One and multi dimensional raw data collections introduce noise and artifacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this thesis is to investigate the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel one/multi-dimensional filtering algorithms. The proposed generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These parallel filtering algorithms and their automated generic architectures tackle the major bottlenecks and limitations of existing multiprocessor systems in wordlength, input data segmentation, boundary conditions as well as inter-processor communications, in order to support high data throughput real-time applications of low-power architectures using a Xilinx Virtex-6 FPGA board. For one-dimensional raw signal filtering case, mathematical model and architectural development of the generalized parallel 1-D filtering algorithms are presented using the 1-D block filtering method. Five generic architectures are implemented on a Virtex-6 ML605 board, evaluated and compared. A complete set of results on area, speed, power, throughput and computation rates are obtained and discussed as performance indices for the 1-D convolution architectures. A successful application of parallel 1-D cross-correlation is demonstrated. For two dimensional greyscale/colour image processing cases, new parallel 2-D/3-D filtering algorithms are presented and mathematically modelled using input decimation and output image reconstruction by interpolation. Ten generic architectures are implemented on the Virtex-6 ML605 board, evaluated and compared. Key results on area, speed, power, throughput and computation rate are obtained and discussed as performance indices for the 2-D convolution architectures. 2-D image reconfigurable processors are developed and implemented using single, dual and quad MAC FIR units. 3-D Colour image processors are devised to act as 3-D colour filtering engines. A 2-D cross-correlator parallel engine is successfully developed as a parallel 2-D matched filtering algorithm for locating any MRI slice within a MRI data stack library. Twelve 3-D MRI filtering operators are plugged in and adapted to be suitable for biomedical imaging, including 3-D edge operators and 3-D noise smoothing operators. Since three dimensional greyscale/colour volumetric image applications are computationally intensive, a new parallel 3-D/4-D filtering algorithm is presented and mathematically modelled using volumetric data image segmentation by decimation and output reconstruction by interpolation, after simultaneously and independently performing 3-D filtering. Eight generic architectures are developed and implemented on the Virtex-6 board, including 3-D spatial and FFT convolution architectures. Fourteen 3-D MRI filtering operators are plugged and adapted for this particular biomedical imaging application, including 3-D edge operators and 3-D noise smoothing operators. Three successful applications are presented in 4-D colour MRI (fMRI) filtering processors, k-space MRI volume data filter and 3-D cross-correlator.

The integration of complementary airborne light detection and ranging (LiDAR) and photogrammetric data continues to receive attention from the relevant research communities. Such an approach requires the optimized registration of the two data types within a common coordinate reference frame and thus enables the cross-calibration of one information source against another. This research assumes airborne LiDAR as a reference dataset against which in-flight camera system calibration and validation can be performed. The novel methodology involves the production of dense photogrammetric point clouds derived using the simultaneous adjustment of GNSS/IMU data and a dense set of photogrammetric tie points. Quality of the generated photogrammetric dataset is further improved through introducing the self-calibration additional parameters in the combined adjustment. A robust least squares surface matching algorithm is then used to minimise the Euclidean distances between the two datasets. After successful matching, well distributed LiDAR-derived control points (LCPs) are automatically identified and extracted. Adjustment of the photogrammetric data is then repeated using extracted LCPs in a self-calibrating bundle adjustment. The research methodology was tested using two datasets acquired using different photogrammetric digital sensor systems, a Microsoft UltraCamX large format camera and an Applanix DSS322 medium format camera. Systematic sensitivity testing included the influence of the number and weighting of LCPs required to achieve optimised adjustment. For the UltraCamX block it was found that when the number of control points exceeded 80, the accuracy of the adjustment stabilized at c. 2 cm in all axes, regardless of point weighting. Results were also compared with those from reference calibration using surveyed ground control points in the test area, with good agreement found between the two. Similar results were obtained for the DSS322 block, with block accuracy stabilizing at 100 LCPs. Moreover, for the DSS322 camera, introducing self-calibration greatly improved the accuracy of aerial triangulation.

