Deterministic Component Model Judging Apparatus for Signal Jitter Analysis
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Solution Overview
Problem
Existing methods for evaluating electronic circuits and communication systems fail to accurately separate and measure deterministic and random components of signal jitter, as they rely on convolution of probability density functions without precise models for deterministic components.
Innovation Solution
A deterministic component model determining apparatus that calculates standard deviation, spectrum, null frequency, and theoretical values for various deterministic component models, allowing for the identification of the correct model by comparing measured values, thereby separating deterministic and random components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of deterministic component models are considered, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the model determination process into distinct functional modules: standard deviation calculation, spectrum calculation, null frequency detection, theoretical value calculation for multiple models, measured value calculation, and model determination. Each module handles a specific aspect of the analysis, making the overall complex process manageable and systematic.
Solution Approach 2:
The patent implements feedback by comparing measured values with theoretical values from multiple deterministic component models and using this comparison to determine the most appropriate model. This feedback mechanism ensures that the selected model accurately represents the actual deterministic component in the probability density function, thereby improving measurement precision.
2Reliability
If deconvolution is performed on probability density function, then separation of deterministic and random components improves, but measurement precision deteriorates without accurate model
Solution Approach 1:
The patent performs preliminary actions by calculating standard deviation, spectrum, and null frequency before performing deconvolution. These preliminary calculations provide essential parameters needed for accurate deconvolution and model determination, ensuring that the separation of deterministic and random components is based on reliable data.
Solution Approach 2:
The patent changes parameters by calculating multiple theoretical values corresponding to different deterministic component models and comparing them with measured values. This parameter comparison approach allows selection of the most appropriate model, ensuring accurate deconvolution and separation of components.
Data Source
AI summary
Provided is a deterministic component model determining apparatus that determines a type of a deterministic component included in a probability density function supplied thereto, comprising a standard deviation calculating section that calculates a standard deviation of the probability density function; a spectrum calculating section that calculates a spectrum of the probability density function; a null frequency detecting section that detects a null frequency of the spectrum; a theoretical value calculating section that calculates a theoretical value of a spectrum for each of a plurality of predetermined types of deterministic components, based on the null frequency; a measured value calculating section that calculates a measured value of the spectrum for the deterministic component included in the probability density function, based on the standard deviation and the spectrum; and a model determining section that determines the type of the deterministic component included in the probability density function to be the type of deterministic component corresponding to a theoretical value closest to the measured value, from among the theoretical values for the plurality of types of deterministic components.