circular convolution造句

• If the signals are processed digitally, the frequency-domain convolution becomes circular convolution.
• A consequence of the circular convolution theorem is that the DFT matrix diagonalizes any circulant matrix.
• N _ 2 ) circular convolution and the linear convolution of x with h are identical.
• The remaining samples of the circular convolution will be identical to the samples of the linear convolution.
• Circular convolution can also be computed in the time / spatial domain and not only in the frequency domain.
• The covariance operator is a circular convolution with R Y and is therefore diagonalized in the discrete Fourier Karhunen-Lo鑦e basis
• After multiplying the DFTs and taking the inverse DFT on the result, the resulting circular convolution is then given by:
• In many situations, discrete convolutions can be converted to circular convolutions so that fast transforms with a convolution property can be used to implement the computation.
• The advantage is that the circular convolution can be computed very efficiently as follows, according to the circular convolution theorem "': "'
• The advantage is that the circular convolution can be computed very efficiently as follows, according to the circular convolution theorem "': "'
• If one sequence is much longer than the other, zero-extension of the shorter sequence and fast circular convolution is not the most computationally efficient method available.
• When either sequence contains a string of zeros, of length L, L + 1 of the circular convolution outputs are equivalent to values of \ mathbf { x * y }.
• An important special case is the "'circular convolution "'of x and y defined by x _ N * y, where x _ N is a periodic summation:
• If these conditions are satisfied, then the results of the circular convolution will equal that of the linear convolution ( taking the main period of the circular convolution as the region of support ).
• If these conditions are satisfied, then the results of the circular convolution will equal that of the linear convolution ( taking the main period of the circular convolution as the region of support ).
• When the DFT or FFT is used, we don't have the option of not computing the affected samples, but the leading and trailing edge-effects are overlapped and added because of circular convolution.
• Similarly, when a sequence x is circularly convolved with another, h, which has a much smaller region of support, only a subset of the samples of that circular convolution will show the effects of the spatial aliasing.
• There could also be a problem with phase diffraction from multiple sources . ( See also Superposition principle and Circular convolution ) ~ It would probably be okay if by " short-range " you mean " a few inches " . ~ E : ( talk ) 01 : 38, 20 January 2013 ( UTC )]
• In practice, when multiplying a column vector \ mathbf { x } by \ mathbf { G } _ 1, we take the inverse FFT of \ mathbf { x }, set the first N values in the result to zero and then take the FFT . This is meant to remove the effects of the circular convolution.
• The motivation behind using the circular convolution approach is that it is based on the DFT . The premise behind circular convolution is to take the DFTs of the input signals, multiply them together, and then take the inverse DFT . Care must be taken such that a large enough DFT is used such that aliasing does not occur.