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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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A review of dielectric optical metasurfaces for spatial differentiation and edge detection

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Frontiers of Optoelectronics 2021年第2期14卷 187-200页

作者： Lei WAN Danping PAN Tianhua FENG Weiping LIU Alexander A.POTAPOV Department of Electronic Engineering College of Information Science and TechnologyJinan UniversityGuangzhou 510632China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan UniversityGuangzhou 510632China Key Laboratory of Optoelectronic Information and Sensing Technologies of Guangdong Higher Education Institutes Jinan UniversityGuangzhou 510632China Institute of Radio Engineering and Electronics Russian Academy of SciencesMoscow 125009Russia

Dielectric metasurfaces-based planar optical spatial differentiator and edge detection have recently been proposed to play an important role in the parallel and fast image processing *** the development of dielectric metasurfaces of different geometries and resonance mechanisms,diverse on-chip spatial differentiators have been proposed by tailoring the dispersion characteristics of subwavelength *** review focuses on the basic principles and characteristic parameters of dielectric metasurfaces as first-and second-order spatial differentiators realized via the Green's function *** spatial bandwidth and polarization dependence are emphasized as key properties by comparing the optical transfer flinctions of metasurfaces for different incident wavevectors and *** present the operational capabilities of a two-dimensional spatial differentiator in image information acquisition,edge detection is described to illustrate the practicability of the *** an application example,experimental demonstrations of edge detection for different biological cells and a flower mold are discussed,in which a spatial differentiator and objective lens or camera are integrated in three optical pathway *** realization of spatial differentiators and edge detection with dielectric metasurfaces provides new opportunities for ultrafast information identification in biological imaging and machine vision.

关键词： dielectric metasurfaces spatial differentiator edge detection optical transfer function

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Hand Hygiene Assessment via Joint Step Segmentation and key Action Scorer

arXiv

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arXiv 2022年

作者： Li, Chenglong Zhu, Qiwen Liu, Tubiao Tang, Jin Su, Yu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230061 China

Hand hygiene is a standard six-step hand-washing action proposed by the World Health Organization (WHO). However, there is no good way to supervise medical staff to do hand hygiene, which brings the potential risk of disease spread. Existing action assessment works usually make an overall quality prediction on an entire video. However, the internal structures of hand hygiene action are important in hand hygiene assessment. Therefore, we propose a novel fine-grained learning framework to perform step segmentation and key action scorer in a joint manner for accurate hand hygiene assessment. Existing temporal segmentation methods usually employ multi-stage convolutional network to improve the segmentation robustness, but easily lead to over-segmentation due to the lack of the long-range dependence. To address this issue, we design a multi-stage convolution-transformer network for step segmentation. Based on the observation that each hand-washing step involves several key actions which determine the hand-washing quality, we design a set of key action scorers to evaluate the quality of key actions in each step. In addition, there lacks a unified dataset in hand hygiene assessment. Therefore, under the supervision of medical staff, we contribute a video dataset that contains 300 video sequences with fine-grained annotations. Extensive experiments on the dataset suggest that our method well assesses hand hygiene videos and achieves outstanding performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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21-cm foreground removal using AI and the frequency-difference technique

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Physical Review D 2024年第6期109卷 063509-063509页

作者： Feng Shi Haoxiang Chang Le Zhang Huanyuan Shan Jiajun Zhang Suiping Zhou Ming Jiang Zitong Wang School of Aerospace Science and Technology Xidian University Xi’an 710126 People’s Republic of China Peng Cheng Laboratory No. 2 Xingke 1st Street Shenzhen 518000 People’s Republic of China School of Physics and Astronomy Sun Yat-sen University 2 Daxue Road Tangjia Zhuhai 519082 People’s Republic of China CSST Science Center for the Guangdong-Hong Kong-Macau Greater Bay Area Zhuhai 519082 People’s Republic of China Shanghai Astronomical Observatory (SHAO) Nandan Road 80 Shanghai 200030 China University of Chinese Academy of Sciences Beijing 100049 People’s Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi’an 710126 People’s Republic of China

The deep learning technique has been employed in removing foreground contaminants from 21-cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input. Combining with the frequency difference, we refer to our method as the UNet-fd (UNet frequency-difference), where the U-Net structure is the same as that in deep21. Based on our tests, we demonstrate that this frequency-difference preprocessing technique can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of k≲0.3 hMpc−1 in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent autocorrelation and cross-correlation power spectrum ratios at the 1σ level across scales k≲0.1 hMpc−1, with only a 10% reduction observed in the cross-correlation power spectrum at k≃0.2 hMpc−1. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching with the target truth. In order to test how thermal noise affects the performance of our method, we simulated various white noise levels in the map. This shows the mean cross-correlation ratio R¯cross≳0.8 when the level of the thermal noise is smaller than or equal to that of the HI signal. In comparison, our method outperforms the traditional principal component analysis (PCA) method. The PCA-derived cross-correlation ratios are underestimated b

关键词： Large scale structure of the Universe

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

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Knowledge-Based Systems 2021年 217卷

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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Continuous Distributed processing of Software Defined Radar

Continuous Distributed Processing of Software Defined Radar

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IEEE International Conference on Radar

作者： Bing Li Qiang Qiu Shiqi Gong Yongjun Liu Yu Lei CAS Key Lab of Network Data Science and Technology Chinese Academy of Sciences Institute of Computing Technology Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China National Laboratory of Radar Signal Processing Xidian University Xi'an China Golaxy Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665468893

Software-defined radar has been an active research field for more than ten years. However, the low performance and low scalability of the traditional processing techniques of SDR make it hard to deal with complex radar tasks. The stream processing engine is a software that supports low latency and high scalability distributed data processing. It is widely used in log, image, and video processing scenarios but rarely used in radar applications. This paper reviews processing software-defined radar data by using the stream processing engine, and we illustrate the differences between processing log data and radar data. Furthermore, we discuss some optimizations for stream processing engines to get better performance in the radar data processing.

关键词： Protocols Scalability Streaming media Radar imaging Data processing Software Hardware

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