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Deep Modulation Recognition in an Unknown Environment [PDF]
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Deep modulation recognition has demonstrated high classification accuracy when a neural network is trained on large-scale datasets. However, when applied in an unknown environment where there are not any ground-truth labels in collected data, its performance can be significantly degraded.
Li, Lei +3 more
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AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK [PDF]
Journal of Engineering Science and Technology, 2014 In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the ...
JIDE JULIUS POPOOLA
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IEEE Access, 2020 Communication signal modulation recognition has important research value in the fields of cognitive electronic warfare, communication countermeasures and non-collaborative communication. However, traditional signal recognition methods usually suffer some
Chong Lin +3 more
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A Method of Noise Reduction for Radio Communication Signal Based on RaGAN
Sensors, 2023 Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise.
Liang Peng +4 more
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Big Data and Cognitive ComputingIn wireless communication, information security, and anti-interference technology, modulation recognition of frequency-hopping signals has always been a key technique.
Mengxuan Lan +2 more
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Recognition of narrowband rausing autoregressive models and pattern comparison approach
Journal of Telecommunications and Information Technology, 2002 This paper presents an improved spectral recognition method for digitally modulated radio signals. It is based on a signal autoregressive (AR) model. Model poles are used as signal features for neural network based on recognition process.
Jerzy Łopatka
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Digital modulation recognition based on discriminative restricted Boltzmann machine
Tongxin xuebao, 2021 In order to improve the performance of digital modulation recognition under high dynamic signal-to-noise ratio, a joint modulation recognition method based on high-order cumulant and discriminative restricted Boltzmann machine was proposed, which ...
Zhengquan LI +5 more
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Auditory-Inspired End-to-End Speech Emotion Recognition Using 3D Convolutional Recurrent Neural Networks Based on Spectral-Temporal Representation [PDF]
, 2018 The human auditory system has far superior emotion recognition abilities compared with recent speech emotion recognition systems, so research has focused on designing emotion recognition systems by mimicking the human auditory system.
Dang, Jianwu +9 more
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Portable software modules for speech recognition [PDF]
Proceedings of the workshop on Speech and Natural Language - HLT '90, 1990 Because speech recognition R&D is conducted in large part through the creation and modification of computer software, productivity depends heavily on software engineering issues - the ease of writing new software and reusing existing software. These issues are relevant not just to the work within a particular R&D group, but also to the exchange of ...
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Communicating Recognition & Rewards experiences: Tips and tricks
These guidelines support communications and marketing employees in the preparation and execution stages of creating a story about someone’s experiences with Recognition & Rewards (R&R) principles and themes.
Recognition & Rewards
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