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arXiv 2021年

作者： Wang, Haide Zhou, Ji Wei, Jinlong Guo, Dong Feng, Yuanhua Liu, Weiping Yu, Changyuan Wang, Dawei Li, Zhaohui Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China Huawei Technologies Duesseldorf GmbH European Research Center Germany School of Information and Electronics Beijing Institute of Technology Beijing100081 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou510275 China State Key Laboratory of Optoelectronic Materials and Technologies 519000 China

In this paper, to the best of our knowledge, we propose the first multi-rate Nyquist-subcarriers modulation (SCM) for C-band 100Gbit/s signal transmission over 50km dispersionuncompensated link. Chromatic dispersion (CD) introduces severe spectral nulls on optical double-sideband signal, which greatly degrades the performance of intensity-modulation and direct-detection systems. Based on the prior knowledge of the dispersive channel, Nyquist-SCM with multi-rate subcarriers is proposed to keep away from the CD-caused spectral nulls flexibly. Signal on each subcarrier can be individually recovered by a digital signal processing, including the feed-forward equalizer with no more than 31 taps, a two-tap post filter, and maximum likelihood sequence estimation with one memory length. Combining with entropy loading based on probabilistic constellation shaping to maximize the capacity-reach, the C-band 100Gbit/s multi-rate Nyquist-SCM signal over 50km dispersion-uncompensated link can achieve 7% hard-decision forward error correction limit and average normalized generalized mutual information of 0.967 at received optical power of -4dBm and optical signal-to-noise ratio of 47.67dB. In conclusion, the multi-rate Nyquist-SCM shows great potentials in solving the CD-caused spectral distortions. © 2021, CC BY.

关键词： Chromatic dispersion

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Physical Adversarial Attack Meets computer Vision: A Decade Survey

arXiv

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arXiv 2022年

作者： Wei, Hui Tang, Hao Jia, Xuemei Wang, Zhixiang Yu, Hanxun Li, Zhubo Satoh, Shin'ichi Van Gool, Luc Wang, Zheng School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing100871 China Colleage of Software and Technology Zhejiang University Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Digital Content and Media Sciences Research Division National Institute of Informatics Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan Computer Vision Lab of ETH Zurich Zürich8092 Switzerland KU Leuven Leuven3000 Belgium INSAIT Sofia Bulgaria

Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs’ performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Parameter Estimation of Redundant System 15th

Parameter Estimation of Redundant System

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15th International Conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2019

作者： Xie, Chao-Fan Xu, Lin Zhang, Fuquan Xu, Lu-Xiong Department of Electrical Engineering I-Shou University KaohsiungTaiwan Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University Fuqing FuzhouFuJian China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University Fuqing FuzhouFuJian China

ISBN: (纸本)9789811397134

The components usually are redundant and they are often connected to larger systems in series or in parallel. For large systems, the order estimation formulas of different types of components are very important which are deduced by collecting the data of overall faults. The estimation properties in the sense of probability are given. It is the asymptotic convergence of probability. Continue to improve variance estimator to achieve asymptotically minimum uniform variance estimator. This ensures the practicability of the theoretical formula. Further study uses computer simulation to estimate series and parallel systems in which the components are redundant, and it gives the specific handing framework. © 2020, Springer Nature Singapore Pte Ltd.

关键词： computer simulation Estimation Redundant

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A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

arXiv

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology

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Fuzzy decision implications: interpretation within fuzzy decision context

引用

CAAI Transactions on Intelligence technology 2019年第4期4卷 231-236页

作者： Jing Zhang Yanhui Zhai Deyu Li School of Foreign Language Shanxi UniversityTaiyuan 030006ShanxiPeople’s Republic of China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information TechnologyShanxi UniversityTaiyuan 030006ShanxiPeople’s Republic of China

Fuzzy decision implication is an extension of decision implication in the fuzzy setting,serving to uncover the dependencies of fuzzy *** study presents the interpretation of fuzzy decision implication in the fuzzy decision ***,they will show that from fuzzy decision contexts one can obtain a closed fuzzy set of fuzzy decision implications,and the semantical characteristic of the obtained fuzzy set can be interpreted by fuzzy decision context and can be represented by some operators of fuzzy decision ***,starting from a fuzzy set of fuzzy decision implications,they can form a fuzzy decision context,from which the given fuzzy set can be *** result actually implies that they have constructed a correspondence between closed fuzzy sets of fuzzy decision implications and fuzzy decision contexts,and thus shows the equivalence of two interpretations of fuzzy decision implications.

关键词： Fuzzy operators implies

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An Adaptive Irregular Convolution U-Net for Reconstructing Ancient Channel of the Yellow River

An Adaptive Irregular Convolution U-Net for Reconstructing A...

