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International Conference on Communication technology (ICCT)

作者： Tingmin Jiang Yue Xiao Lilin Dan Xinrong Ji Wenqi Tang Yan Zhao National Key Laboratory of Science and Technology on Communications UESTC Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Offset spatial modulation (OSM) is a novel modulation scheme with simplified radio-frequency-switching structure. In this paper, the concept of OSM is combined with orthogonal frequency division multiplexing (OFDM) transmission to take advantage of the benefits of these two techniques. We shed light on the effects of carrier frequency offset (CFO) on the OSM-OFDM systems over multipath Rayleigh fading channel, where the inter-carrier interference (ICI) induced by CFO degrades the system performance. We further derive the theoretical bit-error rate (BER) upper bound of OSM-OFDM impaired by CFO. Simulation results show that the theoretical BER is in good agreement with its simulated counterpart, which demonstrates the accuracy of the theoretical analysis.

关键词： OFDM Transmitting antennas Receiving antennas Precoding Radio frequency Frequency division multiplexing Upper bound

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Continuous-time dynamic network node classification algorithm based on autoencoder 9

Continuous-time dynamic network node classification algorith...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Huang, Xin Li, Yun Xiong, Jinyu Liang, Jie Wu, Jie Jing, Jie PLA Strategic Support Force Information Engineering University College of Information System Engineering Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China Officers College of PAP Chengdu China

Node classification is an important issue in network researches. Existing methods lay more emphasis on the classification of static network nodes, while there are insufficient researches on network dynamic characteristics. At the same time, the existing adjacency matrix representation cannot reflect the potential relationship of indirectly connected nodes. To better describe the network structure and utilize network time information, the author proposes a new Node Classification Algorithm based on Autoencoder, namely NCAA. It adopts the random walk in limited spatio-temporal and the matrix representation of node relationship. The low-dimensional representation vector of the node is generated by the autoencoder model, and the LogicRegression classifier is used to classify the representation vectors, thereby achieving node classification. Experiments show that, compared to some classic algorithms, NCAA can better represent the network, and its two-dimensional visualization of node vectors has a superior effect. Under the same conditions, the F1 value of the classification result is significantly higher than that of baseline algorithms. © 2020 IEEE.

关键词： Classification (of information)

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Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

Tag Pollution Detection in Web Videos via Cross-Modal Releva...

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International Workshop on Quality of Service

作者： Yixin Chen Xinye Lin Keshi Ge Wenbo He Dongsheng Li College of Computer National University of Defense Technology National Key Laboratory of Science and Technology on Blind Signal Processing McMaster University

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.

关键词： Videos Visualization Pollution Neural networks Social network services Correlation Unsolicited electronic mail

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Research on Automatic Decoding of Morse Code Based on Deep Learning

Research on Automatic Decoding of Morse Code Based on Deep L...

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2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Li, Weihao Wang, Keren Information Engineering University Zhengzhou China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728161068

The current automatic decoding method of the Morse telegram has limited accuracy, and can't adapt to signal distortion and code length deviation of the manual telegram. This paper introduces the deep learning method and constructs an automatic decoding model, which integrates feature extraction, sequence modeling and transcription into an end-to-end training neural network. The time-frequency diagrams of signals are used for training and testing. Experimental results show that the decoding system has strong adaptability to manual deviation and frequency drift, and is robust in a noisy environment. © 2019 IEEE.

关键词： Decoding

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Sparse Bayesian Learning for Distributed Passive Radar Imaging via Covariance Sparse Representation 6

Sparse Bayesian Learning for Distributed Passive Radar Imagi...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Gong, Fukang Zhang, Shunsheng He, Qing Research Institute of Electronic Science and Technology University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu China

ISBN: (纸本)9781728129129

Due to low imaging cost and robustness, the distributed passive radars using multiple transmitters and multiple receivers to observe targets have become a hot research. In the case of low SNR, the imaging accuracy of the distributed passive radar imaging model via Orthogonal Matching Pursuit (OMP) sparse reconstruction is low. For this problem, a framework consisting of sparse representation of the received multi-snapshot radar signal covariance matrix, Sparse Bayesian Learning (SBL) based reconstruction algorithm has been built. At the end of paper, through the simulation experiment, the imaging results of the original data and covariance data at low SNR are compared, and the reconstruction errors under different SNR are used to verify the effectiveness of the proposed algorithm. © 2019 IEEE.

