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Related Concept Videos

Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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.
Different compounds display unique properties due to their...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
IR Spectrum01:19

IR Spectrum

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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IR Spectrometers01:25

IR Spectrometers

There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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 the 3500–3100 cm−1 range. Even though both O−H and N−H bonds vibrate at a similar...

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Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
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Published on: March 22, 2019

Multichannel infrared receiver performance.

S J Dunning, S R Robinson

    Applied Optics
    |March 10, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study presents an optical receiver for target detection using spectral signatures. Both optimal and approximate receiver designs achieve similar performance, dependent on signal-to-noise ratios.

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    Area of Science:

    • Optical engineering
    • Signal processing
    • Statistical modeling

    Background:

    • Optical receivers are crucial for detecting targets based on their unique spectral signatures.
    • Processing signals across multiple frequency bands requires robust statistical models.

    Purpose of the Study:

    • To present the performance of an optical receiver designed for spectral signature detection.
    • To develop and evaluate optimal and suboptimal receiver structures for multi-channel, sequential detection.

    Main Methods:

    • Utilized a statistical model treating signal fields as Gaussian random processes.
    • Derived the optimal Bayes/Neyman-Pearson receiver structure for M spectral channels and N sequential looks.
    • Developed practical suboptimal and ad hoc receiver structures.
    • Employed numerical methods to compute probabilities of false alarm and detection.

    Main Results:

    • Optimal, approximate, and ad hoc receiver structures demonstrated comparable performance.
    • Receiver performance is primarily determined by the difference in mean-to-variance ratios and the ratio of variances between target and background.
    • Identical parameters were used for all processors to ensure fair comparison.

    Conclusions:

    • Approximate and ad hoc receiver designs offer a practical alternative to optimal structures without significant performance loss.
    • The derived performance metrics provide a clear understanding of factors influencing detection accuracy.
    • This research contributes to the development of advanced optical detection systems.