Transformer-based masked autoencoder with contrastive loss for hyperspectral image classification
X Cao, H Lin, S Guo, T Xiong… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, in order to solve the problem of lacking accurately labeled hyperspectral
image data, self-supervised learning has become an effective method for hyperspectral …
image data, self-supervised learning has become an effective method for hyperspectral …
Automatic modulation classification in impulsive noise: Hyperbolic-tangent cyclic spectrum and multibranch attention shuffle network
Automatic modulation classification plays an essential role in cognitive communication
systems. Traditional automatic modulation classification approaches are primarily …
systems. Traditional automatic modulation classification approaches are primarily …
A transformer-based contrastive semi-supervised learning framework for automatic modulation recognition
W Kong, X Jiao, Y Xu, B Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The application of deep learning improves the processing speed and the accuracy of
automatic modulation recognition (AMR). As a result, it realizes intelligent spectrum …
automatic modulation recognition (AMR). As a result, it realizes intelligent spectrum …
Semi-Supervised Radar Intra-Pulse Signal Modulation Classification With Virtual Adversarial Training
J Cai, M He, X Cao, F Gan - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Radar intrapulse signal modulation classification is an important work for the electronic
countermeasure and there are mainly two categories of algorithms. The deep learning …
countermeasure and there are mainly two categories of algorithms. The deep learning …
Broiler sound signal filtering method based on improved wavelet denoising and effective pulse extraction
W Tao, Z Sun, G Wang, S Xiao, B Liang… - … and Electronics in …, 2024 - Elsevier
There is little attention paid to signal filtering in existing broiler health monitoring or broiler
sound signal classification research, and the only few studies still have issues such as lack …
sound signal classification research, and the only few studies still have issues such as lack …
Feature fusion convolution-aided transformer for automatic modulation recognition
Automatic Modulation Recognition (AMR) is becoming increasingly important due to its key
role in wireless communications. In order to enrich the feature information and reduce …
role in wireless communications. In order to enrich the feature information and reduce …
S3L: Spectrum Transformer for self-supervised learning in hyperspectral image classification
H Guo, W Liu - Remote Sensing, 2024 - mdpi.com
In the realm of Earth observation and remote sensing data analysis, the advancement of
hyperspectral imaging (HSI) classification technology is of paramount importance …
hyperspectral imaging (HSI) classification technology is of paramount importance …
Toward the Automatic Modulation Classification With Adaptive Wavelet Network
With the evolutionary development of modern communications technology, automatic
modulation classification (AMC) has played an increasing role in the complex wireless …
modulation classification (AMC) has played an increasing role in the complex wireless …
Automatic Modulation Recognition of Underwater Acoustic Signals Using a Two-Stream Transformer
Automatic modulation recognition (AMR) of underwater acoustic (UWA) signals is incredibly
challenging due to the complexity of UWA channels and the severity of ocean noise. In the …
challenging due to the complexity of UWA channels and the severity of ocean noise. In the …
Automatic modulation classification with deep neural networks
Automatic modulation classification is an important component in many modern aeronautical
communication systems to achieve efficient spectrum usage in congested wireless …
communication systems to achieve efficient spectrum usage in congested wireless …