Results 311 to 320 of about 113,812 (347)

Bayesian Window Transformer for Image Restoration

IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformers have excelled in image restoration due to their advanced representational abilities. However, their reliance on a fixed local window for attention often undermines translation invariance and local relationship preservation. This limitation can reduce network stability, especially when dealing with positional changes in degradation ...
Jie Xiao 0002, Xueyang Fu, Yurui Zhu, Zheng-Jun Zha   +3 more
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Moving-window discrete Fourier transform

Journal of Real-Time Image Processing, 2006
The moving-window discrete Fourier transform (MWDFT) is a dynamic spectrum analysis in which the next analysis interval differs from the previous one by including the next signal sample and excluding the first one from the previous analysis interval (Dillard in IEEE Trans Inform Theory 13:2–6, 1967, Comput Elect Eng 1:143–152, 1973, USA Patent 4023028,
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Windowed-Kontorovich-Lebedev transforms

Frontiers of Mathematics in China, 2010
For functions \(f,g\in C^\infty_0(\mathbb{R}^+)\), the authors define a transformation \(f,g\to V(f,g)\) which is, in a sense, an iterated Kontorovich-Lebedev transform. Several properties are proved including a necessary and sufficient condition for boundedness. See also the following papers by \textit{S. B.
Zhao, Jiman, Peng, Lizhong
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A relativistic windowed Fourier transform

Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium' (Cat. No.00CH37105), 2002
Drawing upon some earlier work on coherent states of the Poincare' group in l-space and l-time dimensions, we use these states to define a relativistic windowed Fourier transform, as relativistic extension of the usual windowed Fourier transform. We discretize the resulting transform and obtain conditions under which the discretized transform can be ...
M.R. Karim, S.T. Ali, M. Bodruzzaman
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The Windowed Fourier Transform

2002
Fourier analysis, well as wavelet analysis have an intrinsic limitation, which is contained in the uncertainty principle. In order to state this result, we need a definition of the “width” of a function. Here is the one that suits our purpose.
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Windowed Fourier Transforms

2010
Fourier series are ideal for analyzing periodic signals, since the harmonic modes used in the expansions are themselves periodic. By contrast, the Fourier integral transform is a far less natural tool because it uses periodic functions to expand nonperiodic signals.
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Windowed Fourier transform profilometry based on improved S-transform

Optics Letters, 2012
A novel method for windowed Fourier transform (WFT) profilometry is presented. This method is based on improved S-transform. The impact of the second order derivative of the phase (φ''(b)) to the ridge of S-transform is derived, and how to estimate this deviation is discussed.
F, Da, F, Dong
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2D Discrete Fourier Transform on Sliding Windows

IEEE Transactions on Image Processing, 2015
Discrete Fourier transform (DFT) is the most widely used method for determining the frequency spectra of digital signals. In this paper, a 2D sliding DFT (2D SDFT) algorithm is proposed for fast implementation of the DFT on 2D sliding windows. The proposed 2D SDFT algorithm directly computes the DFT bins of the current window using the precalculated ...
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Transformer design using Windows

Proceedings of Electrical/Electronics Insulation Conference, 2002
A number of software packages exist to help a programmer make the transition to the Windows environment. The programs are used to develop subprograms of "scripts" that instruct the Windows application to perform certain functions when a button is pushed or a field is entered. Illustrative examples are provided.
P.K. Goethe, W.D. Goethe
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Magnetic potential of transformer window

1996
We describe how to calculate the magnetic potential in the window of an ideal transformer. The knowledge of this potential is a starting point for the determination of some other quantities of practical importance (such as leakage field, overheating of windings, circular currents, additional losses, short circuit forces in windings, etc.).
Michal Křížek, Pekka Neittaanmäki
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