Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 4 - Phase 1 Implementation of the Concept Demonstrator

This report describes the first phase of the implementation of the Concept Demonstrator. The Concept Demonstrator system is a powerful and flexible experimental test-bed platform for developing sensors, communications systems, and multi-agent based algorithms for an intelligent vehicle health monitoring system for deployment in aerospace vehicles. The Concept Demonstrator contains sensors and processing hardware distributed throughout the structure, and uses multi-agent algorithms to characterize impacts and determine an appropriate response to these impacts

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 3 - Design of the Concept Demonstrator

This report provides an outline of the essential features of a Structural Health Monitoring Concept Demonstrator (CD) that will be constructed during the next eight months. It is emphasized that the design cannot be considered to be complete, and that design work will continue in parallel with construction and testing. A major advantage of the modular design is that small modules of the system can be developed, tested and modified before a commitment is made to full system development. The CD is expected to develop and evolve for a number of years after its initial construction. This first stage will, of necessity, be relatively simple and have limited capabilities. Later developments will improve all aspects of the functionality of the system, including sensing, processing, communications, intelligence and response. The report indicates the directions this later development will take

Accurate non-rigid object segmentation in medical images by fusing statistical features with structural features

This paper presents an accurate non-rigid object segmentation method that fuses both statistical features and structural features. In particular, the approach is detailed and applied on liver segmentation. It consists of three main components. First, an image texture analysis is done to derive pixel level features. It efficiently fuses the statistical features with structural features to achieve better segmentation. Then, a trained classifier based on support vector machine (SVM) is applied to classify the image into liver pixels or non-liver pixels. Finally, composite morphological operations are used to remove small wrongly classified areas and delineate the liver region. The approach is unique in two aspects: it states and provides experimental data to demonstrate that the fusion of the two classes of features does improve segmentation rate, comparing to the cases where only statistical features or structural features are used; it shows that an accurate segmentation can be achieved by combing regional morphological operations with pixel-wised SVM classifier. The algorithm can be applied to general non-rigid object segmentation which is a crucial part of an automatic surgical training and planning system

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Continued overleaf Systemic lupus erythematosus and pregnancy – a challenge to the clinicia

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Continued overleaf Management of pain in chronic pancreatitis. New solutions to an old problem Management of pain in chronic pancreatitis is a challenging clinical problem. Lack of proper understanding of the mechanisms responsible for pain, high morbidity and mortality rates historically associated with pancreatic surgery, and the long held view that pain will eventually subside when the pancreas "burns itself out " as a result of progressive fibrosis have all contributed to a non-surgical therapeutic approach for decades [1]. Many recent studies have challenged this view, and at present there is a shift from the "wait and see " approach to a more pro-active type of therapeutic approach in the management of pancreatic pain [2]. It is accepted that, at least in a majority of cases, the pain results from pressure increase within the pancreatic duct system from obstruction to the main pancreatic duct by stones or from post-inflammatory strictures [3]