Linear Image Sensor Spectral Analysis with Adaptive Pixel Sampling

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Solution Overview

Problem

Spectral analysis systems using CMOS light sensors face challenges with high noise ratios due to uniform sampling rates across pixels, leading to inconsistent signal-to-noise ratios for varying light intensities.

Innovation Solution

Implementing different sample rates and integration times across pixels of a CMOS sensor based on light intensity, with higher rates for brighter pixels and lower rates for dimmer pixels, to enhance signal-to-noise ratio and prevent saturation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If uniform sampling rates are used across all pixels, then the system is simple to implement, but the signal-to-noise ratio deteriorates for pixels with varying light intensities

Engineering Contradiction:

Improveuniform sampling rate implementationVSAvoidsignal-to-noise ratio

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent applies local quality by assigning different integration times to different pixels based on their light intensity characteristics. Bright pixels receive shorter integration times while dim pixels receive longer integration times, optimizing the signal-to-noise ratio for each local region rather than using a uniform sampling rate across the entire sensor array.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts integration times on a per-pixel basis according to the detected light intensity. The controller modifies the integration time parameter in real-time based on the relationship between a pixel's intensity and the overall spectrum peak intensity, transforming a static uniform sampling system into a dynamic adaptive one.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If higher sample rates are used for bright pixels, then the signal-to-noise ratio improves, but the device complexity increases

Engineering Contradiction:

Improvesignal-to-noise ratio for bright pixelsVSAvoidvariable sampling rate control

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system employs self-service by having each pixel automatically determine its own optimal integration time based on its measured light intensity relative to the spectrum peak. The controller autonomously calculates and applies appropriate integration times without external intervention, reducing the need for complex manual calibration or external control systems.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the integration time parameter dynamically based on the relationship between pixel intensity and spectrum peak intensity. By adjusting this single critical parameter on a per-pixel basis, the system optimizes signal-to-noise ratio while avoiding the need for multiple hardware components or complex system architectures.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If longer integration times are used for dim pixels, then the signal-to-noise ratio improves, but the response time worsens

Engineering Contradiction:

Improvesignal-to-noise ratio for dim pixelsVSAvoidspectral analysis response time

Core Design Contradiction:

Measurement precisionVSSpeed

Solution Approach 1:

The patent resolves this contradiction through local quality by applying longer integration times only to dim pixels that require enhanced signal accumulation, while bright pixels use shorter integration times for faster response. This localized optimization ensures that each pixel's integration time is tailored to its specific lighting conditions, balancing signal quality and response speed across the entire sensor array.

Inventive Principle:

Principle #3Local quality

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach improves the signal-to-noise ratio across a wider range of light intensities, providing more accurate spectral analysis and enabling better control of combustion systems.

Implementation Method 1

An optical conditioner is configured to receive light and separate the light into different wavelengths of light

Methodology Applied

Scientific EffectDiffraction: Diffraction

Implementation Method 2

An optical conditioner is configured to receive light and separate the light into different wavelengths of light

Methodology Applied

Scientific EffectRefraction: Refraction

Implementation Method 3

Each pixel of the CMOS linear array light sensor corresponds to a specified range of light frequencies

Methodology Applied

Scientific EffectPhotoelectric effect: Photoelectric Effect

Data Source

PatentUS12517040B2Spectral analysis with linear image sensor

Publication Date: 2026.01.06 BAKER HUGHES CO

AI summary

A first set of data characterizing a spectrum of light emitted or reflected by an object is received from a linear light sensor. Each pixel of the linear light sensor corresponds to a specified range of light frequencies. The data includes a first intensity of each pixel at a first sample rate. A subset of pixels is classified as points of interest within the first set of data. A second sample rate of each pixel is determined for each of the subset of pixels. A second set of data characterizing the spectrum of the light is retrieved. The second set of data includes a second intensity of each of the subset of pixels at the second sample rate. The first set of data and the second set of data are combined to produce a third set of data characterizing the spectrum.