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

Nodal Analysis01:10

Nodal Analysis

1.8K
Nodal analysis is a fundamental method in electrical engineering used to simplify the process of circuit analysis. This method revolves around the concept of using node voltages as the primary variables for circuit analysis. The objective is to determine the voltage at each node in a circuit, which can then be used to find other quantities of interest, such as currents through specific components.
Consider, for instance, a simple circuit composed of three nodes and three resistors, as shown in...
1.8K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

863
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....
863
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.5K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.5K
Control Systems: Applications01:25

Control Systems: Applications

1.1K
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
1.1K
Nodal Analysis with Voltage Sources01:11

Nodal Analysis with Voltage Sources

1.8K
Nodal analysis is a remarkably effective method used in electrical engineering to simplify the analysis of complex circuits, including those with dependent or independent voltage sources. Its strength lies in its systematic approach to breaking down circuits into manageable components, making it easier for engineers to understand and solve.
Consider a circuit that contains four resistors and two voltage sources, as shown in Figure 1. One of these voltage sources is connected between a...
1.8K
Linear time-invariant Systems01:23

Linear time-invariant Systems

828
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
828

You might also read

Related Articles

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

Sort by
Same author

Conceptual qualitative system dynamics model for simulation of perceived workload, stress and performance from industrial work content.

PloS one·2026
Same author

Six-sigma approach-based visibility graphs.

Scientific reports·2026
Same author

Subtractive clustering for spatial resource allocation problems in waste management.

Scientific reports·2026
Same author

Sensitivity Analysis of Long Short-Term Memory-Based Neural Network Model for Vehicle Yaw Rate Prediction.

Sensors (Basel, Switzerland)·2025
Same author

Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties.

Heliyon·2025
Same author

WEBA dataset as the Reflection of Work content effect on Workload perception in Real life Working conditions.

Scientific data·2025

Related Experiment Video

Updated: Jan 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox.

Dániel Leitold1,2, Ágnes Vathy-Fogarassy1,2, János Abonyi2

  • 1Department of Computer Science and Systems Technology, University of Pannonia, Egyetem u. 10, Veszprém, 8200, Hungary.

F1000Research
|October 17, 2019
PubMed
Summary

Network science methods identify key nodes and sensor locations in dynamical systems. This study applies these techniques to the Caenorhabditis elegans neural network, introducing a new toolbox for analysis.

Keywords:
Complex networksControllability and observability analysisDynamical systemsMATLAB toolboxRobustness

More Related Videos

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.3K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.9K

Related Experiment Videos

Last Updated: Jan 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
10:18

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

3.3K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.9K

Area of Science:

  • Dynamical Systems
  • Network Science
  • Neuroscience

Background:

  • Network science methods for driver node identification and sensor placement are increasingly used in dynamical systems.
  • These methodologies offer powerful tools for understanding complex systems.
  • Their application in life sciences, particularly neuroscience, is a growing area of interest.

Purpose of the Study:

  • To introduce and demonstrate the applicability of network science-based methodologies in the life sciences.
  • To analyze the neural network of Caenorhabditis elegans using these techniques.
  • To present a novel NOCAD toolbox for structural controllability and observability analysis.

Main Methods:

  • Application of network science principles to determine driver nodes and optimal sensor placement.
  • Analysis of the Caenorhabditis elegans neural network structure.
  • Development and utilization of an Octave/MATLAB-compatible NOCAD toolbox.
  • Calculation of structural controllability and observability measures for linear/linearized systems.

Main Results:

  • Demonstrated the successful application of network science methodologies to the C. elegans neural network.
  • The NOCAD toolbox facilitates automated generation and comparison of controllability and observability measures.
  • Identified key nodes and potential sensor locations within the neural network.

Conclusions:

  • Network science provides a robust framework for analyzing complex biological systems like neural networks.
  • The proposed NOCAD toolbox enhances the practical application of these methods in life sciences research.
  • This approach aids in understanding system dynamics and optimizing monitoring strategies.