Flight data recorders (FDRs) play a critical role in determining root causes of aviation mishaps. Some aircraft record limited amounts of information during flight (e.g. T-1A Jayhawk), while others have no FDR on board (B-52 Stratofortress). This study explores the use of image-based flight data acquisition to overcome a lack of available digitally-recorded FDR data. In this work, images of cockpit gauges were unwrapped vertically, and 2-D cross-correlation was performed on each image of the unwrapped gauge versus a template of the unwrapped gauge needle. Points of high correlation between the unwrapped gauge and needle template were used to locate the gauge needle, and interpolation and extrapolation were performed (based on locations of gauge tick marks) to quantify the value to which the gauge needle pointed. Results suggest that image-based flight data acquisition could provide key support to mishap investigations when aircraft lack sufficient FDR data.

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The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes are predetermined, and examples (templates) are available for comparison. This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus the field of Correlation Pattern Recognition has developed over the past few decades as an important area of research. From the signal processing point of view, correlation is nothing but a filtering operation. Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to done to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters (QCF). QCFs offer the advantages of linear filters while optimizing a bank of these simultaneously to offer much improved performance. This dissertation develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed. Secondly, a transform domain implementation of the QCF is presented that has the benefits of lower computational complexity and computational requirements while retaining excellent recognition accuracy. Finally, a two dimensional QCF is presented that holds the potential to further save on storage and computations. The techniques are developed based on the recently proposed Rayleigh Quotient Quadratic Correlation Filter (RQQCF) and simulation results are provided on synthetic and real datasets.

A Laser Inertial Fusion Energy (LIFE) target’s flight through a low Reynolds number and high Mach number regime was analyzed with computational fluid dynamics software. This regime consisted of xenon gas at 1,050 K and approximately 6,670 Pa. Simulations with similar flow conditions were performed over a sphere and compared with experimental data and published correlations for validation purposes. Transient considerations of the developing flow around the target were explored. Simulations of the target at different velocities were used to determine correlations for the drag coefficient and Nusselt number as functions of the Reynolds number. Simulations with different target angles of attack were used to determine the aerodynamic coefficients of drag, lift, Magnus moment, and overturning moment as well as target stability. The drag force, lift force, and overturning moment changed minimally with spin. Above an angle of attack of 15°, the overturning moment would be destabilizing. At angles of attack less than 15°, the overturning moment would tend to decrease the target’s angle of attack, indicating the lack of a need for spin for stability at these small angles. This stabilizing moment would cause the target to move in a mildly damped oscillation about the axis parallel to the free-stream velocity vector through the target’s center of gravity.

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No description available.

