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IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Mehdi Sajjadi Rassoul Amirfattahi Mohammad Reza Ahmadzadeh Mohammad Ali Saghafi Digital Signal Processing Research Lab Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Smart Engineering System Research Group Department of Electrical Electronics & System Engineering University Kebangsaan Malaysia Bangi Selangor Darul Ehsan Malaysia

Brain CT images are very useful in diagnosis of cerebrovascular accidents (CVA). These images contain too many information. But most of the times, provided information is contaminated by noise and suffer from poor contrast. On the other hand, there are certain parts of the brain image that is really important to radiologist, while other image details are more or less confusing. This sparks the need for customized enhancement of brain CT images. Translation-invariant wavelet transform is being widely used in most of image processing tasks including image enhancement. This transform is calculated with a filter bank algorithm, called algorithme àtrous. In this paper we propose a filter bank structure similar to algorithme àtrous. This structure is more redundant and offers greater selectivity and flexibility to enhance desired features of brain CT images.

关键词： Computed tomography Wavelet transforms Image edge detection Lesions Image enhancement

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Reports of the aaai 2011 Spring symposia

Reports of the aaai 2011 Spring symposia

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作者： Buller, Mark Cuddihy, Paul Davis, Ernest Doherty, Patrick Doshi-Velez, Finale Erdem, Esra Fisher, Douglas Green, Nancy Hinkelmann, Knut McLurkin, James Maher, Mary Lou Maheswaran, Rajiv Rubinelli, Sara Schurr, Nathan Scott, Donia Shell, Dylan Szekely, Pedro Thönssen, Barbara Urken, Arnold B. U.S. Army Research Institute of Environmental Medicine Natick MA United States General Electric Research Niskayuna NY United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States Department of Computer and Information Sciences IDA Linkoping University Sweden Massachusetts Institute of Technology Cambridge MA United States Computer Science and Engineering Department Sabanci University Turkey Department of Electrical Engineering and Computer Science Vanderbilt University United States Department of Computer Science University of North Carolina Greensboro United States Business Information Systems and Studies School of Business University of Applied Sciences Northwestern Switzerland Department of Computer Science Rice University United States Human-Computer Interaction Lab. University of Maryland United States Computer Science Department University of Southern California United States Department of Health Sciences and Health Policy University of Lucerne Swiss Paraplegic Research Switzerland Aptima Inc. United States Department of Informatics University of Sussex United Kingdom Department of Computer Science and Engineering Texas A and M University United States School of Business University of Applied Sciences Northwestern Switzerland College of Engineering University of Arizona United States

The Association for the Advancement of Artificial Intelligence presented the 2011 Spring Symposium Series Monday through Wednesday, March 21-23, 2011, at Stanford University. This report summarizes the eight symposia. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

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Reports of the AAAI 2010 fall symposia

Reports of the AAAI 2010 fall symposia

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作者： Azevedo, Roger Biswas, Gautam Bohus, Dan Carmichael, Ted Finlayson, Mark A. Hadzikadic, Mirsad Havasi, Catherine Horvitz, Eric Kanda, Takayuki Koyejo, Oluwasanmi Lawless, William F. Lenat, Doug Meneguzzi, Felipe Mutlu, Bilge Oh, Jean Pirrone, Roberto Raux, Antoine Sofge, Donald A. Sukthankar, Gita Van Durme, Benjamin Yorke-Smith, Neil Department of Educational and Counseling Psychology McGill University Canada Center for Intelligent Systems Vanderbilt University United States Microsoft Research Redmond United States Department of Software and Information Systems University of North Carolina Charlotte United States Complex Systems Institute United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Media Lab. Massachusetts Institute of Technology United States ATR Intelligent Robotics and Communication Laboratories Kyoto Japan Department of Electrical and Computer Engineering University of Texas Austin United States Department of Mathematics Paine College Augusta GA United States Cycorp Austin TX United States Robotics Institute Carnegie Mellon University United States Department of Computer Sciences University of Wisconsin Madison United States University of Palermo Italy Honda Research Institute United States Navy Center for Applied Research in Artificial Intelligence Naval Research Laboratory Washington DC United States Department of Electrical Engineering and Computer Science University of Central Florida United States Department of Computer Science Johns Hopkins University United States American University of Beirut Lebanon SRI International's Artificial Intelligence Center United States

