Results 11 to 20 of about 59 (42)

A Real-World Dataset Generator for Specific Emitter Identification

IEEE Access, 2023
Generating high-quality, real-world, well-labeled datasets for radio frequency machine learning (RFML) applications often proves prohibitively cumbersome and expensive, leading to the low availability of high-fidelity, low-cost datasets. Specific emitter
Braeden P. Muller, Lauren J. Wong, William H. Clark, Alan J. Michaels   +3 more
doaj   +1 more source

The Importance of Data in RF Machine Learning

, 2022
While the toolset known as Machine Learning (ML) is not new, several of the tools available within the toolset have seen revitalization with improved hardware, and have been applied across several domains in the last two decades. Deep Neural Network (DNN)
Clark, William Henry IV
core  

Real-World Considerations for RFML Applications

, 2023
Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques.
Muller, Braeden Phillip Swanson
core  

Revolutionary Point‐of‐Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Advanced Science, Volume 11, Issue 36, September 25, 2024.
Point‐of‐care systems, particularly in their wearable format, play a pivotal role in transforming the diagnostics and healthcare realm. Integration of these high‐tech platforms with intelligent systems, such as artificial intelligence (AI), is a promising path for revolutionizing early disease detection, in vitro bioassays, medical image/data analysis,
Fatemeh Haghayegh, Alireza Norouziazad, Elnaz Haghani, Ariel Avraham Feygin, Reza Hamed Rahimi, Hamidreza Akbari Ghavamabadi, Deniz Sadighbayan, Faress Madhoun, Manos Papagelis, Tina Felfeli, Razieh Salahandish   +10 more
wiley   +1 more source

Snow Distribution Patterns Revisited: A Physics‐Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub‐Arctic

Water Resources Research, Volume 60, Issue 9, September 2024.
Abstract Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow ...
R. L. Crumley, C. L. Bachand, K. E. Bennett   +2 more
wiley   +1 more source

Clustering Method for Signals in the Wideband RF Spectrum Using Semi-Supervised Deep Contrastive Learning

Applied Sciences
This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms.
Adam Olesiński, Zbigniew Piotrowski
doaj   +1 more source

An Analysis of RF Transfer Learning Behavior Using Synthetic Data

, 2022
Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but ...
Wong, Lauren J., McPherson, Sean, Michaels, Alan J.   +2 more
core  

Binary Transformer Detectors for Automatic Modulation Detection Under Realistic Radio Frequency Impairment Conditions

Signals
Automatic modulation classification (AMC) is a core capability for spectrum monitoring, adaptive receivers, and electronic support. Most radio-frequency machine learning (RFML) studies train multi-class classifiers on benchmark datasets that contain a ...
AnuraagChandra Singh Thakur, Masudul Imtiaz   +1 more
doaj   +1 more source

One Size Does Not Fit All:  Optimizing Sequence Length with Recurrent Neural Networks for Spectrum Sensing

, 2021
With the increase in spectrum congestion, intelligent spectrum sensing systems have become more important than ever before. In the field of Radio Frequency Machine Learning (RFML), techniques like deep neural networks and reinforcement learning have been
Moore, Megan O.'Neal
core  

Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems

, 2020
Recent innovations in machine learning have paved the way for new capabilities in the field of radio frequency (RF) communications. Machine learning techniques such as reinforcement learning and deep neural networks (DNN) can be leveraged to improve upon
Delvecchio, Matthew David
core