Oscilloscope Principal Component Analysis Data Remapping
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Solution Overview
Problem
Conventional oscilloscopes and test and measurement devices are limited in their data analysis capabilities, unable to effectively perform complex analysis on measurement data beyond simple statistical processing and visualization, particularly when dealing with multiple variables and linear dependencies.
Innovation Solution
Integration of Principal Component Analysis (PCA) into oscilloscopes, which performs matrix decomposition on measurement data to re-map it into a principal component domain, allowing for the extraction of insights and revealing relationships between variables, using Singular Value Decomposition to determine orthogonal principal components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional statistical processing and simple visualization tools are used in oscilloscopes, then device complexity is kept low, but data analysis capability is insufficient for complex multi-variable measurements
Solution Approach 1:
The patent transforms measurement data from the original measurement domain into a principal component domain through mathematical transformation (PCA). This parameter change in the data representation enables complex multi-variable analysis capabilities while the PCA algorithm itself remains a software processing step that does not significantly increase hardware complexity
Solution Approach 2:
The patent introduces principal components as an intermediary representation between the raw measurement data and the final analysis results. These principal components serve as a bridge that simplifies complex multi-variable relationships into orthogonal components with clear physical meanings, enabling better data analysis without requiring direct complex processing of all original variables
2Loss of information
If Principal Component_analysis is integrated into the oscilloscope, then data analysis capability is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential information from complex multi-variable measurement data by performing PCA to identify principal components that capture the most significant variations in the data. This extraction process separates the meaningful information from noise and redundant variables, enabling better insight extraction while the computation is performed as a post-processing step rather than requiring complex real-time hardware
Solution Approach 2:
The patent segments the complex multi-variable measurement problem into distinct principal components through orthogonal transformation. Each principal component represents a separate dimension of variation in the measurement data, allowing independent analysis of different aspects of the measured phenomenon without the complexity of analyzing all variables simultaneously
Data Source
AI summary
A system includes an input for accepting an input signal from a Device Under Test (DUT), a measurement unit for generating first measurement data and second measurement data from the input signal, and one or more processors configured to derive at least one principal component from the first and second measurement data using principal component analysis, and remap the first measurement data and the second measurement data to a principal component domain derived from the at least one principal component. Methods of operation and description of storage media, the operation of which performs the above operations, are also described.