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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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Middle East Conference on Biomedical engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Diseases Image segmentation Biomedical imaging Gaussian distribution

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Containment control of networked Lagrange systems with group dispersion and cohesion behaviors

Containment control of networked Lagrange systems with group...

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Chinese Control Conference (CCC)

作者： Ziyang Meng Zongli Lin Wei Ren Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville 22904-4743 U.S.A. Department of Electrical and Computer Engineering Utah State University Logan 84322 U.S.A.

In this paper, distributed containment control with group dispersion and cohesion behaviors are discussed for a group of Lagrange systems. Both the cases of constant leaders' generalized coordinate derivatives and time-varying leaders' generalized coordinate derivatives are considered. The proposed control algorithms are shown to obtain velocity matching, connectivity maintenance and collision avoidance. In addition, the sum of the steady-state distances between followers and the convex hull formed by the leaders is shown to be bounded and this bound is explicitly given.

关键词： Lead Dispersion Topology Sensors Laplace equations Maintenance engineering Algorithm design and analysis

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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Middle East Conference on Biomedical engineering (MECBME)

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours Image segmentation Charge carrier processes

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AASVMES: An intelligent expert system for realization of Adaptive Autonomy using Support Vector Machine

AASVMES: An intelligent expert system for realization of Ada...

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International Conference on Control, Instrumentation, and Automation

作者： N. Hassanpour M. A. Zamani A. Fereidunian H. Lesani Systems and Machines Research Laboratory (SMRL) Control and Intelligent Processing Center of Excellence (CIPCE) School of ECE University of Tehran Tehran Iran Electrical and Computer Engineering Department K. N. Toosi University of Technology Tehran Iran

We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts' judgments. SVM is used as an expert system inference engine. The practical list of PSFs and the judgments of GTEDC's (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implemented AASVMES in response to GTEDC's network are evaluated against the GTEDC experts' judgment. Evaluations show that AASVMES has the ability to predict the proper LOA for GTEDC's Utility Management Automation (UMA) system, which changes in relevance to the changes in PSFs; thus providing an adaptive LOA scheme for UMA.

关键词： Support vector machines Training Expert systems Complexity theory Automation Adaptive systems Humans

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Effects of plasma exposure on defects in novel dielectric materials

Effects of plasma exposure on defects in novel dielectric ma...

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IEEE International Conference on Plasma Science (ICOPS)

作者： H. Ren M.T. Nichols G. Jiang G.A. Antonelli Y. Nishi J.L. Shohet Plasma Processing & Technology Laboratory and Department of Electrical and Computer Engineering University of Wisconsin Madison Madison WI USA Novellus Systems Inc. Tualatin OR USA Novellus Systems Inc. Albany NY USA University of Stanford Stanford CA USA

Summary form only given. During plasma processing of microelectronic devices, plasma exposure can cause damage to dielectric materials, resulting in defects in the thin dielectric layer. Effects of plasma exposure on defect concentrations in high- and low-k thin dielectric films deposited on high-resistivity silicon were measured with electron-spin resonance (ESR) spectroscopy. For high-k dielectric films, plasma parameters, including pressure and power, were varied to minimize the defect concentrations. For low-k dielectric films, during argon electron cyclotron resonance (ECR) plasma exposure, ion and photon bombardment increased the measured defect concentrations. Dielectric samples with various dielectric constants were examined showing that as the value of the dielectric constant was lowered, the defect concentrations were shown to increase significantly. To investigate further the nature of the defect concentrations and locations, defect states in different dielectric materials with crystalline and amorphous structures were investigated with two models, the Broer model and the Bloch model. For crystalline structures as in hafnium oxide, the defects were found to fit well with the Broer model due to strong local magnetic-field-orientation dependency. In amorphous materials, for example, organosilicate glass, the defects were found to fit to the Bloch model as seen in the field-broadening effects. Using the two models, the behaviors of the defects can be explained as a result of electron spins.

关键词： Dielectrics

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COGNITIVE RADIO SYSTEMS WITH INTERFERENCE CONSTRAINTS

PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COG...

引用

2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

作者： Saeed Motiian Mohammad Aghababaie Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Colleague of EngineeringUniversity of TehranTehran 14399-57131Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences(IPM)Tehran 19548-56316 Iran Image Analysis Laboratory Radiology DepartmentHenry Ford HospitalDetroitMichigan 48202USA

Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service（QoS）for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio（SINR） are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization（PSO）does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.

关键词： Cognitive radio Power allocation Particle Swarm Optimization（PSO）.

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Local spectrum sensing in non-gaussian noise

Local spectrum sensing in non-gaussian noise

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2010 17th International Conference on Telecommunications, ICT 2010

作者： Zahabi, Sayed Jalal Tadaion, Ali A. Department of Electrical and Computer Engineering Statistical Signal Processing Laboratory Yazd University Yazd Iran

ISBN: (纸本)9781424452477

This paper deals with the local spectrum sensing problem in non-Gaussian noise. It is of great importance to reliably detect the presence of licensed users in cognitive radio. This task highly depends on the noise distribution, thus it is important to characterize the noise behavior as best as possible. Although Gaussian assumption for noise is of interest due to many reasons, it sometimes fails to model the behavior of noise in practice. In this paper we present a local spectrum sensing scheme in a broad family of non-Gaussian noise. We reasonably assume that the Primary User (PU) signal is Phase Shift Keying (PSK) modulated, where the number of constellation points, M, and the symbol code sequence are unknown to the Secondary Users (SU), as in practice. We also assume that the SUs are unaware of the PU signal complex amplitude and the non- Gaussian noise variance. Within this framework, we obtain the Generalized Likelihood Ratio Test (GLRT) as our spectrum sensing scheme and propose an approximate implementation of the resulting GLRT. Simulation results show that our proposed scheme has an acceptable performance. © 2009 IEEE.

关键词： Cognitive radio

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Efficient data encoder for low-power capsule endoscopy application

Efficient data encoder for low-power capsule endoscopy appli...

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International Conference on Information Science, Signal processing and Their Applications

作者： Tajallipour, Ramin Wahid, Khan Multimedia Processing and Prototyping Laboratory Department of Electrical and Computer Engineering University of Saskatchewan SK Canada

ISBN: (纸本)9781424471676

The paper presents an efficient data encoder based on Lempel-Ziv-Welch (LZW) algorithm to be used in a lowpower capsule endoscopic system. The encoder is library-based where the size of the library can be set by the user, and hence, the output data rate can be controlled according to the bandwidth requirement. The simulation is carried out with several endoscopic images and the results show that a minimum overall compression ratio of 92.8% can be achieved with a minimum reconstruction quality of 30dB. The energy consumption has been estimated to be 75uJ/frame. Several other encoding algorithms have been studied and the comparative results are discussed. © 2010 IEEE.

关键词： Energy utilization

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Text extraction using document structure features and support vector machines

Text extraction using document structure features and suppor...

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11th IASTED International Conference on computer Graphics and Imaging, CGIM 2010

作者： Zagoris, Konstantinos Papamarkos, Nikos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9780889868243

In order to successfully locate and retrieve document images such as technical articles and newspapers, a text localization technique must be employed. The proposed method detects and extracts homogeneous text areas in document images indifferent to font types and size by using connected components analysis to detect blocks of foreground objects. Next, a descriptor that consists of a set of structural features is extracted from the merged blocks and used as input to a trained Support Vector Machines (SVM). Finally, the output of the SVM classifies the block as text or not.

关键词： Support vector machines

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