摘要
在ARM平台上,设计了基于压缩感知的采集算法,极大地减少了数据存储量,提高了传输效率。分别选择离散傅里叶变换基(DFT)、离散余弦变换基(DCT)作为稀疏基,测试了该算法在2种基底下的工作性能。通过实验仿真分析,在相同稀疏度条件下,DCT具有更小的稀疏化误差,但是DFT具有更好的去噪效果、更低的观测维度和更高的数据压缩比;随着稀疏度的增大,两者的稀疏化误差减小,观测维度升高,数据压缩比降低。与此同时,还在ARM平台上测试了基于DFT压缩采集系统的压缩比和功耗,与常规植物微环境及生理参数监测系统相比,该系统的压缩比达到4.24,并能够节省13.62%的功耗。综上所述,基于压缩感知的植物微环境及生理参数采集方法由于数据压缩比高,在节省数据存储空间和降低数据传输量的同时,达到了降低系统功耗的目的,间接增强了系统的续航能力。
The system for collecting plant micro-environment and physiological parameters was featured with large amount of data, storage and transmission difficulties, which restricted the long-term monitoring of ecological information. A new method for collecting plant micro-environment and physiological parameters based on compressive sensing was proposed. The bases of discrete Fourier transformation （DFT） and discrete cosine transformation （DCT） were selected as sparse bases under which the working performance of system was tested. Through the experimental analysis, DCT had smaller sparse error and DFT had better denoising effect, lower observed dimension, higher compression ratio under condition of the same sparsity. With the increase of sparsity, both sparse error and compression ratio were decreased. In the meantime, both observed dimensions were increased. In addition, data compression ratio and power consumption of the DFT compressive sensing system were tested based on ARM. The conclusion was that the data compression ratio was 4.24, and 13.62% power consumption was saved compared with traditional collecting system for plant micro-environment and physiological parameters. In conclusion, the method proposed had advantage of compressing data, saving storage, decreasing data transmission, which can extend working hours of the system by decreasing power consumption and make an important effect on the development of internet of things in agriculture and forestry.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第3期317-324,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31371537)
中央高校基本科研业务费专项资金项目(BLX2015-36)
北京市共建项目
关键词
微环境及生理参数
数据采集
压缩感知
离散傅里叶变换
离散余弦变换
稀疏度
micro-environment and physiological parameters
data acquisition
compressive sensing
discrete Fourier transformation
discrete cosine transformation
sparsity
