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44 results on '"Deng, Muqing"'

8. Research of CDG to Identify Individuals via Deterministic Learning Theory

Author

Yan, Guohui, Geng, Tong, Deng, Muqing, Wang, Cong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2019
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9. Matlab-Based Myocardial Ischemia Detection System Design via Deterministic Learning

Author

Lai, Hongji, Deng, Muqing, Tang, Min, Wang, Cong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Jia, Yingmin, editor, Du, Junping, editor, and Zhang, Weicun, editor

Published
2019
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20. Gait Recognition Under Different Clothing Conditions Via Deterministic Learning

Author

Deng, Muqing and Wang, Cong

Abstract

Dear Editor, This letter deals with the robustness problem of gait recognition method against maximum number of clothing conditions. By selecting four kinds of time-varying silhouette features, gait dynamics underlying different individuals' gait features is effectively modeled by radial basis function (RBF) neural networks through deterministic learning. This kind of dynamics information has little sensitivity to the variance between gait patterns under different clothing conditions. In order to eliminate the effect of clothing differences, the training patterns under different clothing conditions further constitute a uniform training dataset, containing all kinds of gait dynamics under different clothing conditions. A rapid recognition scheme is presented on published gait databases. Extensive experiments demonstrate the efficacy of the proposed method.

Published
2024
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32. The implementation of clinical data management and statistics system

Author

Wang Cong, Deng Muqing, and Ma Heming

Subjects
SQL, 020205 medical informatics, SIMPLE (military communications protocol), Computer science, business.industry, media_common.quotation_subject, Interface (computing), 020207 software engineering, 02 engineering and technology, computer.software_genre, Server, 0202 electrical engineering, electronic engineering, information engineering, Clinical data management, Business logic, Data mining, Function (engineering), Software engineering, business, computer, Statistic, media_common, computer.programming_language
Abstract

In this paper, a clinical data management and statistic system is implemented for authorized users such as doctors and medical researchers to manage clinical data. The system is able to inquiry patient particulars, diagnostic results, CT (Computerized Tomography) data and electrocardiogram (ECG) data, and it implements comparison function of Cardiodynamicsgram (CDG) data and statistic function for further analysis of medical research. The paper builds a stable and simple operating system based on GUI with MATLAB and SQL Server. The three-tier model which includes the storage platform of the system, abstract business logic layer and the operating interface, is utilized in this application. This system is very helpful for clinicians, as well as for researchers to do research on cardiovascular disease with the cardiovascular clinical data.

Published
2017

33. Kinect-based gait recognition system design via deterministic learning

Author

Deng Muqing, Cheng Fengjiang, and Wang Cong

Subjects
Scheme (programming language), 021110 strategic, defence & security studies, Engineering, business.industry, Feature extraction, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Process (computing), 02 engineering and technology, ComputingMethodologies_PATTERNRECOGNITION, Gait (human), Gait training, 0202 electrical engineering, electronic engineering, information engineering, Recognition system, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business, MATLAB, computer, ComputingMethodologies_COMPUTERGRAPHICS, computer.programming_language, Visual programming language
Abstract

This paper proposes an effective and rapid human gait recognition system based on deterministic learning and Microsoft sensor. In order to deal with the difficulties of feature extraction in the gait recognition system, Kinect sensor is used for realtime skeleton detection and tracking. Dynamical deterministic learning algorithm implementation is achieved and a rapid gait recognition scheme is proposed. The graphical programming language of C# and MATLAB GUI controls are combined for building user-friendly and simple interfaces to display the gait training and recognition process. The recognition results and detailed parameters can be displayed on the system panels, which is also helpful for further data analysis and algorithm improvement. The effectiveness of the gait recognition system under multi-pattern is verified on the self-constructed gait database.

Published
2017

41. A new inertial sensor-based gait recognition method via deterministic learning

Author

Wang Qinghui, Deng Muqing, Liu Yiqi, and Zeng Wei

Subjects
Engineering, Acceleration, Gait (human), Artificial neural network, Biometrics, business.industry, Inertial measurement unit, Estimator, Angular velocity, Computer vision, Artificial intelligence, Similarity measure, business
Abstract

This paper presents a new gait recognition method based on acceleration and angular velocity data captured by inertial sensors via deterministic learning. These gait features describe the motion trajectories of human gait and contain rich information for persons identification. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, the gait dynamics underlying different individuals' gaits are represented by the acceleration and angular velocity features, and are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated. The average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the OU-ISIR biometric gait database: inertial sensor dataset, which includes at most 744 subjects (389 males and 355 females) and is now the world's largest inertial sensor-based gait database, to demonstrate the recognition performance of the proposed algorithm.

Published
2015

43. Deterministic Learning-Based Methodology for Detecting Abnormal Dynamics of Cardiac Repolarization During Ischemia.

Author

Deng M, Wu W, Cao J, Tang M, and Wang C

Subjects
Algorithms, Electrocardiography, Heart, Humans, Coronary Artery Disease, Myocardial Ischemia
Abstract

Objective: This study concentrates on subtle electrocardiogram (ECG) spatiotemporal characteristics in the repolarization phase, and describes a deterministic learning-based methodology for the detection of abnormal cardiac dynamics induced by ischemia., Methods: ST-T complex of the surface 12-lead ECG signals are identified and extracted. Cardiac dynamics underlying ST-T complex signals is captured using deterministic learning algorithm. This kind of dynamics information represents the beat-to-beat temporal change of electrophysiological modifications in ventricular repolarization, which is shown to be sensitive to the variance during myocardial ischemia. Cardiodynamicsgram (CDG) is proposed as the three-dimensional graphic representation of cardiac dynamics information., Results: Encouraging evaluation results are achieved on electrocardiograms from public PTB database and hospital patients. Significant correlations are found between the CDG morphology and ischemia., Conclusion: Anormal dynamics of cardiac repolarization during ischemia can be detected using a deterministic learning-based methodology. The extracted cardiac dynamics information within routine ECG is expected to provide early detection for latent ischemia before obvious pathological changes are present in ECG., Significance: The proposed techniques can be considered as a complementary tool to the generally accepted ECG method for detection of abnormal dynamics in cardiac repolarization, which are important for identifying patients at risk of myocardial ischemia.

Published
2019
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44. Epileptic Signal Classification with Deep Transfer Learning Feature on Mean Amplitude Spectrum.

Author

Wang Y, Cao J, Wang J, Hu D, and Deng M

Subjects
Algorithms, Humans, Neural Networks, Computer, Seizures, Electroencephalography, Epilepsy, Signal Processing, Computer-Assisted
Abstract

Epilepsy, as a sudden and life-threatening nervous system disease, seriously affected around 6% population in the world. Epileptic classification has attracted wide attention in the past and a number of methods have been developed. But currently studies are mainly on three epileptic states classification (preictal, ictal, interictal) or seizure/non-seizure detection. Among them, the one hour before seizure onset was generally considered as preictal, where the division is actually not fine enough for some practical applications. In this paper, the epileptic signal classification with a more granular time-scale of the preictal stage is studied and a novel deep Electroencephalogram (EEG) feature extraction with the convolutional neural network (CNN) based transfer learning is developed. The subband mean amplitude spectrum map (MAS) of multichannel EEGs is computed for signal representation and three popular deep CNNs are exploited for feature transfer learning, respectively. Experiments on the benchmark CHI-MIT epilepsy EEG database show that the proposed algorithm achieves a highest overall accuracy of 92.77% when the one hour preictal stage is divided into small segments with a fine resolution of 20-minutes scale.

Published
2019
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