The Association for the Advancement of Artificial Intelligence (AAAI) presented the 2010 Fall Symposium Series on November 11-13, 2010. The eight symposia included Cognitive and Metacognitive Educational systems, Commonsense Knowledge, Complex Adaptive systems: Resilience, Robustness, and Evolvability, Computational Models of Narrative, Dialog with Robots, Manifold Learning and Its Applications, Proactive Assistant Agents and Quantum Informatics for Cognitive, Social, and Semantic Processes. Cognitive and Metacognitive Educational systems aimed to provide a comprehensive definition of metacognitive educational systems that is inclusive of the theoretical, architectural, and educational aspects of this field. The AAAI Commonsense Knowledge Fall Symposium had the goal of bringing together the diverse elements of this community whose work benefits from or contributes to the representation of general knowledge about the world. One of the specific goals of Proactive symposium was to gather the researchers from various projects in assistant agents to share their wisdom in retrospect.

关键词： Semantics

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Electrocardiogram beat classification using classifier fusion based on Decision Templates

Electrocardiogram beat classification using classifier fusio...

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2011 10th IEEE International Conference on Cybernetic Intelligent systems, CIS 2011

作者： Sajedin, Atena Ebrahimpour, Reza Garousi, Tahmoures Younesi P. O. Box 19395-5746 Niavaran Tehran Iran Islamic Azad University South Tehran Branch No. 209 North Iranshahr St. Tehran Iran Brain and Intelligent Systems Research Lab Department of Electrical and Computer Engineering Shahid Rajaee University P.O.Box:16785-136 Tehran Iran Tolid Va Maskane Khayam Tehran Iran

ISBN: (纸本)9781467306874

This paper presents a "Decision Templates" (DTs) approach to develop customized Electrocardiogram (ECG) beat classifier in an effort to further improve the performance of ECG classification. Taking advantage of the Un-decimated Wavelet Transform (UWT), which also serves as a tool for noise reduction, we extracted 10 ECG morphological, as well as one timing interval features. For classification we have used a number of diverse MLPs neural networks as the base classifiers that are trained by Back Propagation algorithm. Then we employed and compared different combination methods. Tested with MIT/BIH arrhythmia database, we observe significant performance enhancement using this approach. © 2011 IEEE.

关键词： Electrocardiograms

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Bayesian mixed-effects inference on classification performance in hierarchical data sets

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2012年第1期13卷

作者： Kay H. Brodersen Christoph Mathys Justin R. Chumbley Jean Daunizeau Cheng Soon Ong Joachim M. Buhmann Klaas E. Stephan Machine Learning Lab. Dept. of Comp. Sci. ETH Zurich Switzerland and Lab. for Social and Neural Systems Res. Dept. of Economics Univ. of Zurich Zurich Switzerland and Translational Neuromodeling Unit Inst. for Biomed. Eng. ETH Zurich and Univ. of Zurich Zurich Switzerland Laboratory for Social and Neural Systems Research Department of Economics University of Zurich Zurich Switzerland and Translational Neuromodeling Unit Institute for Biomedical Engineering ETH Zurich & University of Zurich Zurich Switzerland Laboratory for Social and Neural Systems Research Department of Economics University of Zurich Zurich Switzerland Machine Learning Laboratory Department of Computer Science ETH Zurich Zurich Switzerland Lab. for Social and Neural Sys. Res. Dept. of Economics Univ. of Zurich Switzerland Wellcome Trust Centre for Neuroimaging Univ. College London London United Kingdom and Translational Neuromodeling Unit Inst. for Biomedical Eng. ETH Zurich and Univ. of Zurich Zurich Switzerland

