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A connectionist architecture is developed that can be used for modeling choice probabilities and reaction times in psychophysics and word recognition. The network architecture consists of a feed-forward network and a decoding module. Learning is by mean-variance back-propagation, an extension of the standard back-propagation learning algorithm. The new learning procedure is interpreted as a selective attention mechanism, and leads to a better model of learning in simple identification tasks than the standard back-propagation. Choice probabilities are modeled by the input/output relations of the network, and reaction times are modeled by the time taken for the network, particularly the decoding module, to achieve a stable state. The model is applied to both unidimensional and multidimensional identification tasks in psychophysics and to word recognition.

This thesis examines the statistical properties of the Poisson AR(1) model of Al-Osh and Alzaid (1987) and McKenzie (1988). The analysis includes forecasting, estimation, testing for independence and specification and the addition of regressors to the model. The Poisson AR(1) model is an infinite server queue, and as such is well suited for modeling short-term disability claimants who are waiting to recover from an injury or illness. One of the goals of the thesis is to develop statistical methods for analyzing series of monthly counts of claimants collecting short-term disability benefits from the Workers' Compensation Board (WCB) of British Columbia. We consider four types of forecasts, which are the k-step ahead conditional mean, median, mode and distribution. For low count series the k-step ahead conditional distribution is practical and much more informative than the other forecasts. We consider three estimation methods: conditional least squares (CLS), generalized least squares (GLS) and maximum likelihood (ML). In the case of CLS estimation we find an analytic expression for the information and in the GLS case we find an approximation for the information. We find neat expressions for the score function and the observed Fisher information matrix. The score expressions leads to new definitions of residuals. Special care is taken to test for independence since the test is on the boundary of the parameter space. The score test is asymptotically equivalent to testing whether the CLS estimate of the correlation coefficient is zero. Further we define a Wald and likelihood ratio test. Then we use the general specification test of McCabe and Leybourne (1996) to test whether the model is sufficient to explain the variation found in the data. Next we add regressors to the model and update our earlier forecasting, estimation and testing results. We also show the model is identifiable. We conclude with a detailed application to monthly WCB claims counts. The preliminary analysis includes plots of the series, autocorrelation function and partial autocorrelation function. Model selection is based on the preliminary analysis, t-tests for the parameters, the general specification test and residuals. We also include forecasts for the first six months of 1995.

Focusing techniques and detection of targets is usually associated to defense and military use. However in recent past things have moved ahead. Now target detection using UWB radars is being done in many industries and corporations. Radarbolaget AB is one of them; one of their projects uses UWB radars to detect steel strips inside a furnace. This research solves a potential problem of detecting middle steel strip out of total three strip edges which can be seen by radar placed on the front. For better understanding of the reader, existing system and introductory UWB radar principles are discussed. As there can be many solutions to focusing of targets here (steel strip edge detection). Available focusing techniques have been discussed in detail along with the possible physical and simulation setups. Later in the document, detection methods have been proposed. UWB time reversed signal detection is a fairly new method and a very limited research has been done so far. PRBS sequence has been focused on in detection mechanism. Results section show that the pulse of the PRBS works better and produces more promising results rather than a repetitive signal. Time reversal methods for locating the target have been used to find the approximate location of the target. Manual distance calculations from target to the transmitter and receiver have been done. Comparison of actual distance from target to the transmitter is compared with simulation results. Different model simulation setups and their results have proved that using UWB Time reversed signals; a still or moving target can be detected with centimeter window precision.

Objective: To estimate the associations between measures of religiousness and depression and to determine if these associations have changed over the period 1952 to 1992. Methods: Data were drawn from 2,398 individuals from the 1952 and 1992 cross sectional surveys of the Stirling County Study as a means of studying time trends. For this thesis, questions about frequency of religious worship attendance, frequency of saying grace, religious importance were employed to develop a scale of secularism. The individual questions and the scale were analyzed in terms of the prevalence of depression at each time point. Logistic regression was used to determine associations of depression with religion variables, adjusted for demographic and other covariates. Results: Individuals who attended religious services weekly were over two times less likely to meet criteria for depression than infrequent attenders and this relationship did not change over time. Associations between religious attendance and depression were stronger among women and the medically healthy compared to men and those with a medical condition. Being more secular was associated with higher odds of depression among females. Conclusions: Religious attendance has consistently been associated with lower depression over a forty year period, irrespective of marked declines in population-level religious behaviors. Associations between religiousness and depression may be stronger in females than in males.

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