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

Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
Entropy01:18

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

You might also read

Related Articles

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

Sort by
Same author

Temporal point process modeling of aggressive behavior onset in psychiatric inpatient youths with autism.

Scientific reports·2026
Same author

Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.

Annals of biomedical engineering·2026
Same author

The Groningen Meander Walking Test: a reliable and valid test for assessing advanced walking ability in people with Parkinson's disease.

Disability and rehabilitation·2026
Same author

Deep Learning-Based Prediction of Cardiopulmonary Disease in Retinal Images of Premature Infants.

JAMA ophthalmology·2026
Same author

Ability of Basic Physiological Monitoring to Identify Excessive Occupational Heat Strain.

Journal of occupational and environmental medicine·2025
Same author

Effectiveness of dual-task training in older adults undergoing total knee arthroplasty: a randomized controlled trial.

Irish journal of medical science·2025
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Continuously differentiable sample-spacing entropy estimation.

Umut Ozertem1, Ismail Uysal, Deniz Erdogmus

  • 1Yahoo! Inc., Sunnyvale, CA 95054, USA. umut@yahoo-inc.com

IEEE Transactions on Neural Networks
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonparametric entropy estimator for adaptive systems. It offers computationally efficient and continuously differentiable estimates, overcoming limitations of existing methods for machine learning and signal processing.

More Related Videos

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Related Experiment Videos

Last Updated: Jun 25, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

Area of Science:

  • Machine Learning
  • Signal Processing
  • Information Theory

Background:

  • Traditional methods rely on second-order statistics, but higher-order statistics, particularly from information theory like error entropy minimization, are increasingly important.
  • Estimating error entropy in adaptive systems is computationally intensive using standard kernel estimators.
  • Existing computationally inexpensive entropy estimators lack differentiability, hindering gradient-based adaptation.

Purpose of the Study:

  • To propose a novel nonparametric entropy estimator for adaptive system training.
  • To develop an estimator that balances computational efficiency with continuous differentiability.
  • To address the drawbacks of both kernel density estimation (KDE) and sample-spacing methods.

Main Methods:

  • A new nonparametric entropy estimator is proposed.
  • The estimator's computational complexity is analyzed and compared to sample-spacing techniques.
  • Performance and computation time are evaluated against KDE-based estimators in a supervised neural network training framework.

Main Results:

  • The proposed estimator provides continuously differentiable entropy estimates.
  • It achieves computational complexity comparable to inexpensive sample-spacing techniques.
  • Empirical comparisons demonstrate competitive performance with KDE-based methods.

Conclusions:

  • The novel nonparametric entropy estimator effectively combines computational efficiency and differentiability.
  • This method offers a viable alternative for adaptive system training, particularly in machine learning and signal processing.
  • The proposed estimator facilitates gradient-based adaptation while managing computational load.