A team led by the Technology Innovation Institute (TII) in Abu Dhabi has developed NATHR-G1, a ground penetrating radar for detecting landmines and unexploded ordnance. The project, involving researchers from Colombia, Germany, Sweden, and Switzerland, builds on earlier work using radar to detect buried objects. NATHR-G1 incorporates machine learning for advanced signal processing and object identification. Why it matters: This humanitarian application of AI and robotics based in the UAE could significantly reduce casualties from landmines and other explosive remnants of war.
KAUST researchers in the Sensors Lab are developing neuromorphic circuits for vision sensors, drawing inspiration from the human eye. They created flexible photoreceptors using hybrid perovskite materials, with capacitance tunable by light stimulation, mimicking the human retina. The team collaborates with experts in image characterization and brain pattern recognition to connect the 'eye' to the 'brain' for object identification. Why it matters: This biomimetic approach promises advancements in AI, machine learning, and smart city development within the region.
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
MBZUAI researchers presented "TransRadar," a study at WACV proposing new uses for radar in object identification. The study, led by Yahia Dalbah, explores fusing radar with other technologies to identify objects, particularly for autonomous vehicles. The "TransRadar" approach uses an adaptive-directional transformer for real-time multi-view radar semantic segmentation. Why it matters: This research addresses the limitations of radar by enhancing its object recognition capabilities, potentially improving the reliability of autonomous systems in adverse conditions.