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Related Concept Videos

Energy Losses in Transformers01:21

Energy Losses in Transformers

820
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
820
Types Of Transformers01:16

Types Of Transformers

943
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...
943
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...
132
Downsampling01:20

Downsampling

126
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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The Ideal Transformer01:26

The Ideal Transformer

344
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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Deconvolution01:20

Deconvolution

129
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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DeepReducer: A linear transformer-based model for MEG denoising.

Hui Xu1, Li Zheng2, Pan Liao3

  • 1McGovern Institute for Brain Research, Peking University, Beijing 100871, PR China; Center for MRl Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, PR China; Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, PR China.

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|February 10, 2025
PubMed
Summary
This summary is machine-generated.

DeepReducer, a new deep learning model, effectively denoises event-related magnetic fields (ERFs) in magnetoencephalography (MEG). This reduces the need for extensive data collection, improving signal quality and participant comfort.

Keywords:
Deep learningDenoiseERFMEGTransformer

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Magnetoencephalography (MEG) measures event-related magnetic fields (ERFs) for cognitive and perceptual research.
  • ERFs are often obscured by noise in single trials, necessitating lengthy data acquisition.
  • Efficiently isolating ERFs is critical for advancing neuroscience and clinical applications.

Purpose of the Study:

  • To introduce DeepReducer, a novel deep learning model for denoising MEG event-related magnetic fields.
  • To reduce the number of trials required for reliable ERF analysis.
  • To enhance the efficiency and practicality of MEG data acquisition.

Main Methods:

  • Developed DeepReducer, a linear transformer-based deep learning model.
  • Trained the model on a combination of limited-trial and multi-trial averaged ERFs.
  • Utilized mean squared error as the loss function to capture signal fluctuations in MEG data.

Main Results:

  • DeepReducer demonstrated superior performance compared to conventional trial-averaging methods.
  • Significantly improved the signal-to-noise ratio (SNR) of event-related magnetic fields.
  • Reduced source localization errors in both semi-synthetic and experimental MEG data.

Conclusions:

  • DeepReducer offers a reliable and efficient method for denoising ERFs in MEG.
  • The model optimizes MEG data acquisition, reducing participant burden and artifacts.
  • Facilitates more accessible and less time-consuming neuroimaging studies.