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Velocity and Position by Integral Method01:13

Velocity and Position by Integral Method

If acceleration as a function of time is known, then velocity and position functions can be derived using integral calculus. For constant acceleration, the integral equations refer to the first and second kinematic equations for velocity and position functions, respectively.
Consider an example to calculate the velocity and position from the acceleration function. A motorboat is traveling at a constant velocity of 5.0 m/s when it starts to decelerate to arrive at the dock. Its acceleration is...
Strategies of Self-Presentation II: Self-Verification01:17

Strategies of Self-Presentation II: Self-Verification

Self-verification is a fundamental psychological drive wherein individuals seek affirmation of their self-concept from others, striving for consistency between their internal self-view and external perceptions. This drive operates even when the self-concept is negative, influencing interpersonal behavior and feedback preferences in complex and often counterintuitive ways. Unlike the self-enhancement motive, which seeks positive evaluations, self-verification prioritizes coherence and...
Instantaneous Velocity - I01:15

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The average velocity during a time interval cannot tell us how fast or in what direction a particle is moving at any given time during the interval. To calculate this, it is important to know the instantaneous velocity, which is the velocity at a specific instant of time or at a specific point along the path. Instantaneous velocity is the quantity that measures how fast an object is moving along its path. In other words, the instantaneous velocity vx of an object is the limit of the average...
Velocity and Acceleration in Steady and Unsteady Flow01:11

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In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
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Average and Instantaneous Velocity Vectors01:12

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To calculate other physical quantities in kinematics, the time variable must be introduced. The time variable not only allows us to state where an object is (its position) during its motion, but also how fast it’s moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position, a particular time is assigned. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity v. This...
Vector Functions and Motion: Problem Solving01:30

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Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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Related Experiment Video

Updated: Jul 19, 2026

Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations
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Velocity-image model for online signature verification.

Mohammad A U Khan1, Muhammad Khalid Khan Niazi, Muhammad Aurangzeb Khan

  • 1Department of Computer Engineering, Kyung Hee University, Sochen-dong, 449-701 Suwon, Korea. mohammad_a_khan@yahoo.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 2, 2006
PubMed
Summary

This study introduces a new algorithm for online signature verification. It extracts simpler strokes from velocity signals, improving the accuracy of signature discrimination.

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Area of Science:

  • Biometrics
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Online signature capturing devices generate shape and velocity signals.
  • Previous methods extracted strokes from velocity signal minimas, resulting in complex shapes that hinder template generation.
  • This complexity impacts the discriminative capability of signature verification systems.

Purpose of the Study:

  • To propose a novel stroke-based algorithm for online signature verification.
  • To address the limitations of existing methods in generating discriminative signature templates.
  • To enhance the accuracy and efficiency of signature verification through improved stroke extraction.

Main Methods:

  • A new algorithm splits the velocity signal into various frequency bands.
  • Strokes are extracted based on these segmented bands, resulting in simpler and smaller stroke representations.
  • The medium-velocity band is identified as the most stable and discriminative for stroke extraction.
  • Euclidean distances of strokes from the medium-velocity band are utilized for verification.

Main Results:

  • The proposed algorithm extracts smaller and simpler strokes compared to traditional methods.
  • The medium-velocity band proves effective for discrimination, while low- and high-velocity bands are found to be unstable.
  • Experiments demonstrate an improved discriminative capability of the stroke-based system.
  • The new method enhances the overall performance of online signature verification.

Conclusions:

  • The proposed stroke-based algorithm offers a more effective approach to online signature verification.
  • By utilizing a medium-velocity band for stroke extraction, the system achieves better discriminative power.
  • This method simplifies stroke generation, leading to more robust and accurate signature templates.
  • The findings suggest a significant improvement in the field of biometric authentication through enhanced signature analysis.