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Contributors to the Lab on a Chip cell and tissue engineering in microsystems special issue

Lab on a Chip 2007年第6期7卷 667-671页

作者： Bhatia, Sangeeta N. Chen, Christoper Bashir, Rashid Beebe, David J. Desai, Tejal Gottwald, Eric Khademhosseini, Ali Langer, Robert S. Piel, Matthieu Takayama, Shuichi Thorsen, Todd Tien, Joe Voldman, Joel Vunjak-Novakovic, Gordana Wu, Mingming Yu, Hanry Massachusetts Institute of Technology Harvard-MIT Division of Health Sciences and Technology Department of Electrical Engineering and Computer Science United States Laboratory for Multiscale Regenerative Technologies United States Department of Innovation University of Pennsylvania Department of Bioengineering United States Cell Biology and Physiology Program Cell Growth and Cancer Program Tissue Microfabrication Laboratory Department of Electrical and Computer Engineering and Biomedical Engineering Purdue University United States Department of Electronics University of Glasgow United Kingdom Department of Physiology and Bioengineering University of California San Francisco CA United States California Institute for Quantitative Biomedical Research UCSF/UC Berkeley Graduate Group in Bioengineering United States Laboratory of Therapeutic Micro and Nanotechnology Institute for Biological Interfaces Germany 3D-Cell Culture Techniques Group Institute for Biological Interfaces Germany Department of Medicine and Health Sciences and Technology Massachusetts Institute of Technology (MIT) United States Institut Curie UMR 144 IC/CNRS France Biomedical Engineering Department University of Michigan United States Department of Biomedical Engineering Macromolecular Science and Engineering Program United States Sibley School of Mechanical and Aerospace Engineering School of Chemical and Molecular Engineering Cornell University United States National University of Singapore Singapore Institute of Bioengineering and Nanotechnology A STAR Singapore

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Decision Fusion of Circulating Markers for Breast Cancer Detection in Premenopausal Women

Decision Fusion of Circulating Markers for Breast Cancer Det...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Jonathan L. Jesneck Sayan Mukherjee Loren W. Nolte Anna E. Lokshin Jeffrey R. Marks Joseph Lo Duke Advanced Imaging Labs Institute for Genome Sciences and Policy Department of Computer Science Duke University USA Department of Electrical and Computer Engineering Division of Hematology/Oncology University of Pittsburgh School of Medicine Pittsburgh PA USA Division of Hematology/Oncology University of Pittsburgh Cancer Institute Pittsburgh PA USA Division of Hematology/Oncology University of Pittsburgh Cancer Institute Pittsburgh PA Institute for Genome Sciences and Policy Department of Experimental Surgery Medical Center Duke University USA Department of Experimental Surgery Duke University Medical Center Medical Physics Graduate Program Duke University Durham NC USA

Current mammographic screening for breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions.

关键词： Breast cancer Cancer detection Lesions Proteins Support vector machines Support vector machine classification Mammography Testing Blood Linear regression

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GA optimization of OBF TS fuzzy models with linear and non linear local models

GA optimization of OBF TS fuzzy models with linear and non l...

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9th Brazilian Symposium on Neural Networks, SBRN'06

作者： Medeiros, Anderson V. Amaral, Wagner C. Campello, Ricardo J. G. B. State University of Campinas School of Electrical and Computer Engineering Campinas SP 13083-970 Brazil Catholic University of Santos Graduate Program in Computer Science Santos SP 11070-906 Brazil

ISBN: (纸本)0769526802

OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Although encouraging application results have been obtained, no automatic procedure had yet been developed to optimize the design parameters of these models. This paper elaborates on the use of a genetic algorithm (GA) especially designed for this task, in which a fitness function based on the Akaike information criterion plays a key role by considering both model accuracy and parsimony aspects. The use of linear (actually affine) and nonlinear local models is also investigated. The proposed methodology is evaluated in the modeling of a real nonlinear magnetic levitation system. © 2006 IEEE.

关键词： Fuzzy sets

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How do you teach engineering in grades K and one?

How do you teach engineering in grades K and one?

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113th Annual ASEE Conference and Exposition, 2006

作者： Bush, Katie Gray, Jennifer Holmes, Megan Kosinski, Karen Orr, John Razzaq, Leena Rulfs, Jill Worcester Polytechnic Institute United States Worcester Polytechnic Institute Graduate School of Biomedical Sciences University of Massachusetts Worcester MA United States Department of Electrical and Computer Engineering WPI United States Computer Science Department Worcester Polytechnic Institute United States Department of Biology and Biotechnology WPI United States

As part of the National Science Foundation (NSF)-funded program titled "K-6 Gets a Piece of the PIEE (Partnerships Implementing engineering Education)," graduate fellows and undergraduate students at Worcester polytechnic Institute (WPI), Worcester, MA have implemented a technology and engineering curriculum in kindergarten and grade one in the Worcester Public schools (WPS) System. This follows successful implementation of the technology and engineering curriculum in the first two years of the program in grades two through six. This project is also part of the NSF graduate Teaching Fellows in K-12 Education (GK-12) program, the goal of which is to involve engineering graduate students with K-12 science and engineering education. Massachusetts is one of the few states to have mandated the teaching of engineering and technology topics from kindergarten through grade twelve. Needless to say, the approach to teaching engineering that is suitable for the university, high school, middle school, or fifth and six grade levels is unworkable in kindergarten and first grade. For example, the students cannot read or write! Nevertheless, concepts such as creative design, materials selection, and proper tool use can be effectively taught if approached in the appropriate way. graduate fellows have written and illustrated a set of picture books that provides a particularly effective introduction to these topics. Curriculum plans, representative lessons, program successes, and lessons learned, are described below. © American Society for engineering Education, 2006.

