Plant Electrical signals based on wavelet transform and self similarity feature detection
Changcheng Li, Laiwu Yin, Shuyun Cai
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.76How to use a DOI?
- Continuous wavelet transform; Plant electrical signals; self similarity; feature detection
- According to the self similarity of plant electrical signal (fractal feature), changes of plant electrical signal amplitude a moment with the physical environment and mutation, causing plant electrical signal is not continuous. Electrical signal fractal characteristics of plant changes along with the time development, but at some point, it does not change with time change. This paper adopts the wavelet coefficient and self similarity relationship, through the index of self similarity calculation between plant electrical signal and wavelet to obtain the wavelet decomposition. Self similarity index is large, and plant electrical of the self similar degree are high. The simulation experiment results show that the self similarity index diagram after wavelet decomposition display can be found in many scales, the wavelet coefficients are very similar looking, providing a new idea for the detection of plant electrical signal characteristics of the physical environment.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Changcheng Li AU - Laiwu Yin AU - Shuyun Cai PY - 2015/04 DA - 2015/04 TI - Plant Electrical signals based on wavelet transform and self similarity feature detection BT - 2015 International Conference on Automation, Mechanical Control and Computational Engineering PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/amcce-15.2015.76 DO - https://doi.org/10.2991/amcce-15.2015.76 ID - Li2015/04 ER -