Search

We propose a modified algorithm for the gradient method to determine the near-edge smoke plume boundaries using backscatter signals of a scanning lidar. The running derivative of the ratio of the signal standard deviation (STD) to the accumulated sum of the STD is calculated, and the location of the global maximum of this function is found. No empirical criteria are required to determine smoke boundaries; thus the algorithm can be used without a priori selection of threshold values. The modified gradient method is not sensitive to the signal random noise at the far end of the lidar measurement range. Experimental data obtained with the Fire Sciences Laboratory lidar during routine prescribed fires in Montana were used to test the algorithm. Analysis results are presented that demonstrate the robustness of this algorithm. OCIS codes: 280.3640, 280.0280, 280.1100, 280.1120.

A pathway has been developed using a multidisciplinary group from within specialist palliative care to ensure a comprehensive approach to the insertion of gastromy tubes for patients with motor neurone disease (MND) with swallowing difficulties. The pathway has ensured that there is a coordinated approach and the professionals involved are clear as to their responsibilities in the discussion and planning of the insertion, ensuring the best support for the patient and family.

Machine vision for navigational purposes is a rapidly growing field. Many abilities such as object recognition and target tracking rely on vision. Autonomous vehicles must be able to navigate in dynamic enviroments and simultaneously locate a target position. Traditional machine vision often fails to react in real time because of large computational requirements whereas the fly achieves complex orientation and navigation with a relatively small and simple brain. Understanding how the fly extracts visual information and how neurons encode and process information could lead us to a new approach for machine vision applications. Photoreceptors in the Musca domestica eye that share the same spatial information converge into a structure called the cartridge. The cartridge consists of the photoreceptor axon terminals and monopolar cells L1, L2, and L4. It is thought that L1 and L2 cells encode edge related information relative to a single cartridge. These cells are thought to be equivalent to vertebrate bipolar cells, producing contrast enhancement and reduction of information sent to L4. Monopolar cell L4 is thought to perform image segmentation on the information input from L1 and L2 and also enhance edge detection. A mesh of interconnected L4's would correlate the output from L1 and L2 cells of adjacent cartridges and provide a parallel network for segmenting an object's edges. The focus of this research is to excite photoreceptors of the common housefly, Musca domestica, with different visual patterns. The electrical response of monopolar cells L1, L2, and L4 will be recorded using intracellular recording techniques. Signal analysis will determine the neurocircuitry to detect and segment images.