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Related Concept Videos

Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...
Types of Collisions - II01:19

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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...

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Related Experiment Video

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Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
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Published on: August 4, 2014

A neural computational model for animal's time-to-collision estimation.

Ling Wang1, Dezhong Yao

  • 1Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China. w_ling@uestc.edu.cn

Neuroreport
|March 7, 2013
PubMed
Summary
This summary is machine-generated.

We developed a simple Weighted Summation of Visual Angle Model (WSVAM) for estimating time-to-collision (TTC). This neuronal-implemented model offers precise TTC estimation for artificial vision and potential insights into biological visual mechanisms.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Animal behavior

Background:

  • Accurate time-to-collision (TTC) estimation is vital for survival in animals and crucial for AI systems avoiding dangers.
  • Existing TTC models (e.g., 1/τ≈θ'/sin θ, θ'/θ) are too complex for biological neuronal implementation.

Purpose of the Study:

  • To propose a novel, simple computational model for TTC estimation that is readily implementable by biological neuronal systems.
  • To introduce the Weighted Summation of Visual Angle Model (WSVAM) as a biologically plausible mechanism for TTC calculation.

Main Methods:

  • Developed a new computational model: 1/τ≈Mθ-P/(θ+Q)+N, termed WSVAM.
  • Constants M, P, Q, and N are dependent on a predefined visual angle.
  • Demonstrated WSVAM's compatibility with widely accepted biological neuronal models.

Main Results:

  • WSVAM provides a precise estimation for TTC.
  • The model is perfectly implementable using biological neuronal mechanisms.
  • WSVAM exhibits natural minimum consumption and simplicity.

Conclusions:

  • WSVAM offers a simple, neuronal-implemented method for TTC estimation.
  • This model has practical applications in artificial vision systems.
  • WSVAM represents a potential mechanism within the biological visual system for TTC calculation.