Poster exhibited at GPSC Student Showcase, February 24th, 2016, University of Arizona. Recipient of the 2016 Katheryne B. Willock Library Research Award. / The nervous system is composed of two types of cells: neurons and glia. In neuronal circuits, neurons communicate through synapses and glia play a crucial modulatory role. To modulate chemical reuptake, glia send processes close to synapses and many glia directly appose or ensheathe a synapse. This structural motif is one of the elements often included in describing a vertebrate tripartite synapse, which includes a bidirectional functional neuron-glia relationship. The exact nature of this neuron-glia communication is not well understood. In the invertebrate fruit fly, we have also found that particular neurons and glia also have a bidirectional functional relationship. This allows us to ask new questions about glial morphology. Throughout multiple images, I identified particular neuronal synapses and surrounding glia. After creating a 3D reconstruction, I measured the distance between a particular neuronal synapse and its closest glial process. Interestingly, the neuronal synapses were not directly apposed or ensheathed by glia, and the distance to the closest glial process varied one-hundred-fold. With variable distance, functional communication is consistently present. These findings provide important insight into invertebrate neuron-glia communication, and offer new avenues to investigate the structural neuron-glia relationships that are required for reciprocal signaling between the two cell classes.

This thesis looks into the examination of polymer composite wreckage from the perspective of the aircraft accident investigator. It develops an understanding of the process of wreckage examination as well as identifying the potential for visual and macroscopic interpretation of polymer composite aircraft wreckage. The in-field examination of aircraft wreckage, and subsequent interpretations of material failures, can be a significant part of an aircraft accident investigation. As the use of composite materials in aircraft construction increases, the understanding of how macroscopic failure characteristics of composite materials may aid the field investigator is becoming of increasing importance. The first phase of this research project was to explore how investigation practitioners conduct wreckage examinations. Four accident investigation case studies were examined. The analysis of the case studies provided a framework of the wreckage examination process. Subsequently, a literature survey was conducted to establish the current level of knowledge on the visual and macroscopic interpretation of polymer composite failures. Relevant literature was identified and a compendium of visual and macroscopic characteristics was created. Two full-scale polymer composite wing structures were loaded statically, in an upward bending direction, until each wing structure fractured and separated. The wing structures were subsequently examined for the existence of failure characteristics. The examination revealed that whilst characteristics were present, the fragmentation of the structure destroyed valuable evidence. A hypothetical accident scenario utilising the fractured wing structures was developed, which UK government accident investigators subsequently investigated. This provided refinement to the investigative framework and suggested further guidance on the interpretation of polymer composite failures by accident investigators.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Mathematics / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Signal and image processing is important in a wide range of areas, including medical and astronomical imaging, and speech and acoustic signal processing. There is often a need for the reconstruction of these objects to be very fast, as they have some cost (perhaps a monetary cost, although often it is a time cost) attached to them. This work considers the development of algorithms that allow these signals and images to be reconstructed quickly and without perceptual quality loss. The main problem considered here is that of reducing the amount of time needed for images to be reconstructed, by decreasing the amount of data necessary for a high quality image to be produced. In addressing this problem two basic ideas are considered. The first is a subset selection problem where the aim is to extract a subset of data, of a predetermined size, from a much larger data set. To do this we first need some metric with which to measure how `good' (or how close to `best') a data subset is. Then, using this metric, we seek an algorithm that selects an appropriate data subset from which an accurate image can be reconstructed. Current algorithms use a criterion based upon the trace of a matrix. In this work we derive a simpler criterion based upon the determinant of a matrix. We construct two new algorithms based upon this new criterion and provide numerical results to demonstrate their accuracy and efficiency. A row exchange strategy is also described, which takes a given subset and performs interchanges to improve the quality of the selected subset. The second idea is, given a reduced set of data, how can we quickly reconstruct an accurate signal or image? Compressed sensing provides a mathematical framework that explains that if a signal or image is known to be sparse relative to some basis, then it may be accurately reconstructed from a reduced set of data measurements. The reconstruction process can be posed as a convex optimization problem. We introduce an algorithm that aims to solve the corresponding problem and accurately reconstruct the desired signal or image. The algorithm is based upon the Barzilai-Borwein algorithm and tailored specifically to the compressed sensing framework. Numerical experiments show that the algorithm is competitive with currently used algorithms. Following the success of compressed sensing for sparse signal reconstruction, we consider whether it is possible to reconstruct other signals with certain structures from reduced data sets. Specifically, signals that are a combination of a piecewise constant part and a sparse component are considered. A reconstruction process for signals of this type is detailed and numerical results are presented.

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Since most people now have a high-performing computing device with an attached camera in their pocket, in the form of a smartphone, robotics and computer vision researchers are thrilled about the possibility this creates. Such devices have previously been used in robotics to create 3D maps of environments and objects by feeding the camera data to a 3D reconstruction algorithm. The big downside with smartphones is that their cameras use a different sensor than what is usually used in robotics, namely a rolling shutter camera.These cameras are cheaper to produce but are not as well suited for general 3D reconstruction algorithms as the global shutter cameras typically used in robotics research. One recent, accurate and performance effective 3D reconstruction method which could be used on a mobile device, if tweaked, is LSD-SLAM. This thesis uses the LSD-SLAM method developed for global shutter cameras and incorporates additional methods developed allow the usage of rolling shutter data.The developed method is evaluated by calculating numbers of failed 3D reconstructions before a successful one is obtained when using rolling shutter data.The result is a method which improves this metric with about 70\% compared to the unedited LSD-SLAM method.