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Related Concept Videos

Aliasing01:18

Aliasing

Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Bandpass Sampling01:17

Bandpass Sampling

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. The spectrum...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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 stretching vibration...

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Related Experiment Video

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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

Removing baseline flame's spectrum by using advanced recovering spectrum techniques.

Luis Arias1, Daniel Sbarbaro, Sergio Torres

  • 1Department of Electrical Engineering, University of Concepcion, Casilla 160 C, Concepcion, Chile. luiarias@udec.cl

Applied Optics
|September 5, 2012
PubMed
Summary

A new automated algorithm accurately removes continuous baseline from flame spectra using a learning database. This method enhances the detection of discontinuous flame emissions for advanced combustion monitoring.

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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

Area of Science:

  • Spectroscopy
  • Combustion Science
  • Chemical Engineering

Background:

  • Accurate baseline estimation is crucial for analyzing discontinuous flame emissions.
  • Existing methods for baseline removal can be complex and time-consuming.
  • Sooty regions in flames present challenges due to predominant continuous emissions.

Purpose of the Study:

  • To develop and validate a novel automated algorithm for estimating and removing continuous baselines from flame spectra.
  • To improve the accuracy of discontinuous flame emission analysis.
  • To provide a robust tool for advanced combustion monitoring.

Main Methods:

  • An automated algorithm was developed, utilizing a learning database of continuous flame spectra.
  • The algorithm estimates the continuous background and subtracts it from measured spectra.
  • Performance was quantified using the goodness-of-fit coefficient (GFC) and compared with the first derivative method (FDM).

Main Results:

  • The proposed algorithm effectively estimated and removed continuous baselines from natural gas and bio-oil flame spectra.
  • The method demonstrated high performance across various combustion conditions.
  • Comparison with FDM indicated the proposed technique's advantages.

Conclusions:

  • The novel automated algorithm offers an effective solution for baseline removal in flame spectroscopy.
  • This technique is a valuable tool for developing advanced combustion monitoring strategies.
  • The method shows significant potential for real-time analysis of discontinuous emissions.