The financial reporting world has recently faced a number of changes due to the impact of the Internet. Today, the revolution in business communication is accelerating and more data is being shared by a large number of participant users, aside from the company’s internal management, including: clients, business partners, financial market analysts, investors and government regulators. These changes have led to the development of eXtensible Business Reporting Language (XBRL), which is an opensource Internet-based financial reporting language. XBRL is an extension of eXtensible Markup Language (XML) that provides machinereadable tags for each individual data element in each financial statement. XBRL is likely to be used as a platform that offers universal standards for defining business information. XBRL can ease the preparation, analysis, and exchange of business information along each part of financial reporting supply chain and across companies around the world. It can also increase the efficiency for all related users of business data. This study has analysed the accuracy of XBRL outputs by conducting a literature review and by checking the accuracy of the real company XBRL filing submissions that are published publicly. This study has found that there were many errors in these public XBRL documents that were caused either through a few basic common errors or from mistakes in related financial information. Therefore, this study has aimed to discover any possible causes of errors in XBRL filings. It has also aimed to find a way to detect those errors. Consequently, this study conducted a semantic checking system that aimed to detect XBRL errors and so enhance the accuracy of financial statements. To develop the semantic checking system, the results of an error finding analysis were combined, filtered, and classified into each category of errors, including the integration of accounting, business, and technology knowledge to fulfil the system. A process flow for the semantic checking system was created to help understand both the method and the rule. The rules were then set up to determine the different aspect of errors, which had a different method to manage and reduce errors. The semantic checking system was created in terms of the information specification of the XBRL filings. The system was designed to be more practical for the users by presenting the relationship between the real data and accounting practice. Moreover, a prototype was produced and the case study method was applied to prove the development of the system. This step was able to ensure the accuracy of the semantic checking system. Finally, this semantic checking system has been shown to improve the accuracy of XBRL filings. It also emphasises the importance of employing XBRL preparers who are aware of all of the possible issues that may arise while preparing an XBRL-based filing submission.

This thesis provides a new space-time statistical rain model and a novel space-time interpolation approach for planning and dimensioning wide area high frequency satellite communication networks. Key characteristics of rainfall rate fields are modelled. These include detailed description of: (i) the first order statistical distribution, (ii) the spatial and temporal correlation functions of rainfall rate and, and (iii) the probability of rain/no-rain. With a focus on their relevance to satellite and terrestrial microwave network design, the key contribution of this study is the assessment of the impact of varying spatial and temporal integration lengths on these quantities. The issue of how these key characteristics of rainfall rate field change with different area sizes are analysed in this thesis and it is novel. A simple but accurate interpolation approach of the key characteristic parameters is presented in this thesis. The novelty of the proposed technique is that it does not rely directly on the radar/raingauge derived rainfall rate data like traditional models do but rather on fitted coefficients and computed rain characteristics. This thesis proposes rain parameter contour maps and databases covering the whole of Western Europe from which users can conveniently obtain the key rain characteristic parameters at any location within the studied area. More speculatively, the space-time interpolation approach can extrapolate to rain parameters at space-time resolutions shorter than those in the NIMROD databases. The results have been validated by comparing them with those from ITU Rec model and measurements by NIMROD rain radar. In addition, a Graphical User Interface (GUI) software has been provided that allows users to interact with the proposed model. The user can easily obtain the information of the key rain characteristics at different space-time scales by simply inputting the longitude, latitude, space resolution and time resolution of the location of interest. The detailed results are then automatically calculated and displayed by the software significantly facilitating rain rate study.

The aim of this work was to investigate signal processing and analysis techniques for Ground Penetrating Radar (GPR) and its use in civil engineering and construction industry. GPR is the general term applied to techniques which employ radio waves, typically in the Mega Hertz and Giga Hertz range, to map structures and features buried in the ground or in manmade structures. GPR measurements can suffer from large amount of noise. This is primarily caused by interference from other radio-wave-emitting devices (e.g., cell phones, radios, etc.) that are present in the surrounding area of the GPR system during data collection. In addition to noise, presence of clutter – reflections from other non-target objects buried underground in the vicinity of the target can make GPR measurement difficult to understand and interpret, even for the skilled human, GPR analysts. This thesis is concerned with the improvements and processes that can be applied to GPR data in order to enhance target detection and characterisation process particularly with multivariate signal processing techniques. Those primarily include Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Both techniques have been investigated, implemented and compared regarding their abilities to separate the target originating signals from the noise and clutter type signals present in the data. Combination of PCA and ICA (SVDPICA) and two-dimensional PCA (2DPCA) are the specific approaches adopted and further developed in this work. Ability of those methods to reduce the amount of clutter and unwanted signals present in GPR data have been investigated and reported in this thesis, suggesting that their use in automated analysis of GPR images is a possibility. Further analysis carried out in this work concentrated on analysing the performance of developed multivariate signal processing techniques and at the same time investigating the possibility of identifying and characterising the features of interest in pre-processed GPR images. The driving idea behind this part of work was to extract the resonant modes present in the individual traces of each GPR image and to use properties of those poles to characterise target. Three related but different methods have been implemented and applied in this work – Extended Prony, Linear Prediction Singular Value Decomposition and Matrix Pencil methods. In addition to these approaches, PCA technique has been used to reduce dimensionality of extracted traces and to compare signals measured in various experimental setups. Performance analysis shows that Matrix Pencil offers the best results.