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AN ADAPTIVE FILTER BASED LEARNING MODEL FOR TIME SERIES SENSOR SIGNAL CLASSIFICATION ON EDGE DEVICES
AN ADAPTIVE FILTER BASED LEARNING MODEL FOR TIME SERIES SENSOR SIGNAL CLASSIFICATION ON EDGE DEVICES
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机译:基于自适应滤波器的时间序列传感器信号分类的基于自适应滤波器的学习模型
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摘要
This disclosure relates generally to method and system for an adaptive filter based learning model for time series sensor signal classification on edge devices. The adaptive filter based learning model for time series sensor signal classification enables automated-computationally lightweight learning (significant reduction in computational resources) and inferring/classification in real-time or near-real-time on CPU/memory/battery life constrained edge devices. The disclosed techniques for time series sensor signal classification on edge devices characterizes the intrinsic signal processing properties of the input time series sensor signals using linear adaptive filtering and derivative spectrum to efficiently construct the adaptive filter based learning model based on standard classification algorithms for time series sensor signal classification.
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