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检索条件"机构=Advanced Control Systems Laboratory Control and Intelligent Processing Center of Excellence"

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High SNR flexible top hat monopole probe for 1.5 T MRI

High SNR flexible top hat monopole probe for 1.5 T MRI

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作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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Rule-based decision-making framework for knowledge-based anatomical landmark localization (K-BALL)

Rule-based decision-making framework for knowledge-based ana...

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IEEE International Symposium on Biomedical Imaging

作者： M.-R. Siadat H. Soltanian-Zadeh K. Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

K-BALL is a general method for localization of anatomical phenomena of the same origin with natural discrepancies distributed over a reference space, e.g., human brain anatomical structures. In this paper, we focus on information analysis step (2nd step) of K-BALL during which landmarks extracted in its first step are evaluated. We provide a framework in which rules are automatically generated based on estimated and derived models. We show that the rules based on the derived models can improve the overall success rate of K-BALL. Each rule evaluates the extracted points by producing an intermediate confidence factor (ICNF). A total confidence factor is calculated using ICNF's to facilitate the acceptance or rejection of a set of points as landmarks of interest. Using the rules merely based on the estimated models, simulation study produced an overall success rate of 91.8%. Using the rules based on both of the estimated and derived models, this rate increased to 92.5%

关键词： Decision making Hippocampus Information analysis Data mining Humans Radiology Image analysis Radio control Process control control systems

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Hippocampus Volume and Texture Analysis for Temporal Lobe Epilepsy

Hippocampus Volume and Texture Analysis for Temporal Lobe Ep...

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IEEE International Conference on Electro-Information Technology

作者： Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Kost Elisevich Henry Ford Health System Radiology Image Analysis Laboratory Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA Control and Intelligent Processing Center of Excellence ECE Department University of Tehran Tehran Iran Neurosurgery Department Henry Ford Health System Detroit MI USA

It has been previously shown that intensity and volume features of the hippocampus from MR images of the brain are useful in detecting the abnormality and consequently candidacy of the hippocampus for temporal lobe epilepsy (TLE) surgery. In this paper, we have studied and compared volume and texture features of hippocampus for TLE lateralization. Volume and some features describing the intensity non-uniformities are used. We use manual segmentation of the hippocampi from T1 weighted images and map them to the FLAIR images. Wavelet features as well as mean and variance of the hippocampus intensities of the FLAIR slices are used as texture features. A dataset of 55 patients with 28 right and 27 left abnormal sides was used in the experiments. Experimental results show that the volume and intensity features are useful in determining the abnormal side

关键词： Hippocampus Temporal lobe Epilepsy Image texture analysis Image segmentation Image analysis Shape Surgery Wavelet packets Radiology

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Medical Data Mining using Particle Swarm Optimization for Temporal Lobe Epilepsy

Medical Data Mining using Particle Swarm Optimization for Te...

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Congress on Evolutionary Computation

作者： M. Ghannad-Rezaie H. Soltanain-Zadeh M.-R. Siadat K.V. Elisevich Henry Ford Health System and Wayne State University Detroit MI USA Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Neurosurgery Department Henry Ford Health System Detroit MI USA

In clinical problems, numerous factors are usually involved in a medical syndrome. New advances in medicine provide a broad range of diagnosis methods to cover all aspects of a disease. However, huge amounts of raw information may confuse clinicians and decrease decision accuracy. Computerized knowledge extraction is an active area of research in medical informatics. This paper suggests a new medical data mining approach using an advanced swarm intelligence data mining algorithm. Considering medical knowledge discovery difficulties, this approach addresses common issues such as missing value management and interactive rule extraction. Here, surgery candidate selection in temporal lobe epilepsy is the main target application. However, the general idea can be applied to other medical knowledge discovery problems. Experimental results show noticeable performance improvement in the final rule-set quality while the method is flexible and fast.

关键词： Data mining Particle swarm optimization Temporal lobe Epilepsy Medical diagnostic imaging Biomedical imaging Coronary arteriosclerosis Diseases Surgery Medical services

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ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGE USING TENSOR VOTING FRAMEWORK

ROAD CENTRAL CONTOUR EXTRACTION FROM HIGH RESOLUTION SATELLI...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： SHENG ZHENG JIAN LIU WEN-ZHONG SHI GUANG-XI ZHU Electronic & Information Engineering Dept. Huazhong University of Science and Technology Wuhan 430 State Education Commission Key Laboratory for Image Processing and Intelligent Control Inst.for Pat Advanced Research Center for Spatial Information Technology Department of Land Surveying and Geo-In Chinese Key lab of optic & electronics Huazhong University of Science and Technology Wuhan 430074

In this paper, a unique road contour extraction approach from high resolution satellite image is proposed, in which the road contour was extracted in two steps. Firstly, support vector machines (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. The identified road group images are the discrete and irregularly distributed sampled points, and they are an uncompleted data set for the road. Secondly, the road contour was extracted from the road group images using the tensor voting framework, since the tensor voting technique is superior to the traditional methods in extracting the geometrical structure from the uncompleted data set. The experimental results on the high resolution satellite image demonstrate that the proposed approach worked well with images comprised by both rural and urban area features.

关键词： Tensor voting framework Road central line extraction High-resolution satellite image

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An evolutionary fuzzy modeling approach for ANFIS architecture

An evolutionary fuzzy modeling approach for ANFIS architectu...

