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A computer-aided system for mass detection and classification in digitized mammograms

Biomedical engineering - Applications, Basis and Communications 2005年第5期17卷 215-228页

作者： Yang, Sheng-Chih Wang, Chuin-Mu Chung, Yi-Nung Hsu, Giu-Cheng Lee, San-Kan Chung, Pau-Choo Chang, Chein-I. Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Chin Yi Institute of Technology Taichung Taiwan Department of Electrical Engineering Da-Yeh University Chunghua Taiwan Department of Radiology Tri-Service General Hospital National Defense Medical Center Taipei Taiwan Administrative Office Suao Veterans Hospital Yilan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD United States

This paper presents a computer-assisted diagnostic system for mass detection and classification, which performs mass detection on regions of interest followed by the benign-malignant classification on detected masses. In order for mass detection to be effective, a sequence of preprocessing steps are designed to enhance the intensity of a region of interest, remove the noise effects and locate suspicious masses using five texture features generated from the spatial gray level difference matrix (SGLDM) and fractal dimension. Finally, a probabilistic neural network (PNN) coupled with entropic thresholding techniques is developed for mass extraction. Since the shapes of masses are crucial in classification between benignancy and malignancy, four shape features are further generated and joined with the five features previously used in mass detection to be implemented in another PNN for mass classification. To evaluate our designed system a data set collected in the Taichung Veteran General Hospital, Taiwan, R.O.C. was used for performance evaluation. The results are encouraging and have shown promise of our system.

关键词： computer aided analysis

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Stability Analysis of a Multivariable Fuzzy Controller

Stability Analysis of a Multivariable Fuzzy Controller

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IEEE International Symposium on Intelligent Control (ISIC)

作者： G. Bitsoris J. Aguilar-Martin A. Acevez M. Vassilaki N. Miliou Department of Electrical and Computer Engineering University of Patras Patras Greece LAAS-CNRS Toulouse France Technologico de Monterrey Mexico ASPETE School of Pedagogical and Technological Education Heraklion Athens Greece ETH Signal and Information Processing Laboratory Zurich Switzerland

In this paper a method for the design of fuzzy controllers that overcome the problems of lack of guaranteed stability and of computational explosion is developed. The proposed fuzzy controller takes into account the technological constraints on the input and output variables and can be easily designed even for multivariable systems. In the first part of the paper, a new fuzzy controller, named natural logic controller, is presented. Then the stability properties of systems controlled by this type of controller are studied. Based on the results of this analysis a systematic approach to the design of multivariable constrained fuzzy control systems with guaranteed stability is developed

关键词： Stability analysis Fuzzy control Control systems Design methodology Fuzzy logic Explosions Fuzzy systems Fuzzy sets Laboratories Educational technology

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Network coding for wireless applications

Network coding for wireless applications

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Unmanned/Unattended Sensors and Sensor Networks II

作者： Lun, Desmond S. Ahmed, Ebad Brothers, L. Reggie DeBardelaben, James A. Effros, Michelle Ho, Tracey Karger, David Koetter, Ralf Lee, Anna MacMullan, Samuel J. Médard, Muriel Zhao, Fang Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 United States DARPA ATO 3701 N. Fairfax Dr. Arlington VA 22203 United States Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd. Laurel MD 20769 United States Data Compression Laboratory California Institute of Technology Pasadena CA 91125 United States Department of Electrical Engineering California Institute of Technology Pasadena CA 91125 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 United States Coordinated Science Laboratory University of Illinois at Urbana-Champaign Urbana IL 61801 United States Lorenz Research Associates Inc. 4443 Brookfield Corporate Dr. Chantilly VA 20151 United States

The advent of network coding promises to change many aspects of networking. Network coding moves away from the classical approach of networking, which treats networks as akin to physical transportation systems. We overview some of the main features of network coding that are most relevant to wireless networks. In particular, we discuss the fact that random distributed network coding is asymptotically optimal for wireless networks with and without packet erasures. These results are extremely general and allow packet loss correlation, such as may occur in fading wireless channels. The coded network lends itself, for multicast connections, to a cost optimization which not only outperforms traditional routing tree-based approaches, but also lends itself to a distributed implementation and to a dynamic implementation when changing conditions, such as mobility, arise. We illustrate the performance of such optimization methods for energy efficiency in wireless networks and propose some new directions for research in the area.

