scanned body images enabling better isolation of hip components in replacement therapy.

Nanocomposite materials are currently attracting much interest due to their possibility of global property improvement – mechanical strength, toughness, electrical breakdown strength, electrical erosion resistance and flame retardancy. In order to disperse montmorillonite clay (MMT) into polyethylene (PE), the clay sheets need to be rendered organophilic. Masterbatches with a high level (~40 %wt) of organomodified clay can then be dispersed into a host by a simple mechanical process. Two chemically different masterbatches were purchased: Nanoblend 2101 from PolyOne Corp. and C30PE from Nanocor Inc. These were let down using a RandcastleTM single screw extruder with a patented mixing device to provide elongational flow. Wide angle X-ray diffraction was used together with transmission electron microscopy to evaluate the particle dispersion, which consisted of intercalated clay organised in clusters up to one micron in diameter. The performance of these materials was assessed in terms of AC ramp breakdown statistics, dielectric spectroscopy, dynamic and tensile mechanical properties. Nanoblend masterbatch consistently improved the breakdown statistics, more than overcoming the inherent demerit of extrusion, which mildly aged the unfilled material (as confirmed by Raman spectroscopy.) On the other hand, even low loading levels of Nanocor could result in reduced breakdown strength and increased scatter. Furthermore, both sets of materials demonstrated large dielectric losses at power frequencies and poorer performance under mechanical tension. These materials would therefore require considerable development before they could confidently be used commercially. The nature of the PE-MMT interactions was examined by investigating the crystallisation kinetics and resulting morphologies with differential scanning calorimetry and scanning electron microscopy. By varying the masterbatch type, loading level and crystallisation temperature, it was possible to study a wide range of supercrystalline morphologies using a permanganic etching technique. This is a useful contribution to the field of nanocomposites research. It is known that the morphologies of polymers can affect their mechanical properties and electrical treeing behaviour, and so it is possible that controlled crystallisation could provide a route toward designer materials with optimised behaviour.

The mean of a data set is one trivial representation of data from one class. This thesis discusses mutual interdependence analysis (MIA) that is successfully used to extract more involved representations, or “mutual features”, accounting for samples in the class. MIA aims to extract a common or mutual signature that is invariant to changes in the inputs. For example, a mutual feature is a speaker signature under varying channel conditions or a face signature under varying illumination conditions. By definition, the mutual feature is a linear combination of class examples that is equally correlated with all training samples in the class. An equivalent view is to find a direction to project the dataset such that projection lengths are maximally correlated. The MIA optimization criterion is presented from the perspectives of canonical correlation analysis and Bayesian estimation. This allows to state and solve the criterion for mutual features concisely and to infer other properties of its closed form, unique solution under various statistical assumptions. Moreover, a generalized MIA solution (GMIA) is defined that enables utilization of a priori knowledge. MIA and GMIA work well even if the mutual signature accounts only for a small part of the energy in the inputs. Real world problems do not exactly fit the signal model of an equally correlated common signature. Therefore, the behavior of MIA is analyzed in situations where its model does not exactly fit. For these situations it is shown that GMIA continues to extract meaningful information. Furthermore, the GMIA result is compared to ubiquitous signal processing methods. It is shown that GMIA extends these current tools visualizing previously hidden information. The utility of both MIA and GMIA is demonstrated on two standard pattern recognition problems: text–independent speaker verification and illumination–independent face recognition. For example, GMIA achieves an equal error rate (EER) of 4.0% in the text–independent speaker verification application on the full NTIMIT database of 630 speakers. On the other hand, for illumination–independent face recognition, MIA achieves an identification error rate of 7.4% in exhausive leave–one–out tests on the Yale database. Overall, MIA and GMIA are found to achieve competitive pattern classification performance to other modern algorithms.

This thesis considers the use of optimal techniques within iterative learning control (ILC) applied to linear systems. Two different aspects are addressed: the first is the duality relationship existing between iterative learning control and repetitive control which allows the synthesis of controllers developed in one domain to be applied in the other. Significant extensions to existing duality framework are made by eliminating an explicit current-error feedback loop and providing the facility of both current error feedback, and previous error feedforward within the control structure. This, in turn, with the case when either state-feedback or output-feedback is used to solve the ILC control paradigm extends the range of underlying plants to which the framework can be applied. In this context optimal control is used to solve the stabilisation problem which yields solutions for both RC and ILC cases in terms of state-feedback, and for ILC in terms of output-injection. These significantly extend the range of underlying plants to which the framework can be applied. The second aspect addressed is the selection of a suitable first input. Whilst ILC algorithms have been shown to over a high level of performance both theoretically and in practical applications, resulting error convergence is generally highly dependent on the initial choice of input applied. Optimal techniques are therefore applied to generate the most appropriate initial input to speed up the learning process over subsequent trials. Two approaches are developed to tackle the problem, both involving optimal solutions. The first is frequency domain bases, and involves a description of system uncertainty. An input is constructed which maximises convergence in the presence of uncertainty and noise, making use of the Fast Fourier Transform (FFT). The second approach is time domain based and an initial input is constructed using a library of previous references and their associated converged inputs. The assumption of system linearity is used to find the choice of previous inputs which maximises robust convergence. It is then shown how the frequency and time domain schemes may be combined. Both the duality and initial input techniques developed in this thesis have been evaluated experimentally on a gantry robot testbed, and the results obtained confirm the success of these additions to the ILC/RC framework