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European Conference on Circuit Theory and Design, ECCTD

作者： Sandor Kocsardi Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputing University of Pannonia Veszprem Hungary Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. Inside that one of the most exciting fields is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In this paper a CNN-UM based solver of 2D inviscid, isothermal, compressible fluids will be presented. Unfortunately the necessity of the coupled multi-layered computational structure with non-linear, space-variant templates does not make it possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution an emulated digital CNN-UM has been used.

关键词： Cellular neural networks Analog computers Aerospace engineering Chemical engineering Civil engineering Partial differential equations mathematics Computational modeling Fluid flow Land vehicles

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Normalized Linear Transform for Cross-Platform Microarray Data Integration

Normalized Linear Transform for Cross-Platform Microarray Da...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Huilin Xiong Ya Zhang Xue-wen Chen Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.

关键词： Costs Gene expression Support vector machines Support vector machine classification Machine learning Application software image processing Pattern recognition computer science Statistical analysis

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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PET image RECONSTRUCTION USING ANATOMICAL INFORMATION THROUGH MUTUAL INFORMATION BASED PRIORS: A SCALE SPACE APPROACH

PET IMAGE RECONSTRUCTION USING ANATOMICAL INFORMATION THROUG...

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IEEE International Symposium on Biomedical Imaging

作者： Sangeetha Somayajula Anand Rangarajan Richard M. Leahy Signal and Image Processing Institute University of Southern California Los Angeles CA USA Department Computer and Information Science and Engineering University of Florida Gainesville FL USA

ISBN: (纸本)9781424406715;1424406714

We propose a mutual information based prior for incorporating information from co-registered anatomical images into PET image reconstruction. The prior uses mutual information between feature vectors that are extracted from the anatomical and functional images using a scale space approach. We perform simulations on a realistic 3D phantom generated by replicating a 2-D autoradiographic cross section of a mouse labelled with F18-FDG. A digital photograph of the cryosection of the same slice is used to generate the anatomical image. The images are registered using mutual information based rigid registration. PET data are then simulated from the autoradiography based phantom. We use a preconditioned conjugate gradient algorithm to compute the PET image that maximizes the posterior density. The performance of this method is compared with that using a Gaussian quadratic penalty, which does not use anatomical information. Simulation results indicate that the mutual information based prior can achieve reduced standard deviation at comparable bias compared to the quadratic penalty

关键词： Positron emission tomography image reconstruction Mutual information Imaging phantoms Feature extraction image analysis Mice Computational modeling Spatial resolution image resolution

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View-Dependent Visibility Estimation for Tree Models

View-Dependent Visibility Estimation for Tree Models

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jessy Lee Jingliang Peng C.-C. Jay Kuo Ming Hsieh Department of Electrical Engineering and Signal and Image Processing Institute University of Southern California Los Angeles CA USA Department of Computer Science Sun Yat-sen University Guangzhou China

A view-dependent visibility estimation technique for tree models is proposed in this work. While most previous work focused on visibility estimation of large objects in architectural walkthroughs, we consider the visibility estimation problem of tree leaves in complex natural scenes such as forests. Exact calculation of how each leaf object blocks another from a given viewpoint is difficult due to the large amount of computational cost. To address this issue, we propose three simple yet effective methods for efficient visibility estimation of tree leaves: the distance-based, the normal-based, and the layer-plus-normal-based methods. Experimental results show that the distance-based method always produces the best analytical performance, the normal-based method works better in preserving volumetric visual quality at low budget constraints, and the layer-plus-normal-based method preserves volumetric visual quality while yielding excellent analytical performance.

关键词： Layout Computational efficiency Rendering (computer graphics) Geometry Performance analysis Signal processing image processing computer science Sun computer graphics

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FAST image RECONSTRUCTION METHODS FOR FULLY 3D MULTISPECTRAL OPTICAL BIOLUMINESCENCE TOMOGRAPHY

FAST IMAGE RECONSTRUCTION METHODS FOR FULLY 3D MULTISPECTRAL...

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IEEE International Symposium on Biomedical Imaging

作者： Sangtae Ahn Abhijit J. Chaudhari Felix Darvas Charles A. Bouman Richard M. Leahy Signal and Image Processing Institute Department of Electrical Engineering University of Southern California Los Angeles CA USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (纸本)9781424406715;1424406714

We investigate fast iterative image reconstruction methods for fully 3D multispectral optical bioluminescence tomography where inhomogeneous optical properties are modeled using the finite element method (FEM). We compare two approaches to incorporating the forward model into the solution of the inverse problem. In the direct calculation approach we compute the full forward model by repeated solution of the PEM problem, once for each potential source location. In the on-the-fly approach, we do not explicitly solve for the full forward model. Instead, the solution to the forward problem is included implicitly in the formulation of the inverse problem, and the FEM is solved once at each iteration for the current image estimate. In both approaches we use reordered Cholesky factorization and substitution (CFS) to solve the FEM problem. We compare the computation cost of these two approaches, concluding that the on-the-fly approach can lead to substantial reductions in cost when combined with a rapidly converging iterative algorithm

关键词： image reconstruction Biomedical optical imaging Bioluminescence Tomography Inverse problems Iterative methods Finite element methods Position measurement Computational efficiency Costs

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CNN-UM based transversely isotropic elastic wave propagation simulation

CNN-UM based transversely isotropic elastic wave propagation...

