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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...
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Uniform Depth Channel Flow: Problem Solving01:18

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Fast Decoupled and DC Powerflow01:24

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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:
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Reconstruction of Signal using Interpolation01:10

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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...
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Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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Convolution: Math, Graphics, and Discrete Signals01:24

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Related Experiment Video

Updated: Jan 10, 2026

Meso-Scale Particle Image Velocimetry Studies of Neurovascular Flows In Vitro
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Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve.

Pedro Pessoa1,2, Max Schweiger1,2, Lance W Q Xu1,2

  • 1Center for Biological Physics, Arizona State University, Tempe, AZ, USA.

Iscience
|November 24, 2025
PubMed
Summary

This study introduces normalizing flows to recover underlying stochastic signals from combined measurements, avoiding noise amplification from subtraction or division. The NFdeconvolve software package implements this novel approach for signal recovery.

Keywords:
Computer scienceEngineering

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Area of Science:

  • Signal processing
  • Statistical inference
  • Computational science

Background:

  • Stochastic signals are common in science, often combined through addition or multiplication.
  • Directly isolating one signal (e.g., fluorescence background) from a combined measurement (e.g., total fluorescence) is challenging.
  • Traditional methods like subtraction or division amplify noise, hindering accurate statistical learning.

Purpose of the Study:

  • To develop a method for recovering the statistics of a component stochastic signal (b) from a combined signal (x) and known statistics of another component (a).
  • To avoid the noise amplification issues inherent in direct subtraction or division operations.
  • To provide a practical software implementation for this novel signal recovery technique.

Main Methods:

  • Utilizing normalizing flows, a class of deep generative models, to approximate probability distributions.
  • Applying normalizing flows to model the distribution of the unobserved signal 'b' given the observed signal 'x' and the statistics of signal 'a'.
  • Developing the NFdeconvolve software package to implement the normalizing flow-based deconvolution.

Main Results:

  • Demonstrated that normalizing flows can effectively approximate the probability distribution of the target signal 'b'.
  • Showcased the ability to recover signal statistics without resorting to direct subtraction or division.
  • Successfully implemented the method in the NFdeconvolve software, making it accessible to researchers.

Conclusions:

  • Normalizing flows offer a powerful alternative to traditional methods for deconvolving stochastic signals.
  • This approach mitigates noise amplification, leading to more robust statistical inference.
  • The NFdeconvolve package provides a valuable tool for scientific research involving complex stochastic signal analysis.