This thesis investigates robust and fast methods for single and cooperative 2D/3D image mosaicing to enhance field of view of images by joining them together. Image mosaicing is underlined by the process of image registration and a significant portion of the contributions of this work are dedicated to it. Image features are identified as a solution to the problem of image registration that uses feature-to-feature matching between images to solve for inter-image transformations. We have developed a novel two signature distribution based feature descriptor that combines grey level gradients and a colour histogram. This descriptor is robust to illumination changes and shows better matching accuracy compared to state of the art. Furthermore, we introduce a feature clustering technique that uses colour codes assigned to each feature to group them together. This allows fast and accurate feature matching as the search space is reduced. Taking into account feature location uncertainty we have introduced a novel information fusion technique to reduce this error by covariance intersection. This reduced error location is consequently fed to an H∞ filter taking into account system uncertainty for parameter estimation. We show that this technique outperforms costly nonlinear optimisation techniques. We have also developed a novel coupled filtering scheme based on H∞ filtering that estimates inter-image geometric and photometric transformations simultaneously. This is shown to perform better than standard least square techniques. Furthermore, we have introduced time varying parameter estimation using recursive techniques that facilitate in tracking changing parameters of inter-image transformations, suitable for image mosaicing between moving platforms. A method for rapid 3D scene reconstruction is developed that uses homographic lines between images for semi-dense pixel matching. Triangular meshes are then used for a complete visualisation of the scene and to fill in the gaps. To tackle cooperative mosaicing scenarios, additional methods are presented that include descriptor compression using principal components and 3D scene merging using the trifocal tensor. Capabilities of the proposed techniques are illustrated with real world images.

This thesis describes the process and technique used to fabricate reproducible optical fibre long period gratings (LPG) of various types. It explores how they can be exploited for use as highly selective and sensitive sensors. A versatile method for fabricating LPG sensors has been demonstrated. The single system has the capability of fabricating LPGs of different configurations, such as uniform period, those operating at the phase matching turning point (PMTP), as well as phase shifted and chirped. LPGs were characterised for their sensitivities to temperature, axial strain and surrounding refractive index. The gratings at the PMTP were found to show higher sensitivities to external influences. Novel sensing configurations that exploit the properties of LPGs were also constructed. An LPG coated with a molecularly imprinted ceramic coating was demonstrated to offer a selective method for porphyrin detection. A composite nanoscale of a titanium oxide (TiO2) matrix and 5, 10, 15, 20 Tetrakis-(N-methyl-piridinium4- yl)-21H, 23H-porphine tertakis (p-toluenesulfonate) [TMPyP] porphyrin template film was deposited on the LPG via liquid phase deposition. Attempts to rebind porphyrins to the matrix were carried out. The LPGs transmission spectrum exhibited a higher sensitivity to the target TMPyP template than it did to other, structurally similar porphyrins, showing high selectivity. A continuously chirped long period grating (CCLPG) sensor for monitoring directional flow and cure of an epoxy resin is also presented. The asymmetric properties of the CCLPG were exploited to facilitate the measurement of the direction of the flow. The CCLPG was also used to monitor changes in the refractive index of the resin during its cure, showing close agreement with a fibre optic Fresnel refractometer.

This thesis investigates the fundamental limitations to optical transmission at a bit-rate of 40Gbit/s. The signal distortion due to nonlinear effects, noise and dispersion are analysed and techniques for their suppression through dispersion management and optimum choice of modulation format are demonstrated. The high launch powers required for overcoming noise from the amplifiers result in an increase in fibre nonlinearities. Transmission at 40Gbit/s favours the RZ modulation format. However, RZ signals were found to be limited by intra-channel cross phase modulation (IXPM) and intra-channel four-wave-mixing (IFWM). These intra-channel nonlinear effects take place as a result of nonlinear interaction between overlapping pulses of the same wavelength channel. Minimising such pulse overlap by controlling the dispersion-induced pulse broadening during propagation in the fibre was investigated by reducing the fibre local dispersion and by pre-compensating the signal at the transmitter. Dispersion compensation using higher-order-mode devices with high nonlinear tolerance was also investigated, enabling transmission over in-line pre-compensated amplifier spans. In the second part of this thesis, the nonlinear tolerance of the RZ modulation format was increased by use of alternate-polarisation and alternate-phase between adjacent pulses. These techniques were found to improve the transmission performance by approximately 50% and required simple modifications to the transmitter only. These advanced RZ signals were found to be compatible with dispersion management techniques. However, the optimum pre-compensation at the transmitter was found to be dependent on the modulation format and dominant intra-channel effect. A novel modulation format combining alternate-polarisation and phase simultaneously was demonstrated for maximum nonlinear suppression without the use of dispersion management. Finally, a new experimental technique was demonstrated for the investigation of dispersion tolerance. It was found that the choice of optimum modulation format requires a trade-off between nonlinear tolerance and dispersion tolerance. The results of this work can be applied to optimise the design rules of future optical networks.

