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

State Space Representation01:27

State Space Representation

785
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
785
Traits and States01:17

Traits and States

886
Personality traits represent consistent patterns in behavior, thoughts, and emotions, reflecting an individual's tendencies across various situations. For example, extraversion, a well-known trait, manifests in individuals as talkative, energetic, and enthusiastic behaviors. These traits are stable over time, offering a reliable framework for predicting how people might act in different contexts. However, they do not define every moment of an individual's life. In contrast to traits,...
886
State Space to Transfer Function01:21

State Space to Transfer Function

691
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
691
The Entropy as a State Function01:14

The Entropy as a State Function

134
Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
134
Transfer Function to State Space01:23

Transfer Function to State Space

985
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
985
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
1.1K

You might also read

Related Articles

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

Sort by
Same author

Uncovering Sex Differences in the Drosophila Ventral Nerve Cord Through Connectome Alignment.

bioRxiv : the preprint server for biology·2026
Same author

Oral [<sup>18</sup>F]-Fluoro-Thia-Heptadecanoic Acid Positron Emission Tomography Reveals Mesenteric-to-Central Lymphatic Flow.

Gastro hep advances·2026
Same author

The redundant protein synthesis gene Aimp1 challenges the canonical inverse relationship between translation and autophagy.

Communications biology·2026
Same author

A Gut-Restricted Liver X Receptor Agonist Ameliorates Liver Injury in Experimental Short Bowel Syndrome.

Gastroenterology·2026
Same author

Lymphatic disruption drives lung transplant fibrosis through interleukin-1-mediated hyaluronan accumulation.

Science translational medicine·2026
Same author

Proteomic and Lipidomic Atlas of Gut-Associated Lymph and Venous Depots in Female Piglets.

Arteriosclerosis, thrombosis, and vascular biology·2026
Same journal

Cichlid fish as a model for understanding social dysfunction.

Current opinion in neurobiology·2026
Same journal

On aims and methods in field neuroethology: Investigating neural mechanisms of behavior in semi-natural and natural contexts.

Current opinion in neurobiology·2026
Same journal

Neurobiological interfaces connecting environmental change to monarch butterfly migration.

Current opinion in neurobiology·2026
Same journal

Learning how to experience the world: From circuits to cell types to genes.

Current opinion in neurobiology·2026
Same journal

Editorial overview for neurobiology of disease 2026.

Current opinion in neurobiology·2026
Same journal

Optical voltage imaging: ready to spark systems neuroscience.

Current opinion in neurobiology·2026
See all related articles

Related Experiment Video

Updated: May 2, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

9.9K

Dynamic belief state representations.

Daniel D Lee1, Pedro A Ortega1, Alan A Stocker2

  • 1Electrical and Systems Engineering Department, University of Pennsylvania, Philadelphia, PA 19104, United States.

Current Opinion in Neurobiology
|March 18, 2014
PubMed
Summary
This summary is machine-generated.

Accurate state estimation in robotics requires dynamic belief states, not single vectors, to manage uncertainty. This review covers algorithms and biological links, highlighting navigation correlations and future research needs.

More Related Videos

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.7K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K

Related Experiment Videos

Last Updated: May 2, 2026

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior
12:38

State-Dependency Effects on TMS: A Look at Motive Phosphene Behavior

Published on: December 28, 2010

9.9K
Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.7K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

2.5K

Area of Science:

  • Robotics
  • Perception
  • Control Systems

Background:

  • Accurate and efficient dynamic state estimation of environmental objects is crucial for perceptual and control systems.
  • Representing uncertainty necessitates dynamic belief states over single state vectors.

Purpose of the Study:

  • To review canonical algorithms for computing and updating belief states in robotic applications.
  • To highlight connections between robotic belief state algorithms and biological systems.
  • To illustrate the importance of belief state correlations using a navigation example.

Main Methods:

  • Review of established algorithms for belief state computation and updating.
  • Analysis of correlations within belief state components.
  • Exploration of psychophysical and neurobiological parallels.

Main Results:

  • Canonical algorithms for belief state representation in robotics are presented.
  • The significance of accounting for correlations in belief states is demonstrated.
  • Connections to biological systems offer insights into perception and control.

Conclusions:

  • Dynamic belief states are essential for robust state estimation under uncertainty.
  • Understanding correlations in belief states is key for advanced robotic perception.
  • Further research in psychophysics and neurobiology can inform future robotic systems.