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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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The Ideal Transformer01:26

The Ideal Transformer

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential...
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Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Updated: Jan 9, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Voxel-Level Brain States Prediction Using Swin Transformer.

Yifei Sun, Daniel Chahine, Qinghao Wen

    IEEE Journal of Biomedical and Health Informatics
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    This study predicts future brain states using functional magnetic resonance imaging (fMRI) and a novel Swin Transformer model. The AI accurately forecasts brain activity, potentially reducing fMRI scan times.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Understanding brain dynamics is crucial for neuroscience and mental health.
    • Functional magnetic resonance imaging (fMRI) measures neural activity via blood-oxygen-level-dependent (BOLD) signals, reflecting brain states.

    Purpose of the Study:

    • To predict future human resting brain states using fMRI data.
    • To develop a novel deep learning architecture for accurate spatio-temporal fMRI analysis.

    Main Methods:

    • A novel architecture combining a 4D Shifted Window (Swin) Transformer encoder and a convolutional decoder was proposed.
    • The model was trained and tested on fMRI data from 100 unrelated subjects from the Human Connectome Project (HCP).

    Main Results:

    • The model achieved high accuracy in predicting 7.2s of resting-state brain activity from a prior 23.04s fMRI time series.
    • The predicted brain states closely matched the BOLD contrast and dynamics of actual brain activity.

    Conclusions:

    • The Swin Transformer model effectively learns the spatiotemporal organization of human brain activity from fMRI data at high resolution.
    • This approach shows potential for reducing fMRI scan duration and advancing brain-computer interfaces.