Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Deconvolution01:20

Deconvolution

485
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...
485
Classification of Signals01:30

Classification of Signals

1.2K
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...
1.2K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

627
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...
627
Discrete Fourier Transform01:15

Discrete Fourier Transform

741
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
741
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

580
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
580
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

815
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
815

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Author Correction: Dietary intervention of mice using an improved Multiple Artificial-gravity Research System (MARS) under artificial 1 g.

NPJ microgravity·2026
Same author

Compost fermented with thermophilic Bacillaceae reduces heat stress-induced mortality in laying hens through gut microbial modulation.

Animal microbiome·2026
Same author

Maternal administration of octanoate, a medium-chain fatty acid, improves feed efficiency of Japanese black calves through influencing gut bacteriome structure.

Scientific reports·2025
Same author

Causal estimation of the relationship between reproductive performance and the fecal bacteriome in cattle.

Animal microbiome·2025
Same author

Feed Components and Timing to Improve the Feed Conversion Ratio for Sustainable Aquaculture Using Starch.

International journal of molecular sciences·2024
Same author

Fecal metagenomic and metabolomic analyses reveal non-invasive biomarkers of <i>Flavobacterium psychrophilum</i> infection in ayu (<i>Plecoglossus altivelis</i>).

mSphere·2024

Related Experiment Video

Updated: Dec 23, 2025

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

1.2K

Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic

Shunji Yamada1,2, Atsushi Kurotani2, Eisuke Chikayama2,3

  • 1Graduate School of Bioagricultural Sciences, Nagoya University, Furo-cho, Nagoya 464-8601, Chikusa-ku, Japan.

International Journal of Molecular Sciences
|April 29, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new informatics tool for nuclear magnetic resonance (NMR) data cleansing. The tool enhances signal-to-noise ratio (SNR) and separates complex molecular signals by reducing noise in NMR spectra.

Keywords:
FIDNMRT2* relaxation timeacquisition parameterscorrelation network analysisdiffusion-edited spectrummatrix factorizationmolecular complexityshort-time Fourier transformsignal-to-noise ratio

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K

Related Experiment Videos

Last Updated: Dec 23, 2025

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

1.2K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.1K

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Data Science

Background:

  • Nuclear magnetic resonance (NMR) spectroscopy provides atomic-resolution data for molecular characterization.
  • Analyzing complex NMR data from mixtures is challenging due to noise and signal overlap.
  • Essential data-cleansing steps include quality checking, noise reduction, and signal deconvolution.

Purpose of the Study:

  • To develop an NMR measurement informatics tool for effective data cleansing.
  • To improve signal-to-noise ratio (SNR) and enable better analysis of complex NMR datasets.
  • To identify key experimental factors influencing NMR data quality.

Main Methods:

  • Developed a novel informatics tool combining short-time Fourier transform (STFT) and probabilistic sparse matrix factorization (PSMF).
  • Applied the tool to raw free induction decay (FID) signals of one-dimensional NMR spectra.
  • Utilized noise factor analysis to correlate SNR with acquisition parameters.

Main Results:

  • The developed signal deconvolution method increased the signal-to-noise ratio (SNR) by approximately tenfold.
  • The tool successfully separated signals of macromolecules and small molecules in diffusion-edited spectra based on T2* relaxation time.
  • Noise factor analysis revealed significant correlations between SNR and specific experimental acquisition parameters.

Conclusions:

  • The NMR informatics tool provides effective data cleansing for complex mixtures.
  • The method significantly enhances spectral quality and aids in resolving overlapping signals.
  • Understanding the impact of acquisition parameters on SNR is crucial for optimizing NMR experiments.