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Related Concept Videos

Three-Winding Transformers01:19

Three-Winding Transformers

263
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
263
Plane Electromagnetic Waves I01:30

Plane Electromagnetic Waves I

3.7K
The existence of combined electric and magnetic fields that propagate through space as electromagnetic (EM) waves is the most significant prediction of Maxwell's equations. As Maxwell's equations hold in free space, the predicted electromagnetic waves do not require a medium for their propagation. An EM wave comprises an electric field, defined as the force per charge on a stationary charge, and a magnetic field, which is the force per charge on a moving charge.
The EM field is assumed...
3.7K
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

176
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...
176
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

94
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
94
Reducing Line Loss01:18

Reducing Line Loss

173
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
173
Equivalent Circuits for Practical Transformers01:28

Equivalent Circuits for Practical Transformers

466
The practical equivalent circuits of single-phase two-winding transformers exhibit significant deviations from their idealized versions due to the inherent properties of winding resistance and finite core permeability. These properties result in real and reactive power losses, affecting the transformer's performance. Understanding these deviations is crucial for designing more efficient transformers.
In a practical transformer, each winding exhibits resistance and leakage reactance. The...
466

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Related Experiment Video

Updated: Jul 20, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

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Complex Transformer Network for Single-Angle Plane-Wave Imaging.

Xiaolei Qu1, Chujian Ren1, Zihao Wang1

  • 1School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China.

Ultrasound in Medicine & Biology
|August 6, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning enhances plane-wave imaging (PWI) quality using a complex transformer network (CTN). The CTN improves image contrast and resolution for high-frame-rate ultrasound applications.

Keywords:
Adaptive beamformingDeep learningPlane-wave imagingUltrasound

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Area of Science:

  • Medical Imaging
  • Ultrasound Technology
  • Artificial Intelligence in Medicine

Background:

  • Plane-wave imaging (PWI) offers high frame rates but compromises image quality.
  • Processing complex in-phase and quadrature (IQ) data and suppressing incoherent signals are key challenges in deep learning for PWI.

Purpose of the Study:

  • To develop a novel deep learning model for enhancing PWI quality.
  • To address the challenges of complex IQ data processing and incoherent signal suppression in PWI.

Main Methods:

  • A complex transformer network (CTN) integrating complex convolution and complex self-attention (CSA) modules was proposed.
  • The CTN processes delayed complex IQ data, extracts features, suppresses irrelevant signals using CSA, and forms output images.
  • Minimum variance (MV) beamforming was used to generate training labels.

Main Results:

  • CTN achieved image quality comparable or superior to MV, with significantly reduced computation time.
  • Quantitative evaluation showed strong performance in contrast ratio, signal-to-noise ratio, and resolution across simulation, phantom, and in vivo experiments.
  • CTN successfully enhanced previously unclear or invisible details in delay-and-sum (DAS) and MV images.

Conclusions:

  • The CTN significantly improves image contrast, resolution, and clarity compared to MV beamforming.
  • The CTN is an efficient deep learning tool for high-frame-rate ultrasound imaging.
  • GPU acceleration made CTN's runtime comparable to conventional DAS methods.