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

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...

Ampere-Maxwell's Law: Problem-Solving

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Machines: Problem Solving II

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.

You might also read

Related Articles

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

Sort by

Same author

Potential increase of the U.S. total fertility rate resulting from restorative treatment of unresolved subfertility: a simulation study.

Frontiers in reproductive health·2026

Same author

Analysis of cortical morphometric variability using labeled cortical distance maps.

Statistics and its interface·2023

Same author

Isomer-selected ion-molecule reactions of acetylene cations with propyne and allene.

Physical chemistry chemical physics : PCCP·2020

Same author

Machine-learned modeling of PM<sub>2.5</sub> exposures in rural Lao PDR.

The Science of the total environment·2019

Same author

An ion trap time-of-flight mass spectrometer with high mass resolution for cold trapped ion experiments.

The Review of scientific instruments·2018

Same author

Features of the non-contact carotid pressure waveform: Cardiac and vascular dynamics during rebreathing.

The Review of scientific instruments·2016

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Representing and computing regular languages on massively parallel networks.

M I Miller¹, B Roysam, K R Smith

¹Dept. of Electr. Eng., Washington Univ., St. Louis, MO.

IEEE Transactions on Neural Networks

|January 1, 1991

Summary

This summary is machine-generated.

This study introduces a unified method for integrating rule-based language constraints into stochastic inference, enabling combined stochastic and syntactic pattern analysis. This approach facilitates the generation of rule-constrained sequences using parallel computing, demonstrated in image segmentation.

Related Experiment Videos

Last Updated: Jul 7, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Computational linguistics
Statistical inference
Computer vision

Background:

Stochastic inference and rule-based constraints are often treated separately.
Existing methods lack a unified framework for integrating syntactic and stochastic pattern recognition.
Generalizing Shannon's work on channel encoding to maximum entropy Markov chains is a key theoretical development.

Purpose of the Study:

To propose a general method for incorporating rule-based constraints from regular languages into stochastic inference problems.
To establish a formal connection between rules and Chomsky grammars.
To enable a unified representation of stochastic and syntactic pattern constraints.

Main Methods:

Generalizing Shannon's encoding of rule-based sequences to maximum entropy Markov chains.
Developing a maximum entropy probabilistic view leading to Gibbs representations.
Coupling Gibbs representations to stochastic diffusion algorithms for sampling language-constrained sequences.
Deriving parallel stochastic cellular automata for generating samples from rule-based constraint sets.

Main Results:

A unified representation for stochastic and syntactic pattern constraints is achieved.
The number of minima in Gibbs representations grows exponentially with the language complexity.
Fully parallel stochastic cellular automata are derived for generating rule-constrained sequences.
The method was successfully mapped to the DAP-510 massively parallel processor for image segmentation.

Conclusions:

The proposed method offers a unified framework for integrating rule-based and stochastic constraints in pattern recognition.
The derived stochastic cellular automata are efficient for generating complex, rule-governed data.
This approach has practical applications in areas like automated image segmentation using massively parallel processing.