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IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Wei Xin Yingsong Li Harbin Engineering University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nested-array (TNA) is constructed, which reduces the element redundancy via rearranging the density of sub-arrays of the constructed new TNA. Compared to former TNAs, the constructed TNA gets higher DOFs and maximization signals, numerical simulation are given for getting its superior behaviors.

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Detection of Photoplethysmography Manipulation in Video Forgery 31

Detection of Photoplethysmography Manipulation in Video Forg...

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31st European Signal processing Conference, EUSIPCO 2023

作者： Lin, Yuzhen Li, Bin Maiorana, Emanuele Campisi, Patrizio Guangdong Key Lab of Intelligent Information Processing Shenzhen Key Lab of Media Security Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen China Dept. of Industrial Electronic and Mechanical Engineering Roma Tre University Rome Italy

ISBN: (纸本)9789464593600

Recent studies have shown that physiological signals related to blood pressure and heart rate can be estimated in a contactless modality from facial videos using remote photoplethysmography (rPPG). This has paved the way to the development of techniques that can acquire and manipulate the rPPG signals recoverable from facial videos without affecting their visual appearance. The goal of this paper is to analyze the detectability of this new kind of forgery, here referred to as rPPG deepfake videos. Specifically, we propose a two-stream method based on the analysis of rPPG deepfake videos at both spatial and temporal levels. The experimental results obtained from tests performed on samples taken from two distinct databases demonstrate that our method performs better than popular deep learning methods in the rPPG deepfake detection task. © 2023 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Photoplethysmography

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Generalizing quotient space theory from structural viewpoint

Generalizing quotient space theory from structural viewpoint

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IEEE International Conference on Granular computing (GRC)

作者： W.L. Chen J.X. Cheng Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University of Science and Technology Hefei Anhui China

Quotient space theory of problem solving, a formal model of granular computing, is generalized in the sense that topological structure is replaced by Cech's closure space. Some basic issues of granular computing, such as the representation of real world at different levels of granularity, property preserving and the construction of granular world, are discussed in detail. It turns out that most of conclusions of the classical quotient space theory keep being valid, so intension and applicable fields are enriched and enlarged respectively.

关键词： Problem-solving Topology Signal processing Set theory Humans Artificial intelligence Processor scheduling Path planning Lattices intelligent structures

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Generating Manipulation Sequences using Reinforcement Learning and Behavior Trees for Peg-In-Hole Task

Generating Manipulation Sequences using Reinforcement Learni...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Xu, Jiahua Lin, Yunhan Zhou, Haotian Min, Huasong Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Reinforcement Learning (RL), a method of learning skills through trial-and-error, has been successfully used in many robotics applications in recent years. We combine manipulation primitives (MPs), behavior trees (BTs), and reinforcement learning to propose an algorithm for peg-in-hole tasks, which speeds up the convergence of the RL model and enhance the adaptability of the dynamic environment. Manipulation primitives are used as actions for RL, which can reduce the gap between control instruction and robotic actions and speed up the convergence of the RL model. Behavior trees are used as robot behavior control, which makes robots can actively adapt to the changes in the environment. In experiments, RL-BT, from the combination of RL and BT, is designed for the peg-in-hole task in the Gazebo simulation environment by using UR5 as the actuator. The experiments are conducted on a simple peg and a complex multi-hole peg by three aspects, which include convergence speed verification experiment, adaptability of dynamic environment experiment, and algorithm robustness experiment. The experiment result proves that our RL-BT can speed up the convergence and adapt to the changes in the environment. © 2022 IEEE.

关键词： Reinforcement learning

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Sparse Representation Optimization of Gaussian Mixed Feature of Image Based on Convolution Neural Network

Research Square

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Research Square 2021年

作者： Ye, Yuguang Quanzhou Normal University Fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing Fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Fujian Quanzhou362000 China

With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image. © 2021, CC BY.

关键词： Data mining

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Optimized Statistical Model for Complex Chemical Processes Monitoring

Optimized Statistical Model for Complex Chemical Processes M...

