Results 21 to 30 of about 59 (42)

Adversarial RFML: Evading Deep Learning Enabled Signal Classification

, 2019
Deep learning has become an ubiquitous part of research in all fields, including wireless communications. Researchers have shown the ability to leverage deep neural networks (DNNs) that operate on raw in-phase and quadrature samples, termed Radio ...
Flowers, Bryse Austin
core  

Deep learning for wireless communications : flexible architectures and multitask learning

Demand for wireless connectivity has never been higher and continues to grow rapidly. Connecting more devices requires mindfulness in managing the limited resources of energy and radio spectrum.
Kalade, Sarunas
core   +1 more source

Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence. [PDF]

Cureus, 2023
Talukdar D, De Deus LF, Sehgal N.
europepmc   +1 more source

Considerations for Radio Frequency Fingerprinting across Multiple Frequency Channels. [PDF]

Sensors (Basel), 2022
Gutierrez Del Arroyo JA, Borghetti BJ, Temple MA.   +2 more
europepmc   +1 more source

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications. [PDF]

Sensors (Basel), 2022
Moore MO, Buehrer RM, Headley WC.
europepmc   +1 more source

On the Use of Convolutional Neural Networks for Specific Emitter Identification

, 2018
Specific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint ...
Wong, Lauren J.
core  

Foundations of Radio Frequency Transfer Learning

The introduction of Machine Learning (ML) and Deep Learning (DL) techniques into modern radio communications system, a field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when ...
Wong, Lauren Joy
core  

Sensitivity Analysis of RFML-based SEI Algorithms

Radio Frequency Machine Learning (RFML) techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used
Olds, Brennan Edson
core  

Intelligently Leveraging Multi-Channel Image Processing Neural Networks for Multi-View Co-Channel Signal Detection

The evolution of technology and gadgets has led to a significant increase in the number of transmitted signals, making RF sensing more complex than ever.
Koppikar, Nidhi Nitin
core  

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RESEARCH AND ANALYSIS RADIO FREQUENCY MACHINE LEARNING RFML

Электросвязь, 2023
Статья посвящена проблеме использования машинного обучения на радиочастотах в беспроводных сетях. Отмечается, что машинное обучение широко применяется в телекоммуникациях для решения задач классификации сигналов и причин отказов, прогнозирования событий с помощью, например, нейронных сетей.
openaire   +3 more sources