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113th Annual ASEE Conference and Exposition, 2006

作者： Strangeway, Robert Chandler, Edward Brown, Roger Exworthy, Kenneth Fancher, Jeffrey Fleischman, Terry Hedges, Walter Hoppe, Patrick Lokken, Richard Martin, Thomas Merkel, Daniel O'Donnell, Michael O'Laughlin, Terry Porubsky, Mark Steker, Robert Tewalt, Timothy Milwaukee School of Engineering United States United States Medical College of Wisconsin Milwaukee WI United States Electrical Engineering Technology Program MSOE United States United States L-3 Communications Corp. United States Chippewa Valley Technical College United States United States Northeast Wisconsin Technical College United States Western Wisconsin Technical College United States Electronic Technologies Department United States Electronics and Electrical Engineering Technology Programs WWTC Fox Valley Technical College United States United States United States Gateway Technical College United States Milwaukee Area Technical College United States United States Green Bay WI United States Madison Area Technical College United States United States Electronics Technology Department United States Waukesha County Technical College United States

A two-plus-two transfer agreement structure in electrical engineering Technology (EET) has been established between eight two-year colleges in the Wisconsin Technical College System (WTCS) and Milwaukee School of engineering (MSOE). The EET programs at most of the two-year colleges are based on and overlap significantly with their electronics Technology programs. This format creates a valuable opportunity for students: they may earn the AAS-EET degree, the AAS electronics Technology degree, or both. The electronics Technology and EET programs at the two-year colleges, despite distinct curricula, all meet common statewide core electronics standards as established by the Statewide electronics Leadership Team (SWELT) in the WTCS. All AAS-EET programs also meet entrance standards for the plus-two BS-EET (+2 BS-EET) program at MSOE. Two technical calculus courses and two advanced electronics bridge courses, which promote a successful student transition from the two-year AAS-EET programs into the +2 BS-EET program, were adopted by most of the two-year programs. The bridge courses are described in detail. The courses have also run at several two-year colleges via an instructional television system. A comparison of the performances of students from the first two years of the former four-year MSOE EET program and the first group of EET transfer students to MSOE under the transfer agreements was performed in the first EET course within the plus-two curriculum at MSOE. This comparison determined no performance difference, i.e., the preparations of both student groups were indistinguishable. Coordination among the WTCS AAS-EET programs and with the MSOE +2 BS-EET program is facilitated by regular statewide meetings of electronics faculty as well as other mechanisms. The transfer agreements are renewed annually to counter program drift and to enhance program evolution through input from multiple colleges and their industrial advisory committees. The performance of the transfer s

关键词： electrical engineering

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Edge detection using clustering algorithms

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WSEAS Transactions on Computers 2005年第5期4卷 417-423页

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gokmen, Muhittin Informatics Institute Advanced Technologies in Engineering Computer Science and Engineering Istanbul Technical University Istanbul 34469 Turkey Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Istanbul Technical University Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Electronics and Communication Department Istanbul Technical University Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Computer Engineering Department Istanbul Technical University Istanbul 34469 Turkey

Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.

关键词： Edge detection

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Sparse KPCA for feature extraction in speech recognition

Sparse KPCA for feature extraction in speech recognition

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2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Lima, A. Zen, H. Nankaku, Y. Tokuda, K. Kitamura, T. Resende, F.G. Nagoya Institute of Technology Department of Computer Science and Engineering Nagoya 466-8555 Japan Federal University of Rio de Janeiro Dept. of Electronics and Computer Engineering/EPoli Program of Electrical Engineering/COPPE Rio de Janeiro 21945-970 Brazil

