Search Results - "Zhou, Huaji"
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Spectrum sensing based on deep learning classification for cognitive radios
Published in China communications (01-02-2020)“…Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep…”
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A Synthetic Aperture Radar Imaging Simulation Method for Sea Surface Scenes Combined with Electromagnetic Scattering Characteristics
Published in Remote sensing (Basel, Switzerland) (01-09-2024)“…Synthetic aperture radar (SAR) simulation is a vital tool for planning SAR missions, interpreting SAR images, and extracting valuable information. SAR imaging…”
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Generative adversarial network-based electromagnetic signal classification: A semi-supervised learning framework
Published in China communications (01-10-2020)“…Generative adversarial network (GAN) has achieved great success in many fields such as computer vision, speech processing, and natural language processing,…”
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Deep spectrum prediction in high frequency communication based on temporal-spectral residual network
Published in China communications (01-09-2018)“…High frequency (HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising…”
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Electromagnetic Signal Classification Based on Class Exemplar Selection and Multi-Objective Linear Programming
Published in Remote sensing (Basel, Switzerland) (01-03-2022)“…In the increasingly complex electromagnetic environment, a variety of new signal types are appearing; however, existing electromagnetic signal classification…”
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Deep radio signal clustering with interpretability analysis based on saliency map
Published in Digital communications and networks (01-10-2024)“…With the development of information technology, radio communication technology has made rapid progress. Many radio signals that have appeared in space are…”
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7
Primary user adversarial attacks on deep learning-based spectrum sensing and the defense method
Published in China communications (01-12-2021)“…The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we…”
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Weight-Variable Scattering Convolution Networks and Its Application in Electromagnetic Signal Classification
Published in IEEE access (2019)“…Deep learning is an important support for the development of cognitive communication in the cognitive Internet of Things (IoT). Deep convolution neural…”
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Percutaneous snare-retrieval of intracardiac thrombus under fluoroscopic and transesophageal echocardiography guidance: case report and systematic review
Published in Frontiers in cardiovascular medicine (09-05-2023)“…Intracardiac foreign bodies (IFB) are rare clinical conditions. There are now several reports on the percutaneous retrieval of IFB under fluoroscopy. However,…”
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An electromagnetic signal classification method inspired by the visual characteristics of biological dual-channel
Published in Wireless networks (01-02-2024)“…Automatic modulation classification of electromagnetic signals has an important role in the field of civil and military signal processing, its researches are…”
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SR2CNN: Zero-Shot Learning for Signal Recognition
Published in IEEE transactions on signal processing (2021)“…Signal recognition is one of the significant and challenging tasks in the signal processing and communications field. It is often a common situation that…”
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A New Method for Joint Sparse DOA Estimation
Published in Sensors (Basel, Switzerland) (12-11-2024)“…To tackle the issue of poor accuracy in single-snapshot data processing for Direction of Arrival (DOA) estimation in passive radar systems, this paper…”
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13
Energy-Constrained Completion Time Minimization in UAV-Enabled Internet of Things
Published in IEEE internet of things journal (01-06-2020)“…Unmanned-aerial-vehicles (UAVs)-enabled wireless communication for Internet-of-Things (IoT) applications has attracted increasing attention. This article…”
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MsmcNet: A Modular Few-Shot Learning Framework for Signal Modulation Classification
Published in IEEE transactions on signal processing (2022)“…With the development of deep learning (DL) technology, the feature extraction capability of networks has gradually been enhanced, and high accuracy has been…”
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SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification
Published in IEEE transactions on wireless communications (01-10-2024)“…Due to the uncertainty of non-cooperative communication channels, the received signals often contain various impairment factors, leading to a significant…”
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16
Class Incremental Learning With Few-Shots Based on Linear Programming for Hyperspectral Image Classification
Published in IEEE transactions on cybernetics (01-06-2022)“…Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred…”
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A Deep Learning-Based Intelligent Receiver for Improving the Reliability of the MIMO Wireless Communication System
Published in IEEE transactions on reliability (01-06-2022)“…Multiple-input-multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas,…”
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18
An Interpretable Explanation Approach for Signal Modulation Classification
Published in IEEE transactions on instrumentation and measurement (2024)“…Signal modulation classification (SMC) has attracted extensive attention for its wide application in the military and civil fields. The current direction of…”
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Toward the Intelligent OFDM Receiving Method With Hybrid Knowledge and Data Driven in IoT
Published in IEEE internet of things journal (15-05-2024)“…Orthogonal frequency-division multiplexing (OFDM) is regarded as one of the key technologies in wireless communications, particularly in the integration of…”
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Multi-dimensional spectrum map construction: A tensor perspective
Published in 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP) (01-10-2016)“…Spectrum data, which is usually characterized by many dimensions, such as location, frequency, time and signal strength, presents formidable challenges in…”
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