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Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
Harmonic Mean01:09

Harmonic Mean

The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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...
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...

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Coherent averaging in the frequency domain.

A K Khitrin1, Jiadi Xu, Ayyalusamy Ramamoorthy

  • 1Department of Chemistry, Kent State University, Kent, Ohio 44240-0001, USA.

The Journal of Chemical Physics
|June 16, 2012
PubMed
Summary
This summary is machine-generated.

We present a new frequency-domain method for analyzing quantum systems with periodically changing Hamiltonians. This approach simplifies relating theoretical models to experimental data, improving spectral peak intensity calculations.

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Area of Science:

  • Quantum mechanics
  • Solid-state physics
  • Spectroscopy

Background:

  • Periodic time-modulated Hamiltonians are common in quantum systems.
  • Effective interactions derived from these Hamiltonians can be challenging to link to experimental observations.
  • Existing methods for calculating average Hamiltonians may lack direct experimental relevance.

Purpose of the Study:

  • To introduce a novel frequency-domain approach for analyzing quantum systems with time-modulated Hamiltonians.
  • To develop a method that better connects theoretical models to measurable quantities.
  • To provide a formalism suitable for calculating spectral peak intensities.

Main Methods:

  • Development of a frequency-domain formalism.
  • Calculation of approximate solutions for the density matrix.
  • Application to fast magic-angle-spinning Nuclear Magnetic Resonance (NMR) spectra of solids.

Main Results:

  • The proposed frequency-domain approach offers advantages over traditional methods.
  • The formalism yields an approximate density matrix solution more amenable to experimental interpretation.
  • The method is demonstrated to be effective for calculating intensities of narrowed spectral peaks.

Conclusions:

  • The frequency-domain approach provides a more experimentally relevant way to study quantum systems with time-dependent Hamiltonians.
  • This method enhances the ability to interpret spectral data, particularly in solid-state NMR.
  • The formalism facilitates accurate prediction of spectral peak intensities.