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Related Concept Videos

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...
Sampling Theorem01:15

Sampling Theorem

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.
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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...

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Resampling improves the efficiency of a "fast-switch" equilibrium sampling protocol.

Edward Lyman1, Daniel M Zuckerman

  • 1Center for Biophysical Modeling and Simulation, University of Utah, 315 S 1400 E, Rm. 2020, Salt Lake City, Utah 84112-0850, USA. elyman@hec.utah.edu

The Journal of Chemical Physics
|March 5, 2009
PubMed
Summary
This summary is machine-generated.

We improved peptide sampling efficiency using a statistical resampling technique. This method enhances nonequilibrium simulations, offering a threefold improvement for penta-alanine systems.

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Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Biophysics

Background:

  • Peptide simulations require efficient methods to sample equilibrium configurations.
  • Nonequilibrium methods like simulated annealing can generate trajectories, but extracting equilibrium properties is challenging.
  • Previous work established a multistage reweighting scheme for this purpose.

Purpose of the Study:

  • To improve the efficiency of sampling equilibrium peptide configurations from nonequilibrium trajectories.
  • To evaluate the impact of statistical variance reduction techniques on enhanced sampling protocols.
  • To explore the applicability of these improvements to other nonequilibrium sampling methods.

Main Methods:

  • Applied a multistage reweighting scheme to simulated annealing trajectories.
  • Utilized a statistical variance reduction technique: resampling.
  • Tested the protocol on a penta-alanine system.

Main Results:

  • Resampling improved the efficiency of the multistage reweighting protocol by approximately a factor of 3.
  • Demonstrated successful sampling of equilibrium configurational distributions for peptides.
  • Identified resampling as a key improvement for nonequilibrium sampling.

Conclusions:

  • Resampling significantly enhances the efficiency of sampling peptide configurations from nonequilibrium simulations.
  • The demonstrated improvements are expected to benefit other nonequilibrium sampling protocols.
  • This approach holds promise for applications in Jarzynski-relation calculations and annealing-based NMR structure determination.