Compressive Sensing Algorithm for Data Compression on Weather Monitoring System
Compressive Sensing Algorithm for Data Compression on Weather Monitoring System
复制标题
气象监测系统数据压缩的压缩感知算法
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
B. Sugiarto
中科院分区：
文献类型：
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作者：
Rika Sustika;B. Sugiarto
压缩感知（CS）是一种可用于压缩的新型数据采集算法。 CS 理论证明，可以从比奈奎斯特速率少得多的样本或测量中恢复信号。在本文中，压缩传感技术应用于我们的天气监测系统的数据压缩。在这个天气监测系统中，使用压缩传感和更少的样本或测量进行压缩意味着最大限度地减少传感和总体能源成本。本文的重点在于选择矩阵作为表示基础，在该矩阵下对天气数据进行稀疏表示。我们使用实际测量的数据评估了三种类型的表示基础。通过比较数据恢复的性能，结果表明DCT（离散余弦变换）在稀疏天气数据上具有最佳性能
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