Adaptive Filtering for Lightweight Time-Series Sensor Classification
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Solution Overview
Problem
Existing machine learning approaches for IoT data analysis on edge devices are computationally heavy and resource-constrained, making it difficult to construct learning models efficiently under computational and energy constraints, particularly for time series sensor signal analytics.
Innovation Solution
An adaptive filter-based learning model for time series sensor signal classification on edge devices, utilizing linear adaptive filtering and derivative spectrum to construct a computationally lightweight model for real-time or near-real-time classification, using techniques like Finite Impulse Response Recursive Least Squares (FIR-RLS) and Fast Fourier Transform (FFT).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning approaches (Deep Learning, Machine Learning, reinforcement learning) are used for IoT data analysis, then classification accuracy can be achieved, but computational resources and energy consumption increase significantly
Solution Approach 1:
The patent extracts and utilizes only the essential signal processing properties of time series sensor signals through linear adaptive filtering and derivative spectrum analysis, rather than employing full deep learning architectures. This extraction approach maintains classification accuracy while dramatically reducing computational resource requirements and energy consumption on edge devices.
Solution Approach 2:
The patent replaces computationally intensive machine learning mechanisms with signal processing-based adaptive filtering techniques. By substituting traditional ML approaches with adaptive filter-based methods that leverage intrinsic signal properties, the system achieves comparable classification performance with significantly lower computational overhead and energy usage.
2Measurement precision
If traditional machine learning approaches are used for time series sensor signal classification, then classification performance can be improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts and utilizes only the essential signal processing properties of time series sensor signals through linear adaptive filtering and derivative spectrum analysis, rather than employing full deep learning architectures. This extraction approach maintains classification accuracy while dramatically reducing computational resource requirements and energy consumption on edge devices.
Solution Approach 2:
The patent replaces computationally intensive machine learning mechanisms with signal processing-based adaptive filtering techniques. By substituting traditional ML approaches with adaptive filter-based methods that leverage intrinsic signal properties, the system achieves comparable classification performance with significantly lower computational overhead and energy usage.
3Productivity
If automated learning is implemented on edge devices, then real-time classification can be achieved, but computational resources are severely constrained
Solution Approach 1:
The patent implements self-service learning where the adaptive filter automatically adapts to each new time series signal instance without requiring external retraining or supervision. The filter autonomously extracts features and performs classification using only the intrinsic properties of the incoming signal, enabling real-time operation on resource-constrained edge devices with minimal computational overhead.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The model achieves automated, computationally efficient learning and classification on CPU/memory/battery-constrained devices, balancing training and inference time with performance accuracy, reducing computational resources while maintaining classification efficiency.
Implementation Method 1
constructing a linear filter using the predicted optimal filter co-efficient filter to obtain a deterministic component and a stochastic component for each of the training data
Implementation Method 2
deriving a derivative spectrum from the deterministic component and the stochastic component based on fast fourier transform techniques
Data Source
AI summary
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.