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Conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Li, Xu Song, Junjie Wu, Qiong Zhu, Benpeng Jiang, Shenglin Ding, Mingyue Ming Yuchi Huazhong Univ Sci & Technol Sch Life Sci & Technol Dept Biomed Egineering Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Optic & Elect Informat Dept Microelect & Solid State Elect Wuhan Hubei Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Hubei Peoples R China

ISBN: (纸本)9780819498335

This paper present a preliminary work on a pre-beamformed data acquisition ultrasound imaging system for a 3-MHz, 32x32 2-D array tranducer. The row-column addressing scheme is adopted for the transducer fabrication. This scheme provides a simple interconnection, consisting of one top and one bottom single-layer flex circuits. The designed system can acquire pre-beamformed data with 12-bit resolution at 40-MHz sampling rate. The digitized data of all channels are first fed through FPGAs to deserialize and stored in a 4GB RAM buffer. The acquired data can be transferred through a 1000 Mbps Ethernet link to a computer for off-line processing and analysis. The system design is based on high-level commercial integrated circuits to obtain the maximum flexibility and minimum system complexity. Partial beam summation have been performed to help finish the 3-D B-mode volumetric imaging.

关键词： ultrasound imaging system 2-D array transducer row-column addressing off-line processing

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Noise cancellation of memristive neural networks

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Neural networks : the official journal of the International Neural Network Society 2014年 60卷 74-83页

作者： Shiping Wen Zhigang Zeng Tingwen Huang Xinghuo Yu Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: wenshiping226@***. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074 China. Electronic address: zgzeng527@***. Texas A & M University at Qatar Doha 5825 Qatar. Electronic address: tingwen.huang@qatar.tamu.edu. Platform Technologies Research Institute RMIT University VIC 3001 Australia. Electronic address: x.yu@rmit.edu.au.

This paper investigates noise cancellation problem of memristive neural networks. Based on the reproducible gradual resistance tuning in bipolar mode, a first-order voltage-controlled memristive model is employed with asymmetric voltage thresholds. Since memristive devices are especially tiny to be densely packed in crossbar-like structures and possess long time memory needed by neuromorphic synapses, this paper shows how to approximate the behavior of synapses in neural networks using this memristive device. Also certain templates of memristive neural networks are established to implement the noise cancellation.

关键词： Memristor Neural network Stability

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Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

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Stochastic Environmental Research and Risk Assessment 2014年第8期28卷 1957-1972页

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.

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Performance of Combined Artificial Neural Networks for Forecasting Landslide Displacement

Performance of Combined Artificial Neural Networks for Forec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lian, Cheng Zeng, Zhigang Yao, Wei Tang, Huiming Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Hubei Peoples R China South Central Univ Nationalities Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Fac Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781479914845

An efficient and accurate method for landslide displacement prediction is very important to reduce the casualties and property losses caused by this type of natural hazard. In recent years, many kinds of artificial neural networks (ANNs) have been widely applied to landslide displacement prediction. But we can't know which type of ANN is the best until we have calculated the prediction error. An improper choice of ANN may result in bad prediction results. In this paper, we use a neural networks combination prediction method based on the discounted MSFE (mean squared forecast error) to reduce the risk of selecting the types of ANNs. Four popular ANNs, radial basis function neural network (RBFNN), support vector regression (SVR), least squares support vector machine (LSSVM) and extreme learning machine (ELM), are selected as candidate neural networks. The performance of our model is verified through two case studies in Baishuihe landslide and Bazimen landslide. Experimental results reveal that the combining neural networks can improve the generalization abilities of ANNs.

关键词： Landslides

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Nonlinear Terminal Guidance for Hypervelocity Vehicles Impact in Specific Direction and Speed

Nonlinear Terminal Guidance for Hypervelocity Vehicles Impac...

