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Reconstruction of Signal using Interpolation

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...

Aliasing

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...

NMR Spectrometers: Resolution and Error Correction

NMR Spectrometers: Resolution and Error Correction

When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...

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Related Experiment Video

Updated: Jul 4, 2025

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Corrigendum: Forest stand spectrum reconstruction using spectrum spatial feature gathering and multilayer perceptron.

Fan Wang^1,2, Linghan Song^1,2, Xiaojie Liu^1,2

¹College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China.

Frontiers in Plant Science

|January 29, 2024

Summary

This study corrects a previous article DOI. The corrected DOI is 10.3389/fpls.2023.1223366.

Keywords:

UAV-based remote sensing deep learning masson pine forest spectrum-spatial fusion three-dimensional spectral

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Last Updated: Jul 4, 2025

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Area of Science:

Correction of scientific literature references.
Ensuring accurate citation for plant science research.

Context:

Previous publication identified with an incorrect Digital Object Identifier (DOI).
Need for precise referencing in scientific communication.

Purpose:

To provide the correct DOI for the article.
To facilitate accurate retrieval and citation of the research.

Summary:

The article DOI has been corrected to 10.3389/fpls.2023.1223366.
This ensures proper identification of the published work.

Impact:

Improves the integrity of scientific records.
Aids researchers in accessing and citing the correct publication.