| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥300,000)
Fiscal Year 2007: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Research Abstract |
The research focused on the sparse Fourier sampling algorithm, which is a prominent example of summarizing information structures by random sampling algorithms, an approach that quickly summarizes a large scale data keeping its structure and using random sampling. The algorithm enables estimating major Fourier coefficients of a huge signal data depending only on relatively small number of samples from the signal., and is expected to be useful in summarizing large-scale multimedia data The main contribution of the research is a series of performance analyses based on an implementation. In particular, the algorithm was applied to many types of data, setting values of various parameters to be smaller than that would be derived from theoretical performance guarantee, so that it was clear in what parameter and to what type of data the algorithm runs with/without performance margin. The algorithm was implemented using GP/PARI language and the running time, the success probability and the acc
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uracy of the Fourier coefficients were examined. It turned out that (a) replacing the original digital filter used in the frequency identification procedure with a equripple filter with the same filter degree and a better frequency selectivity increases the success probability of identifying major frequencies by 15 percent points, at a cost of small increment of the running time (b) in the same procedure, when one considers distributing a limited amount of computational resource to "'the number of isolated signals" and "filter degree", it is good to put more weight on "the number of isolated signals" (c) the algorithm runs with some performance margin on such a signal that consists of one significant frequency component and many small frequency components, on the other hand, it runs without margin when the signal contains two or more major frequency components of approximately same magnitudes (d) when the required accuracy of the Fourier coefficients is around 5 percent, the time taken by the estimation of Fourier coefficients is very small compared to that taken by the identification of major frequencies. These results will be good hint for practical implementation that can be used to summarize large multimedia data. Less
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