ISBN: (纸本)0780388747

This paper presents an analysis of the applicability of Sparse Kernel Principal Component Analysis (SKPCA) for feature extraction in speech recognition, as well as, a proposed approach to make the SKPCA technique realizable for a large amount of training data, which is an usual context in speech recognition systems. Although the KPCA (Kernel Principal Component Analysis) has proved to be an efficient technique for being applied to speech recognition, it has the disadvantage of requiring training data reduction, when its amount is excessively large. The standard approach to perform this data reduction is to randomly choose frames from the original data set, which does not necessarily provide a good statistical representation of the original data set. In order to solve this problem a likelihood related re-estimation procedure was applied to the KPCA framework, thus creating the SKPCA. The experimental results show the efficiency of SKPCA technique with the proposed approach over the KPCA with the standard sparse solution using randomly chosen frames and the standard feature extraction techniques. © 2005 IEEE.

关键词： Speech recognition

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Progressive multigrid V-cycle phase unwrapping for MRI phase images

Progressive multigrid V-cycle phase unwrapping for MRI phase...

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7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005

作者： Dewi, Dyah Ekashanti O. Suksmono, Andriyan B. Mengko, Tati Latifah R. Department of Electrical Engineering Imaging and Image Processing Research Group Institut Teknologi Bandung Jalan Ganesha 10 Bandung 40132 Indonesia Biomedical Engineering Program Department of Electrical Engineering Institut Teknologi Bandung Jalan Ganesha 10 Bandung 40132 Indonesia Department of Electrical Engineering Radio Telecommunication and Microwave Laboratory Institut Teknologi Bandung Jalan Ganesha 10 Bandung 40132 Indonesia Department of Electrical Engineering Electronics and Component Laboratory Institut Teknologi Bandung Jalan Ganesha 10 Bandung 40132 Indonesia

ISBN: (纸本)0780389409

We propose a progressive method which improves the conventional Multigrid V-Cycle Phase Unwrapping (PU) scheme. The method is aimed to accommodate the need of early visualization in solving wrapping problem in phase image. In essence, this technique formulates the wrapped phase image in a Partial Differential Equation (PDE) and solves it by performing a rough global scale solution in the first grid of Multigrid structure and constructing the subsequent levels of detail utilizing the gradients of residual error in coarse and fine grids. The intermediate solution is then superposed to the previous solutions and displayed to the user gradually to get an improved solution. In this paper, we look into the feasibility of utilizing this approach to Magnetic Resonance Imaging (MRI) application. The proposed system is evaluated on simulated as well as actual MRI phase images and shows promising progressive improvement in estimating the unwrapped phase image over the conventional one. © 2005 IEEE.

关键词： Magnetic resonance imaging

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A comparative evaluation of competitive learning algorithms for edge detection enhancement

A comparative evaluation of competitive learning algorithms ...

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13th European Signal Processing Conference, EUSIPCO 2005

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gokmen, Muhittin Ersoy, Okan Informatics Institute Advanced Technologies in Engineering Computer Science and Engineering Maslak Istanbul 34469 Turkey Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Maslak Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Electronics and Communication Department Maslak Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Computer Engineering Department Istanbul Technical University Maslak Istanbul 34469 Turkey Purdue University School of Electrical and Computer Engineering West Lafayette IN United States

ISBN: (纸本)1604238216

Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.

关键词： Edge detection

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Progressive multigrid V-cycle phase unwrapping for MRI phase images

Progressive multigrid V-cycle phase unwrapping for MRI phase...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： D.E.O. Dewi A.B. Suksmono T.L.R. Mengko Department of Electrical Engineering Biomedical Engineering Program Bandung Indonesia Department of Electrical Engineering Radio Telecommunication & Microwave Laboratory Bandung Indonesia Department of Electrical Engineering Electronics & Component Laboratory Bandung Indonesia

We propose a progressive method which improves the conventional multigrid V-cycle phase unwrapping (PU) scheme. The method is aimed to accommodate the need of early visualization in solving wrapping problem in phase image. In essence, this technique formulates the wrapped phase image in a partial differential equation (PDE) and solves it by performing a rough global scale solution in the first grid of multigrid structure and constructing the subsequent levels of detail utilizing the gradients of residual error in coarse and fine grids. The intermediate solution is then superposed to the previous solutions and displayed to the user gradually to get an improved solution. In this paper, we look into the feasibility of utilizing this approach to magnetic resonance imaging (MRI) application. The proposed system is evaluated on simulated as well as actual MRI phase images and shows promising progressive improvement in estimating the unwrapped phase image over the conventional one.

