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Region of Convergence01:17

Region of Convergence

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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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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.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Centroid of a Body: Problem Solving01:03

Centroid of a Body: Problem Solving

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The centroid of a body is a crucial concept in engineering and physics. Finding the centroid of a body can help determine its stability, its balance point, and even its design. In this context, consider a thin wire bent in the form of a quarter circular arc. Polar coordinates are used to calculate the centroid. The wire is first divided into small differential elements of a length equal to the radius multiplied by the differential angle.
The x-coordinates and y-coordinates of each element's...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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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.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Related Experiment Video

Updated: Nov 2, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence.

Chi Zhang, Guankai Li, Guosheng Lin

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 14, 2021
    PubMed
    Summary

    CycleSegNet introduces a novel framework for image co-segmentation, enhancing information transfer between images. This computer vision approach significantly outperforms existing methods on benchmark datasets.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Image co-segmentation is a challenging computer vision task requiring effective inter-image information transfer for accurate object segmentation.
    • Existing learning-based algorithms face difficulties in leveraging cross-image data for conditional predictions.

    Purpose of the Study:

    • To present CycleSegNet, a novel framework designed to improve image co-segmentation performance.
    • To address the challenge of information transfer between images in co-segmentation tasks.

    Main Methods:

    • CycleSegNet employs a region correspondence module for exchanging information between local image regions.
    • A cycle refinement module, utilizing ConvLSTMs, iteratively updates image representations and facilitates information exchange.

    Main Results:

    • CycleSegNet achieved superior performance compared to state-of-the-art methods on four benchmark datasets: PASCAL VOC, MSRC, Internet, and iCoseg.
    • Performance improvements were 2.6% on PASCAL VOC, 7.7% on MSRC, 2.2% on Internet, and 2.9% on iCoseg.

    Conclusions:

    • The proposed CycleSegNet framework effectively enhances image co-segmentation by improving inter-image information transfer.
    • The novel architecture, combining region correspondence and cycle refinement, represents a significant advancement in the field of computer vision for co-segmentation.