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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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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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.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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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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IR Spectrometers01:25

IR Spectrometers

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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...
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Bandpass Sampling01:17

Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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[Hyperspectral Band Selection Based on Spectral Clustering and Inter-Class Separability Factor].

Fang-pu Qin, Ai-wu Zhang, Shu-min Wang

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |September 30, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel spectral clustering algorithm for hyperspectral image band selection, effectively reducing data redundancy and enhancing classification accuracy. The method improves upon traditional approaches, achieving high overall accuracy with Support Vector Machine and Minimum Distance Classification.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Data Science

    Context:

    • Hyperspectral remote sensing images offer rich spectral information but present significant data processing challenges due to high dimensionality.
    • Effective band selection is crucial for reducing redundancy, improving classification accuracy, and enhancing processing efficiency in hyperspectral imagery.

    Purpose:

    • To develop an optimized band selection method for hyperspectral images using a spectral clustering algorithm based on graph theory.
    • To reduce data dimensionality while preserving essential spectral information for improved classification.

    Summary:

    • The proposed method utilizes spectral clustering, calculating mutual information between bands to form a similarity matrix.
    • Graph partitioning and spectral decomposition identify clusters with low inter-band similarity and high intra-band similarity.
    • An inter-class separability factor is computed to select representative bands, followed by classification using Support Vector Machine and Minimum Distance methods.

    Impact:

    • The novel approach demonstrates superior performance compared to traditional adaptive and subspace-divided band selection algorithms.
    • Achieved high overall classification accuracies of approximately 94.08% (SVM) and 87.98% (MDC), validating the effectiveness of the proposed band selection technique.