Signal Waveform Classification for Oscilloscope Measurement Automation
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Solution Overview
Problem
Conventional oscilloscopes require manual user selection of numerical parameters for signal waveforms, which is time-consuming and prone to errors, especially for less experienced users, as they need to identify the signal waveform and select appropriate measurements.
Innovation Solution
A method and apparatus that automatically classify signal waveforms by analyzing input signals, determining their form, and selecting the most appropriate numerical parameters, using techniques such as noise filtering, amplitude histogram analysis, matched filter testing, and decision trees to identify signal types and select corresponding measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user selection of numerical parameters is used, then user control over measurement selection is maintained, but operation time increases and accuracy decreases for less experienced users
Solution Approach 1:
The system automatically classifies signal waveforms and selects appropriate numerical parameters without requiring user intervention. The oscilloscope performs self-service by analyzing the input signal, determining its type through classification algorithms, and automatically enabling the most relevant measurements, thereby eliminating manual selection errors and reducing operation time
Solution Approach 2:
The system performs preliminary classification and analysis of the signal waveform before the user needs to make measurement selections. By pre-processing the signal through multiple classification stages and predetermined decision logic, the system prepares the optimal measurement configuration in advance, ready for immediate activation without user delay
2Ease of operation
If manual user identification of signal waveform is required, then user understanding of signal characteristics is maintained, but ease of operation decreases
Solution Approach 1:
The signal classification process is divided into multiple sequential stages: initial classification, secondary classification, and tertiary classification. Each stage narrows down the signal type using different criteria and algorithms, breaking down the complex task of waveform identification into manageable segments that automatically process without user involvement
Solution Approach 2:
Multiple classification algorithms and predetermined decision logic act as intermediaries between the raw signal input and the final measurement selection. These intermediary processing layers analyze signal characteristics, compare them against known waveform patterns, and translate complex signal properties into simple classification decisions that automatically drive measurement selection
3Productivity
If automated classification is implemented, then user intervention is reduced and measurement speed increases, but device complexity increases
Solution Approach 1:
The classification system dynamically adapts its analysis depth and methodology based on the signal characteristics encountered. The system can adjust between different classification pathways, enabling or disabling specific analysis techniques depending on the complexity of the input signal, thereby optimizing processing speed while managing computational resources efficiently
Data Source
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AI summary
There is provided a method of classifying a signal waveform under test for appropriate numerical measurement, comprising acquiring waveform data indicative of the signal waveform under test, analysing the waveform data, determining a signal waveform type from the analysed waveform data, and activating an appropriate numerical measurement of the signal waveform under test based upon the determined signal waveform type. There is also provided a test and measurement instrument configured to carry out any of the disclosed methods.