Title: Detecting Frequency from Randomly Sampled Data Implementation of random sampling in BRATUMASS

  • Luxi Li School of Info Science and Tech, East China Normal University, Shanghai, China
  • Yizhou Yao College of Science and Engineering, Central Michigan University, Mt Pleasant, MI, U.S.
  • Meng Yao School of Info Science and Tech, East China Normal University, Shanghai, China
    BEACON Center, Michigan State University, East Lansing, MI, U.S.
  • Erik. D Goodman BEACON Center, Michigan State University, East Lansing, MI, U.S.

Abstract

In this article, a system which implements random sampling theory is presented—the system obtains each sample after a random time interval, and it is a part of the Breast Tumor Microwave Sensor System (BRATUMASS) which is designed to refine the data of BRATUMASS.

The first part introduces the system in the following aspects—the components of the system, how the signal is obtained, and the algorithm we used to calculate the spectrum of non-uniformly sampled data. The second part introduces a set of experimental performances based on random sampling method to explore the features of random sampling, and the signals used in the experiment are single frequency sinusoidal waves, mixed sinusoidal waves, and a piece of bass music waves since the random sampling method is a prototype and not integrated with BRATUMASS yet.

The data from BRATUMASS is a uniformly sampled data interpolated with the same mechanism—random time interval interpolation—random time interval interpolation—will be the next step of this study.

download PDF
DOI
10.32392/biomed.5
Published
2018-09-06
Citation
Yao M. et al. (2018) Detecting Frequency from Randomly Sampled Data Implementation of random sampling in BRATUMASS. Science Publishing Group Journal 1(2).
Corresponding author

Meng Yao
E-mail: myao@ee.ecnu.edu.cn

Author Guide

Full Text