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2009 WRI World Congress on computer science and Information engineering, CSIE 2009

作者： Wu, Shaozhi Xu, Peng Asgari, Shadnaz Bergsneider, Marvin Hu, Xiao Department of Neurosurgery David Geffen School of Medicine University of California Los Angeles CA United States Biomedical Engineering Graduate Program Henry Samueli School of Engineering and Applied Science University of California Los Angeles CA United States School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China

ISBN: (纸本)9780769535074

A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least squares (LLS), truncated singular value decomposition (TSVD) and standard Tikhonov regularization (STR) are systemically tested to compare the possible effects of different solutions on the non-invasive ICP estimation. The conducted comparison demonstrated that the selection of mapping function solution actually influences the estimation. Among the tested three solutions for mapping function, TSVD and STR show better ICP estimation performance with smaller ICP errors than LLS. © 2008 IEEE.

关键词： Flow velocity

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Fatigue and Non-Fatigue Mathematical Muscle Models During Functional Electrical Stimulation of Paralyzed Muscle

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IFAC Proceedings Volumes 2009年第12期42卷 365-370页

作者： Zhijun Cai Er-Wei Bai Richard K. Shield Electrical and Computer Engineering The University of Iowa Iowa City IA 52242USA Graduate Program in Physical Therapy and Rehabilitation Science The University of Iowa Iowa City IA 52242 USA

Electrical muscle stimulation demonstrates potential for restoring functional movement and for preventing muscle atrophy after spinal cord injury (SCI). To optimize delivery of electrical stimulation protocols, an accurate and easy-to-implement mathematical skeletal muscle model is essential. The existing models are either accurate but complex, making them hard to implement, such as the Hill-Huxley-type model, or simple but inaccurate, such as second order model. In this paper, we propose a Wiener-Hammerstein system to model the paralyzed skeletal muscle dynamics under electrical stimulus conditions. The proposed model has substantial advantages in identification algorithm analysis and implementation including computational complexity and convergence. Experimental data sets were collected on soleus muscles under 15 Hz frequency stimulation for fourteen subjects with SCI. The simulation results show that the proposed model outperforms the Hill-Huxley-type model not only in peak force prediction, but also in fitting performance for force output of each individual stimulation train.

关键词： modeling identification muscle modeling fatigue modeling

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Abstracts from the 2011 Joint Annual Meeting of the Society for Northwestern Vertebrate Biology and Washington Chapter of the Wildlife Society, Held at the Wesley Inn, Gig Harbor, Washington, March 23–25, 2011

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Northwestern Naturalist 2011年第2期92卷 136-166页

