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 ...

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 ...

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 ...

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 ...

Deep Convolutional Neural Networks (DCNN) have the ability to learn complex features and are thus widely used in the field of seismic signal denoising with low signal-to-noise ratio (SNR). However, ...