Abstract: In response to the dual bottleneck of difficulty in obtaining the clean signal and extracting key features, which leads to poor denoising effect in mechanical equipment acoustic signal, a ...

You can use these live scripts as demonstrations in lectures, class activities, or interactive assignments outside class. This module covers the definition and computation of 1D and 2D convolution, as ...

@InProceedings{Saijo2024_TFLoco, author = {Saijo, Kohei and Wichern, Gordon and Germain, Fran\c{c}ois G. and Pan, Zexu and {Le Roux}, Jonathan}, title = {TF-Locoformer: Transformer with Local Modeling ...

In order to accurately detect series arc fault, this paper proposes a series arc fault detection method based on voltage signal which introduces inception with multi-scale parallel convolution ...

A windowed sinc function can implement a low-pass filter, and a two-dimensional convolutional filter can blur or sharpen images. In part 3 of this series, we introduced a low-pass filter based on the ...

RF signals historically are measured using spectrum analyzers, at least that was before oscilloscopes offered sufficient bandwidth for those measurements. With oscilloscope bandwidths over 100 GHz, RF ...

Abstract: Subsampled blind deconvolution is the recovery of two unknown signals from samples of their convolution. To overcome the ill-posedness of this problem, solutions based on priors tailored to ...

The Laplace transform convolution theorem is a powerful tool in the field of engineering and mathematics. It relates the convolution of two functions in the time domain to the multiplication of their ...

The world of artificial intelligence (AI) is rapidly evolving, and AI is increasingly enabling applications that were previously unattainable or very difficult to implement. A subsequent article, ...

Convolutional neural networks are an important category of deep learning, currently facing the limitations of electrical frequency and memory access time in massive data processing. Optical computing ...

Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out‐of‐hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ...