计算平台:Windows/Linux
硬件:CPU
项目网址:http://www.acousticsathome.ru/boinc/
项目主办方:V.I. Ilichev Pacific Oceanological Institute, Far Eastern Federal University, Matrosov Institute for System Dynamics and Control Theory, A.A. Kharkevich Institute for Information Transmission Problems, Dorodnicyn Computing Centre
项目介绍:
大致是研究水下声学问题的,有空我详细翻译一下试试。
What is Acoustics@home?Acoustics @home is a BOINC-based volunteer computing project aimed at solving inverse problems in underwater acoustics. You can contribute to our research by running a free program on your computer. In underwater acoustics the notion of geoacoustic inversion refers to a collection of techniques that can be used for the reconstruction of medium parameters. The medium parameters are usually understood as the sound speed profile in water (i.e., the dependence of the sound speed on the depth) and the sound speed and density in the sea bottom sediment layers. Medium parameters reconstruction is of great importance for problems of underwater communication and for the development of underwater navigation systems.
While normally measurements for the geoacoustic inversion are performed using expensive receiver arrays, recently it was shown that single- hydrophone recording of a broadband pulse signal can be also successfully used for estimating the medium parameters.
The method of geoacoustic inversion developed in our study is based on using modal dispersion data. Waveguide dispersion is usually induced by the difference in group velocities of propagation of normal waves of different mode numbers at different frequencies. By using special algorithms of the frequency-time analysis of signals, it is possible to filter out the modal components of a pulse signal from its time series recorded by a single hydrophone. The implementation of this method in practice can be thought of as a solution of an optimization problem in a (very large) discrete search space, and every evaluation of the cost function requires numerous solutions of an acoustic spectral problem. Thus, the whole computational burden can be easily divided into a large number of relatively simple independent tasks, which can be solved using volunteer computing.
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