transformWaveletsTranslation and convolution associated with the discrete wavelet transform are investigated using properties of Calder贸n-Zygmund operator and Riesz fractional integral operator. Dual convolution is also studied. The wavelet convolution is applied to approximate functions belonging to certain ...
为了解决短时傅里叶变换中出现的问题,就出现了小波变换(Wavelet Transform),它在时域和频域上都有相当高的分辨率,不仅可以告诉我们信号中存在哪些频率,同时还能告诉不同频率出现的具体时刻。这通过使用不同的缩放实现。 小波变换(wavelet transform,WT)是一种新的变换分析方法,它继承和发展了短时傅立叶变换局部化的思...
After the wavelet transform, the number of wavelet coefficients is the same as the number of variables in the original signal. All operations of convolution and downsampling can be represented as a multiplication of the signal with two matrices WK and VK,. The approximation coefficients a 1 and...
Finally, we design the wavelet transform temporal convolution network to directly predict the two-dimensional Gaussian distribution parameters of the future trajectory. Extensive experiments on the ETH, UCY, and SDD datasets demonstrate that our method outperforms the state-of-the-art methods in ...
(window) # Apply the filter to the signal using convolution filtered_signal = np.convolve(signal, window, mode='same') return filtered_signal def wavelet_filter(signal): """ Applies a wavelet filter to a signal to remove noise. Inputs: signal: numpy array containing the signal Returns: ...
optimizing 3d convolutions for wavelet transforms on cpus with sse units and gpus optimizing 3d convo- lutions for wavelet transforms on cpus with sse units and gpus. optimizing 3d convolutions for wavelet transforms on cpus with sse units and gpus ...
The spectral analysis of signals is currently either dominated by the speed–accuracy trade-off or ignores a signal’s often non-stationary character. Here we introduce an open-source algorithm to calculate the fast continuous wavelet transform (fCWT). T
Structured Convolution Matrices for Energy-efficient Deep learning We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired b... R Appuswamy,T Nayak,J Arthur,... 被引量: 2发表: 2016年 SADCNN-ORBM: a ...
For the finite impulse response filter bank benchmark, a fast convolution FFT-based frequency-domain approach on the GPU performed 4 to 35 times faster than the comparable calculation on a CPU. A non-transform time-domain approach ... MP Mcgraw 被引量: 19发表: 2007年 GPU-Accelerated Video ...
In this work, we demonstrate that by leveraging the Wavelet Transform (WT), it is, in fact, possible to obtain very large receptive fields without suffering from over-parameterization, e.g., for a $k imes k$ receptive field, the number of trainable parameters in the proposed method grows ...