In the fall of 2006, soils from three small valleys in the Laxemar/Oskarshamn area were sampled. A total of eight composite samples were characterized for a number of soil parameters that are important for geochemical sorption and were later also used in batch sorption experiments. Solid/liquid partition coefficients (Kd values) were then determined for seven radionuclides in each of the eight samples. To contribute to the interpretation of the sorption results together with the soil characterizations, this study aims to describe the sorption behavior of the radionuclides caesium, nickel and uranium and also discern which parameters that could provide a basis for estimating the strength of sorption of radionuclides in general. The methodology included quantitative methodologies such as compilation of chemical equilibrium diagrams by the software Hydra/Medusa and correlation analyses using the statistical software SPSS statistics. Based on the speciation diagrams of each radionuclide and identified important linear and non-linear relationships of the Kd values with a number of soil parameters, the following soil- and soil solution properties were found to have controlled the sorption of Cs, Ni and U, respectively, in the Laxemar soils. Cs: the specific surface area of the soil coupled to the clay content. Ni: the cation exchange capacity, alkaline solution pH, soil organic matter and dissolved organic matter. U: the cation exchange capacity, soil organic matter, dissolved organic matter, dissolved carbonate and alkaline solution pH. The soil that showed the strongest sorption varied between the nuclides, which can be related to the individual sorption behavior of caesium, nickel and uranium, as well as the different physicochemical properties of the soils. The parameters that should be prioritized in characterizations of soil samples are identified to be: solution pH, the cation exchange capacity, the specific surface area of the soil, soil organic matter and soil texture (clay content). / För att kunna fatta beslut relaterade till hypotetisk framtida kontaminering från slutförvar av radioaktivt avfall är det direkt avgörande att förstå mobiliteten av radioaktiva element i miljön. Sorption är en av de viktigaste kemiska mekanismerna som kan minska spridningen av radionuklider i vatten/jord/bergssystem, där nukliderna fördelar sig mellan vätskefasen och ytor på fasta partiklar i dessa system. Fördelningskoefficienter (Kd värden) används generellt som ett kvantitativt mått på sorptionen, där ett högt Kd värde innebär att en större andel av ämnet i fråga är bundet till den fasta fasen. Under hösten 2006 togs jordprover från tre dalgångar i Laxemar/Oskarshamn. Totalt åtta jordprover karakteriserades för ett antal jordparametrar som är viktiga för geokemisk sorption och användes senare i batchförsök tillsammans med ett naturligt grundvatten. Fördelningskoefficienter (Kd värden) bestämdes för sju radionuklider (Cs, Eu, I, Ni, Np, Sr and U) för vart och ett av de åtta jordproverna. För att bidra till tolkningen av sorptionsresultaten tillsammans med jordprovernas egenskaper syftar denna studie till att beskriva sorptionsbeteendet hos radionukliderna caesium, nickel och uran samt urskilja vilka parametrar som kan fungera som grund för att uppskatta sorptionsstyrkan av radionuklider i allmänhet. För att uppnå detta syfte så har studien följande mål. Identifiera de jord- och marklösningsegenskaper som kontrollerar sorptionen av Cs, Ni respektive U i de åtta Laxemar proverna. Bestämma vilket Laxemar-jordprov som starkast sorberar de tre radionukliderna. Identifiera de jordparametrar som bör prioriteras vid jordkarakteriseringar, baserat på deras sorptionsinflytande, för att kunna uppskatta Kd värden endast med begränsad information om ett jordsystem. Metoden innefattade kvantitativa metoder såsom sammanställning av kemiska jämviktsdiagram med programvaran Hydra/Medusa och korrelationsanalyser med hjälp av statistikprogramvaran SPSS statistics. De kemiska jämviktsdiagrammen bidrog till att beskriva specieringen av respektive nuklid som en funktion av pH och korrelationsanalyserna bidrog till att identifiera linjära samband mellan par av variabler, tex mellan Kd och jordparametrar. Baserat på specieringsdiagrammen för varje radionuklid och identifierade viktiga linjära och icke-linjära förhållanden mellan Kd-värdena och ett antal jordparametrar har följande egenskaper hos jordarna och marklösningen visat sig huvudsakligen kontrollera sorptionen av Cs, Ni respektive U i de åtta Laxemar jordarna: För caesium gäller jordens specifika ytarea kopplad till lerinnehållet, medan för nickel är det katjonbytarkapaciteten, organiskt material, alkaliska pH-värden samt löst organiskt material. Sorptionen av uran befanns kontrolleras av katjonbytarkapaciteten, organiskt material, löst organiskt material, alkaliska pH-värden samt lösta karbonater. Den jord som visade starkast sorption varierar mellan de tre nukliderna, vilket kan relateras till nuklidernas individuella sorptionsbeteende i jord samt jordarnas olika fysikaliska och kemiska egenskaper. Parametrarna som bör prioriteras vid karaktärisering av jordprov identifierades vara: pH, katjonbytarkapaciteten, jordens specifika ytarea, mängden organiskt material samt jordtexturen (lerinnehåll).

Yes / Motion-in-depth can be detected by using two different types of binocular cues: change of disparity (CD) and inter-ocular velocity differences (IOVD). To investigate the underlying detection mechanisms, stimuli can be constructed that isolate these cues or contain both (FULL cue). Two different methods to isolate the IOVD cue can be employed: anti-correlated (aIOVD) and de-correlated (dIOVD) motion signals. While both types of stimuli have been used in studies investigating the perception of motion-in-depth, for the first time, we explore whether both stimuli isolate the same mechanism and how they differ in their relative efficacy. Here, we set out to directly compare aIOVD and dIOVD sensitivity by measuring motion coherence thresholds. In accordance with previous results by Czuba et al. (2010), we found that motion coherence thresholds were similar for aIOVD and FULL cue stimuli for most participants. Thresholds for dIOVD stimuli, however, differed consistently from thresholds for the two other cues, suggesting that aIOVD and dIOVD stimuli could be driving different visual mechanisms.

Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.

Neste trabalho foi analisada a influência de um evento sobre o mercado de ações brasileiro a partir das redes, e suas árvores geradoras mínimas, obtidas de medidas de dependência baseadas nas correlações de Pearson, de Spearman e de Kendall. O evento considerado foi a notícia da noite de 17 de maio de 2017 em que o dono da empresa brasileira JBS, Joesley Batista, gravou o então Presidente da República Michel Temer autorizando a compra do silêncio de um Deputado Federal. O dia seguinte a notícia, 18 de maio de 2017, foi definido como o dia do evento. Foram coletados dados de alta frequência de 58 ações do Ibovespa no período de 11 a 25 de maio de 2017. As alterações nas redes das ações do mercado foram analisadas comparando-se o período anterior e posterior ao evento em duas escalas de tempo: (1) Redes diárias: cinco pregões antes do evento, o dia do evento e, cinco pregões depois do evento, com cotações a cada 15 minutos; (2) Agrupadas em antes e depois: agrupando os dados dos 5 dias antes e dos 5 dias depois do evento. O estudo das redes diárias indicou mudança de tendência nas suas propriedades no decorrer do período que contém o evento, com cotações a cada 15 minutos. Isto sugeriu que análise do efeito médio contido nos dados agrupados antes de depois do evento poderiam tornar mais evidente as mudanças na estrutura de rede das ações. As redes antes e depois do evento apresentaram mudanças significativas nas suas métricas que ficaram mais evidenciadas nas árvores geradoras mínimas. As redes geradas pelas correlações de Kendall e Spearman apresentaram um número maior de agrupamentos antes e depois do evento e, após o evento, as árvores geradoras mínimas apresentaram uma redução do número de agrupamentos de ações para todos os tipos de correlação. As distribuições de grau ponderado após o evento indicam uma probabilidade maior de vértices com graus distante da média. As métricas das árvores geradoras mínimas por correlação de Spearman sofreram a maior variação, seguidas pelas de Kendall e Pearson, e também, indicaram que as redes após o evento ficaram mais robustas, ou seja, mais rígidas. A maior robustez das redes após o evento indica maior conectividade do mercado, tornando-o, como um todo, mais suscetível ao impacto de novos acontecimentos. / In this work the influence of an event on the Brazilian stock market was analyzed from networks and its minimum spanning trees obtained from measures of dependence based on the Pearson, Spearman, and Kendall\'s correlations. The event considered was the news in the evening of May 17, 2017 in which the owner of the Brazilian company JBS, Joesley Batista, recorded the Brazilian President Michel Temer authorizing the purchase of the silence of a congress member. The day just after the news, May 18, 2017, was defined as the event day. High-frequency data from 58 Ibovespa shares were collected from 11 to 25 May 2017. Changes in the stocks networks were analyzed comparing the period before and after the event in two time scales: (1) Daily networks: five trade sections before the event, the day of the event and, five trade sections after the event, with price every 15 minutes; (2) Grouped before and after do evento: grouping data from 5 days before and 5 days after event. The study of the daily networks indicated a change of trend in their properties during the period that contains the event, with quotations every 15 minutes. The study of daily networks indicated a change of trend in their properties during the period containing the event. This suggested that analysis of the mean effect of grouped data before and after the event could highlight the changes in the network structure. The networks before and after the event showed significant changes in their metrics, which became more evident from the minimum spanning trees. After the event, the minimum spanning trees for grouped data got a smaller number of clusters in the networks for all kind of correlations. The networks generated by Kendall and Spearman correlations presented a larger number of clusters before and after the event. The weighted degree distributions after the event suggest a power law decay tail for all the correlations considered and indicates a higher probability of vertices with weighted degrees far away from the mean weighted degree. The minimum spanning tree metrics generated by Spearman correlation suffered the greatest variation, followed by those of Kendall and Pearson; and their values indicates that after the event the networks became more robust, that is, more rigid. The increase in the networks robustness after the event indicates a higher market connectivity, making it as a whole, more susceptible to the impact of new events.