The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis: tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a similar sound to the target. The techniques for achieving this that are investigated are genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis space explorer provides a user with a representation of the space of possible sounds that a synthesizer can produce and allows them to interactively explore this space. The applications of automatic sound synthesizer programming that are investigated include studio tools, an autonomous musical agent and a self-reprogramming drum machine. The research employs several methodologies: the development of novel software frameworks and tools, the examination of existing software at the source code and performance levels and user trials of the tools and software. The main contributions made are: a method for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme.

Diagnosis of ageing mechanisms in supercapacitors is made difficult by the enforcement of various ageing factors in the current ageing tests. The thesis presents the exact determination of the ageing mechanism by separating the impacts of high temperature, current cycling and constant voltage applications in accelerated ageing tests. The state of health (SOH) of the supercapacitors are monitored periodically with electrochemical impedance spectroscopy, cyclic voltammetry and constant current test to observe the evolution of ageing. The thesis identifies patterns of ageing from the changes at supercapacitor impedance. The thesis also presents the cause of the increase in ESR and the loss of capacitance in supercapacitors. High temperature application causes the appearance of high frequency semicircle which reflects the damage at the electrode-current collector interface. A tilt of the impedance line at low frequencies reflects modifications of electrodes and it is most sensitive to current cycling and constant voltage applications. The increase in ESR is observed to be caused by a single ageing mechanism while the capacitance loss is caused by multiple interactions of these ageing mechanisms at the same time. The thesis develops a supercapacitor model by means of electrical equivalent circuit. The model is divided into two parts based on the changes in its SOH: the baseline model represents the early stage of the supercapacitor life and the ageing model represents the phase of ageing. The models show dynamic interactions between ageing process and supercapacitor electrical performance. The supercapacitor model, in the form of fractional-order model, reduces the number of circuit components and shows excellent electrical behaviour particularly at the open circuit voltage decay and voltage recovery period. The parameterisation of model parameters shows that aged supercapacitors experience an increase of distributed resistance in the electrode pores and an increase of diffusion impedance at high temperature.

In this work, a versatile electrochemical biosensing platform was developed based on graphene/zinc oxide (G/ZnO) nanocomposite. For the synthesis of G/ZnO nanocomposite, two facile and green approaches were employed to eradicate the issues associated with conventional methods, which use harsh chemicals and high temperature. The G/ZnO nanocomposite synthesised via low temperature hydrothermal growth method exhibited approximate 58 times improvement in terms of sensitivity as compared to the G/ZnO nanocomposite synthesised via the mechanical stirring method. Therefore, the G/ZnO nanocomposite with higher sensitivity was employed for the following work. Results from cyclic voltammetry and amperometry showed that the G/ZnO-modified electrode displayed a wide linear range up to 15 mM for the detection of H2O2 and sensitivity improvements of 200% in comparison to the control sample. Subsequently, an electrochemical genosensor based on G/ZnO nanocomposite was fabricated for the detection of Avian Influenza H5N1 virus. The G/ZnO-based genosensor displayed its potential in replacing the conventional detection method, with result showing higher sensitivity and efficiency. The G/ZnO-based genosensor was further applied for the detection of Coconut Cadang-Cadang Viroid disease (CCCVd) single stranded RNA (ssRNA). Promising results were obtained with high specificity in discriminating the target from mismatched ssRNA sequences.

Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood. The pixel's neighbourhood could be considered to contain more that textural information. Second order methods using mean greylevel, Laplacian and variance values derived from the pixel neighbourhood are developed to provide the neural network with a three neuron input vector. This method provides the neural network with additional information, improving the strength of the relationship between the input and output neurons, and therefore reducing the training time and improving the classification accuracy. A third method using a hierarchical set of two or more neural networks is proposed as a method of identifying the high level objects in the images. The methods were applied to representative data sets and the results were compared with manually classified images to quantify the results. Classification accuracy varied from 69% with a window of raw pixel values and 84% with a three neuron input vector of second order values.

In order to combat the climate change caused by increasing emissions of CO2 from industrial processes, Carbon Capture and Storage (CCS) technologies have been accepted worldwide to address these pressing global warming concerns. So as to efficiently manage material and financial losses across the entire stream, accurate accounting and monitoring through fiscal metering of CO2 in CCS transportation pipelines are core and required features for the deployment of CCS technologies. Moreover, these technical requirements are part of the legal compliance schemes and guidelines from various regulatory bodies. This thesis reports experimental studies of two different metering technologies, an Averaging Pitot Tube (APT) and a Coriolis mass flowmeter (CMF), for CO2 flow measurement, together with the design and implementation of a CO2 flow calibration facility. A prototype system for the leak detection of the gas phase of CO2 is also developed. A review of the methodologies and technologies for the flow measurement and leak detection of CO2 gas is firstly given, followed by the discussion of the main challenges and technical requirements in their applications. Based on this review, two flow metering technologies, APT and CMF, are selected for experimental studies and a calibration platform using both volumetric and direct mass measurement methods for the gas phase of CO2 is also developed. The APT and CMF were calibrated and evaluated in the test facility. Experimental results obtained in this test facility demonstrate that the instruments are capable of accurately metering gaseous CO2 within a measurement uncertainty of ±1.5%. Flow characterization of the fluid under wet and mixed components conditions were further assessed with both meters. Under wet CO2 flow, results obtained show that both flow instruments are subject to significant measurement errors. The APT gave an error of up to ±25%, for a liquid fraction of 20%, while the error of the CMF was up ±6%, for a liquid fraction of 10%. Further investigations show that these errors can, however, be corrected through simple and straightforward algorithms that can be easily incorporated into computing processes in the flowmeters. In binary gaseous mixture tests, the CMF proved to be very reliable in the gas combination processes and likewise in the metering of the CO2 mixture (≤ ±1%), while a higher degree of uncertainty was registered for the APT (≤ ±4%). Overall analyses from investigations confirmed the APT and CMF instruments as promising technologies for CO2 flow measurement and can be further improved for application in actual CCS conditions. In addition, this thesis describes experimental investigations of the leak detection of CO2 gas from a pipeline, with emphasis on full controllability and flexibility of the operational process. An imaging system using passive temperature change detection techniques is designed, implemented and evaluated. The effectiveness of the developed technique is examined on a laboratory-scale flow rig system. Results obtained from tests confirmed the operability of the system configuration and validation of the thermal imaging approach. Suggestions for future development and enhancement of the proposed techniques are finally given.