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Data Driven Control and Learning Systems (DDCLS)

作者： Jian Zhang Xiaohe Qu Zhiwei Lei Haifeng Zhang Kai Zhong Datang East China Electric Power Test and Research Institute Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

The traditional kernel principal components analysis (KPCA) and linear discriminant analysis (LDA) have been verified to be two effective approaches for fault detection and diagnosis in recent years. Nevertheless, the conventional method and corresponding improved ones still exposed their deficiencies in some ways. Facing this dilemma, this paper presents a combination of optimized KPCA and modified LDA (OKPCA-MLDA), in which the OKPCA avoids the loss of original features after centralizing data in the eigenspace by adjusting covariance matrix's eigenvalue and transforming the distribution of variables thus providing representative and abundant principal components for the classifier. In addition, the MLDA maximizes a brand new objective function in feature space which achieves better classification performance than the conventional LDA, then utilizing diagnostic thresholds and similarity coefficients to identify the fault types. Based on the combined model, not only the fault detection and diagnosis can be realized simultaneously but also the accuracy of detection and diagnosis can be guaranteed. Furthermore, the simulation experiments on Tennessee Eastman (TE) benchmark process clearly illustrated the superiority of our proposed strategy.

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An ECG Monitoring System Based on Android

An ECG Monitoring System Based on Android

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International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Ruiqi Zhang Zhengchun Hua Chen Chen Guangyuan Liu Wanhui Wen School of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China

ISBN: (数字)9781728165172

ISBN: (纸本)9781728165189

This study aims to design a real-time electrocardiogram (ECG) and heart rate monitoring system based on Android. The system connects Android device and a Shimmer3 ECG module, which is a wearable ECG device collecting ECG data with five electrodes. The ECG signal is amplified and filtered by internal circuit processing in Shimmer3 ECG module, and the ECG data is transmitted to an Android phone via Bluetooth. The baseline drift in ECG signal is removed through wavelet decomposition and reconstruction. Then, the processed ECG data are used to find the R wave peaks by an algorithm for automatic R peak locating and RR interval calculation. RR interval waveform is plotted and displayed on an Android phone in real time. Finally, ECG and RR intervals are saved for data playback and subsequent analysis.

关键词： Electrocardiography Bluetooth Real-time systems Smart phones Monitoring Heart rate

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Accelerated Distributed Optimization over Directed Graphs with Row and Column-Stochastic Matrices

Accelerated Distributed Optimization over Directed Graphs wi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jinhui Hu Yifan Zhu Huaqing Li Zheng Wang Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

In this paper, we study distributed optimization problem over multi-agent networks where the goal is to find the global optimal of a sum of convex functions over strongly connected and directed graphs. A novel distributed algorithm is proposed where both row and column-stochastic matrices are utilized to bypass the limits of the implementation of doubly-stochastic matrices or eigenvector estimation in related work. Besides, it has an evident expression and accelerated convergence by introducing the momentum term. Combining the Generalized Small Gain Theorem with Linear Time Invariant (LTI) system inequality, the algorithm is proved to be able to linearly converge to the exact optimal solution. Furthermore, the ranges of stepsize and momentum paramater are characterized, respectively. Finally, simulation results illustrate effectiveness of the method and correctness of theoretical analysis.

关键词： Radio frequency Convergence Optimization Acceleration Eigenvalues and eigenfunctions Convex functions Linear matrix inequalities

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A Modified Soft-Decision Algorithm of V-BLAST Architecture

A Modified Soft-Decision Algorithm of V-BLAST Architecture

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International Conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： Dongfeng Zhao Daoben Li Key Laboratory of Information Processing and Intelligent Technology Information Engineering School BUPT Beijing University of Posts and Telecommunications Beijing China

A method based on modified sphere-decoding to compute the soft-information for the V-BLAST architecture is deduced in this paper. The system bit error ratio (BER) and computation complexity are simulated and compared with the classical methods. Simulation indicates that the new methods can reduce the decoding complexity with negligible performance degradation.

关键词： Maximum likelihood decoding Computer architecture Receiving antennas Telecommunication computing Computational modeling Random variables Laboratories information processing Computer errors Bit error rate

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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： Big Data Hierarchical Rough Set Knowledge Acquisition MapReduce big data information Extraction Decision tables

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