Dynamic Sampling Density for Signal Response Analysis
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Solution Overview
Problem
Current signal analysis methods for evaluating device performance analyze all portions of the response function with constant resolution, regardless of the device's response properties, leading to suboptimal analysis.
Innovation Solution
A signal analysis method and measurement system that dynamically adjust sampling point density based on the output signal and its derivatives, focusing higher resolution on relevant and interesting portions with varying sampling point density, such as Gaussian distribution or doubled/tripled density compared to other portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant sampling point density is used across all frequency ranges, then the analysis covers the entire response function uniformly, but the resolution is suboptimal for relevant portions and wasteful for non-interesting portions
Solution Approach 1:
The patent applies local quality by varying the sampling point density according to the specific characteristics of different frequency regions. Relevant portions (e.g., passband, stopband, transition regions) are analyzed with higher sampling density to achieve better resolution, while non-interesting portions use lower density. This resolves the contradiction by making measurement precision high where needed and reducing analysis time in less critical regions.
Solution Approach 2:
The patent implements dynamics by adapting the sampling point density dynamically based on the device under test's response properties. The sampling density is adjusted according to the actual characteristics of the response function (such as identifying passband, stopband, and transition regions), rather than using a static uniform density. This allows the system to optimize both resolution and analysis time based on real-time analysis needs.
2Measurement precision
If higher sampling point density is used to improve resolution, then the characterization accuracy increases, but the analysis time increases
Solution Approach 1:
The patent applies local quality by concentrating higher sampling density only in relevant portions of the response function where accurate characterization is critical (such as passband, stopband, and transition regions). Non-interesting portions are analyzed with lower density, thereby maintaining high characterization accuracy where needed while improving overall analysis throughput by reducing unnecessary sampling in less critical regions.
3Measurement precision
If uniform resolution is applied to all portions of the response function, then the analysis is simplified, but relevant portions lack sufficient detail
Solution Approach 1:
The patent applies preliminary action by first performing a coarse analysis or preliminary identification of the response function characteristics to identify relevant portions (passband, stopband, transition regions). Based on this preliminary information, the sampling point density is then optimized for subsequent detailed analysis. This two-stage approach enables high detail resolution in relevant portions while keeping the overall system complexity manageable through a structured, phased methodology.
Data Source
AI summary
A signal analysis method is described. The signal analysis method comprises the following steps. An output signal is received from a device under test. A sampling point density is received and/or the sampling point density is determined based on the output signal. A response function of the device under test is determined based on the output signal and based on the sampling point density. The sampling point density represents a number of sampling points per frequency interval for determining the response function. The response function characterizes at least one property of the device under test as a function of frequency. Moreover a measurement system for determining a response function of a device under test is described.