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).
Keywords: compressive sensing, DCT, representation basis, weather monitoring
Author: Rika Sustika, Bambang Sugiarto
Journal Code: jptkomputergg160244

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