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第25届中国控制与决策会议

作者： Kun Hu Lei Liu Yongji Wang Zhishen Wang Key Laboratory of Ministry of Education for Image Processing & Intelligent Control Department of Control Science & Engineering Huazhong University of Science & Technology

ISBN: (纸本)9781467355322

The terminal guidance problem of a hypervelocity gliding vehicle to intercept a stationary target in the planar scenario is considered. In addition to impact position accuracy, the guidance law must meet the impact angle and speed demand. This problem is formulated as an infinite-time horizon nonlinear regulator problem, and solved with the state-dependent Riccati equation (SDRE) control technique. We convert the system to a linear-like structure with state-dependent coefficient (SDC) matrices and derive a closed-loop state-feedback control law using the SDRE method. A new state is introduced concerning the impact speed constraint. By rotating the coordinate system, the guidance scheme is extended to satisfy arbitrary impact angle. The state weighting matrix is chosen as the function of time-to-go to include the distance information between the vehicle and target. The numerical simulations are carried out for different impact angles and speeds, the results of which verify the effectiveness of the proposed guidance approach.

关键词： Terminal Guidance, Nonlinear Regulator, State-dependent Riccati Equation

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Evolutionary membrane computing: A comprehensive survey and new results

Evolutionary membrane computing: A comprehensive survey and ...

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作者： Zhang, Gexiang Gheorghe, Marian Pan, Linqiang Pérez-Jiménez, Mario J. School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 China Department of Computer Science University of Sheffield Regent Court Portobello Street Sheffield S1 4DP United Kingdom Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Computer Science and Artificial Intelligence University of Sevilla Avda. Reina Mercedes s/n Sevilla 41012 Spain

Evolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations. © 2014 Elsevier Inc. All rights reserved.

关键词： Surveys

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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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Event-triggered Consensus Of Nonlinear Multi-Agent Systems With Nonlinear Interconnections

Event-triggered Consensus Of Nonlinear Multi-Agent Systems W...

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第三十二届中国控制会议

作者： YIN Xiuxia YUE Dong Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology

ISBN: (纸本)9781479900305

This paper investigates the consensus problem for a set of nonlinear multi-agent systems with nonlinear interconnections. First, in order to reduce the communication burden in the multi-agent network, a distributed event-triggered consensus control is designed by taking into account the effect of the nonlinear interconnections. Then, based on the Lyapunov functional method and the Kronecker product technique, sufficient conditions are obtained to guarantee the consensus in the form of linear matrix inequality (LMI). Finally, a simulation example is proposed to illustrate the effectiveness of the developed theory.

关键词： Consensus, Multi-Agent System (MAS), Nonlinear Interconnection, Event-Triggered control, LMI.

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Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays

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INFORMATION SCIENCES 2013年 232卷 386-396页

作者： Zhang, Guodong Shen, Yi Yin, Quan Sun, Junwei Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The paper presents theoretical results on the global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays. The dynamic analysis in the paper employs the theory of differential equations with discontinuous right-hand side as introduced by Filippov. By using the inequality techniques and a useful Lyapunov functional, some new testable algebraic criteria are obtained for ensuring the existence and global exponential stability of periodic solution of the system. The model based on the memristor widens the application scope for the design of neural networks, and the new effective results also enrich the toolbox for the qualitative analysis of neural networks. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Periodic solution Exponential stability Memristor Recurrent neural network Time delay

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Cluster synchronisation of directed complex dynamical networks with nonidentical nodes via pinning control

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2013年第9期44卷 1577-1586页

作者： Wang, Guan Shen, Yi Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

In this article, we study the cluster synchronisation problem of directed complex dynamical networks through controlling some specific nodes. This method explains which nodes should be chosen as pinned candidates. Owing to the special structure of cluster network, the number of such nodes is extremely small. In our model, we consider a directed network whose nodes in different clusters are nonidentical. Then some sufficient conditions to guarantee cluster synchronisation in the coupled network are presented. Finally, numerical examples are given to verify our theoretical analysis.

关键词： cluster synchronisation directed complex dynamical networks delayed coupling nonidentical nodes pinning control

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