关键词： Magnetic resonance imaging Laboratories Visualization Phase estimation Magnetic field measurement Microwave imaging Image processing Wrapping Partial differential equations Demodulation

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Edge detection in multispectral remote sensing images

Edge detection in multispectral remote sensing images

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RAST 2005 - 2nd International Conference on Recent Advances in Space Technologies

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gökmen, Muhittin Ersoy, Okan Istanbul Technical University Informatics Institute Advanced Technologies in Engineering Maslak/Istanbul Turkey Istanbul Technical University Informatics Institute Satellite Communication and Remote Sensing Program Maslak/Istanbul Turkey Istanbul Technical University Faculty of Electrical and Electronic Engineering Electronics and Communication Department Maslak/Istanbul Turkey Istanbul Technical University Faculty of Electrical and Electronic Engineering Computer Engineering Department Maslak/Istanbul Turkey Purdue University School of Electrical and Computer Engineering West Lafayette IN United States

ISBN: (纸本)0780389778

In this paper, we discuss edge detection by first using a clustering algorithm followed by a known edge detection filter such as Canny or Generalized Edge Detector (GED). We developed a new clustering method called Self-Organizing Global Ranking (SOGR). Comparative results with multispectral satellite images including SOGR, SOM and K-Means clustering methods are discussed. The results show that the two-stage algorithms are better than single stage edge detector algorithms. © 2005 IEEE.

关键词： Edge detection

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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Reduced-order model-based feedback controller design for subsonic cavity flows

Reduced-order model-based feedback controller design for sub...

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43rd AIAA Aerospace Sciences Meeting and Exhibit

作者： Yuan, X. Caraballo, E. Yan, P. Özbay, H. Serrani, A. DeBonis, J. Myatt, J.H. Samimy, M. Collaborative Center of Control Science Ohio State University United States Department of Electrical and Computer Engineering Department of Mechanical Engineering United States Bilkent Univ. Dept. of Electrical and Electronics Eng. Ankara Turkey NASA Glenn's Quiet Aircraft Technology Program United States Air Force Research Laboratory Air Vehicles Directorate WPAFB United States

This paper explores feedback controller design for cavity flows based on reduced-order models derived using Proper Orthogonal Decomposition (POD) along with Galerkin projection method. Our preliminary analysis shows that the equilibrium of the POD model is unstable and a static output feedback controller cannot stabilize it. We develop Linear Quadratic (LQ) optimal state feedback controllers and LQ optimal observers for the linearized models. The linear controllers and observers are applied to the nonlinear system using simulations. The controller robustness is numerically tested with respect to different POD models generated at different forcing frequencies. An estimation for the region of attraction of the linear controllers is also provided.

关键词： Subsonic flow

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A comparative evaluation of competitive learning algorithms for edge detection enhancement

A comparative evaluation of competitive learning algorithms ...

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European Signal Processing Conference (EUSIPCO)

作者： Tuba Sirin Mehmet Izzet Saglam Isin Erer Muhittin Gokmen Okan Ersoy Informatics Institute Computer Science and Engineering Informatics Institute Satellite Communication and Remote Sensing Program Faculty of Electrical and Electronic Engineering Electronics and Communication Department Faculty of Electrical and Electronic Engineering Istanbul Technical University Maslak Istanbul TURKEY Purdue University School of Electrical and Computer Engineering West Lafayette Indiana USA

关键词： Image edge detection Image segmentation Vectors Abstracts Filtering algorithms Erbium Training

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