作者： Michael J. Adams Christopher A. Pearl Jessica Y. Adkins Daniel D. Roby Ladd Bayliss Peter J. Loschl Donald E. Lyons Yasuko Suzuki Hannah E. Anderson Sarah T. Hamman Keith B. Aubry Catherine M. Raley John J. Rohrer Eric C. Lofroth Scott H. Fitkin Michael L. Best Hartwell H. Welsh Jamie B. Bettaso Don T. Ashton Hartwell H. Welsh Scott McBain Lindsay M. Biga Andrew R. Blaustein Susan F. Brady R. Steven Wagner Daniel J. Selski James E. Johnson R. Bruce Bury Wynn W. Cudmore Hartwell H. Welsh Nathan D. Chelgren Tara Chestnut Chaucney W. Anderson Julie Kirshtein Megan T. Cook Tiffany S. Garcia Stephen Corn Erin Muths Amanda Kissel Rick Scherer Eric Delvin Jonathan D. Bakker Peter W. Dunwiddie Diana L. Dishman Deborah A. Duffield Harriet R. Huber Dyanna M. Lambourn Karen M. Dvornich Greg Falxa Gary M. Fellers Patrick M. Kleeman David Miller Brandon Fessler Jason T. Irwin Daniel D. Beck R. Steven Wagner Marc P. Hayes John A. Frew Christian E. Grue Chris R. Friesen Robert T. Mason Stevan J. Arnold Suzanne Estes Tiffany S. Garcia Katie M. Dugger Josh Johnson Mike Rochelle A. J. Kroll Stephanie S. Gervasi Caren S. Goldberg Dan Davis Erica Bree Rosenblum Bill Bosworth Lisette P. Waits Purnima Govindarajulu Gregory A. Green Steve Negri Jennifer R. Griffiths Daniel E. Schindler Christian E. Grue Karen M. Dvornich Adam G. Hansen David A. Beauchamp Casey M. Baldwin Marc P. Hayes Gretchen Padgett-Flohr Blake R. Hossack Winsor H. Lowe Paul Stephen Corn Aaron N. Johnston Kris Kendell Dave Prescott Scott Stevens Peter M. Kiffney Carol Volk Kerensa A. King Christian E. Grue James M. Grassley Andrew J. Kroll Matt Hane Josh Johnston Mike Rochelle Stuart Farber Niels Leuthold Michael J. Adams John P. Hayes Bo Li Michael T. Brett Kent B. Livezey Ilon E. Logan Teresa J. Lorenz Kimberly A. Sullivan Amanda V. Bakian Deanna Lynch Nancy Thomas Krysten Schuler Steven Jeffries Joshua Malt Kelly R. McAllister Joanne P. Schuett-Hames Aimee P. McIntyre Jay E. Jones Eric M. Lund Frithiof T. Waterstrat Jack N. Giovanini Stephen D. Duke Marc P. Hayes US Forest Service Pacific Northwest Research Station Olympia WA 98512 kaubry@fs.fed.us US Forest Service Okanogan-Wenatchee National Forest Methow Valley Ranger District Winthrop WA 98862 British Columbia Ministry of Environment Victoria BC V8W9M1 Washington Department of Fish and Wildlife Winthrop WA 98862 Humboldt State University 1 Harpst St. Arcata CA 95521 addsmike14@*** US Forest Service Pacific Southwest Research Station 1700 Bayside Dr. Arcata CA 95521 hwelsh@fs.fed.us US Fish and Wildlife Service Arcata Field Office 1655 Heindon Road Arcata CA 95521 jamie_bettaso@fws.gov Redwood Sciences Laboratory US Forest Service1700 Bayview Drive Arcata CA 99521 dashton@fs.fed.us hwelsh@fs.fed.us McBain &amp Trush 980 7th Street Arcata CA 95521 scott@*** Oregon State University Environmental Sciences Graduate Program Corvallis OR 97331 lindsay.biga@science.oregonstate.edu Oregon State University Department of Zoology Corvallis OR 97331 US Geological Survey Forest and Rangeland Ecosystem Science Center 3200 SW Jefferson Way Corvallis OR 97331 buryb@usgs.gov Life Science Department Chemeketa Community College Salem OR 97309 wynn.cudmore@chemeketa.edu US Forest Service Redwood Sciences Laboratory 1700 Bayview Drive Arcata CA 95521 hwelsh@fs.fed.us USGS Oregon Water Science Center 2130 SW 5th Avenue Portland OR 97207 chestnut@usgs.gov USGS Headquarters 12201 Sunrise Valley Drive Reston VA 20192 US Geological Survey Northern Rocky Mountain Science Center Aldo Leopold Wilderness Research Institute 790 E. Beckwith Avenue Missoula MT 59801 scorn@usgs.gov US Geological Survey Fort Collins Science Center 2150 Centre Avenue Building C Fort Collins CO 80526 Department of Biological Sciences Simon Fraser University Burnaby BC V5A 1S6 Colorado State University Department of Fishery and Wildlife Biology Fort Collins CO 80523 The Nature Conservancy 120 Union Ave SE Olympia WA 98501 edelvin@tnc.org School of Forest Resources University of Washington Seattle WA

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Probabilistic structure from sound

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Advanced Robotics 2009年第12-13期23卷 1687-1702页

作者： Wang, Chieh-Chih Lin, Chi-Hao Hu, Jwu-Sheng Department of Computer Science and Information Engineering Graduate Institute of Networking and Multimedia National Taiwan University Taipei 10617 Taiwan Intelligent Robotics Program of Industrial Technology Research Institute Department of Electrical and Control Engineering National Chiao Tung University HsinChu 310 Taiwan

Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. In practice, microphone array calibration can be tedious and other devices or means are required. The structure from sound (SFS) approach addresses the problem of simultaneously localizing a set of microphones and a set of acoustic events that provides a great flexibility to calibrate different setups of microphone arrays. However, the existing method does not take measurement uncertainty into account and does not provide uncertainty estimates of the SFS results. In this paper, we propose a probabilistic structure from sound (PSFS) approach using the unscented transform in which the uncertainties of the PSFS results are also available. In addition, a probabilistic sound source localization approach using the PSFS results is provided to improve sound source localization accuracy. The ample results of simulation and experiments using low-cost, off-the-shelf microphones demonstrate the feasibility and performance of the proposed PSFS approach. © 2009 VSP.

