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Induction01:16

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An emf is induced when the magnetic field in a coil is changed by pushing a bar magnet into or out of the coil. emfs of opposite signs are produced by motion in opposite directions, and the directions of emfs are also reversed by reversing poles. The same results are produced if the coil is moved rather than the magnet—it is the relative motion that is important. The faster the motion, the greater the emf. Additionally, there is no emf when the magnet is stationary relative to the coil.
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Self-Inductance01:24

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Mutual inductance arises when a current in one circuit produces a changing magnetic field that induces an emf in another circuit. On the other hand, self-inductance arises when the current passing through the circuit changes, creating a changing magnetic flux, resulting in inductance in the same circuit.
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Inductance is the property of a device that tells us how effectively it induces an emf in another device. In other words, it is a physical quantity that expresses the effectiveness of a given device.
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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
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Mathematical induction is a structured method of proof used to confirm the truth of statements involving natural numbers. Consider the sum of the first n natural numbers:This formula describes a pattern that appears to hold true as more terms are added. To verify that it is valid for all natural numbers, mathematical induction proceeds in two essential steps. The first is the base case, where the formula is tested for the initial value, typically n = 1. Substituting into both sides confirms the...
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Calculation of Self-inductance01:29

Calculation of Self-inductance

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The self-inductance of a circuit, often simply called the inductance, is a purely geometric factor that depends only on the circuit component's structure. More specifically, it depends on the shape and size of the component that lets the flux pass through it, thus inducing an electric field that opposes any current passing through it.
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A parsimonious model of brightness induction.

Ashish Bakshi1, Kuntal Ghosh2,3

  • 1Machine Intelligence Unit, Indian Statistical Institute, 203 B T Road, Kolkata, 700108, India. ashishbakshi@outlook.com.

Biological Cybernetics
|January 23, 2018
PubMed
Summary

We developed a new model explaining brightness illusions. This model uses a difference of difference-of-Gaussian filter and attentive vision to explain contrast and assimilation effects, improving upon prior models.

Keywords:
Brightness perceptionComputational modelVisual pathways

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Area of Science:

  • Vision Science
  • Computational Neuroscience
  • Perceptual Psychology

Background:

  • Brightness perception is complex, involving various illusions like contrast and assimilation.
  • Existing models, such as the oriented difference of Gaussian, have limitations in explaining certain visual phenomena.
  • Understanding the neural mechanisms underlying brightness perception is crucial.

Purpose of the Study:

  • To present a parsimonious model of brightness induction.
  • To account for both brightness-contrast and brightness-assimilation illusions.
  • To offer insights into the role of attention in brightness perception via magnocellular and parvocellular pathways.

Main Methods:

  • Developed a computational model based on a difference of difference-of-Gaussian filter.
  • Incorporated a two-pass model of attentive vision.
  • Utilized the concept of parallel channels in the central visual pathway (magnocellular and parvocellular).

Main Results:

  • The proposed model successfully accounts for various brightness illusions.
  • It addresses limitations of previous models, including those related to Mach band and checkerboard illusions.
  • Demonstrates the model's ability to explain phenomena not covered by the oriented difference of Gaussian model.

Conclusions:

  • The parsimonious model provides a unified explanation for diverse brightness illusions.
  • It highlights the interplay between visual processing channels and attention in brightness perception.
  • Suggests a framework for understanding the neural basis of visual attention and perception.