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

SFG Algebra01:16

SFG Algebra

107
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
107
Signal Flow Graphs01:18

Signal Flow Graphs

169
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
169
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

164
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
164
Even and Odd Signals01:17

Even and Odd Signals

713
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
713
Upsampling01:22

Upsampling

195
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...
195
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

167
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...
167

You might also read

Related Articles

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

Sort by
Same author

A physics-informed alternative to Richardson-Lucy deconvolution across SNR regimes without iteration cutoffs.

Nature communications·2026
Same author

Mitochondria directly interact with the nuclear pore complex.

Nature·2026
Same author

Stochastic colonization and host-to-host transmission shape gut bacterial variability.

bioRxiv : the preprint server for biology·2026
Same author

Resolving fluorescently labeled species using highly multiplexed spectral FLIM.

Scientific reports·2026
Same author

Simulation-based inference captures non-Markovian effects as exemplified in protein production kinetics through cell division.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Substrate-interacting pore loops of two ATPase subunits determine the degradation efficiency of the 26S proteasome.

Nature communications·2026

Related Experiment Video

Updated: May 30, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

5.8K

Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve.

Pedro Pessoa, Max Schweiger, Lance W Q Xu

    Arxiv
    |January 29, 2025
    PubMed
    Summary

    This study introduces normalizing flows to recover underlying stochastic signals without noisy subtraction or division. The NFdeconvolve software package provides a novel method for signal deconvolution, enhancing statistical analysis in scientific research.

    More Related Videos

    Blood Flow Imaging with Ultrafast Doppler
    05:57

    Blood Flow Imaging with Ultrafast Doppler

    Published on: October 14, 2020

    7.5K
    Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events
    06:26

    Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

    Published on: November 7, 2017

    15.9K

    Related Experiment Videos

    Last Updated: May 30, 2025

    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
    09:39

    Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

    Published on: November 18, 2019

    5.8K
    Blood Flow Imaging with Ultrafast Doppler
    05:57

    Blood Flow Imaging with Ultrafast Doppler

    Published on: October 14, 2020

    7.5K
    Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events
    06:26

    Capturing Flow-weighted Water and Suspended Particulates from Agricultural Canals During Drainage Events

    Published on: November 7, 2017

    15.9K

    Area of Science:

    • Computational Science
    • Statistical Modeling
    • Signal Processing

    Background:

    • Scientific measurements often involve stochastic signals combined through addition or multiplication.
    • Directly isolating a signal of interest (b) from a combined measurement (x) using subtraction or division amplifies inherent noise.
    • Existing methods struggle with noise amplification when deconvolving stochastic signals.

    Purpose of the Study:

    • To develop a method for recovering the statistics of a target stochastic signal (b) from a combined measurement (x) and known signal statistics (a).
    • To avoid the noise amplification associated with traditional subtraction or division methods.
    • To introduce a novel computational approach for stochastic signal deconvolution.

    Main Methods:

    • Utilized normalizing flows, a class of generative models, to approximate probability distributions.
    • Developed the NFdeconvolve software package to implement the normalizing flow approach.
    • Applied the method to scenarios involving both additive ($x=a+b$) and multiplicative ($x=ab$) signal combinations.

    Main Results:

    • Normalizing flows successfully generated an approximation of the probability distribution for the signal of interest (b).
    • The proposed method effectively bypasses the need for direct signal subtraction or division, thereby mitigating noise amplification.
    • Demonstrated the practical application and accessibility of the method through the NFdeconvolve software and tutorial.

    Conclusions:

    • Normalizing flows offer a robust alternative for deconvolving stochastic signals in scientific applications.
    • The NFdeconvolve package provides a valuable tool for researchers dealing with noisy signal data.
    • This approach significantly improves the ability to learn signal statistics from combined measurements.