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In image fusion, information from a set of images is extracted and then combined intelligently toform a new composite image with extended information content. The original data may come from different viewing conditions (bracketed focus or exposure) or various sensors (visible and infrared or a cat scan and magnetic resonance imagery). tininhabited Airborne Vehicles (UAVs) often have visible, infrared and synthelic aperture radar imaging sensors, so image fusion is an appropriate onboard processing task for tiA V's. Some forms of image fusion are computationally intensive task, but like many other image processing applications are naturally suited Io acceleralion in hardware. This potenrial for hardware acceleration, and the abiliry to reconfigure the UAV to implement new algorithms as it moves towards objects of inferest make reconfgurable computing a natural route for a hardware implementation. I n this paper we present what we believe is rhe firsr implementarion of image fusion on a reconfigurable plarform alone, and the first investigation of adaptive image fusion which m a k s use of dynamic reconfiguration to change the fusion algorithm as the tiAVapproaches an object ofinterest.

Hotspots or smouldering embers left in the wake of a bushfire can, if not extinguished, reignite causing further destruction and loss of life as was the case on Eyre Peninsular in Australia in January 2005. The current method of detecting hotspots is very labour intensive, time consuming and inexact. To overcome these limitations, we propose a system that employs small uninhabited aerial vehicles (UAV) and reconfigurable computing (RC) technologies to enable fire fighting personnel to quickly and effectively locate hotspots. This paper explores the technologies proposed for the hotspot detection system including the algorithms for detecting and tracking of hotspots. It investigates the characterisation of these hotspots for autonomous detection, including data collection and testing techniques. It also describes the system's requirements as well as its components and architecture 12

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Many augmented reality systems use general purpose computing hardware to perform tasks such as rendering computer graphics, video overlay, and vision tracking. This can result in systems being large and bulky due to the hardware complexity required and the power consumed. We have developed a hand tracking solution in a reconfigurable computer, which reduces power consumption and transfers some of the processing into specialised hardware. This paper presents a summary of the design and its implementation details.