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Upsampling

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...

Aliasing

Aliasing

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

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Bandpass Sampling

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Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Published on: December 27, 2012

Suppressing sampling noise in linear and two-dimensional spectral simulations.

Johannes F Kruiger¹, Cornelis P van der Vegte¹, Thomas L C Jansen¹

¹Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.

The Journal of Chemical Physics

|February 10, 2015

Summary

This summary is machine-generated.

A novel adaptive apodization method effectively suppresses sampling noise in spectroscopy simulations. This technique improves accuracy and speeds up simulations for complex molecular systems like proteins.

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Area of Science:

Computational Chemistry
Spectroscopy
Biophysics

Background:

Sampling noise is a significant challenge in time-domain simulations of spectroscopic techniques.
Existing apodization methods can introduce spectral aberrations or require extensive computational resources.

Purpose of the Study:

To develop and validate a new adaptive apodization scheme for noise reduction in spectroscopy simulations.
To assess the impact of this scheme on spectral accuracy and simulation efficiency.

Main Methods:

An adaptive apodization method based on physical principles was devised.
The method was tested on an artificial dimer system for slope analysis.
Simulated two-dimensional infrared spectra of lysozyme were analyzed, focusing on the cross-polarization component.

Main Results:

The adaptive apodization scheme effectively suppresses sampling noise.
It introduces minimal spectral aberrations, allowing for reduced disorder realizations.
The method demonstrated improved accuracy compared to lifetime and Gaussian apodization schemes, especially for the cross-polarization component.

Conclusions:

The developed adaptive apodization scheme offers a more accurate and efficient approach for time-domain spectroscopy simulations.
This method has broad applicability in studying spectral dynamics and molecular systems.