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IEEE SENSORS

作者： Zhishun Guo Jianhui Zhao Ning Li Lin Wu College of Computer and Information Engineering Henan University Kaifeng China Henan. Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. This paper, based on Gaofen-3 full-polarization synthetic aperture radar (SAR) data, analyzed scattering characteristics of archaeolandscapes, which provided reliable evidences for the existence of AYR between Lankao and Sui County. Then, an adaptive irregular convolution kernel U-Net (AICK-U-Net) was proposed to reconstruct the part of channel of AYR, based on the image obtained by polarization decomposition, and the precision reached 96.21%. In summary, polarization decomposition and neural network in spaceborne SAR field, provided new insights for detecting and reconstructing archaeolandscapes.

关键词： Convolution Spaceborne radar Neural networks Scattering Sensors Rivers Reliability

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Univariate Forecasting of time series data Based on Seq2Seq Model 4

Univariate Forecasting of time series data Based on Seq2Seq ...

引用

4th International Conference on Electronic information technology and computer Engineering, EITCE 2020

作者： Guo, Yanwen Wu, Tao Liu, Yanbing Jiang, Yixin Department of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Laboratory of Internet and Information Sccurity Chongqing University of Posts and Telecommunications Chongqing China Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)9781450387811

With the release of country's 2020 new infrastructure projects, the increasing demand for electricity in various projects has further promoted the intelligent upgrade of the power grid. The processing of grid data at the edge has become one of the future trends of next generation grid. In the field of power grid edge time series data prediction, the inability to obtain the variable characteristics of other systems and the uncertainty of the length of various edge input data, this paper proposes a Seq2seq prediction model based on a Bi-directional Long Short Term Memory (Bi-LSTM). Bi-LSTM can make full use of contextual information to achieve a more accurate prediction effect. The Seq2Seq model can process variable-length sequence data. In this paper, Seq2Seq model and BI-LSTM model are combined to solve the problem of uncertain edge length and univariate prediction. At the same time, in order to solve the information loss caused by Seq2Seq in the encoding stage, the Attention mechanism is introduced, it can compute the allocation of resources to more important tasks while addressing the problem of information overload. Through experimental research, it is found that the proposed ABSeq2Seq model is superior to other classic models in univariate prediction, and it has the better generalization for various types of data. We have proved its effectiveness in the single variable power prediction. © 2020 ACM.

关键词： Time series

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Multi-Exemplar Learning Particle Swarm Optimization for Regional Traffic Signal Timing Optimization with Multi-Intersections

Multi-Exemplar Learning Particle Swarm Optimization for Regi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhuang-Jie Deng Zhi-Hui Zhan Sam Kwong Jun Zhang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou China City University of Hong Kong Hong Kong Hanyang University Chaoyang University of Technology

ISBN: (纸本)9781665442084

Traffic congestion has become one of the major problems of smart travel. The application of evolutionary computation (EC) for traffic signal timing optimization (TSTO) can effectively alleviate traffic congestion at a single intersection. However, while in more complicated regional traffic signal timing optimization (RTSTO) problems, the canonical EC algorithm such as particle swarm optimization (PSO) still has limitation due to population prematurity. In this paper, a multi-exemplar learning (MEL) strategy is adopted to improve the diversity of the population, so that the particles can have more opportunities to explore the search space. Furthermore, multiple traffic indicators are used in this paper to measure the comprehensive performance of the solution. Moreover, the microsimulation software is adopted to evaluate the solution to simulate the real-world intersection, making the obtained solution more practical in real-world application. Experiments are conducted to investigate the effectiveness and efficiency of MEL-PSO. The results show that the MEL-PSO algorithm is more effective and efficient than the compared algorithms on RTSTO problems.

关键词： Software algorithms Sociology Traffic control Software Timing Space exploration Statistics

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Bi-Objective Search Method for Bayesian Network Structure Learning

Bi-Objective Search Method for Bayesian Network Structure Le...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Ting Wu Hong Qian Aimin Zhou Zhenzi Li Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China 2012 Labs Huawei Technologies CO. LTD Shenzhen China

ISBN: (纸本)9781665441506

Bayesian network (BN) is a probability graph model, which makes uncertain reasoning logically clearer and more understandable. Structure learning is the first step to learn a BN model. And the score + search methods are a kind of the effective methods to learn the structure. This paper proposes a Bi-Objective Search (BOS) method for Bayesian network structure learning, which considers two objectives, i.e., the log-likelihood score and network complexity. To avoid the illegal structures, BOS samples edges and generates permutations to add directions to the edges for the initial population. To improve the diversity, BOS designs the genetic operators to generate new solutions. The new approach is applied to a set of discrete Bayesian networks, and the experimental results show that the algorithm is superior to the existing algorithms in BN structure learning.

关键词： Conferences Sociology Search problems Genetics Inference algorithms Bayes methods Complexity theory

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Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

arXiv

引用

arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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