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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

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

ISBN: (纸本)9781424470006

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). © 2011 IEEE.

关键词： computerized tomography

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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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2011 1st Middle East Conference on Biomedical engineering, MECBME 2011

ISBN: (纸本)9781424470006

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. © 2011 IEEE.

关键词： Pulmonary diseases

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Flexible Electronics: Stretchable Semiconductor Technologies with High Areal Coverages and Strain-Limiting Behavior: Demonstration in High-Efficiency Dual-Junction GaInP/GaAs Photovoltaics (Small 12/2012)

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Small 2012年第12期8卷

作者： Jongho Lee Jian Wu Jae Ha Ryu Zhuangjian Liu Matthew Meitl Yong-Wei Zhang Yonggang Huang John A. Rogers Department of Mechatronics Gwangju Institute of Science and Technology (GIST) 123 Cheomdan-gwagiro Buk-gu Gwangju 500-712 Korea Department of Engineering Mechanics Tsinghua University Beijing 100084 China Department of Materials Science and Engineering Frederick Seitz Materials Research Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 USA Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis 138632 Singapore Semprius 4915 Prospectus Dr. Durham NC 27713 USA Department of Mechanical Engineering Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA Department of Materials Science and Engineering Department of Chemistry Department of Mechanical Science and Engineering Department of Electrical and Computer Engineering Frederick Seitz Materials Research Laboratory Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana IL 61801 USA

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基于漏电变化表征的参数偏差在线预测方法

基于漏电变化表征的参数偏差在线预测方法

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第七届中国测试学术会议

作者： Jin Song 靳松 Han Yinhe 韩银和 Xu Qiang 徐强 Department of Electronic and Communication Engineering School of Electrical and Electronic Engineer 华北电力大学电气与电子工程学院电子与通信工程系保定071003 State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of 中国科学院计算技术研究所计算机体系结构国家重点实验室北京100190 CUhk REliable Computing Laboratory(CURE) Department of Computer Science&Engineering The Chinese Un 香港中文大学计算机科学与工程系

负偏置温度不稳定(NBTI)引发的动态参数偏差给集成电路的生命期可靠性带来严重威胁。已有的方法主要通过设计片上检测电路实时监测关键通路的时延偏差来阻止NBTI导致的电路失效。然而，不断恶化的静、动参数偏差，不断增加的设计复杂度... 详细信息

负偏置温度不稳定(NBTI)引发的动态参数偏差给集成电路的生命期可靠性带来严重威胁。已有的方法主要通过设计片上检测电路实时监测关键通路的时延偏差来阻止NBTI导致的电路失效。然而，不断恶化的静、动参数偏差，不断增加的设计复杂度以及片上检测电路所引入的面积开销会影响上述方法的应用效果。为了弥补已有方法的不足，本文提出以测量到的电路漏电变化为表征，预测NBTI导致的电路时延偏差。首先，通过施加一组测量向量得到的电路漏电变化量被联立成一个方程组;求解方程组后可以得到关键通路上关键门的漏电变化量。随后，根据描述漏电变化与时延偏差相关性的分析表达式来预测任意关键通路的时延偏差。提出的方法对电路工作过程中的实时噪声免疫，且可以通过增加漏电测量时间抵消参数偏差对预测结果的影响。实验结果表明，提出的方法能够以可接受的精度损失有效地预测NBTI导致的电路时延偏差。

关键词：纳米集成电路负偏置温度不稳定性漏电变化时延监测

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Knowledge Granulation in Interval-Valued Information Systems

Knowledge Granulation in Interval-Valued Information Systems

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International Workshop on Intelligent Systems and Applications, ISA

作者： Nan Zhang Duoqian Miao Can Gao Department of Computer Science and Technology University of Tongji Shanghai China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Key Laboratory of Embedded System and Service Computing of Ministry of Education University of Tongji Shanghai China

Rough set theory, proposed by Pawlak, has been proved to be a mathematical tool to deal with vagueness and uncertainty in intelligent information processing. In this paper, we propose the concept of knowledge granulation in interval-valued information systems, and discuss some important properties. From these properties, it can be shown that the proposed knowledge granulation provides important approaches to measuring the discernibility of different knowledge. It may be helpful for rule evaluation and knowledge discovery in interval-valued information systems.

关键词： Information systems Approximation methods Uncertainty Rough sets computers Entropy

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Degree of polarization of Mie scattered stationary partially coherent electromagnetic fields

Degree of polarization of Mie scattered stationary partially...

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International Conference on Quantum Electronics (IQEC)

作者： Madara L. Marasinghe Malin Premaratne David M. Paganin Advanced Computing and Simulation Laboratory (AL) Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton VIC Australia School of Physics Faculty of Science Monash University Clayton VIC Australia

The polarization of non-paraxial partially coherent light scattered from nanospheres cannot be described easily. We utilize the degree of polarization to quantify Mie scattered partially coherent light and demonstrate... 详细信息

ISBN: (纸本)9780977565771

关键词： Polarization Lattices Electromagnetic fields Optics Coherence Dielectrics

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Memory Efficient On-Line Streaming for Multichannel Spike Train Analysis

Memory Efficient On-Line Streaming for Multichannel Spike Tr...