关键词： Covariance matrix

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Active Learning for Spam Email Classification 19

Active Learning for Spam Email Classification

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Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence

作者： Zheng Chen Ruiwen Tao Xiaoyang Wu Zhimin Wei Xiao Luo School of Information & Software Engineering University of Electronic Science & Technology of China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781450372619

Deep learning has yielded state-of-the-art performance on text classification tasks. In this paper, a new neural network based on Long-Short-Term-Memory model is applied to classify spam emails. Using deep learning method to classify spam emails requires large amounts of labeled data. To solve this problem, active learning method is used to reduce labeling cost and increase model adaptability. In this paper, it is found that the new model performs better than standard CNNs and RNNs on email classification task, and active learning methods can match state-of-the-art performance with just 10% of the labeled data.

关键词： LSTM

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Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

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Knowledge-Based Systems 2021年 217卷 106833-106833页

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

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Uncertainty estimation by boosting with Gaussian process for expensive optimization problems

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Applied Soft Computing 2025年 180卷

作者： Hanhua Zou Sanyou Zeng Changhe Li Ruwang Jiao Fei Zhao School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China School of Future Science and Engineering Soochow University Suzhou China National Key Laboratory of Blind Signal Processing Chengdu China

In surrogate-assisted evolutionary algorithms (SAEAs), uncertainty measures the confidence level of the predicted fitness and plays an important role in selecting candidate points for expensive evaluation, as evaluating those with a large degree of uncertainty can enhance the exploration ability of algorithms and significantly improve model reliability. Gaussian process (GP) gains great popularity with a predictive variance to estimate uncertainty. However, the performance of GP-assisted SAEAs is still limited with weakened predictions and underutilized uncertainty. In this paper, we propose a novel SAEA that leverages a boosting ensemble with GP as the base learner to enhance optimization performance. First, GP is leveraged to boost a weak learner that captures global trends, refining its prediction in guiding the search. Then, the uncertainty provided by GP is integrated into boosting ensemble model to estimate its overall ensemble uncertainty, enhancing the model reliability and promoting the exploration capabilities. Moreover, a mechanism study is performed through standardized residual and a distance-based metric to have deep insights into search behaviors, demonstrating that the migrated uncertainty basically covers the statistical boundary of Gaussian distribution and contributes to exploitation and exploration trade-off. Experimental results across multiple benchmark problems and an antenna design problem demonstrate that the proposed method significantly enhanced GP-assisted SAEAs in statistical results of the best fitness value and convergence profile without increasing too much running time, particularly in high-dimensional problems.

关键词： Gaussian distribution

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Experimental study of mode distortion induced by stimulated Raman scattering in high-power fiber amplifiers

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Photonics Research 2020年第4期8卷 595-600页

作者： QIUHUI CHU QIANG SHU ZENG CHEN FENGYUN LI DONGLIN YAN CHAO GUO HONGHUAN LIN JIANJUN WANG FENG JING CHUANXIANG TANG RUMAO TAO Research Center of Laser Fusion China Academy of Engineering PhysicsMianyang 621900China Key Laboratory of Particle&Radiation Imaging(Tsinghua University) Ministry of EducationDepartment of Engineering PhysicsTsinghua UniversityBeijing 100084China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610000China

The experimental investigation of mode distortion induced by stimulated Raman scattering(SRS)in a high-power fiber amplifier,which includes the evolutions of optical spectra,spatial beam profiles,and time-frequency characteristics,has been carried out in ***-frequency characteristics have been studied for the first time,to the best of our knowledge,by using a low-speed camera and high-speed photodiode traces,which revealed that temporal-frequency characteristics of SRS-induced mode distortion are different from traditional dynamic mode instability(MI).The experimental results show that the output beam profile remains stable before the mode distortion occurs and fluctuates obviously after the onset of SRS-induced MI but on a time scale of seconds,which is much lower than that of Yb-gain-induced MI featuring millisecond-level beam profile *** also shows that the mode distortion became measurable in company with the onset of inter-mode four-wave mixing(IM-FWM)when the ratio of Raman light reaches 3%;further,the beam quality factor M2degrades gradually from 1.4 to 2.1 as the ratio of Raman light *** mode distortion is accompanied by an obvious temperature increase of the output passive fiber,which further confirms that the mode distortion originates from *** cause of the mode distortion induced by SRS has been explained in the context of core-pumped SRS effect,and the investigation on the accompanying IM-FWM effect indicates that the main content of the SRSinduced high-order mode is the LP21 mode.

关键词： Experimental mode distortion stimulated Raman scattering in high-power fiber amplifiers Raman

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Active learning for spam email classification 2

Active learning for spam email classification

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2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Chen, Zheng Tao, Ruiwen Wu, Xiaoyang Wei, Zhimin Luo, Xiao School of Information and Software Engineering University of Electronic Science and Technology of China China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China

ISBN: (纸本)9781450372619

关键词： Long short-term memory

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