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Infrared (IR) Spectroscopy: Overview01:09

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
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Lensless Fluorescent Microscopy on a Chip
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Local structure preserving sparse coding for infrared target recognition.

Jing Han1, Jiang Yue1, Yi Zhang1

  • 1Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.

Plos One
|March 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a Local Sparse Structure Matching (LSSM) model for infrared target recognition. The model enhances robustness by preserving local structures, enabling accurate identification with fewer templates.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Infrared Imaging

Background:

  • Sparse coding is effective for image classification but requires extensive templates and complex learning for robust target recognition.
  • Existing methods struggle with background interference and variations in target appearance.

Purpose of the Study:

  • To develop a robust infrared target recognition model using sparse coding and template matching.
  • To improve the stability and accuracy of target identification in challenging environments.

Main Methods:

  • Proposed a Local Sparse Structure Matching (LSSM) model incorporating sparsity into template matching.
  • Introduced a Local Structure Preserving Sparse Coding (LSPSc) formulation to maintain local object structures.
  • Developed a Kernel LSPSc (K-LSPSc) to extend LSPSc to nonlinear data spaces.

Main Results:

  • LSPSc and K-LSPSc formulations enhance sparse representation stability and reduce background interference.
  • The LSSM model achieves robust target identification using a limited set of template images.
  • Demonstrated high performance across various datasets, showing stable detection despite scene, shape, and occlusion variations.

Conclusions:

  • The LSSM model, utilizing LSPSc and K-LSPSc, offers an effective solution for general infrared target recognition.
  • The approach provides anti-interference and fault-tolerant capabilities for reliable target identification.
  • The method is suitable for diverse environments and imaging conditions, requiring only a few templates for dictionary learning.