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

作者： Bo Yu Terrence Mak Leslie Smith Yihe Sun Alex Yakovlev Chi-Sang Poon Tsinghua National Laboratory for Information Science and Technology Institute of Microelectronics Tsinghua University School of Electrical Electronic and Computer Engineering Department of Computing Science and Mathematics University of Stirling Harvard-MIT Division of Health Sciences and Technology MIT

ISBN: (纸本)9781424441211

Rapid advances in multichannel neural signal recording technologies in recent years have spawned broad applications in neuro-prostheses and neuro-rehabilitation. The dramatic increase in data bandwidth and volume associated with multichannel recording requires a significant computational effort which presents major design challenges for brain-machine interface (BMI) system in terms of power dissipation and hardware area. In this paper, we present a streaming method for implementing real-time memory efficient neural signal processing hardware. This method exploits the pseudo-stationary property of neural signals and, thus, eliminates the need of temporal storage in batch-based processing. The proposed technique can significantly reduce memory size and dynamic power while effectively maintaining the accuracy of algorithms. The streaming kernel is robust when compared to the batch processing over a range of BMI benchmark algorithms. The advantages of the streaming kernel when implemented on field-programmable gate array (FPGA) devices are also demonstrated.

关键词： Streaming Batch processing Field programmable gate arrays Power dissipation signal processing

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Effect of visual stimulus locations on pattern-reversal visual evoked potential An epidural electrocorticogram study

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Neural Regeneration Research 2011年第26期6卷 2042-2046页

作者： Wensheng Hou Weiwei Shi Xiaolin Zheng Na Liu Zongxia Mou Yingtao Jiang Zhengqin Yin Key Laboratory of Biorheological Science and Technology （Chongqing University） Ministry of Education Bioengineering College Chongqing University Chonqqing 400044 China Department of Ophthalmology Southwest Hospital Third Military Medical University of Chinese PLA Chongqing 400038 China Department of Electrical and Computer Engineering University Of Nevada Las vegas NV 89154 USA

To explore the effect of the location of a visual stimulus on neural responses in the primary visual cortex （V1）, a micro-electromechanical system-based microelectrode array with nine channels was implanted on the cerebral dura mater of V1 in adult cats. 2 Hz pattern reversal checkerboard stimul were used to stimulate the four visual quadrants （i.e., upper left, upper right, lower left, and lower right fields）. The results showed that there was a N75 component of the visual evoked potential around 50-80 ms after the onset of a checkerboard stimulus, and the onset of these N75 peaks varied with different stimulus locations. The checkerboard stimuli Jnduced shorter latencJes in the contralateral V1 than in the ipsilateral V1, while the checkerboard stimulus in the upper half visual field induced shorter latencies for N75. These results suggested that the pattern-reversal stimuli induced neural activities in V1 that can be recorded with multichannel microelectrodes, and more detailed temporal and spatial properties can be measured.

关键词： checkerboard cerebral dura mater visual evoked potential microelectrode array primary visual cortex

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Discriminant Component Analysis and Self-Organized Manifold Mapping for Exploring and Understanding Image Face Spaces

Discriminant Component Analysis and Self-Organized Manifold ...

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Tutorials of the Brazilian Symposium on computer Graphics and Image Processing (SIBGRAPI TUTORIALS)

作者： Gilson A. Giraldi Edson C. Kitani Emilio Del-Moral-Hernandez Carlos E. Thomaz Department of Computer Science National Laboratory for Scientific Computing Petropolis Brazil Polytechnic School University of São Paulo Sao Paulo Brazil Department of Electrical Engineering Centro Universitario da FEI Sao Bernardo do Campo Brazil

Face recognition is a multidisciplinary field that involves subjects in neuroscience, computer science and statistical learning. Some recent research in neuroscience has indicated that the ability of our memory relies on the capability of orthogonalizing (pattern separation) and completing (pattern prototyping) partial patterns in order to encode, store and recall information. From a computational viewpoint, pattern separation can be cast in the subspace learning area while pattern prototyping is closer to manifold learning methods. So, subspace (or manifold) learning techniques have a close biological inspiration and reasonability in terms of computational methods to possibly exploring and understanding the human behavior of recognizing faces. Therefore, the aim of this paper is threefold. Firstly, we review some theoretical aspects about perceptual and cognitive processes related to the mechanisms of pattern separation and pattern prototyping. Then, the paper presents the basic idea of manifold learning and its relationship with subspace learning with focus on the dimensionality reduction problem. Finally, we present the Discriminant Principal Component Analysis (DPCA) and the Self-Organized Manifold Mapping (SOMM) algorithm to exemplify respectively pattern separation and completion techniques. We show experimental results to demonstrate the effectiveness of DPCA and SOMM algorithms on well-framed face image analysis.

关键词： Manifolds Humans Face recognition Vectors Support vector machines Neurons Principal component analysis

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Robust principal component analysis?

Robust principal component analysis?

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作者： Candès, Emmanuel J. Li, Xiaodong Ma, Yi Wright, John Departments of Mathematics and Statistics Stanford University Building 380 450 Serra Mall Stanford CA 94305 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign 145 Coordinated Science Laboratory 1308 West Main Street Urbana IL61801 United States Visual Computing Group Microsoft Research Asia Building 2 No. 5 Dan Ling Street Beijing 100080 China

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit;among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the l1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as *** discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces. © 2011 ACM.

关键词： Face recognition

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