Bio-inspired camouflage to prevent shark attacks on surfers. Bio-inspired camouflage to prevent shark attacks on surfers. This project aims to develop a new shark deterrent technology to protect surfers and paddlers. Shark attacks are physically and emotionally devastating for the victims, and make the community disproportionately afraid. Surfers are at most risk of attack, but current surfboard-mounted deterrents are ineffective and not widely used. This project will build on the recent discove ....Bio-inspired camouflage to prevent shark attacks on surfers. Bio-inspired camouflage to prevent shark attacks on surfers. This project aims to develop a new shark deterrent technology to protect surfers and paddlers. Shark attacks are physically and emotionally devastating for the victims, and make the community disproportionately afraid. Surfers are at most risk of attack, but current surfboard-mounted deterrents are ineffective and not widely used. This project will build on the recent discovery that white sharks do not attack counter-illuminated (light emitting) seal-shaped decoys, and use new information about shark vision to understand why this ‘camouflage’ is so successful. This will also help to protect threatened shark species by reducing reliance on culling programs to keep people safe in the water.Read moreRead less
Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less