Abstract
Compressive sensing (CS) is new data acquisition algorithm that can be used for compression. CS theory certifies that signals can be recovered from far fewer samples or measurements 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. We evaluated three types of representation basis using data from real measurement. By comparing performance of data recovery, result show that DCT (Discrete Cosine Transform) is the best performance on sparsifying weather data
Published Version
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