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関連する概念動画

Deconvolution01:20

Deconvolution

537
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...
537
Reducing Line Loss01:18

Reducing Line Loss

353
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 in...
353
The Ideal Transformer01:26

The Ideal Transformer

1.4K
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...
1.4K
Three-Winding Transformers01:19

Three-Winding Transformers

676
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...
676
Downsampling01:20

Downsampling

596
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...
596
Upsampling01:22

Upsampling

574
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
574

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関連する実験動画

Updated: Jan 14, 2026

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy
08:47

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Published on: December 7, 2017

10.2K

軽量マルチディレーテッド・トランスフォーマーによる画像鮮明化

Zhihao Zhao, Zhulin Tao, Jinshan Pan

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    まとめ
    この要約は機械生成です。

    MDFormerは、画像鮮明化のためのマルチディレーテッド・トランスフォーマーです。非局所情報を効果的にキャプチャし、ピクセル間の相互作用を強化し、計算コストを抑えながら最先端の結果を達成します。

    キーワード:
    画像鮮明化トランスフォーマーディレーテッド畳み込み非局所情報計算効率

    さらに関連する動画

    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

    8.8K

    関連する実験動画

    Last Updated: Jan 14, 2026

    Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy
    08:47

    Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

    Published on: December 7, 2017

    10.2K
    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

    8.8K

    科学分野:

    • コンピュータビジョン
    • ディープラーニング
    • 画像処理

    背景:

    • ウィンドウベースのトランスフォーマーは、画像鮮明化において有望です。
    • 非局所情報のキャプチャの制限は、パフォーマンスの向上を制限します。

    研究 の 目的:

    • 効果的なマルチディレーテッド・トランスフォーマー（MDFormer）を開発し、画像鮮明化を強化します。
    • 非局所情報とピクセル間の相互作用をキャプチャする際の既存の方法の限界に対処します。

    主な方法:

    • 効率的な非局所情報の抽出のために、マルチディレーテッド特徴集約（MDFA）モジュールを開発しました。
    • ピクセル間情報の相互作用を改善するために、ディレーテッド・フィードフォワード・ネットワーク（DiFFN）を提案しました。
    • 画像再構成ガイダンスの改善のために、マルチスケール特徴融合（MSFF）モジュールを導入しました。

    主要な成果:

    • MDFormerは、最先端の方法と同等の結果を示します。
    • 提案されたモジュールは、非局所情報を効果的に抽出し、特徴間の相互作用を強化します。
    • 既存のアプローチと比較して、計算コストの大幅な削減を達成しました。

    結論:

    • MDFormerは、非局所情報の制限に対処することにより、画像鮮明化の効果的なソリューションを提供します。
    • 新しいモジュールは、画像鮮明化のパフォーマンスと計算効率の向上に貢献します。
    • この方法は、画像復元の将来の研究に有望な方向性を提供します。