摘要
物联网是当前人们的研究热点,本文提出使用压缩感知理论处理大规模的物联网中产生的海量数据.压缩感知是一种能够在采样的同时实现数据压缩的采样方法,它可以通过降低采样率显著减少采集的数据量,但压缩感知算法的计算复杂度高、对信号的适应性差.针对压缩感知方法的缺点,本文尝试对压缩感知算法并行处理方法以提高压缩感知的计算速度,同时引入冗余字典构造稀疏变换基以提高压缩感知对信号的适应性.
Internet of Things is one of the most popular scientific and technical terminologies. In this paper, we use compressed sensing theory to process mass data in Internet of Things. Compressed sensing is a sampling method that data sampling and compressing can be done simultaneously. Compressed sensing can significantly lower the data size by reducing sampling rates of sensors, but its algorithm has high computational complexity and its transformation basis is nonadaptive. This paper puts forward parallel processing of compressed sensing algorithm for high computational complexity. At the same time, we introduce redundant dictionary into compressed sensing for increasing the flexibility.
出处
《微电子学与计算机》
CSCD
北大核心
2012年第11期116-119,共4页
Microelectronics & Computer
基金
江苏省"九七三"项目(BK2011022)
国家自然科学基金(61170035)
南京理工大学重点基金(2011YBXM18)
关键词
海量数据
压缩感知
优化重构
并行处理
mass data
compressed sensing
optimal recovery
parallel processing
