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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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.
Transient and Steady-state Response01:24

Transient and Steady-state Response

In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state response.
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
State Space Representation01:27

State Space Representation

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...

You might also read

Related Articles

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

Sort by
Same author

Signaling pathways involved in the effects of HMGB1 on mesenchymal stem cell migration and osteoblastic differentiation.

International journal of molecular medicine·2016
Same author

Presence of retinal pericyte-reactive autoantibodies in diabetic retinopathy patients.

Scientific reports·2016
Same author

Computational Analysis of Structure-Based Interactions for Novel H₁-Antihistamines.

International journal of molecular sciences·2016
Same author

Mixed Spectrum Analysis on fMRI Time-Series.

IEEE transactions on medical imaging·2016
Same author

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.

IEEE transactions on biomedical circuits and systems·2016
Same author

Hepatic Stellate Cells Directly Inhibit B Cells via Programmed Death-Ligand 1.

Journal of immunology (Baltimore, Md. : 1950)·2016
Same journal

Feedback Control of Coupled Nonlinear Oscillators with Uncertain Parameters.

Systems & control letters·2025
Same journal

Spatialized epidemiological forecasting applied to Covid-19 pandemic at departmental scale in France.

Systems & control letters·2022
Same journal

Feedback control of the COVID-19 pandemic with guaranteed non-exceeding ICU capacity.

Systems & control letters·2022
Same journal

Conditions for Global Stability of Monotone Tridiagonal Systems with Negative Feedback.

Systems & control letters·2010
Same journal

Detectability of Discrete Event Systems with Dynamic Event Observation.

Systems & control letters·2010
Same journal

Positive feedback may cause the biphasic response observed in the chemoattractant-induced response of Dictyostelium cells.

Systems & control letters·2007
See all related articles

Related Experiment Video

Updated: May 31, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Generalized Detectability for Discrete Event Systems.

Shaolong Shu1, Feng Lin

  • 1School of Electronics and Information Engineering, Tongji University, Shanghai, China.

Systems & Control Letters
|June 22, 2011
PubMed
Summary
This summary is machine-generated.

This study enhances discrete event system detectability by extending it to nondeterministic systems and introducing polynomial-time algorithms for strong detectability. New D-detectability concepts improve practical applications.

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: May 31, 2026

A Precise and Autonomous System for the Detection of Insect Emergence Patterns
06:22

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Published on: January 9, 2019

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

  • Control Theory
  • Computer Science
  • Systems Engineering

Background:

  • Previous work defined four types of detectability for deterministic discrete event systems.
  • Detectability is crucial for determining system states from observations.

Purpose of the Study:

  • Extend detectability to nondeterministic systems.
  • Develop efficient algorithms for strong detectability.
  • Introduce D-detectability for relaxed state identification.

Main Methods:

  • Generalization of detectability to nondeterministic discrete event systems.
  • Development of polynomial-time algorithms using a novel 'detector' automaton.
  • Extension of detectability to D-detectability, focusing on distinguishing state pairs.

Main Results:

  • Successful extension of detectability theory to nondeterministic systems.
  • Efficient polynomial-time algorithms for checking strong detectability.
  • Introduction of D-detectability, broadening the scope of state analysis.

Conclusions:

  • The extended theory of detectability is more applicable to real-world problems.
  • New algorithms offer significant computational advantages over previous methods.
  • D-detectability provides a flexible approach for state distinguishability.