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

Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
Nonlinear Pharmacokinetics: Overview01:19

Nonlinear Pharmacokinetics: Overview

Nonlinear or dose-dependent pharmacokinetics is a phenomenon that occurs when the pharmacokinetic parameters of certain drugs deviate from linear pharmacokinetics at higher doses. These drugs do not follow the expected first-order kinetics, where the rate of drug elimination is directly proportional to the drug concentration. Instead, they exhibit a nonlinear relationship, which can be attributed to several factors.
Nonlinearity can arise due to the saturation of plasma protein-binding or...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...

You might also read

Related Articles

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

Sort by
Same author

Continuous-surface 3D reconstruction from kilometer-range single-photon LiDAR using score-based priors.

Scientific reports·2026
Same author

WAFFLE - an automated platform for enhancing the performance of electrochemical biosensors.

Lab on a chip·2026
Same author

Bayesian Multifractal Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

On-the-fly adaptive SNR protocol to accelerate Brillouin microscopy.

Optics express·2025
Same author

High-resolution multispectral three-dimensional profiling using a metasurface-enhanced SPAD quanta image sensor.

Optics express·2025
Same author

Physics-based forward model for near-real-time quantitative imaging of spent nuclear fuel assemblies.

Scientific reports·2025

Related Experiment Video

Updated: May 20, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model.

Yoann Altmann1, Nicolas Dobigeon, Jean-Yves Tourneret

  • 1IRIT/INP-ENSEEIHT/TéSA, University of Toulouse, Toulouse 31071, France. yoann.altmann@enseeiht.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 2, 2012
PubMed
Summary

This study introduces a polynomial post-nonlinear mixing model for hyperspectral image unmixing. It develops a detection strategy to distinguish between linear and nonlinear mixing models, showing good agreement with prior research.

More Related Videos

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
08:49

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Related Experiment Videos

Last Updated: May 20, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle
15:06

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
08:49

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Area of Science:

  • Remote Sensing
  • Image Processing
  • Signal Processing

Background:

  • Hyperspectral imaging often assumes linear mixing, which may not accurately represent complex natural phenomena.
  • Nonlinear mixing models are crucial for accurately unmixing hyperspectral data in various applications.
  • Existing methods for nonlinearity detection in hyperspectral unmixing are limited.

Purpose of the Study:

  • To propose and validate a polynomial post-nonlinear mixing model for hyperspectral image unmixing.
  • To develop a generalized likelihood ratio test for detecting nonlinearity in hyperspectral pixels.
  • To evaluate the performance of the proposed nonlinearity detection strategy using synthetic and real data.

Main Methods:

  • Approximation of nonlinear functions using polynomials to form a polynomial post-nonlinear mixing model.
  • Least squares methods for estimating model parameters.
  • Generalized likelihood ratio test utilizing the nonlinearity parameter estimator and its constrained Cramér-Rao bound for variance approximation.

Main Results:

  • The proposed polynomial post-nonlinear mixing model effectively handles nonlinear spectral mixing.
  • Simulations on synthetic data demonstrate the accuracy of the nonlinearity detection strategy.
  • Analysis of the Cuprite hyperspectral image reveals evidence of nonlinear mixing in certain mineral compositions.

Conclusions:

  • The developed polynomial post-nonlinear model and detection strategy offer a robust approach to hyperspectral image unmixing.
  • The method accurately distinguishes between linear and nonlinear mixing scenarios.
  • The findings suggest that nonlinear mixing is a significant factor in certain real-world hyperspectral datasets, improving abundance estimation.