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Related Concept Videos

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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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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Related Experiment Video

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.

Jiang Liu, Srivathsa Pasumarthi, Ben Duffy

    IEEE Transactions on Medical Imaging
    |April 8, 2023
    PubMed
    Summary

    This study introduces a novel Transformer-based method for imputing missing multi-contrast MRI data. The approach effectively synthesizes missing contrasts, improving diagnostic accuracy and automated analysis.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Multi-contrast magnetic resonance imaging (MRI) provides complementary diagnostic information but faces challenges due to varying contrast availability.
    • Existing methods for missing contrast imputation, often based on Convolutional Neural Networks (CNNs), have limitations in handling variable inputs/outputs and capturing long-range dependencies.

    Purpose of the Study:

    • To develop a flexible and interpretable deep learning framework for synthesizing missing multi-contrast MRI data.
    • To overcome the limitations of CNNs in handling variable input contrasts and capturing complex dependencies.

    Main Methods:

    • Formulated missing data imputation as a sequence-to-sequence learning problem using a multi-contrast multi-scale Transformer (MMT).
    • Employed a multi-scale Transformer encoder for hierarchical input representation and a multi-scale Transformer decoder for coarse-to-fine output generation.
    • Utilized multi-contrast Swin Transformer blocks to capture intra- and inter-contrast dependencies.

    Main Results:

    • The proposed MMT model successfully synthesizes missing MRI contrasts from available ones, handling arbitrary subsets of input contrasts.
    • MMT demonstrated superior performance over state-of-the-art methods on two large-scale multi-contrast MRI datasets, both quantitatively and qualitatively.
    • The inherent interpretability of the Transformer's attention maps allows for understanding the contribution of each input contrast to the synthesized output.

    Conclusions:

    • MMT offers a robust and interpretable solution for missing multi-contrast MRI data imputation.
    • The Transformer-based approach effectively addresses the limitations of CNNs, enabling more accurate and flexible medical image synthesis.
    • This method has significant potential for improving clinical workflow and automated analysis in radiology.