First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Force sensing for measuring human body movement

The research developed in this thesis explores the sensing and inference of human movement in a dynamic way, as opposed to conventional measurement systems, that are only concerned with discrete evaluations of stimuli in sequential time. Typically, conventional approaches are used to infer the dynamic movement of the body; such as vision and motion tracking devices, with either a human diagnosis or complex image processing algorithm to classify the movement. This research is therefore the first of its kind to attempt and provide a movement classifying algorithm through the use of minimal sensing points, with the application for this novel system, to classify human movement during a golf swing. There are two main categories of force sensing. Firstly, array-type systems consisting of many sensing elements, and are the most commonly researched and commercially available. Secondly, reduced force sensing element systems (RFSES) also known as distributive systems have only been recently exploited in the academic world. The fundamental difference between these systems is that array systems handle the data captured from each sensor as unique outputs and suffer the effects of resolution. The effect of resolution, is the error in the load position measurement between sensing elements, as the output is quantized in terms of position. This can be compared to a reduced sensor element system that maximises that data received through the coupling of data from a distribution of sensing points to describe the output in discrete time. Also this can be extended to a coupling of transients in the time domain to describe an activity or dynamic movement. It is the RFSES that is to be examined and exploited in the commercial sector due to its advantages over array-based approaches such as reduced design, computational complexity and cost.

Glycated hemoglobin, body weight and blood pressure in type 2 diabetes patients initiating dapagliflozin treatment in primary care:a retrospective study

Introduction - The present study aimed to describe characteristics of patients with type 2 diabetes (T2D) in UK primary care initiated on dapagliflozin, post-dapagliflozin changes in glycated hemoglobin (HbA1c), body weight and blood pressure, and reasons for adding dapagliflozin to insulin. Methods - Retrospective study of patients with T2D in the Clinical Practice Research Datalink with first prescription for dapagliflozin. Patients were included in the study if they: (1) had a first prescription for dapagliflozin between November 2012 and September 2014; (2) had a Read code for T2D; (3) were registered with a practice for at least 6 months before starting dapagliflozin; and (4) remained registered for at least 3 months after initiation. A questionnaire ascertained reason(s) for adding dapagliflozin to insulin. Results - Dapagliflozin was most often used as triple therapy (27.7%), dual therapy with metformin (25.1%) or added to insulin (19.2%). Median therapy duration was 329 days [95% confidence interval (CI) 302–361]. Poor glycemic control was the reason for dapagliflozin initiation for 93.1% of insulin-treated patients. Avoiding increases in weight/body mass index and insulin resistance were the commonest reasons for selecting dapagliflozin versus intensifying insulin. HbA1c declined by mean of 9.7 mmol/mol (95% CI 8.5–10.9) (0.89%) 14–90 days after starting dapagliflozin, 10.2 mmol/mol (95% CI 8.9–11.5) (0.93%) after 91–180 days and 12.6 mmol/mol (95% CI 11.0–14.3) (1.16%) beyond 180 days. Weight declined by mean of 2.6 kg (95% CI 2.3–2.9) after 14–90 days, 4.3 kg (95% CI 3.8–4.7) after 91–180 days and 4.6 kg (95% CI 4.0–5.2) beyond 180 days. In patients with measurements between 14 and 90 days after starting dapagliflozin, systolic and diastolic blood pressure decreased by means of 4.5 (95% CI −5.8 to −3.2) and 2.0 (95% CI −2.9 to −1.2) mmHg, respectively from baseline. Similar reductions in systolic and diastolic blood pressure were observed after 91–180 days and when follow-up extended beyond 180 days. Results were consistent across subgroups. Conclusion - HbA1c, body weight and blood pressure were reduced after initiation of dapagliflozin in patients with T2D in UK primary care and the changes were consistent with randomized clinical trials.