Avi Baum, CTO at Hailo, discusses why AI infrastructure needs are already shifting from centralized to edge, how telcos can evolve and future-proof their business to keep up with that transition, and ...

Researchers have developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal ...

A new white paper from Ross Video and TV Tech dives deep into the multifaceted world of today’s signal processing for video production ...

Deep learning, particularly convolutional neural networks (CNNs), has yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures ...

Herein, we propose a deep learning-powered colloidal digital SERS platform. This innovation converts SERS spectra into binary “ON/OFF” signals based on defined intensity thresholds, which allows ...

This Research Topic is dedicated to the deep learning technology of high-dimensional IoT and signal processing. Its purpose is to emphasize the new research results and development, open problems, and ...

The goal of this special issue on Machine Learning and Deep Learning of Physiological Signal Analysis was to disseminate the articles related to the (a) application of ML and DL for cardiovascular ...

Signal decomposition (analysis) and reconstruction (synthesis) are cornerstones in signal processing and feature recognition tasks. Signal decomposition is traditionally achieved by projecting data ...

Deep neural network modeling of auditory processing identifies distorted cross-frequency interactions as the key problem for the processing of speech in noise after hearing loss.

Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss eLife 12:e85108.

In Chapter 6, deep stacking networks and several of the variants are discussed in detail, which exemplify the discriminative deep architectures in the three-way classification scheme. From Chapters ...