Chaotic Signal Processing for Small-Change Detection Under Noise
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Solution Overview
Problem
Conventional measurement systems face challenges in accurately detecting small changes in input signals due to noise and inherent measurement errors, requiring expensive and sensitive equipment to achieve high precision.
Innovation Solution
The system employs non-linear dynamical functions, such as the Tent Map and Logistic Map, to generate iteration values that form signatures, allowing for high detection resolution independent of the input range, using electronic circuitry to convert these values into digital data and measure signal changes through divergence analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ADC systems are used to detect small signal changes, then measurement precision can be achieved, but the cost of equipment increases significantly
Solution Approach 1:
The patent transforms the measurement approach by changing from direct linear amplification to non-linear chaotic iteration. The system applies chaotic maps (e.g., tent map, logistic map) to iteratively amplify small signal differences, converting unmeasurably small changes into detectable divergences in iteration sequences. This parameter transformation enables high-resolution measurement without requiring expensive high-resolution ADC hardware.
Solution Approach 2:
The patent replaces the traditional mechanical/electronic amplification system with a computational chaos-based system. Instead of using physical amplifiers and high-resolution ADCs, the system uses software-based chaotic iteration algorithms to achieve signal differentiation. This substitution of physical measurement mechanisms with computational methods reduces hardware cost while maintaining or improving measurement precision.
2Measurement precision
If signal amplification is applied to detect small changes, then detection resolution improves, but noise and measurement errors also increase
Solution Approach 1:
The patent implements feedback through iterative chaotic mapping where the output of each iteration becomes the input for the next. This feedback mechanism allows the system to continuously refine the measurement by comparing successive iterations. The chaotic nature of the feedback loop amplifies small initial differences while the systematic comparison of iteration sequences enables noise filtering through pattern recognition and divergence analysis.
Solution Approach 2:
The patent segments the measurement process into multiple discrete chaotic iterations rather than using a single amplification step. Each iteration produces a sequence of values that can be individually analyzed. By segmenting the signal processing into multiple computational stages, the system can identify and filter noise patterns while preserving the underlying small signal changes, as genuine signals will show consistent divergence patterns across iterations while noise will appear random.
3Measurement precision
If high resolution ADCs are used to measure small signal changes, then measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent creates multiple copies of the same signal through iterative chaotic mapping, generating a sequence of transformed versions rather than requiring a single high-resolution measurement. Each iteration produces a copy of the signal processed through the chaotic map, and these copies are then compared to extract the small changes. This copying approach using simple chaotic functions replaces the need for complex high-resolution ADC hardware.
Solution Approach 2:
The patent implements a universal measurement system where the same chaotic iteration algorithm can measure small signal changes across different input ranges and applications. The chaotic map functions (tent map, logistic map, etc.) serve multiple purposes: signal transformation, amplification of small differences, and generation of comparison sequences. This multi-functional approach eliminates the need for different specialized hardware components for different measurement requirements, reducing overall system complexity.
Data Source
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AI summary
A method of measuring signal change including performing at least one calculation or iteration step based on one or more chaos non-linear dynamical functions on first and second input signals, or sample of signals, to produce iteration values. Performing at least a second iteration step by repeating the at least one calculation or iteration step based on one or more non-linear dynamical functions on the first iteration values to produce a second iteration values and subtracting one set of iteration values generated from either the first or second input signal from the corresponding iteration values generated from the other input signal.