Classification algorithms are frequently used on data with a natural hierarchical structure. For instance, classifiers are often trained and tested on trial-wise measurements, separately for each subject within a group. One important question is how classification outcomes observed in individual subjects can be generalized to the population from which the group was sampled. To address this question, this paper introduces novel statistical models that are guided by three desiderata. First, all models explicitly respect the hierarchical nature of the data, that is, they are mixed-effects models that simultaneously account for within-subjects (fixed-effects) and across-subjects (random-effects) variance components. Second, maximum-likelihood estimation is replaced by full Bayesian inference in order to enable natural regularization of the estimation problem and to afford conclusions in terms of posterior probability statements. Third, inference on classification accuracy is complemented by inference on the balanced accuracy, which avoids inflated accuracy estimates for imbalanced data sets. We introduce hierarchical models that satisfy these criteria and demonstrate their advantages over conventional methods usingMCMC implementations for model inversion and model selection on both synthetic and empirical data. We envisage that our approach will improve the sensitivity and validity of statistical inference in future hierarchical classification studies.

关键词： Bayesian inference balanced accuracy beta-binomial group studies normal-binomial

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A digital processor for full calibration of pipelined ADCS

A digital processor for full calibration of pipelined ADCS

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2010 IEEE International Conference on Electronics, Circuits, and systems, ICECS 2010

作者： Fardad, Mohammad Frounchi, Javad Karimian, Ghader Microelectronic and Microsensor Research Lab. Faculty of Electrical and Computer Engineering University of Tabriz Iran Digital Systems Research Lab. Faculty of Electrical and Computer Engineering University OfTabriz Iran

ISBN: (纸本)9781424481576

A digital full calibration processor is proposed to calibrate most known errors in Pipe lined ADCs. This approach does not change internal parts of an ADC and compensates errors by digital post-processing of the output bits. The main idea behind the presented technique is to design a processor to find a correction function that gives the relation between the uncalibrated outputs and the calibrated ones. To verify the algorithm, a 12-bit pipelined ADC based on 1.5-bit per stage architecture is simulated with non ideal factors in the SIMULINK with a 5.34MHz sinusoidal input and a 100MHz sampling frequency. The SNDR is improved from 45dB to 69dB and the SFDR is increases from 45.5dB to 90dB. The described algorithm has been implemented on a virtex-4 LX25 FPGA from Xilinx. ©2010 IEEE.

关键词： Calibration

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A toolkit to explore lived experience of motivation: When words are not enough

A toolkit to explore lived experience of motivation: When wo...

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2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011

作者： Axelrod, Lesley Balaam, Madeline Burridge, Jane Smith, Penny Probert Fitzpatrick, Geraldine Mawson, Sue Ricketts, Ian Rodden, Tom User Centred Technology Lab Interactive Systems Informatics University of Sussex Falmer Brighton BN1 9QH United Kingdom Culture Lab Newcastle University Newcastle upon Tyne NE1 7RU United Kingdom School of Health Sciences University of Southampton Southampton S017 1BJ United Kingdom Dept Engineering Science University of Oxford Oxford 0X1 3PJ United Kingdom Human Computer Interaction Group Technical University of Vienna Favoritenstrase 9-11 A-1040 Vienna Austria Centre for Health and Social Care Research Sheffield Hallam University Sheffield S10 2BP United Kingdom School of Computing University of Dundee Dundee DD1 4HN United Kingdom Dept. Computer Science University of Nottingham NG8 IBB United Kingdom

ISBN: (纸本)9781936968152

Designing to support motivation is an increasingly important issue, especially as pervasive technologies are used to facilitate various healthy behaviour changes. There are many motivation theories but these do not map specifically to inform design. In 'Motivating Mobility' we explore the lived experiences of motivation of people with stroke, in order to design rehabilitation technologies. Motivation varies between people, between contexts and over time and can be 'difficult to express', particularly for those with communication problems. We describe development of a theoretically based toolkit, principled in both content and form, and using multiple modes of communication, aimed at gathering motivational requirements in order to inspire design. We show use of the toolkit, discuss the rich data collected and reflect on how well the approach works and ties requirements, via their elicitation tool, back to theory. This toolkit has potential to inform design for motivational effect in similar pervasive health applications. © 2011 ICST.

