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Understanding biochemical reactions inside cells of individual organisms is a key factor for improving our biological knowledge. Signaling pathways provide a road map for a wide range of these chemical reactions that convert one signal or stimulus into another. In general, each signaling pathway in a cell involves many different proteins, each with one or more specific roles that help to amplify a relatively small stimulus into an effective response. Since proteins are essential components of a cells activities, it is important to understand how they work and in particular, to determine which of species proteins participate in each role. Experimentally determining this mapping of proteins to roles is difficult and time consuming. Fortunately, many individual pathways have been annotated for some species, and the pathways of other species can often be inferred using protein homology and the protein properties.

Manufacturing companies today face increasingly uncertain and volatile market demands. Product designs and the required quantities change rapidly to meet the needs of customers. To maintain competitiveness in this uncertain environment, manufacturing companies need to possess agility to dynamically and effectively adapt to the changing environment. Agility at the machine level can be thought of as the ability to reconfigure manufacturing machines in response to changing needs and opportunities. This thesis is concerned with a design method for machine level agility for reconfigurable manufacturing machines. This thesis is divided into two portions: a design approach for reconfigurable manufacturing machines and the embodiment of this approach in a computational synthesis example. In developing this design method, various approaches and reconfigurable systems are presented to develop an overview of the applications and current related research to reconfigurable manufacturing machines. From this related research, a research gap is identified pertaining to the identification of the evolving architecture of reconfigurable manufacturing machines. The key contribution is the design approach based on co-evolution. This design approach involves the implementation of agent based co-evolutionary algorithms. In this implementation, each agent synthesizes the configuration of a machine for a product in the range of products it is to manufacture and co-evolves with other agents which are synthesizing machines for other products to reduce the reconfiguration cost. Finally, an in-depth case study of the design approach is presented in which the approach is tested relative to various product changes; thus, showing the advantages of employing an evolving reconfigurable machine architecture. These product changes include batch size variations, geometry changes, and material changes. Hence, the core objective is to identify the necessary reconfigurable manufacturing machine architecture for the range of configurations required for machining various products.

[Abstract]: Machine vision systems in agricultural applications are becoming commonplace as technology becomes both affordable and robust. Applications such as fruit and vegetable grading were amongst the earliest applications, but the field has diversified into areas such as yield monitoring, weed identification and spraying, and tractor guidance. Machine vision systems generally consist of a number of steps that are similar between applications. These steps include image pre-processing, analysis, and post- processing. This leads the way towards a generalisation of the systems to an almost ‘colour by number’ methodology where the platform may be consistent between many applications, and only algorithms specific to the application differ. Shape analysis is an important part of many machine vision applications. Many methods exist for determining existence of particular objects, such as Hough Transforms and statistical matching. A method of describing the outline of objects, called s-ψ (s-psi) offers advantages over other methods in that it reduces a two dimensional object to a series of one dimensional numbers. This graph, or chain, of numbers may be directly manipulated to perform such tasks as determining the convex hull, or template matching. A machine vision system to automate yield monitoring macadamia harvesting is proposed as a partial solution to the labour shortage problems facing researchers undertaking macadamia varietal trials in Australia. A novel method for objectively measuring citrus texture is to measure the shape of a light terminator as the fruit is spun in front of a video camera. A system to accomplish this task is described. S-psi template matching is used to identify animals to species level in another case study. The system implemented has the capability to identify animals, record video and also open or shut a gate remotely, allowing control over limited resources.

This thesis investigates the application of structured sequence classification models to multilingual natural language processing (NLP). Many tasks tackled by NLP can be framed as classification, where we seek to assign a label to a particular piece of text, be it a word, sentence or document. Yet often the labels which we’d like to assign exhibit complex internal structure, such as labelling a sentence with its parse tree, and there may be an exponential number of them to choose from. Structured classification seeks to exploit the structure of the labels in order to allow both generalisation across labels which differ by only a small amount, and tractable searches over all possible labels. In this thesis we focus on the application of conditional random field (CRF) models (Lafferty et al., 2001). These models assign an undirected graphical structure to the labels of the classification task and leverage dynamic programming algorithms to efficiently identify the optimal label for a given input. We develop a range of models for two multilingual NLP applications: word-alignment for statistical machine translation (SMT), and multilingual super tagging for highly lexicalised grammars.

[Abstract]: Machine vision systems in agricultural applications are becoming commonplace as technology becomes both affordable and robust. Applications such as fruit and vegetable grading were amongst the earliest applications, but the field has diversified into areas such as yield monitoring, weed identification and spraying, and tractor guidance. Machine vision systems generally consist of a number of steps that are similar between applications. These steps include image pre-processing, analysis, and post- processing. This leads the way towards a generalisation of the systems to an almost ‘colour by number’ methodology where the platform may be consistent between many applications, and only algorithms specific to the application differ. Shape analysis is an important part of many machine vision applications. Many methods exist for determining existence of particular objects, such as Hough Transforms and statistical matching. A method of describing the outline of objects, called s-ψ (s-psi) offers advantages over other methods in that it reduces a two dimensional object to a series of one dimensional numbers. This graph, or chain, of numbers may be directly manipulated to perform such tasks as determining the convex hull, or template matching. A machine vision system to automate yield monitoring macadamia harvesting is proposed as a partial solution to the labour shortage problems facing researchers undertaking macadamia varietal trials in Australia. A novel method for objectively measuring citrus texture is to measure the shape of a light terminator as the fruit is spun in front of a video camera. A system to accomplish this task is described. S-psi template matching is used to identify animals to species level in another case study. The system implemented has the capability to identify animals, record video and also open or shut a gate remotely, allowing control over limited resources.