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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...
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...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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...
Downstream Processing01:29

Downstream Processing

Downstream processing begins once fermentation is complete and involves a series of steps to recover and purify products such as acids, vitamins, antibiotics, or proteins.Cell HarvestingFor example, for intracellular protein-based products, the first step is harvesting the cells. This is typically achieved using centrifugation or filtration to separate the cells from the liquid phase.Cell Disruption for Intracellular ProductsIf the target product is intracellular, the harvested cells must be...

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Related Experiment Video

Updated: Jun 2, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Post-processing of individual signals for de-noising.

Jun Fukazawa1, Kazuyuki Takeda, K Takegoshi

  • 1Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.

Journal of Magnetic Resonance (San Diego, Calif. : 1997)
|May 7, 2011
PubMed
Summary

This study analyzes NMR signal fluctuations to identify genuine peaks versus noise. Phase information is more crucial than intensity for de-noising spectra and distinguishing signals.

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Nuclear Magnetic Resonance (NMR)

Background:

  • NMR spectroscopy is vital for chemical analysis.
  • Distinguishing true signals from noise is challenging.
  • Signal intensity and phase fluctuations can indicate peak authenticity.

Purpose of the Study:

  • To investigate NMR signal intensity and phase fluctuations during repeated experiments.
  • To assess the utility of these fluctuations in identifying genuine NMR signals versus noise.
  • To develop and evaluate a novel de-noising method based on phase analysis.

Main Methods:

  • Analysis of NMR signal intensity and phase variations across repeated acquisitions.
  • Quantification of phase standard deviation at each spectral point for de-noising.
  • Comparison of the proposed phase-based de-noising method with the phase-covariance method.
  • Evaluation of the impact of dispersion components on signal analysis.

Main Results:

  • Phase fluctuations provide more information than intensity fluctuations for accumulated NMR spectra.
  • A de-noising technique utilizing the standard deviation of phase effectively reduces noise in NMR spectra.
  • The proposed phase-based de-noising method demonstrates comparable or superior performance to the phase-covariance method.
  • The influence of dispersion on spectral analysis was examined.

Conclusions:

  • NMR signal phase analysis is a powerful tool for differentiating true signals from noise.
  • The developed phase-based de-noising method offers an effective approach for improving NMR spectral quality.
  • This technique enhances the reliability of NMR data interpretation by mitigating noise interference.