关键词： Microphone array

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Hardware Architecture for Particle Swarm Optimization Using Floating-Point Arithmetic

Hardware Architecture for Particle Swarm Optimization Using ...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Daniel M. Muñoz Arboleda Carlos H. Llanos Leandro dos_S. Coelho Mauricio Ayala-Rincón Department of Mechanical Engineering University of Brasilia Brasília Brazil Industrial and Systems Engineering Graduate Program Pontificial Catholic University of Paraná Curitiba Brazil Departments of Computer Science and Mathematics University of Brasilia Brasília Brazil

High computational cost for solving large engineering optimization problems point out the design of parallel optimization algorithms. Population based optimization algorithms provide parallel capabilities that can be explored by their implementations done directly in hardware. This paper presents a hardware implementation of particle swarm optimization algorithms using an efficient floating-point arithmetic which performs the computations with high precision. All the architectures are parameterizable by bit-width, allowing the designer to choose the suitable format according to the requirements of the optimization problem. Synthesis and simulation results demonstrate that the proposed architecture achieves satisfactory results obtaining a better performance in therms of elapsed time than conventional software implementations.

关键词： Hardware Particle swarm optimization Floating-point arithmetic computer architecture Design optimization Computational efficiency Design engineering Algorithm design and analysis High performance computing Computational modeling

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Time Series Mining Approach for Noninvasive Intracranial Pressure Assessment: An Investigation of Different Regularization Techniques

Time Series Mining Approach for Noninvasive Intracranial Pre...

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computer science and Information engineering, CSIE, WRI World Congress on FLAGGED

作者： Shaozhi Wu Peng Xu Shadnaz Asgari Marvin Bergsneider Xiao Hu Biomedical Engineering Graduate Program Henry Samueli School of Engineering and Applied Science University of California Los Angeles USA Neural Systems and Dynamics Laboratory Department of Neurosurgery the David Geffen School of Medicine University of California Los Angeles USA School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China

关键词： Cranial pressure Data mining Biomedical engineering Data engineering Spatial databases computer science Feature extraction Arterial blood pressure Biomedical measurements Laboratories

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An agent framework to support sensor networks' setup and adaptation

An agent framework to support sensor networks' setup and ada...

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International Multiconference on computer science and Information Technology, IMCSIT

作者： Edison Pignaton de Freitas Flávio Rech Wagner Tales Heimfarth Carlos Eduardo Pereira Armando Morado Ferreira Tony Larsson IDE - Halmstad University Sweden Computer Science Department UFLA Lavras Brazil Institute of Informatics UFRGS Porto Alegre Brazil Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil IDE - Halmstad University Halmstad Sweden

Sensor networks are being used in several emerging applications not even imagined some years ago due to advances in sensing, computing, and communication techniques. However, these advances also pose various challenges that must be faced. One important challenge is related to the autonomous capability needed to setup and adapt the networks, which decentralizes the control of the network, saving communication and energy resources. Middleware technology helps in addressing this kind of problem, but there is still a need for additional solutions, particularly considering dynamic changes in users' requirements and operation conditions. This paper presents an agent-based framework acting as an integral part of a middleware to support autonomous setup and adaptation of sensor networks. It adds interoperability among heterogeneous nodes in the network, by means of autonomous behavior and reasoning. These features also address the needs for system setup and adaptations in the network, reducing the communication overhead and decentralizing the decision making mechanism. Additionally, preliminary results are also presented.

关键词： Middleware Image sensors computer networks Communication system control Energy resources Decision making

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Learning a nonlinear color distance metric for the identification of skin immunohistochemical staining

Learning a nonlinear color distance metric for the identific...

