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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

315
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
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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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UV–Vis Spectroscopy of Conjugated Systems01:32

UV–Vis Spectroscopy of Conjugated Systems

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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
One of the factors influencing λmax is the extent...
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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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UV–Vis Spectrometers01:14

UV–Vis Spectrometers

1.6K
The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
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Related Experiment Video

Updated: Sep 25, 2025

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis
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Convolutional neural network-based spectrum reconstruction solver for channeled spectropolarimeter.

Chan Huang, Su Wu, Yuyang Chang

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    |April 27, 2022
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    Summary
    This summary is machine-generated.

    A new convolutional neural network (CNN) method improves spectral reconstruction for channeled spectropolarimetry. This enhances the accuracy of measuring light

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    Area of Science:

    • Optical Engineering
    • Spectroscopy
    • Data Science

    Background:

    • Channeled spectropolarimetry measures light's Stokes parameters using spectral demodulation.
    • Spectrometer imperfections limit polarimetric accuracy by distorting the modulation spectrum.
    • Reconstructing high-frequency, continuous modulation spectra is challenging for existing methods.

    Purpose of the Study:

    • To develop an improved spectral reconstruction solver for channeled spectropolarimetry.
    • To enhance the accuracy of polarimetric measurements by addressing spectrometer limitations.
    • To leverage deep learning for more precise spectral feature recovery.

    Main Methods:

    • A convolutional neural network (CNN) based spectral reconstruction solver was proposed.
    • Measured spectra were preprocessed to enhance spectral features.
    • The CNN was trained to map preprocessed spectra to real spectra, improving reconstruction quality.
    • Simulations and real-world experiments validated the CNN approach.

    Main Results:

    • The CNN-based solver significantly improved spectral reconstruction accuracy and robustness.
    • Enhanced spectral reconstruction led to more accurate demodulation results compared to traditional methods.
    • Experiments demonstrated superior performance against other advanced demodulation techniques.
    • System matrices constructed with reconstructed spectra improved Stokes parameter estimation.

    Conclusions:

    • The proposed CNN-based spectral reconstruction solver effectively addresses limitations in channeled spectropolarimetry.
    • This method offers a substantial improvement in polarimetric reconstruction accuracy.
    • The approach shows promise for advancing optical measurement techniques.