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Related Concept Videos

Sequence Networks of Rotating Machines01:24

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...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Ampere-Maxwell's Law: Problem-Solving01:17

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?
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For the first part of the problem,...
Cartesian Vector Notation01:28

Cartesian Vector Notation

Cartesian vector notation is a valuable tool in mechanical engineering for representing vectors in three-dimensional space, performing vector operations such as determining the gradient, divergence, and curl, and expressing physical quantities such as the displacement, velocity, acceleration, and force. By using Cartesian vector notation, engineers can more easily analyze and solve problems in various areas of mechanical engineering, including dynamics, kinematics, and fluid mechanics. This...
Cartesian Form for Vector Formulation01:26

Cartesian Form for Vector Formulation

The Cartesian form for vector formulation is a process to calculate  the moment of force using the position and force vectors. The moment of force is defined as the cross-product of these vectors, making it a vector quantity. The Cartesian form of the position and force vectors involves unit vectors, which can be used to express the cross-product in determinant form.

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Related Experiment Videos

Four-quadrant optical matrix-vector multiplication machine as a neural-network processor.

S Abramson, D Saad, E Marom

    Applied Optics
    |September 8, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an opto-electronic four-quadrant matrix-vector multiplier for optical neural networks. This advancement enables bipolar neuron outputs, enhancing network capabilities for faster learning and recall.

    Related Experiment Videos

    Area of Science:

    • Optoelectronics
    • Artificial Intelligence
    • Computer Science

    Background:

    • Optical processors offer parallelism for neural networks but often lack bipolar neuron output support.
    • Existing systems are limited to two-quadrant multiplication, hindering network capabilities and learning rates.

    Purpose of the Study:

    • To propose and demonstrate an opto-electronic four-quadrant matrix-vector multiplier.
    • To enable bipolar neuron outputs for enhanced feed-forward neural network performance.

    Main Methods:

    • Development of an opto-electronic system for four-quadrant matrix-vector multiplication.
    • Utilizing common commercial components for practical implementation.

    Main Results:

    • Successful demonstration of a four-quadrant matrix-vector multiplier.
    • Experimental validation of the system's utility for feed-forward neural network recall and learning.

    Conclusions:

    • The proposed opto-electronic multiplier provides a novel, useful, and reliable method for four-quadrant multiplication.
    • This approach significantly advances the capabilities of optical neural networks for training and recall.