Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Nano-confined synthesis of fullerene mesoporous carbon (C60-FMC) with bimodal pores: XRD, TEM, structural properties, NMR, and protein immobilization

Nanoconfined synthesized crystalline fullerene mesoporous carbon (C60-FMC) with bimodal pore architectures of 4.95 nm and 10-15 nm pore sizes characterized by XRD, TEM, nitrogen adsorption/ desorption isotherm and solid-state NMR, and the material was used for protein immobilization. The solid-state 13C NMR spectrum of C60-FMC along with XRD, BET and TEM confirms the formation of fullerene mesoporous carbon structure C60-FMC. The immobilization of albumin (from bovine serum, BSA) protein biomolecule in a buffer solution at pH 4.7 was used to determine the adsorption properties of the C60-FMC material and its structural changes investigated by FT-IR. We demonstrated that the C60-FMC with high surface area and pore volumes have excellent adsorption capacity towards BSA protein molecule. Protein adsorption experiments clearly showed that the C60-FMC with bimodal pore architectures (4.95 nm and 10-15 nm) are suitable material to be used for protein adsorption