Coincidence techniques have been employed in a detailed study of Compton scattering of 279.2-keV y rays by K-shell electrons of tin and gold. Double-differential
Neural Network is used as a tool for estimating interconnection wire-length in VLSI standard cell placement problem. Conventional methods for estimating the interconnection wire-length viz.. Bounding Rectangle method, provide inaccurate estimate of the interconnection wire-length and does not depict die interconnection procedure in a layout and separates routing and placement tasks distinctly. The proposed mechanism utilizes the neural network characteristics in understanding the functional mapping between input and output, to estimate the interconnection wire-length. Experiments were performed for different number of cells with varying complexity of interconnections. In all the cases, the performance of the Neural Network is found to be superior to the results obtained using Bounding Rectangle procedure.
The ATLAS Trigger requires high efficiency and selectivity in order to keep the full physics potential of the experiment and to reject uninteresting processes from the 40 MHz event production rate of the LHC. These goals are achieved by a trigger composed of three sequential levels of increasing accuracy that have to reduce the output event rate down to ∼100 Hz . This work focuses on muon reconstruction and identification for the third level (Event Filter), for which specific algorithms from the off-line environment have been adapted to work in the trigger framework. Two different strategies for accessing data are described and their reconstruction potential is shown in terms of efficiency, resolution and fake muon rejection power.
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