The esp-nn optimized convolution functions are producing incorrect outputs, leading to a significant drop in model accuracy from 92% to below 70%. When using the standard ANSI C implementation, the ...
With the rapid development of machine learning, Deep Neural Network (DNN) exhibits superior performance in solving complex problems like computer vision and natural language processing compared with ...
Convolution is a remarkable property of the Fourier transform, often cited in the literature as the “faltung theorem”. Convolution is a remarkable property of the Fourier transform, often cited in the ...
A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks
Abstract: Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based ...
Convolution is used in a variety of signal-processing applications, including time-domain-waveform filtering. In a recent series on the inverse fast Fourier transform (FFT), we concluded with a ...
Abstract: Complex data is commonly encountered in several domains of signal and image processing, where two important components, namely the magnitude and phase of the complex data, convey valuable ...
ABSTRACT: Wiener amalgam spaces are a class of function spaces where the function’s local and global behavior can be easily distinguished. These spaces are ex-tensively used in Harmonic analysis that ...
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