Industrial processes, such as casting and welding, are frequently simulated using computer models which require the materials thermophysical properties of alloys as a function of temperature with higher precision. These properties include the heat capacity, the enthalpy, the thermal conductivity the liquid fraction and the solid fraction. The thermophysical data is normally measured using thermal analysis methods; the most common instrument used is the Differential Scanning Calorimeter (DSC). However, accuracy in the DSC measurement is limited by the instrument kinetics because it measures the thermal response of a furnace to a crucible that contains the sample, rather than a direct measurement. In particular, when phase transition occurs in a sample, several problems arise including significant errors in measuring apparent transition temperatures and enthalpy changes of the transition. Continuous efforts are often devoted to designing calorimeters that can measure materials thermophysical properties with a higher accuracy. Recently Dong and Hunt proposed a Single-Pan Scanning Calorimetry (SPSC) which can significantly reduce the measurement error via a novel single-pan concept. In this study, the systems approach has been adopted to examine the product for the SPSC. The systems approach involves the use of appropriate methods in a strategic manner that should lead to better product design. Measuring the transition temperature and enthalpy change of pure aluminium and some commercial aluminium alloys has tested the designed and built calorimeter. The measured results revealed a high accuracy in enthalpy data, excellent reproducibility and high resolution in determining transient temperature. It is concluded that the new instrument is a promising device that can achieve reliable and reproducible materials thermophysical data.

Some chemical processes are potentially explosive and make use of metallic vessels. Monitoring such kinds of processes using the Three-Dimensional Electrical Impedance Tomography (3D EIT) method is of interest to Industry. The work focuses on the challenge of producing a three-dimensional forward solver for 3D EIT of domains with conducting boundaries. This was limited to two dimensions prior to the start of this project. The problems and shortcomings of 3D EIT in industrial vessels that have a metallic structure are explored with an emphasis on intrinsic safety. This has been evaluated by addressing the project "PROCEss tomography for MONintoring industrial pressure filters (PROCEMON)". It is suggested here that, the ElT software and hardware, presently used in PROCEMON, requires further investigation.

The work described in this thesis is concerned with two main areas of investigation. The first involves the measurement and characterisation of the fluorescence of various doped glasses when excited by a pulsed ultra-violet laser beam, with a view to finding a material which acts as a suitable ultra-violet to visible image converter. A system is described, based on a glass fluorescer, which writes the beam profile of a single-shot KrF laser directly into computer memory and hence permits powerful image processing, and measurements to be made on the laser beam profile. The system was developed primarily for the spatial profiling of 'Sprite', Europe's largest ultra-violet laser, and is currently in routine use at the Rutherford Appleton Laboratory for this purpose.

This thesis is concerned with image reconstruction problems encountered in signal or image processing and with the numerical methods used to tackle these problems. We present a theoretical basis for image reconstruction methods and discuss their implementation within the framework of an underlying linearity assumption. The linearity assumption reduces the problem, in most practical situations, to solving a system of linear equations. This simplifies the investigation of the difficulties inherent in the problem. However, most existing methods for mitigating those difficulties have a common estimation structure, called "regularization" in spite of their apparent variety. This is used here as a unifying framework for understanding this rather complex field of image processing and in this setting, image reconstruction methods are investigated. The major emphasis is given to consideration of methods which are based on the principle of the maximum entropy, known to be a useful tool in image processing. We attempt to improve the existing maximum entropy methods used in the literature and propose two new approaches for generating maximum entropy images. The performances of all methods introduced in the thesis are tested for one and two-dimensional images with a variety of noise levels and are compared to each other. In addition, applications to real observations in x-ray astronomy and in nuclear medicine are presented

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