A method to improve l1 performance of the CS (Compressive Sampling) for signals with known spectral energy density is proposed. Instead of random sampling, the proposed method selects the location of samples to follow the distribution of the spectral energy. Samples collected from three different measurement methods; the uniform sampling, random sampling, and energy equipartition sampling, are ...

Probability proportional to size (PPS) sampling finds application in business surveys both for the first stage of a multi-stage design, and for direct sampling of businesses from a list frame. In both cases there is a need to update samples from time to time, while retaining as many units as possible from the old sample. In the second type of application there is also a need for negative coordi...

The possibility that researchers should be able to obtain data from all cases is questionable. There a need; therefore, this article provides probability and non-probability sampling. In paper we studied the differences similarities of two with approach more fritter away time, cost sufficient energy required throughout sample observed. pair shows between them, different articles were reviewed c...

Abstract In this research work we introduce a new sampling design, namely two-stage cluster sampling, where probability proportional to size with replacement is used in the first stage unit and ranked set second order address issue of marked variability sizes population units concerned sampling. We obtained an unbiased estimator mean total, as well variance estimator. calculated relative effici...

We investigate the use of biased sampling according to the density of the data set to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional data sets. In density-biased sampling, the probability that a given point will be included in the sample depends on the local density of the data set. We propose a general technique for densi...

We investigate the use of biased sampling according to the density of the dataset, to speed up the operation of general data mining tasks, such as clustering and outlier detection in large multidimensional datasets. In densitybiased sampling, the probability that a given point will be included in the sample depends on the local density of the dataset. We propose a general technique for density-...