关键词： Motivation

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Impact of colinearity of sensors selected for location estimation

Impact of colinearity of sensors selected for location estim...

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17th International Conference on High Performance Computing, HiPC 2010

作者： Sadaphal, Vaishali P. Jain, Bijendra N. Systems Research Lab. Tata Research Development and Design Centre Pune India Dept. of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9781424485185

We consider estimating the location of a target moving in a 2D plane by combining distance measurements from multiple sensors. Given that available energy in sensors is at a premium, it is desirable that energy be conserved by selecting fewer number of sensors that measure distance and communicate to the central tracker. We propose heuristics on the basis of which a handful of sensors may be selected. In this paper, we present one of the heuristic. The particular heuristic discussed at length is based on colinearity of sensors. In this paper, we provide a formal and theoretical basis for arriving at the heuristic and evaluate its utility. ©2010 IEEE.

关键词： distance measurement distance measurement location estimation moving target sensor colinearity sensor energy sensor fusion target tracking

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Towards a real-time cognitive radio network testbed: Architecture, hardware platform, and application to smart grid

Towards a real-time cognitive radio network testbed: Archite...

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5th IEEE Workshop on Networking Technologies for Software-Defined Radio , SDR 2010

作者： Qiu, Robert C. Chen, Zhe Guo, Nan Song, Yu Zhang, Peng Li, Husheng Lai, Lifeng Department of Electrical and Computer Engineering Center for Manufacturing Research Cognitive Radio and Smart Grid Institute Cookeville TN United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Department of Systems Engineering University of Arkansas Little Rock AR United States

ISBN: (纸本)9781424472130

A real-time cognitive radio network testbed is being built. This is the first paper to capture the overall picture of this project. Project scope and philosophy, design architecture, hardware platform, and key algorithms are reported. The use of cognitive radio network for smart grid is for the first time proposed in this paper. This unique testbed is ideal for such purpose. © 2010 IEEE.

关键词： smart power grids

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One Day smart Grid Supersession - Analytics and Integration

One Day Smart Grid Supersession - Analytics and Integration

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IEEE Power and Energy Society General Meeting

作者： W. H. Edwin Liu Stephen McArthur Jay Giri Karen Miu Rob Pratt Jun Wen Bob Uluski Luther Dow Gerard labut Pacific Gas & Electric Company USA Bechtel Institute for Energy and Environment British Energy Advanced Diagnostics Centre University of Strathclyde UK Power Systems Technology and Strategic Initiatives ALSTOM Grid Redmond WA USA Electrical and Computer Engineering Department Drexel University USA Smart Grid Research and Development Pacific Northwest National Laboratory's (PNNL's) USA Transmission and Interconnection Planning Group Southern California Edison (SCE) Company USA EPRI's Power USA West Coast Operations Quanta Technology USA SmartCurrentssm Program Strategy DTE Energy Detroit MI USA

ISBN: (纸本)9781457710001

The effort of "smart Grid" is to modernize grid infrastructure and build-in intelligence to power grids and delivery systems, and their interfaces to customer premises. However, the perspectives range from an emphasis on infrastructure to an emphasis on new paradigm-shifting applications. Alternatively, the smart grid can be thought of as the advanced information technologies that enable the desired analytical applications. As a general understanding, we believe that smart Grid needs to integrate power system analysis, computing and economics to enhance grid reliability, efficiency, and security, and contributes to the climate change strategic goal. In this Supersession, the analytics that empowers smart grid applications is discussed;and the practical implementation and integration challenges will be presented.

关键词： smart grid Analytical applications System integration smart grids systems Integration Power system analysis computing information technology DNA Integration integration INFRASTRUCTURE

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