A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network

Zhou, Xuanhan; Xiong, Jun; Zhang, Xiaochen; Liu, Xiaoran; Wei, Jibo

doi:10.1109/lwc.2021.3074135

Cited by 34 publications

(13 citation statements)

References 10 publications

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“…In this way, the proposed solution can achieve a detection accuracy of 99% which is much higher than those of CNN and DNN approaches, i.e., 81.6% and 96.6%, respectively. Similarly, the authors in [93] also point out that the time-varying characteristics of wireless channels introduce more difficulties to conventional DL-based approaches in detecting abnormal RF signals. In contrast, GAN, with its capabilities of anomaly detection and uncertainty estimation, can deal with this issue effectively.…”

Section: Channel Equalizationmentioning

confidence: 96%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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In this study, three clones of the duckweed genus Lemna, collected from three different regions of Vietnam Binh Thuan Bac Giang and Ben Tre were used to evaluate the growth and development in a culture medium containing different concentrations of As3+, Cd2+, or Pb2+. During 14 days of incubation, the growth rate was determined daily by measuring the surface area of all plants of each sample. Experimental results show that the three Lemna clones could grow in nutrient medium contaminated by heavy metal at a concentration of 0.3-0.5 mg/L for As3+, 0.15-0.3 mg/L for Cd2+, and 0.15 mg/L for Pb2+. At these concentrations, the division rate of the treated clones was several times higher than that of the controls. The highest increase in the surface area was recorded for Lemna BTN (68.47 times higher when cultured in medium supplemented in 0.3 mg/L Pb2+ than under control conditions). The surface area of Lemna BTR cultured in a medium supplemented with 0.3 mg/L As3+ was 54.65 times higher than in medium without arsenic. The obtained results showed that Lemna BGG and Lemna BTR can tolerate arsenic and cadmium pollution while Lemna BTN tolerates lead pollution.

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Section: Channel Equalizationmentioning

confidence: 96%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

View full text Add to dashboard Cite

show abstract

“…Li and Boulanger [30] combined the short-time Fourier transform spectra of the ECG signal with hand-made features to detect more complex cardiac abnormalities, including 16 distinct rhythm abnormalities and 13 heartbeat abnormalities. Zhou et al [31] proposed a radio anomaly detection algorithm based on an improved GAN, which uses short-time Fourier transform to obtain the spectral graph image from the received signal, then reconstructs the spectral graph by combining the encoder network in the original GAN, and detects the anomaly according to the reconstruction error and discriminator loss. Chong et al [32] studied the feasibility of detecting adverse substructure conditions by using bullet train load through finite element numerical simulation.…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion

Yang

Gao

et al. 2023

Sensors

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The detection of anomalies in multivariate time-series data is becoming increasingly important in the automated and continuous monitoring of complex systems and devices due to the rapid increase in data volume and dimension. To address this challenge, we present a multivariate time-series anomaly detection model based on a dual-channel feature extraction module. The module focuses on the spatial and time features of the multivariate data using spatial short-time Fourier transform (STFT) and a graph attention network, respectively. The two features are then fused to significantly improve the model’s anomaly detection performance. In addition, the model incorporates the Huber loss function to enhance its robustness. A comparative study of the proposed model with existing state-of-the-art ones was presented to prove the effectiveness of the proposed model on three public datasets. Furthermore, by using in shield tunneling applications, we verify the effectiveness and practicality of the model.

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“…Although the Fourier transform (FT) spectral parameters are useful for detecting radio anomalies [3], the Fourier domain does not exhibit any time domain characteristics, leading to suboptimal feature extraction. Short-time Fourier transform (STFT) does not yield a multiresolution analysis due to the fixed bandwidth for signal decomposition at all frequencies [4,5]. With an underlying assumption of a stationary signal, wavelet analysis is very useful [6].…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detector Using Filtering Stockwell Transform and Siamese Convolutional Neural Network in Radio Monitoring (AnoFSTSCNN)

2023

RSL

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Detecting anomalous behavior in the radio spectrum is a demanding task due to high levels of interference behaviors caused by massive wireless devices. Stockwell transform (ST) has grown in popularity for the analysis of nonstationary and nonlinear signals, but it is yet to be adequately explored in the radio monitoring domain. An approach that consists of FST (filtering Stockwell transform) and SCNN (siamese convolutional neural network) for radio spectrum anomaly detection (AnoFSTSCNN) is proposed in this paper. Four types of anomaly behaviors, including tone, chirp, pulse, and noise frequency modulation (FM) are simulated using MATLAB. Simulation results show that the AnoFSTSCNN has a 13.18% and 29.52% improvement in detection performance over short-time Fourier transform (STFT) and ST, respectively. The proposed approach provides a possible reference solution for radio monitoring and anomalous signal detection.

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A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network

Cited by 34 publications

References 10 publications

Effect of heavy metals on duckweed growth

Effect of heavy metals on duckweed growth

DCFF-MTAD: A Multivariate Time-Series Anomaly Detection Model Based on Dual-Channel Feature Fusion

An Anomaly Detector Using Filtering Stockwell Transform and Siamese Convolutional Neural Network in Radio Monitoring (AnoFSTSCNN)

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