Dynamic Signal Distribution for Sensor Gain Variation
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Solution Overview
Problem
Conventional parallel readout devices for spatially distributed signals suffer from sensor gain variations, leading to spectrum artifacts due to varying sensitivities among sensors, which are poorly calibratable and can change over time.
Innovation Solution
The apparatus and method involve a signal distribution device that continuously changes the spatial distribution of signals according to a deterministic function over time, ensuring every sensor receives signal from every channel, thereby eliminating gain variations across the sensor array without the need for calibration, even if sensor gains change over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional parallel readout devices use fixed sensor-signal associations, then the device complexity is reduced, but sensor gain variations cause spectrum artifacts and reduce measurement precision
Solution Approach 1:
The patent applies dynamics by making the signal distribution dynamic rather than fixed. The signal distribution device changes the spatial distribution of signals over time according to a deterministic function, so that each sensor receives signals from multiple channels across different time instances. This dynamic approach eliminates the need for precise gain calibration of individual sensors while maintaining measurement precision.
Solution Approach 2:
The patent introduces the time dimension to the traditionally spatial signal-sensor mapping. By distributing signals across both space (sensors) and time (multiple time instances), the system transforms a static 2D problem into a dynamic 3D problem, allowing each sensor to sample multiple signal channels over time and eliminating gain variation artifacts.
2Measurement precision
If sensor gains are calibrated to eliminate variations, then measurement precision improves, but the calibration process increases device complexity and maintenance requirements
Solution Approach 1:
The system performs self-correction by using the deterministic time-varying distribution to inherently compensate for sensor gain variations. Each sensor's output is processed with knowledge of the distribution function to reconstruct the original signal intensities, making the system self-calibrating and eliminating the need for external calibration procedures.
3Productivity
If multiple sensors are used for parallel processing, then productivity increases, but sensor gain variations increase harmful factors affecting data quality
Solution Approach 1:
The signal distribution follows a periodic or cyclic pattern over time, where each signal channel is systematically distributed to different sensors in a deterministic sequence. This periodic action ensures that all sensors experience similar gain characteristics across the full signal spectrum, eliminating artifacts while maintaining parallel processing throughput.
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 allows for accurate data collection by eliminating the impact of gain variations, ensuring reliable and artifact-free spectrum analysis while maintaining parallel processing efficiency.
Implementation Method 1
chromatic dispersion may provide spatial separation of an optical signal
Implementation Method 2
an array of sensors distributed along the dispersed electron positions may be utilized to collect the spatially distributed signals
Data Source
AI summary
A readout apparatus and method for processing spatially distributed signals is disclosed. The readout apparatus and method may reduce/eliminate the impact gain variations among a plurality of sensing channels. This is done by continuously varying the dispersion properties of a signal distribution device, which may induce a spatial shift of the signal distribution during data acquisition, allowing the distributed signals to move across the sensor area. Shifting of the distributed signals may occur multiple times, hence eliminating the impact of gain variation across the sensor array. The accumulated data may be re-assembled subsequently to complete the readout operation.