We present an algorithm, Shared-State Sampling (S 3 ), for the problem of detecting large flows in high-speed networks. While devised with different principles in mind, S 3 turns out to be a generalization of two existing algorithms tackling the same problem: Sample-and-Hold and Multistage Filters. S 3 is found to outperform its predecessors, with the advantage of smoothly adapting to the memory technology available, to the extent of allowing a partial implementation in DRAM. S 3 exhibits mild tradeoffs between the different metrics of interest, which greatly benefits the scalability of the approach. The problem of detecting frequent items in streams appears in other areas. We also compare our algorithm with proposals appearing in the context of databases and regarded superior to the aforementioned. Our analysis and experimental results show that, among those evaluated, S 3 is the most attractive and scalable solution to the problem in the context of high-speed network measurements.

In this work a noise probe method to detect Raman bands in spectra has been developed. The effectiveness of this approach is demonstrated with the use of silica and gypsum Raman spectra obtained under variable experimental conditions. The advantage of the proposed method stems from the fact that the location of the Raman bands is automated by using software based upon a signal processing algorithm with the use of a personal computer. This approach avoids subjective errors. The method is shown to be suitable to locate Raman bands and so to be an efficient tool to identify materials on the basis of their characteristic Raman spectrum.

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