Compressive Sensing Algorithm for Data Compression on Weather Monitoring System
Abstract: Compressive sensing
(CS) is new data acquisition algorithm that can be used for compression. CS
theory certifies that signals can be recovered from fewer samples than Nyquist
rate. On this paper, the compressive sensing technique is applied for data
compression on our weather monitoring system. On this weather monitoring
system, compression using compressive sensing with fewer samples or measurements
means minimizing sensing and overall energy cost. Our focus on this paper lies
in the selection of matrix for representation basis under which the weather
data are sparsely represented. Results from simulation show that the using of
DCT (Discrete Cosine Transform) as representation basis has a better performance
on weather data recovery compared with other transform methods such as Walsh-Hadamard
Transform (WHT) and Discrete Wavelet Transform (DWT).
Author: Rika Sustika, Bambang
Sugiarto
Journal Code: jptkomputergg160244