The most common and conventional way to collect people’s opinions has always been random sampling and asking several survey questions by phone. For instance, if a news media is interested in how popular Obama is among Americans, it would probably call up a thousand people and ask how they would rate Obama. The data collected in this manner is considered a gold standard; there has been only few ...

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 ...

SIMULATING DEPENDENT BINARY DATA WITH RANDOM EFFECTS. Aobo Wang, Roy T. Sabo, Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia 23298-0032. Dependent binary data can be simply simulated using the multinomial sampling method. We extend this method to simulate dependent binary data with clustered random effect structures. Several distributions are considered for co...

This article develops a method of ratio estimate in fuzzy sense. By both the simple random sampling and stratified random sampling, we can obtain the ratio estimate in usual statistical sense. However, the sampling data may be ambiguous in some uncertain circumstance. To solve such kind of problem, we probe into the simple random sampling and stratified random sampling in fuzzy sense, obtain th...

Data structures for efficient sampling from a set of weighted items are an important building block many applications. However, few parallel solutions known. We close these gaps. give efficient, fast, and practicable distributed algorithms data that support single (alias tables, compressed structures). This also yields simplified more space-efficient sequential algorithm alias table constructio...

Compressed sensing and magnetic resonance imaging are hot topics in the field of signal processing. In this study we introduced in Lustig’s variable density sampling method, integrated it to compressed sensing, and applied it to brain MRI acquisition. The realistic experiment shows the variable density sampling recovery better than traditional random sampling method on a 256x256 brain magnetic ...