关键词： engineering education

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Blank slate engineering at Florida Gulf Coast University - Innovative and multidisciplinary from the ground up

Blank slate engineering at Florida Gulf Coast University - I...

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113th Annual ASEE Conference and Exposition, 2006

作者： Blanchard, Susan Egiebor, Nosa Sweeney, James Zidek, Lisa Sechrist, Chalmers Hulbert, Sam Osborn, James O'Neill, Robert Florida Gulf Coast University United States FGCU School of Engineering Institute of Electrical and Electronics Engineers Biomedical Engineering Society American Institute for Medical and Biological Engineering United Kingdom Department of Environmental and Civil Engineering Arizona State University United States Department of Bioengineering Department of Bioengineering Arizona State University United States Flinn Foundation Funded Multi- and Interdisciplinary Graduate Training Program in Bioengineering Milwaukee School of Engineering United States Graduate Programs Industrial Engineering Program Department of Electrical and Computer Engineering Department of Engineering University of Illinois Urbana-Champaign United States Institute of Electrical and Electronics Engineers Rose-Hulman Institute for Technology School of Engineering FGCU School of Engineering's Advisory Board Roger Williams University United States Department of Civil Engineering Department of Engineering Roger Williams University United States

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GA Optimization of OBF TS Fuzzy Models with Linear and Non Linear Local Models

GA Optimization of OBF TS Fuzzy Models with Linear and Non L...

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Brazilian Symposium on Neural Networks

作者： Anderson V. Medeiros Wagner C. Amaral Ricardo J. G. B. Campello School of Electrical and Computer Engineering State University of Campinas Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

ISBN: (纸本)9780769531960

关键词： Fuzzy control Fuzzy systems Nonlinear systems Control system synthesis Automatic control Nonlinear control systems Nonlinear dynamical systems Topology Design optimization Genetic algorithms

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Custom error control schemes for energy efficient bluetooth sensor networks

Custom error control schemes for energy efficient bluetooth ...

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International Symposium on Telecommunications

作者： Joao H. Kleinschmidt Walter C. Borelli Marcelo E. Pellenz Department of Telematics School of Electrical and Computer Engineering State University of Campinas Sao Paulo Brazil Graduate Program in Computer Science Pontifical Catholic University of Paraná Curitiba Parana Brazil

This paper analyzes the effect of custom error control schemes on the energy efficiency in Bluetooth sensor networks. The energy efficiency metric considers in just one parameter the energy and reliability constraints of the wireless sensor networks. New packet types are introduced using some error control strategies in the AUX1 packet, such as Hamming and BCH codes, with and without CRC for error detection. Two adaptive techniques are proposed that change the error control strategy based on the number of hops traversed by a packet through the network. The performance results are obtained through simulations in a channel with Rayleigh fading for networks with different number of hops, showing that error control can improve the energy efficiency of a Bluetooth-based sensor network.

关键词： Error correction Energy efficiency Bluetooth Wireless sensor networks Media Access Protocol Energy consumption Sensor phenomena and characterization Automatic repeat request Analytical models Cyclic redundancy check

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Enabling a strong U.S. engineering workforce for leadership of technology development and innovation in industry: The economic multiplier of skill-set development for engineering innovation and leadership

Enabling a strong U.S. engineering workforce for leadership ...

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113th Annual ASEE Conference and Exposition, 2006

作者： Tidwell, Joseph McHenry, Albert Keating, Donald Stanford, Thomas Bardo, John Dunlap, Duane Burbank, Kenneth Zhang, James Quick, David Truesdale, Samuel Boeing Co. United States JACMET College of Technology and Applied Sciences Arizona State University Polytechnic United States College Industry Partnership Division Corporate Members Council American Society for Engineering Education United States Arizona State University United States American Society for Engineering Education United States University of South Carolina United States Department of Mechanical Engineering University of South Carolina United States Graduate Studies Division Corporate Members Council College Industry Partnership Division American Society for Engineering Education United States Department of Chemical Engineering University of South Carolina United States Western Carolina University United States Kimmel School of Construction Management Engineering and Technology United States College of Applied Sciences Western Carolina University United States Graduate Studies Division American Society for Engineering Education United States Electronics Engineering Technology Program Kimmel School of Construction Management Engineering and Technology Western Carolina University United States Kimmel School of Construction Management Engineering and Technology Western Carolina University United States Rolls-Royce United States Rolls-Royce Corporation United States

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A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

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Power and ground bounce reduction in buffers

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WSEAS Transactions on Electronics 2006年第9期3卷 478-483页

作者： Ortiz-Balbuena, Laura E. Perez-Meana, Hector M. Department of Electrical Engineering Universidad Autónoma Metropolitana Av. R. Atlixco 186 Col. Vicenta Iztapalapa 09340 D.F. Mexico Graduate School ESIME National Polytechnic Institute Av. Sta. Ana 1000 Col. Culhuacan Mexico

This paper propose a technique to reduce the delta power and ground bounce and the settling time of these noise signals in buffer structure. Used technique, is compared with no compensation circuit and a decoupling capacitor circuit well known technique. The results were obtained using SPICE simulations with a realistic 0.25um BSIM device model technology and a 2.5V power supply.

关键词： Buffer circuits

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