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Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

404
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
404
Discrete Fourier Transform01:15

Discrete Fourier Transform

404
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
404
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

578
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
578
Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
7.0K
Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Fast Fourier Transform01:10

Fast Fourier Transform

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
465

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関連する実験動画

Updated: Sep 10, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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骨格ベースの連続した手話認識のための部分賢明なグラフフーリエ学習

Dong Wei1, Hongxiang Hu1, Gang-Feng Ma1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Journal of imaging
|August 27, 2025
PubMed
まとめ
この要約は機械生成です。

この研究では,手話認識のための新しい部分的なグラフフフーリエ学習法が導入され,精度が向上し,計算コストが削減されます. この新しいアプローチは 複雑な身体部位の動きを効果的にモデル化して 視覚言語の理解を向上させます

キーワード:
フーリエ完全接続グラフ継続的な手話認識周波数強化部分的な行動認識

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関連する実験動画

Last Updated: Sep 10, 2025

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科学分野:

  • コンピュータ科学
  • 人工知能
  • 人とコンピュータの相互作用

背景:

  • 手話認識はRGB入力で計算コストとノイズによる課題に直面しています.
  • 非線形時間動力学と 身体の各部位における非同期性を 正確にモデル化することは 難しそうです

研究 の 目的:

  • 骨格ベースの連続した手話認識のための新しい部分的なグラフフ・フーリエ学習法 (PGF-SLR) を提案する.
  • 身体の各部位の時空関係を均一にモデル化することで,既存の方法の限界を解決する.

主な方法:

  • ボディパーツをノードとして,周波数領域の注意をエッジとして扱う部分レベルのフーリエ完全接続グラフを構築した.
  • 差別的な行動特性を増幅するために,適応的な周波数増強法を使用した.
  • 拡張された理解のために補助的な予測ブランチを備えたダブルブランチアクション学習モジュールを利用しました.

主要な成果:

  • PHOENIX14とPHOENIX14- Tのデータセットではそれぞれ3. 31%/ 3. 70%と2. 81%/ 7. 33%の相対的な改善を達成した.
  • CSL-Dailyのデータセットで競争力のあるパフォーマンスを示し,強い汎用性を示しています.
  • オフラインとオンラインの両方で計算コストを削減します.

結論:

  • 提案されたPGF-SLR方法は,サイン言語の認識のための周波数領域の空間時間的依存性を効果的に捉えます.
  • PGF-SLRは,継続的な手話認識のための軽量で堅牢で計算効率の高いソリューションを提供します.
  • この方法は,正確で迅速な手話の理解を必要とする現実世界のアプリケーションのための大きな可能性を示しています.