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Mathematical Model and Synthetic Data Generation for Infra-Red Sensors.

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This study presents a mathematical model and synthetic data framework to improve uncooled infrared (IR) sensor data pre-processing. This enables better algorithm design for enhanced IR sensor performance.

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Area of Science:

  • Sensor Technology
  • Data Science
  • Optical Engineering

Background:

  • Improving infrared (IR) sensor capabilities requires efficient data pre-processing algorithms.
  • Uncooled IR sensors face challenges in data handling due to complex physical characteristics.

Purpose of the Study:

  • To develop a mathematical model for uncooled IR sensor data generation.
  • To create a synthetic data framework for designing data pre-processing algorithms.

Main Methods:

  • Mathematical modeling of focal plane array, bolometer readout, optics, and environment.
  • Development of a synthetic data generation framework for uncooled IR sensors.
  • Simulation of sensor configurations, pixel defects, non-uniformity, and noise parameters.

Main Results:

  • A validated mathematical model capable of generating realistic synthetic data for uncooled IR sensors.
  • A flexible framework supporting diverse sensor configurations and defect scenarios.
  • Demonstrated utility in designing and testing data pre-processing algorithms.

Conclusions:

  • The developed model and framework are crucial for advancing uncooled IR sensor data pre-processing.
  • This approach facilitates the creation of more robust and efficient IR sensing systems.
  • Enables tailored algorithm development for specific sensor characteristics and environmental conditions.