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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

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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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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...
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Harmonic Mean01:09

Harmonic Mean

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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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Transformers01:26

Transformers

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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A Human Cerebral Organoid Model of Neural Cell Transplantation
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MR Image Harmonization with Transformer.

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    Summary
    This summary is machine-generated.

    This study introduces a Transformer-based method for medical image harmonization, improving magnetic resonance imaging (MRI) data consistency. The approach enhances automated analysis and diagnosis accuracy in clinical settings.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Medical image harmonization is crucial for integrating data from diverse scanners and protocols in clinical applications.
    • Existing methods may struggle with variations in image modality, resolution, and noise.

    Approach:

    • A novel Transformer-based method is proposed for magnetic resonance imaging (MRI) harmonization.
    • The self-attention mechanism of Transformers is utilized to learn cross-domain image characteristics.

    Key Points:

    • The Transformer-based method achieves superior quantitative metrics and visual quality compared to state-of-the-art approaches.
    • The approach demonstrates high robustness against variations in image modality, resolution, and noise.
    • Effective transfer of imaging characteristics between source and target domains is achieved.

    Conclusions:

    • The proposed medical image harmonization method shows significant promise for clinical applications.
    • Improved accuracy and reliability in automated analysis and diagnosis are anticipated.
    • This technique can enhance the utility of multi-center and multi-protocol MRI data.