BackgroundAUTODOCK Vina is an open-source program which is steadfast and authentic to perform docking simulations. Though, Auto Dock Tools can help perform docking simulations with Vina, it largely remains as a platform for docking single molecule at a time.Findings"AUDocker LE" is designed with an aim to develop a software tool as a front end graphical interface with C# language to perform docking experiments in Windows based computers. It helps users to perform automated continuous docking of large ligand databases into a set of predefined protein targets. It will also help the user to analyze the results to select promising lead molecules.ConclusionAUDocker LE provides a straight forward graphical interface which can be used in a standard personal computer with Microsoft Windows XP or Windows 7 as the operating system where Autodock Vina, Python 2.5 and .net frame work are preinstalled.
Low complexity decoding schemes are presented for combined space time block coding and V-BLAST options in Enhanced Wireless Consortium draft [1] for IEEE 802.11n. We exploit the structure of the transmitted data in developing the schemes. They are introduced for a frequency flat fading scenario and the diversity orders they yield are analyzed. The techniques are extended to orthogonal frequency division multiplexing mode of baseband modulation, and the impact of spatial correlation among the antennas is studied.Simulations are used to validate the predicted performance of the proposed schemes, predicted from the diversity order analysis. Also, their performance is compared with that of Zero Forcing Successive Interference Cancellation (ZF-SIC). Computational complexities of the proposed as well as that of ZF-SIC are evaluated. The results show that one of the proposed schemes performs like ZF-SIC while its computational complexity is about one-half of that of ZF-SIC. Simulations with TGn channels [2], which are frequency selective channels and have spatial correlation, show that the performance degrades with increasing correlation.
Fire fighting is always a risk involved mission. To facilitate the fire extinguishing operations, autonomous robots are being developed in recent days. In this paper, we propose a fire fighting robot which uses a modular design concept to implement Fire detection, Path directing and Extinguishing operations. The usual trend in the previous implementation is the use of smoke detectors and physical sensors for fire detection as well as depth manipulation. Generally most sensors have low range and are sensitive to environmental changes. In this paper, we propose a computer vision based algorithm for fire detection and for directing the robot towards the detected fire, thereby overcoming the above limitations. Color segmentation is used in initial detection. Correlation is used to extract the non-static property of fire. Temperature sensor and UV-TRON sensor are used to confirm the presence of fire along with depth mapping. Finally, a water sprinkler is used to extinguish the detected fire.
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