The investigation of the high-latitude aurora by means of the V.H.F. auroral radar SABRE, (Swedish And British auroral Radar Experiment), developed by the Leicester University Ionospheric Physics group, is described. The first two chapters review previous studies of the atmosphere by radar techniques and includes a description of the SABRE radar. The basic radar equation and velocity relationships are derived and the various loss factors examined. From analysis of the echo signal data for the diffuse aurora, and examination of signal data from a radio star, the SABRE radar parameters are established. An investigation is then undertaken of the variation of the electrojet parameters, (derived from application of the radar equation to the SABRE data), with height, electric field strength and geomagnetic conditions. Evidence is found of backscattering from irregularities generated by both two-stream and gradient drift processes. An examination is also made of the echo signals detected at relatively short ranges, of below 370 km, and several causal mechanisms are investigated including scattering processes in the neutral atmosphere. It is evident that aircraft are predominantly the cause of these short-range echoes. This investigation also reveals a number of software and hardware faults in the initial radar configuration. This Thesis is concluded with suggestions for future development of the radar system, which include the implementation of a height-finding facility and improved spectral resolution.

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Remote sensing is becoming an increasingly important tool for ocean wave measurement, and over the past decade much progress has been made in the development of the wave measuring capabilities of HF (High Frequency) radar. This system is able to make detailed and near continuous observations of the sea surface over a wide area. However, because the mathematics of the data extraction process is rather difficult, the statistical properties of the observed data have to date been poorly understood. In this study, the approximate sampling distributions of a variety of measurements from HF radar (including significant waveheight, mean wave period, wind direction, and various spectral parameters) are derived in terms of quantities that are either known or estimable. The resulting confidence intervals are, in the case of significant waveheight and mean wave period, of comparable width to those obtained from the corresponding NURWEC2 (Netherlands UK Radar Wave buoy Experimental Comparison) wave buoy measurements, and in the case of spectral power, they are narrower. Furthermore, methods are derived by which such radar measurements may be compared with their corresponding wave buoy measurements in a statistically valid manner, and their relative biases estimated. These methods are then applied to data taken during the NURWEC2 field trial, which suggest that the radars and the wave buoy show good correspondence for measurements of significant waveheight and of spectral power (over 85 - 125mHz - the frequencies with most wave power, and hence those of most importance). There is also a fair correspondence for mean period measurements in the range 6.8 - 11.0secs. Spectral mean direction shows good correspondence over 85 - 155mHz over the somewhat limited directional range (i. e. as observed during the NURWEC2 storm) of the data.

