Oman is one of the most important countries engaged in fishing in the Middle East. Fishing and agriculture have been traditional Omani occupations and sources of food and employment for the people in Oman. Over the last 40 years, many major food-importing countries have established strict hygiene regulations and legislation, including definitive standards for fishery products. Many countries exporting fishery products, particularly developing ones, did not have the mechanisms in place to meet such requirements. This led to rejection of consignments and economic losses, a fate suffered by Oman in 1997. Since 1997 Oman, has adopted a preventive approach to food safety, inspired by Council Directive 91/493/EEC and Commission Decision 94/356/EC. The acronym HACCP (standing for Hazard Analysis Critical Control Point) denotes the management philosophy and family of techniques employed to implement the preventive approach. In the light ofthese factors, it was considered important in this study to examine, through case studies, the extent to which HACCP principles and associated practices were being applied within the fish industry. Thus the difficulties of their application in practice would be assessed, and their reception in the fish processing industry reviewed. To meet this gap in knowledge, a survey was designed and carried out in all Omani regions. Such a study would determine the problems, as seen by the industry, that obstruct the proper implementation ofHACCP. The aim of this study is to explore the process of HACCP implementation in the Omani food sector, using the seafood processing sector as a case study. To carry out this study,a triangulation method was employed to collect and validate both qualitative and quantitative data. A questionnaire was employed as the main method of data collection supplemented by semi-structured interviews of key-informants together with the application of a checklist against existing practices in the plants. The analysis of the food safety policy and management in Oman, in relation to the food industry as a whole, reveals that most problems experienced are those related to: a poorly developed institutional and legal framework; weak technical regulations; ill-defined inspection and approval procedures; lack of skilled staff for inspection and laboratory testing; many sub-standard processing factories; and the absence of adequate infrastructure for fish marketing. At the level of individual businesses, fish processing strategies for HACCP system implementation were investigated. The findings of this study are that most Omani fish processors are focused primarily on the development of their HACCP plans. Although developing of the HACCP plan is a fundamental part of the HACCP process, it is not widely understood among managers that this is just the beginning. The implementation and sustaining of a HACCP system can be a difficult and time-consuming mission. The study attributes this weakness to three main elements: poor training of personnel; shortcomings in prerequisite programmes; and a lack of commitment to maintenance of HACCP.

This thesis extends the utility of probabilistic methods in two diverse domains: multimodal biometrics and machine inspection. The attraction for this approach is that it is easily understood by those using such a system; however the advantages extend beyond the ease of human utility. Probabilistic measures are ideal for combination since they are guaranteed to be within a fixed range and are generally well scaled. We describe the background to probabilistic techniques and critique common implementations used by practitioners. We then set out our novel probabilistic framework for classification and verification, discussing the various optimisations and placing this framework within a data fusion context. Our work on biometrics describes the complex system we have developed for collection of multimodal biometrics, including collection strategies, system components and the modalities employed. We further examine the performance of multimodal biometrics; particularly examining performance prediction, modality correlation and the use of imbalanced classifiers. We show the benefits from score fused multimodal biometrics, even in the imbalanced case and how the decidability index may be used for optimal weighting and performance prediction. In examining machine inspection we describe in detail the development of a complex system for the automated examination of ophthalmic contact lenses. We demonstrate the performance of this system and describe the benefits that complex image processing techniques and probabilistic methods can bring to this field. We conclude by drawing these two areas together, critically evaluating the work and describing further work that we feel is necessary in the field.

Probabilistic model checking is a powerful formal verification method that can ensure the correctness of real-life systems that exhibit stochastic behaviour. The work presented in this thesis aims to solve the scalability challenge of probabilistic model checking, by developing, for the first time, fully-automated compositional verification techniques for probabilistic systems. The contributions are novel approaches for automatically learning probabilistic assumptions for three different compositional verification frameworks. The first framework considers systems that are modelled as Segala probabilistic automata, with assumptions captured by probabilistic safety properties. A fully-automated approach is developed to learn assumptions for various assume-guarantee rules, including an asymmetric rule Asym for two-component systems, an asymmetric rule Asym-N for n-component systems, and a circular rule Circ. This approach uses the L* and NL* algorithms for automata learning. The second framework considers systems where the components are modelled as probabilistic I/O systems (PIOSs), with assumptions represented by Rabin probabilistic automata (RPAs). A new (complete) assume-guarantee rule Asym-Pios is proposed for this framework. In order to develop a fully-automated approach for learning assumptions and performing compositional verification based on the rule Asym-Pios, a (semi-)algorithm to check language inclusion of RPAs and an L*-style learning method for RPAs are also proposed. The third framework considers the compositional verification of discrete-time Markov chains (DTMCs) encoded in Boolean formulae, with assumptions represented as Interval DTMCs (IDTMCs). A new parallel operator for composing an IDTMC and a DTMC is defined, and a new (complete) assume-guarantee rule Asym-Idtmc that uses this operator is proposed. A fully-automated approach is formulated to learn assumptions for rule Asym-Idtmc, using the CDNF learning algorithm and a new symbolic reachability analysis algorithm for IDTMCs. All approaches proposed in this thesis have been implemented as prototype tools and applied to a range of benchmark case studies. Experimental results show that these approaches are helpful for automating the compositional verification of probabilistic systems through learning small assumptions, but may suffer from high computational complexity or even undecidability. The techniques developed in this thesis can assist in developing scalable verification frameworks for probabilistic models.