Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

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,

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

You might also read

Related Articles

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

Sort by

Same author

Tibetans exhibit lower hemoglobin concentration and decreased heart response to hypoxia during poikilocapnia at intermediate altitude relative to Han Chinese.

Frontiers in physiology·2024

Same author

Correction: Long‑term changes of Th17 and regulatory T cells in peripheral blood of dogs with spinal cord injury after intervertebral disc herniation.

BMC veterinary research·2023

Same author

Long-term changes of Th17 and regulatory T cells in peripheral blood of dogs with spinal cord injury after intervertebral disc herniation.

BMC veterinary research·2023

Same author

Measurement of canine Th17 cells by flow cytometry.

Veterinary immunology and immunopathology·2021

Same author

Intraoperative serum lactate is not a predictor of survival after glioblastoma surgery.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2017

Same author

Seasonal exposure of carbon dioxide ice on the nucleus of comet 67P/Churyumov-Gerasimenko.

Science (New York, N.Y.)·2016

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

The upper bound neural network and a class of consistent labeling problems.

¹Dept. of Math. Sci., Clemson Univ., SC.

IEEE Transactions on Neural Networks

|January 1, 1995

Summary

The upper bound neural network (UBNN) solves consistent labeling problems (CLP) by finding stable attractors. This AI approach offers a scalable, fast alternative for complex tasks like crossbar switch control.

Related Experiment Videos

Last Updated: Jul 7, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Artificial Intelligence
Computational Neuroscience
Operations Research

Background:

Consistent Labeling Problems (CLP) are a general class of NP-complete problems with applications in AI, logic, and OR.
CLP formulations include image segmentation, graph spanning trees, and Euler tours.
Traditional algorithms for problems like maximal matching in large switches face prohibitive time complexity.

Purpose of the Study:

Introduce the Upper Bound Neural Network (UBNN) for solving Consistent Labeling Problems (CLP).
Demonstrate UBNN's efficacy using crossbar packet switch control as an illustrative example.
Propose UBNN as a computationally efficient alternative for large-scale CLP applications.

Main Methods:

Formulating the crossbar switch control as a maximal matching problem.
Utilizing the Upper Bound Neural Network (UBNN) as a dynamical system.
Analyzing the stable attractors of the UBNN to identify feasible solutions.

Main Results:

The set of stable attractors of the UBNN dynamical system corresponds to the feasible solutions of the CLP.
UBNN effectively approximates the maximal matching problem for crossbar switch control.
UBNN demonstrates excellent scalability and near-constant convergence time, unlike traditional O(V^3) algorithms.

Conclusions:

UBNN provides a viable and efficient method for solving Consistent Labeling Problems.
The UBNN's analog circuit properties make it suitable for high-speed applications like nanosecond-range crossbar switches.
UBNN offers a scalable, convergent, and fast solution for complex computational problems.