This thesis focuses on assessing and improving statistical methods implemented in two areas of public health research. The first topic involves estimation of national influenza-associated mortality rates via mathematical modeling. The second topic involves the timely detection of infectious disease outbreaks using statistical process control monitoring. For over fifty years, the Centers for Disease Control and Prevention has been estimating annual rates of U.S. deaths attributable to influenza. These estimates have been used to determine costs and benefits associated with influenza prevention and control strategies. Quantifying the effect of influenza on mortality, however, can be challenging since influenza infections typically are not confirmed virologically nor specified on death certificates. Consequently, a wide range of ecologically based, mathematical modeling approaches have been applied to specify the association between influenza and mortality. To date, all influenza-associated death estimates have been based on mortality data first aggregated at the national level and then modeled. Unfortunately, there are a number of local-level seasonal factors that may confound the association between influenza and mortality - thus suggesting that data be modeled at the local level and then pooled to make national estimates of death. The first component of the thesis topic involving mortality estimation addresses this issue by introducing and implementing a two-stage hierarchical Bayesian modeling approach. In the first stage, city-level data with varying trends in mortality and weather were modeled using semi-parametric, generalized additive models. In the second stage, the log-relative risk estimates calculated for each city in stage 1 represented the “outcome” variable, and were modeled two ways: (1) assuming spatial independence across cities using a Bayesian generalized linear model, and (2) assuming correlation among cities using a Bayesian spatial correlation model. Results from these models were compared to those from a more-conventional approach. The second component of this topic examines the extent to which seasonal confounding and collinearity affect the relationship between influenza and mortality at the local (city) level. Disentangling the effects of temperature, humidity, and other seasonal confounders on the association between influenza and mortality is challenging since these covariates are often temporally collinear with influenza activity. Three modeling strategies with varying representations of background seasonality were compared. Seasonal covariates entered into the model may have been measured (e.g., ambient temperature) or unmeasured (e.g., time-based smoothing splines or Fourier terms). An advantage of modeling background seasonality via time splines is that the amount of seasonal curvature can be controlled by the number of degrees of freedom specified for the spline. A comparison of the effects of influenza activity on mortality based on these varying representations of seasonal confounding is assessed. The third component of this topic explores the relationship between mortality rates and influenza activity using a flexible, natural cubic spline function to model the influenza term. The conventional approach of fitting influenza-activity terms linearly in regression was found to be too constraining. Results show that the association is best represented nonlinearly. The second area of focus in this thesis involves infectious disease outbreak detection. A fundamental goal of public health surveillance, particularly syndromic surveillance, is the timely detection of increases in the rate of unusual events. In syndromic surveillance, a significant increase in the incidence of monitored disease outcomes would trigger an alert, possibly prompting the implementation of an intervention strategy. Public health surveillance generally monitors count data (e.g., counts of influenza-like illness, sales of over-the-counter remedies, and number of visits to outpatient clinics). Statistical process control charts, designed for quality control monitoring in industry, have been widely adapted for use in disease and syndromic surveillance. The behavior of these detection methods on discrete distributions, however, has not been explored in detail. For this component of the thesis, a simulation study was conducted to compare the CuSum and EWMA methods for detection of increases in negative binomial rates with varying amounts of dispersion. The goal of each method is to detect an increase in the mean number of cases as soon as possible after an upward rate shift has occurred. The performance of the CuSum and EWMA detection methods is evaluated using the conditional expected delay criterion, which is a measure of the detection delay, i.e., the time between the occurrence of a shift and when that shift is detected. Detection capabilities were explored under varying shift sizes and times at which the shifts occurred.

Visual memory has been demonstrated to play a role in both visual search and attentional prioritization in natural scenes. However, it has been studied predominantly in experimental paradigms using multiple two-dimensional images. Natural experience, however, entails prolonged immersion in a limited number of three-dimensional environments. The goal of the present experiment was to recreate circumstances comparable to natural visual experience in order to evaluate the role of scene memory in guiding eye movements in a natural environment. Subjects performed a continuous visual-search task within an immersive virtual-reality environment over three days. We found that, similar to two-dimensional contexts, viewers rapidly learn the location of objects in the environment over time, and use spatial memory to guide search. Incidental fixations did not provide obvious benefit to subsequent search, suggesting that semantic contextual cues may often be just as efficient, or that many incidentally fixated items are not held in memory in the absence of a specific task. On the third day of the experience in the environment, previous search items changed in color. These items were fixated upon with increased probability relative to control objects, suggesting that memory-guided prioritization (or Surprise) may be a robust mechanisms for attracting gaze to novel features of natural environments, in addition to task factors and simple spatial saliency. / text

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This thesis gives an account of experiments which investigate the detection of light by, and the emission of light from, planar metal-oxide-metal (M-O-M) tunnel junctions. The particular focus of attention is the mediation of these processes by surface plasmons, or surface electromagnetic waves bound to metal-dielectric interfaces, in the two processes. It describes how the coupling of incident bulk radiation to a surface plasmon supported by the junction structure may enhance the response of the device when used as a photodetector. This idea is then extended to cover other electromagnetic resonances supported by the junction system in different operating configurations. There is a brief departure from M-O-M devices to consider how a metal-semiconductor Schottky barrier diode may also have its photoresponse enhanced in a similar manner by coupling to a surface plasma wave localised at the metal-vacuum interface before returning to M-O-M devices to show that, in addition to their use as discrete detectors, they may also be used as integrated detectors of guided radiation. Attention is then turned onto the reverse process of light emission from M-O-M tunnel junctions. When these devices are 'rough' or are corrugated in some manner and pass a current they emit broadband light with an upper frequency cut-off determined by the applied bias, hv= e<SUP>V<SUB>bias</SUB></SUP>. This light emission process is mediated by the surface plasmons of the structure, of which there are three in the energy range considered. Experimental results on the light emission from residually rough and deliberately roughened junctions are reported. In particular, the results of an experiment are presented which show that the majority of the radiation outcoupled from statistically rough devices is derived from the 'fast' surface plasmon localised at the outer metal surface.

No description available.

No description available.