4 years ago

Reducing noise in computed correlation functions using techniques from signal processing

Michael L. Greenfield, Mohammad Masoori

Time correlation functions invariably suffer from random noise, especially at longer time intervals for which fewer data pairs are available. This noise is particularly of concern when calculating correlations that cannot be averaged over per-molecule contributions, such as stress in molecular simulations. In this work, a set of methods based in signal processing has been developed to reduce the inherent noise that is present in time- and frequency-domain representations of correlation functions. The stress time autocorrelation function, which leads to stress relaxation modulus and complex modulus, is used as an example. The difference between initial and final values of a time correlation function over a finite time domain is found to create so-called ‘leakage’ of noise from disallowed into harmonic frequencies during fast Fourier transformation. Decreasing this leakage effect through reflection to negative time and through applying a window function reduces noise levels significantly. Removing frequency components of insignificant magnitudes also provides significant noise reduction. Applying moving averages in the frequency and time domains also contributes to noise reduction. Specific results obtained by applying these methods to a model asphalt system enable more clear physical interpretations of the underlying relaxations after dramatic noise level reductions were attained.

Publisher URL: http://www.tandfonline.com/doi/full/10.1080/08927022.2017.1321753

DOI: 10.1080/08927022.2017.1321753

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.