关键词： signal encoding

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Journal of Information Science and engineering: Editorial notice

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Journal of Information Science and engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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Corrections to “Stability of Multidimensional Systems Using Genetic Algorithms”

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IEEE Transactions on Circuits and Systems I: Regular Papers 2005年第9期52卷 1982-1982页

作者： N.E. Mastorakis I.F. Gonos M.N.S. Swamy Department of Computer Science Military Institution Hellenic Naval Academy University Education Piraeus Greece High Voltage Laboratory School of Electrical and Computer Engineering National and Technical University of Athens Athens Greece Center for Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

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Spatial matched processing for multipath propagation

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The Journal of the Acoustical Society of America 2005年第S1期82卷 S73-S73页

作者： Matthew Dzieciuch T. G. Birdsall Communication and Signal Processing Laboratory 4242 EECS Building The University of Michigan Ann Arbor MI 48104 Department of Electrical Engineering and Computer Science The University of Michigan Ann Arbor MI 48104

Underwater acoustic propagation is characterized by multipath or multimode propagation. Ray theory and mode theory are not fully adequate for modeling physical reality. Impulse responses can be more accurately calculated using Gaussian beam theory. signal processors can be designed to take advantage of the channel complexity if the propagation is actually known so that detectability is increased. The proposed technique, channel matched filtering, synthetically backpropagates the wave front to a hypothesized source location. Accurate passive estimates of source location can be made without knowledge of the signal characteristics. GB theory can easily accommodate a range‐dependent deep water environment. [This research supported by the Office of Naval Research.]

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Multifractal characterization of synthetic ECG in the presence of coloured noise

Multifractal characterization of synthetic ECG in the presen...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The electrocardiogram (ECG) is a signal resulting from a complicated dynamical process. The paper describes an extension to a previous method of reconstruction and characterization of real ECG to a new "realistic" dynamical model of an ECG. A noise-free ECG is generated at 360 samples per second from the dynamical model, and contaminated with 10 dB of white, pink, or brown noise. The dynamical phase-space is then reconstructed. It is observed that the noise-free ECG has a Renyi fractal dimension spectrum (RS) bounded between 1 and 2. It is also observed that noise introduces a positive bias in the RS estimate. This bias is greater for uncorrelated noise, but more asymmetric with increasing correlation. These are the first characterizations of a realistic estimate of the noise-free ECG attractor. It is also the first analysis of the effect of noise correlation on the RS of ECGs in isolation of sampling effects. This demonstrates that a disagreement with the RS of a real ECG signal is not due to noise.

关键词： Fractals Electrocardiography Colored noise 1f noise signal processing Biomedical monitoring Noise generators Condition monitoring Equations signal analysis

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A study of outliers for robust independent component analysis

A study of outliers for robust independent component analysi...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： N. Gadhok W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The impact of outliers on the signal separation performance of an independent component analysis (ICA) algorithm is an important characteristic in assessing the algorithm's utility in real-world applications. If an ICA estimator has the property of B-robustness, the influence of an extreme point is bounded, leading to good separation performance in the presence of outliers. In recent work, major ICA estimators, such as FastICA, have been proven not to be B-robust. We seek to enhance the non-B-robust FastICA estimator by the introduction of K-means clustering for outlier mitigation. We compare our algorithm with the B-robust /spl beta/-divergence algorithm by conducting a simulation to reproduce published results. The paper demonstrates the utility of the K-means clustering algorithm to mitigate a class of outliers such that our ICA separation performance is at least equal to that of published results for the B-robust /spl beta/-divergence estimator.

关键词： Robustness Independent component analysis signal processing algorithms Clustering algorithms Higher order statistics Statistical analysis Blind source separation Vectors Data compression Laboratories

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signal separation by independent component analysis and fuzzy estimators

Signal separation by independent component analysis and fuzz...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Independent component analysis (ICA) is a developing field of interest for researchers in signal processing and artificial neural networks. ICA is an "intelligent signal processing" extension to the principal component analysis that becomes sensitive to non-Gaussian higher-order statistics. This paper presents the motivation of ICA and a treatment of the theory with a guiding example. The limitations of current ICA algorithms are discussed in general and the possible benefits of developing fuzzy engines as ICA estimators are discussed. In particular, a Mamdani-type fuzzy inference system for determining an optimal ICA rotation of whitened two-dimensional uniform noise is implemented as an example of the feasibility of this new direction in ICA.

关键词： Source separation Independent component analysis signal processing algorithms Artificial neural networks Artificial intelligence Principal component analysis Higher order statistics Inference algorithms Engines Fuzzy systems

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Estimating outlier impact on FastICA using fuzzy inference

Estimating outlier impact on FastICA using fuzzy inference

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： N. Gadhok W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The impact of outliers on signal separation performance of an independent component analysis (ICA) algorithm is an important factor when selecting an ICA algorithm. If an ICA estimator has the property of B-robustness, the influence of an extreme point is bounded, leading to good separation performance in the presence of outliers. Since this property is binary, it does not give the degree of influence an outlier has on the separation performance. To address this issue, a Mamdani-type fuzzy inference, based on the location of a potential outlier and on the skewness of the data set, has been developed. It creates an outlier sensitivity map for an ICA algorithm. The implication of this work is a criterion to switch from one ICA algorithm to another in real time, as determined by the algorithms sensitivity to the data set under consideration. This paper describes estimation of the outlier impact on the separation performance of the non-B-robust FastICA algorithm using a Mamdani-type fuzzy inference. In simulations with data sets contaminated by outliers, the FastICA sensitivity map resembles the separation performance measured by the Amari performance index.

关键词： Independent component analysis signal processing algorithms Inference algorithms Switches Pollution measurement Jitter Higher order statistics Robustness Blind source separation Statistical analysis

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