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Related Concept Videos

The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

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The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
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The Electromagnetic Spectrum01:24

The Electromagnetic Spectrum

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Electromagnetic waves are categorized according to their wavelengths and frequencies, giving the electromagnetic spectrum. These waves are classified as radio, infrared, ultraviolet, etc. Radio waves refer to electromagnetic radiation with wavelengths ranging from millimeters to kilometers. Radio waves are commonly used for audio communications (i.e., radios) and typically result from an alternating current in the wires of a broadcast antenna. They cover a broad wavelength range and are used...
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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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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Mass Spectrum01:23

Mass Spectrum

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A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...
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UV–Vis Spectrum01:30

UV–Vis Spectrum

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When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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[Terahertz Spectrum Features Extraction Based on Kernel Optimization Relevance Vector Machine].

Yi-wei Zhong, Tao Shen, Cun-li Mao

    Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu
    |September 21, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Kernel Optimization Relevance Vector Machine (KO-RVM) effectively extracts global features from terahertz spectra, improving material identification. This method reduces data while preserving spectral characteristics for accurate analysis.

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    Area of Science:

    • Spectroscopy
    • Materials Science
    • Machine Learning

    Context:

    • Terahertz (THz) spectra are sensitive to molecular vibrations, making them susceptible to physical and chemical changes.
    • Traditional methods relying on fixed spectral peaks for material identification can lead to errors due to waveform deformation.
    • Existing component analysis and material identification techniques face challenges with spectral variability.

    Purpose:

    • To develop a robust method for terahertz spectral analysis and material identification.
    • To overcome limitations of local feature extraction by employing global graphic features.
    • To compare the proposed Kernel Optimization Relevance Vector Machine (KO-RVM) with Support Vector Regression (SVR).

    Summary:

    • A novel method using Kernel Optimization Relevance Vector Machine (KO-RVM) is proposed for terahertz transmission spectrum analysis.
    • The KO-RVM method optimizes basis functions using an expectation-maximization algorithm for effective feature extraction.
    • This approach enables sparse representation of spectra, significantly reducing extracted features while retaining overall spectral characteristics.

    Impact:

    • The optimized KO-RVM method provides more consistent fitting results across different spectral bands.
    • Extracted spectral features serve as a basis for similarity measurement and investigation of common characteristics between materials.
    • This advancement enhances the accuracy and reliability of material identification using terahertz spectroscopy.