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2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005

作者： Rastegar, Farzad Araabi, Babak N. Lucas, Caro Control and Intelligent Processing Center of Excellence Electrical and Computer Eng. Department University of Tehran Tehran Iran School of Intelligent Systems Institute for Studies in Theoretical Physics and Mathematics Tehran Iran

ISBN: (纸本)0780393635

This paper proposes a cooperative evolutionary method for optimizing the properties of an ANFIS-architecture-based model where only the input-output data of the identified system are available. The primary tasks of fuzzy modeling are structure identification and parameter optimization: the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. The proposed approach manages all mentioned attributes simultaneously. Particularly, number of rules and parameters of membership functions are realized by applying a novel approach using genetic programming and genetic algorithm whereas consequent parameters are tuned by using least-squares estimation. Finally, two examples of nonlinear system are given to illustrate the effective-ness of the proposed approach. © 2005 IEEE.

关键词： Evolutionary algorithms

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An evolutionary fuzzy modeling approach for ANFIS architecture

An evolutionary fuzzy modeling approach for ANFIS architectu...

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Congress on Evolutionary Computation

作者： F. Rastegar B.N. Araabi C. Lucast Institute for Studies in Theoretical Physics and Mathematics School of Intelligent Systems Tehran Iran Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran School of Intelligent Systems Institute for studies in theoretical Physics and Mathematics Tehran Iran

关键词： Fuzzy sets Genetic programming Fuzzy systems Mathematical model Genetic algorithms Optimization methods Nonlinear systems Evolutionary computation Testing Computer architecture

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Learning distributed grasp in presence of redundant agents

Learning distributed grasp in presence of redundant agents

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IEEE/ASME (AIM) International Conference on advanced intelligent Mechatronics

作者： A.H. Elahibakhsh M. Nili Ahmadabadi F. Janabi-Sharifi B. Nadjar Araabi AI & Robotics Laboratory Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Eng. Institute for Studies on Theoretical Physics and Mathematics University of Tehran Tehran Iran Robotics and Manufacturing Automation Lab Department of Mechanical and Industrial Engineering Ryerson University Toronto ONT Canada

Learning distributed object grasp by a group of robots with redundant members is the main focus of this paper. In Elahibakhsh, A. H., et al. (2004), we tackled the problem of learning form closure grasp for planar convex objects by multiple non-communicating robots without any information about the shape of objects. In this paper, the problem in presence of redundant agents is investigated. Agents' states and actions are designed such that the group learns grasping different objects using Q-learning method. As the environment is not intelligent enough to assess each agent's effect on the team performance, a credit assignment algorithm based on knowledge evaluation is designed. The proposed method considers the environment credit for the team, number of redundant agents, and the expertness level of each agent in its credit assignment. Applicability of the designed approach is verified through a set of simulations. It is shown that the team learns grasping different objects. Therefore, it is expected that the proposed method can be extended for distributed grasp of deformable objects

关键词： intelligent robots Learning Robot sensing systems Shape Cognitive robotics Robotics and automation intelligent agent Algorithm design and analysis Grasping Redundancy

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Movement prediction using an MLP without internal feedback

Movement prediction using an MLP without internal feedback

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IEEE International Conference on systems, Man and Cybernetics

作者： M. Emadi F. Bahrami M.J. Yazdanpanah A. Patla Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Iran Gait and Posture Laboratory University of Waterloo Waterloo Canada Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran

In functional electrical stimulation controllers developed based on a tracking approach, the desired movement (usually the joint angles trajectories) is used as the input of the controller. In answer to the question, how the desired movement should be individually tailored, a new method based on a multi-layer perceptron (MLP) has been developed to generate the subject-dependent trajectories of the joint angles during sit-to-stand transfer. The size of the MLP has been reduced significantly by choosing a suitable set of inputs. Outputs of the implemented MLP are the coefficients of the Fourier half amplitude cosine expansions of the joint angles. Since these coefficients describe the inherent dynamics of the system, we could avoid implementing the usual embedded feedback in the body of the MLP. In comparison with a model-based algorithm (with a maximum prediction error of 10.5%), this method predicted the movement more accurately (with a maximum error of 5.2%).

关键词： Feedback Joints Predictive models Sociotechnical systems Mathematical model Trajectory Multilayer perceptrons Torque Optimization methods Artificial neural networks

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A distributed deterministic help scheme to improve the system fault tolerance

A distributed deterministic help scheme to improve the syste...

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Proceedings of the Biannual World Automation Congress

作者： M.S. Mirian M.N. Ahmadabadi Control and Intelligent Processing Center of Excellence and AI and Robotics Laboratory Department of ECE University of Tehran Iran

This paper proposes some methods to improve the fault-tolerance in distributed systems specifically in deterministic situations by distributed decision-making and coordination. Providing a distributed system with fault tolerance is a feasible but hard problem due to the intrinsic aspects of such systems such as: independency, unpredictability and communication problem. A multi-agent system as an instance of distributed system, can handle different kinds of fault by using traditional fault tolerance techniques. But what focused in this paper are agent-based techniques. In fact proposed methods are based on agent-based help provision by distributed cooperation among helper agents. The helpers try to tune their normal roles such that they can undertake the faulty agents' tasks too. These helpers go through a sub-optimal task selection algorithm, to decide whom to help. It is important to remark that there is no explicit interaction; instead they coordinate their decisions implicitly by adopting the most appropriate task in terms of their speed, relative reliability and the task's criticality coefficient. Proposed ideas are tested on a DCS-like tested to improve its fault tolerance. The results illustrate the effectiveness of the approaches in comparison to the case of no help situation and the case of using purely redundant components.

关键词： Fault tolerant systems Multiagent systems control systems Distributed decision making Redundancy Power system reliability Process control intelligent control Artificial intelligence Testing

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