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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

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Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: Jul 24, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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A Two-Step Discrete Cosine Basis Oriented Motion Modeling Approach for Enhanced Motion Compensation.

Ashek Ahmmed, Manoranjan Paul, Mark Pickering

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 4, 2023
    PubMed
    Summary

    This study introduces a new video coding method using discrete cosine basis oriented (DCO) motion modeling to improve efficiency. The approach blends global and local motion information, achieving significant bit rate savings in HEVC and VVC encoders.

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    Area of Science:

    • Digital Signal Processing
    • Video Compression
    • Computer Vision

    Background:

    • Video coding relies on minimizing redundancy within sequences.
    • Current block-based methods model commonality locally.
    • Advanced standards offer improved efficiency over predecessors.

    Purpose of the Study:

    • To develop a novel commonality modeling approach for video coding.
    • To seamlessly blend global and local motion homogeneity information.
    • To enhance prediction accuracy and reduce computational complexity.

    Main Methods:

    • A two-step discrete cosine basis oriented (DCO) motion modeling for frame prediction.
    • Utilizing DCO motion models for smooth and sparse representation of complex motion.
    • Partitioning frames to investigate conformance to global motion and introducing local DCO models for non-conforming regions.

    Main Results:

    • Improved rate-distortion performance in a High Efficiency Video Coding (HEVC) encoder.
    • Achieved up to 9% bit rate savings by using DCO prediction frames.
    • Reported 2.37% bit rate savings when compared to the Versatile Video Coding (VVC) encoder.

    Conclusions:

    • The proposed method effectively minimizes both global and local motion commonality.
    • DCO motion modeling offers advantages over traditional motion models for complex motion.
    • This approach enhances video compression efficiency in modern coding standards.