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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Related Experiment Video

Updated: Jun 20, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Scene-based nonuniformity correction with reduced ghosting using a gated LMS algorithm.

Russell C Hardie1, Frank Baxley, Brandon Brys

  • 1Department of Electrical and Computer Engineering, University of Dayton, 300 College Park, Dayton, OH 45459-0232, USA. rhardie@udayton.edu

Optics Express
|August 19, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new scene-based nonuniformity correction (NUC) method that reduces ghosting artifacts in infrared images. The novel gating operation improves image quality but slightly increases processing time.

Related Experiment Videos

Last Updated: Jun 20, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Area of Science:

  • Infrared imaging
  • Image processing
  • Signal processing

Background:

  • Nonuniformity correction (NUC) is crucial for infrared imaging systems.
  • Existing scene-based NUC methods can produce ghosting artifacts.
  • Adaptive least mean square (LMS) algorithms are commonly used for NUC.

Purpose of the Study:

  • To develop a scene-based NUC method that minimizes ghosting artifacts.
  • To improve the accuracy and quality of infrared imagery.
  • To present a modified adaptive LMS algorithm with a novel gating operation.

Main Methods:

  • A modified adaptive least mean square (LMS) algorithm was developed.
  • A novel gating operation was incorporated to halt updates during low temporal variation.
  • The proposed method was tested using simulated and real infrared image sequences.

Main Results:

  • The proposed NUC method significantly reduced ghosting artifacts compared to existing methods.
  • The gating operation effectively minimized artifacts caused by temporal variations.
  • A slight increase in convergence time was observed.

Conclusions:

  • The novel gating operation in scene-based NUC effectively reduces ghosting artifacts.
  • The modified LMS algorithm offers improved image quality in infrared sequences.
  • The trade-off between artifact reduction and convergence time is a key consideration.