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2009 22nd IEEE International Symposium on computer-Based Medical Systems, CBMS 2009

作者： Sobieranski, Antonio Carlos Neto, Sylvio Luiz Mantelli Coser, Leandro Comunello, Eros Von Wangenheim, Aldo Cargnin-Ferreira, Eduardo Di Giunta, Gabriella LAPIX Laboratory of Image Processing and Computer Graphics UFSC Federal University of Santa Catarina Brazil CPTEC Center for Weather Forecasts and Environment Studies INPE - Brazilian Institute for Space Research Brazil EGC Knowledge Engineering Department UFSC - Federal University of Santa Catarina Brazil MCA Graduate Program in Applied Computer Science UNIVALI - Itajai Valley University Brazil Laboratory of Histological Markers Department of Biology Embriology and Genetics UFSC - Federal University of Santa Catarina Brazil Pathology Service UFSC - Federal University of Santa Catarina Brazil

ISBN: (纸本)9781424448784

This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained regions that will be used as a reference for the construction of a distance metric. In this learning process, the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the IHC pattern distributions. The results of the proposed method showed a high correlation to a pathologist's manual analysis, which was used as a golden standard, presenting a more robust discrimination between stained and non-stained areas with little bias. © 2009 IEEE.

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Comparison of Different Approaches to ICP Dominated Pulse Extraction

Comparison of Different Approaches to ICP Dominated Pulse Ex...

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International Conference on Bioinformatics and Biomedical engineering (ICBBE)

作者： Peng Xu Shaozhi Wu Shadnaz Asgari Magdalena Kasprowicz Marvin Bergsneider Xiao Hu Center of Neuroinformatics School of Life Science and Technology University of Electronic Science and Technology Chengdu China Neural Systems and Dynamics Laboratory Department of Neurosurgery the David Geffen School of Medicine University of California Los Angeles USA School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Biomedical Engineering Graduate Program Henry Samueli School of Engineering and Applied Science University of California Los Angeles USA University of California Los Angeles Los Angeles CA US

Changes of ICP waveform morphology are characterized with different patients' states like hypertension, hydrocephalus and traumatic brain injury etc. Morphological clustering and analysis of ICP pulse (MOCAIP) approach is recently developed to extract ICP morphology feature, in which hierarchical clustering is used to extract the dominated pulse. In this paper, we evaluate the feasibility of using principle component analysis (PCA) and independent component analysis (ICA) to extract dominated pulse. The comparative study among clustering, PCA and ICP based approaches shows that PCA approach may be an alternative of clustering approach to extract dominated pulse in a faster fashion when dataset is of large size.

关键词： Independent component analysis Morphology Principal component analysis Biomedical engineering Feature extraction Electrocardiography Tellurium Hypertension Data mining Laboratories

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Identification of cell cycle-related regulatory motifs using a kernel canonical correlation analysis

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BMC Genomics 2009年第SUPPL. 3期10卷 1-10页

作者： Rhee, Je-Keun Joung, Je-Gun Chang, Jeong-Ho Fei, Zhangjun Zhang, Byoung-Tak Graduate Program in Bioinformatics Seoul National University Seoul 151-744 South Korea Center for Biointelligence Technology (CBIT) Seoul National University Seoul 151-744 South Korea Boyce Thompson Institute for Plant Research Cornell University Ithaca NY 14853 United States Konan Technology Inc Seoul 135-080 South Korea USDA Robert W. Holley Center for Agriculture and Health Ithaca NY 14853 United States School of Computer Science and Engineering Seoul National University Seoul 151-744 South Korea

Background: Gene regulation is a key mechanism in higher eukaryotic cellular processes. One of the major challenges in gene regulation studies is to identify regulators affecting the expression of their target genes in specific biological processes. Despite their importance, regulators involved in diverse biological processes still remain largely unrevealed. In the present study, we propose a kernel-based approach to efficiently identify core regulatory elements involved in specific biological processes using gene expression profiles. Results: We developed a framework that can detect correlations between gene expression profiles and the upstream sequences on the basis of the kernel canonical correlation analysis (kernel CCA). Using a yeast cell cycle dataset, we demonstrated that upstream sequence patterns were closely related to gene expression profiles based on the canonical correlation scores obtained by measuring the correlation between them. Our results showed that the cell cycle-specific regulatory motifs could be found successfully based on the motif weights derived through kernel CCA. Furthermore, we identified co-regulatory motif pairs using the same framework. Conclusion: Given expression profiles, our method was able to identify regulatory motifs involved in specific biological processes. The method could be applied to the elucidation of the unknown regulatory mechanisms associated with complex gene regulatory processes. © 2009 Rhee et al;licensee BioMed Central Ltd.

关键词： Canonical Correlation Analysis Regulatory Motif Upstream Sequence Gene Expression Dataset Yeast Cell Cycle

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