Zhijin Zhao
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View article: Simple yet effective heuristic community detection with graph convolution network
Simple yet effective heuristic community detection with graph convolution network Open
View article: Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network
Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network Open
Community detection is crucial in data mining. Traditional methods primarily focus on graph structure, often neglecting the significance of attribute features. In contrast, deep learning-based approaches incorporate attribute features and …
View article: Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide
Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide Open
View article: Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide
Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide Open
View article: Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide
Surface Oxygen Vacancies Induced Formation of Crystalline/Amorphous Heterostructures for Enhanced Zinc-Storage Capacity of Vanadium Oxide Open
View article: Deep Learning-Based DOA Estimation
Deep Learning-Based DOA Estimation Open
Direction-of-arrival (DOA) estimation is a vital research topic in array signal processing, with extensive applications in many fields. In recent years, deep learning has been applied to DOA estimation to improve the performance. However, …
View article: Detection of Radar Pulse Signals Based on Deep Learning
Detection of Radar Pulse Signals Based on Deep Learning Open
Radar is widely used in aviation, meteorology, and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we pro…
View article: Deep Learning-Based SNR Estimation
Deep Learning-Based SNR Estimation Open
The signal-to-noise ratio (SNR) is an important metric for measuring signal quality and its estimation has received widespread attention in various application scenarios. In this paper, we propose an SNR estimation framework based on deep …
View article: FM-Based Positioning via Deep Learning
FM-Based Positioning via Deep Learning Open
Frequency modulation (FM) broadcast signals, as opportunity signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on received signal strength (RSS) for…
View article: FM-Based Positioning via Deep Learning
FM-Based Positioning via Deep Learning Open
Frequency modulation (FM) broadcast signals, as opportunity signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on received signal strength (RSS) for…
View article: Detection of Radar Pulse Signals Based on Deep Learning
Detection of Radar Pulse Signals Based on Deep Learning Open
Radar is widely used in aviation, meteorology and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we prop…
View article: Detection of Radar Pulse Signals Based on Deep Learning
Detection of Radar Pulse Signals Based on Deep Learning Open
Radar is widely used in aviation, meteorology and military fields, and radar pulse signal detection has become an indispensable and essential function of cognitive radio systems as well as electronic warfare systems. In this paper, we prop…
View article: OFDM sensing based on deep learning
OFDM sensing based on deep learning Open
Detection of OFDM signals has received paramount interest especially for spectrum sensing in cognitive radios. We present a deep learning-based OFDM signal detection method, namely OFDM-DetNet, which combines feature learning and classifie…
View article: Radar Active Jamming Recognition under Open World Setting
Radar Active Jamming Recognition under Open World Setting Open
To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutio…
View article: Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks Open
Although the spectrum sensing algorithms based on deep learning have achieved remarkable detection performance, the sensing performance is easily affected by adversarial attacks due to the fragility of neural networks. Even slight adversar…
View article: Intelligent Reception of Frequency Hopping Signals Based on CVDP
Intelligent Reception of Frequency Hopping Signals Based on CVDP Open
The frequency hopping communication systems have been widely used in anti-jamming communication due to their anti-interception and anti-interference capabilities. With the increasingly complex electromagnetic environment, the transmitter o…
View article: A High-Performance FPGA-Based Depthwise Separable Convolution Accelerator
A High-Performance FPGA-Based Depthwise Separable Convolution Accelerator Open
Depthwise separable convolution (DSC) significantly reduces parameter and floating operations with an acceptable loss of accuracy and has been widely used in various lightweight convolutional neural network (CNN) models. In practical appli…
View article: Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning
Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning Open
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast le…
View article: Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications Open
The frequency-hopping communication system has been widely used in anti-jamming communication due to its anti-interception and anti-jamming performance. With the increasingly complex electromagnetic environment, the frequency-hopping commu…
View article: Contrastive Clustering for Unsupervised Recognition of Interference Signals
Contrastive Clustering for Unsupervised Recognition of Interference Signals Open
Interference signals recognition plays an important role in anti-jamming communication. With the development of deep learning, many supervised interference signals recognition algorithms based on deep learning have emerged recently and sho…
View article: Frequency hopping signal detection based on optimized generalized S transform and ResNet
Frequency hopping signal detection based on optimized generalized S transform and ResNet Open
The performance of traditional frequency hopping signal detection methods based on time frequency analysis is limited by the tradeoff of time-frequency resolution and spectrum leakage. Machine learning-based frequency hopping signal detec…
View article: Adaptive training‐feedback scheme for FDD in massive MIMO systems
Adaptive training‐feedback scheme for FDD in massive MIMO systems Open
Accurate acquisition of channel state information (CSI) is crucial but difficult in frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) systems. To improve the estimation accuracy and to minimize the training cons…
View article: User Selection Scheme based on Reference Frames
User Selection Scheme based on Reference Frames Open
For a multi-user (MU) massive multiple-input multiple-output (MIMO) system, its total throughput is directly related to the degree of mutual interference among user's channel vectors accessing the communication system. Selecting a group of…
View article: Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms
Deep Reinforcement Learning Based Decision Making for Complex Jamming Waveforms Open
With the development of artificial intelligence, intelligent communication jamming decision making is an important research direction of cognitive electronic warfare. In this paper, we consider a complex intelligent jamming decision scenar…
View article: Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning Open
Interference classification plays an important role in anti-jamming communication. Although the existing interference signal recognition methods based on deep learning have a higher accuracy than traditional methods, these have poor robust…
View article: Intelligent Anti-Jamming Decision Algorithm of Bivariate Frequency Hopping Pattern Based on DQN With PER and Pareto
Intelligent Anti-Jamming Decision Algorithm of Bivariate Frequency Hopping Pattern Based on DQN With PER and Pareto Open
To improve the anti-jamming performance of frequency hopping system in complex electromagnetic environment, a Deep Q-Network algorithm with priority experience replay (PER) based on Pareto samples (PPER-DQN) is proposed, which makes intell…
View article: PCA‐Based Adaptive Training‐Feedback Scheme in Time‐Varying FDD Massive MIMO Systems
PCA‐Based Adaptive Training‐Feedback Scheme in Time‐Varying FDD Massive MIMO Systems Open
For massive multiple‐input multiple‐output (MIMO) systems, the real‐time channel state information (CSI) acquisition is crucial but difficult in fast time‐varying scenarios, especially for downlink (DL) channels in frequency division duple…
View article: Self-organizing fuzzy inference ensemble system for big streaming data classification
Self-organizing fuzzy inference ensemble system for big streaming data classification Open
View article: Open set recognition algorithm based on Conditional Gaussian Encoder
Open set recognition algorithm based on Conditional Gaussian Encoder Open
For the existing Closed Set Recognition (CSR) methods mistakenly identify unknown jamming signals as a known class, a Conditional Gaussian Encoder (CG-Encoder) for 1-dimensional signal Open Set Recognition (OSR) is designed. The network r…
View article: Hardware Decoding Accelerator of (73, 37, 13) QR Code for Power Line Carrier in UPIoT
Hardware Decoding Accelerator of (73, 37, 13) QR Code for Power Line Carrier in UPIoT Open
The proposal of the ubiquitous power Internet of Things (UPIoT) has increased the demand for communication coverage and data collection of smart grid; the quantity and quality of communication networks are facing greater challenges. This b…