Jove
Visualize
Contact Us

Related Concept Videos

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.0K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
4.0K
Entropy01:18

Entropy

3.3K
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...
3.3K
Entropy02:39

Entropy

33.7K
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...
33.7K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.0K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.0K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

6.3K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
6.3K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

26.0K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
26.0K

You might also read

Related Articles

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

Sort by
Same author

Improving the Patient Experience During Care in the Orthopedic Cast Room.

Orthopedics·2023
Same author

Special Issue on Rate Distortion Theory and Information Theory.

Entropy (Basel, Switzerland)·2020
See all related articles
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 Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K

Entropy Power, Autoregressive Models, and Mutual Information.

Jerry Gibson1

  • 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

We introduce the entropy rate power for autoregressive processes, simplifying calculations for differential entropy and mutual information changes. This method enhances analysis in signal processing applications like speech and seismic data.

Keywords:
autoregressive modelsentropy rate powermutual information

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
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

34.2K

Related Experiment Videos

Last Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
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

34.2K

Area of Science:

  • Signal Processing
  • Information Theory
  • Time Series Analysis

Background:

  • Autoregressive (AR) processes are fundamental in various signal processing fields, including speech, seismic, and biological signals.
  • Shannon's entropy rate power is a key information-theoretic quantity with significant analytical potential.
  • Understanding changes in mutual information with model order is crucial for AR process analysis.

Purpose of the Study:

  • To explore the utility of the entropy rate power for analyzing autoregressive processes.
  • To establish a relationship between the log ratio of entropy powers and the difference in differential entropy.
  • To investigate the simplification of differential entropy and mutual information calculations using minimum mean squared prediction error.

Main Methods:

  • Utilizing Shannon's entropy rate power definition (1948).
  • Deriving the relationship between the log ratio of entropy powers and differential entropy.
  • Analyzing changes in mutual information by increasing the model order of autoregressive processes.
  • Examining the substitution of minimum mean squared prediction error for entropy power.

Main Results:

  • The log ratio of entropy powers is shown to equal the difference in differential entropy between two processes.
  • The study demonstrates how minimum mean squared prediction error can substitute for entropy power, simplifying calculations.
  • This simplification enhances the practical utility of analyzing differential entropy and mutual information in AR models.

Conclusions:

  • The entropy rate power offers a valuable tool for analyzing autoregressive processes.
  • Simplified calculations of differential entropy and mutual information are achievable, increasing the approach's applicability.
  • The findings have direct applications in speech processing and coding, with potential in seismic, EEG, and ECG classification.