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European Conference on Circuit Theory and Design, ECCTD

作者： Peter Sonkoly Istvan Noe J. M. Carcione Zoltan Nagy Peter Szolgay Department of Image Processing and Neurocomputong University of Pannonia Veszprem Hungary Geophysics of the Lithosphere National Institute of Oceanography and Experimental Geophysics Trieste Italy Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Several previous studies have proved the effectiveness of the CNN-UM solution of isotropic elastic wave equations. Most crustal rocks of interest to exploration geophysics are either inherently anisotropic or behave as anisotropic materials when probed by seismic waves. This paper describes modeling of wave propagation in transversely isotropic media. Numerical methods which are based on finite-difference (FD) techniques (in time and space) were not efficient when applied to realistic 3D models, because they require enormous computer memory and power. This work investigates the simulation speedup of FD techniques by CNN-UM architecture. Unfortunately the huge number of space- dependent equations and the low computational precision do not make it possible to utilize the huge computing power of the analogue VLSI CNN-UM chips so the FPGA based Falcon emulated digital CNN-UM architecture is used to implement our solution.

关键词： Anisotropic magnetoresistance computer architecture Analog computers Partial differential equations Geophysics Seismic waves Finite difference methods Computational modeling Very large scale integration Field programmable gate arrays

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Using supervised fuzzy clustering and cwt for ventricular late potentials (vlps) detection in high-resolution ecg signal

Using supervised fuzzy clustering and cwt for ventricular la...

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5th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2007

作者： Khorshidi, Gholamreza Salimi Jaafari, Ayyoub Pourmorteza, Amir Motie, Ali Golpayegani, Mohammadreza Hashemi School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics Tehran Iran Faculty of Biomedical Engineering Amirkabir University of Technology Tehran Iran Iran Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran Nasrabadi Group of Biomedical Engineering Faculty of Engineering Shahed University Tehran Iran

ISBN: (纸本)9788073683863

Ventricular Late Potentials (VLPs) are lowamplitude, high-frequency components that appear at the end of the QRS complex of a High-Resolution ECG (HRECG) record. VLPs are clinically useful for identifying post-MI (Myocardial Infarction) patients prone to Ventricular Tachycardia (VT) and Sudden Cardiac Death (SCD). In this paper, the Continuous Wavelet Transform (CWT) and a supervised fuzzy clustering algorithm are used together to detect VLP. The terminal part of the QRS complex in the Vector Magnitude (VM) waveform is processed with the CWT to extract a feature vector. Resulting time-frequency representation is subdivided into several sub bands, and the sum of the squared decomposition coefficients is computed in each region. Finally, a supervised Fuzzy clustering method, trained by an appropriate set of these feature vectors, is applied to this data in order to identify VLP.

关键词： Electrocardiograms

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On the Diversity-Multiplexing Tradeoff for Multi-Antenna Multi-Relay Channels

On the Diversity-Multiplexing Tradeoff for Multi-Antenna Mul...

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IEEE International Conference on Communications (ICC)

作者： Y. Fan J. S. Thompson A. Adinoyi H. Yanikomeroglu Institute for Digital Communications Joint Research Institute for Signal and Image Processing School of Engineering and Electronics University of Edinburgh Edinburgh UK Broadband Communications and Wireless Systems centre Department of Systems and Computer Engineering Carleton University Ottawa Canada

In this paper we analyze the performance of multiple relay channels when multiple antennas are deployed only at relays. Specifically, we investigate the simple repetition-coded decode-and-forward protocol and apply two antenna combining techniques at relays, namely maximum ratio combining (MRC) on receive and transmit beamforming (TB). We assume that the total number of antennas at all relays is fixed to N. With a reasonable power constraint at the relays, we show that the antenna combining techniques can exploit the full spatial diversity of the relay channels and can achieve the same diversity multiplexing tradeoff as achieved by more complex space-time distributed coding techniques, such as those proposed by Laneman and Womell (2003).

关键词： Relays Diversity reception Decoding Transmitting antennas Diversity methods Performance analysis Broadband antennas Power system relaying Receiving antennas Array signal processing

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NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL image SEGMENTATION

NONPARAMETRIC ENTROPY-BASED COUPLED MULTI-SHAPE MEDICAL IMAG...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhoundi-Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424406715;1424406714

We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures. For each structure, we consider a similarity transform using seven parameters. In addition, to generate most accurate results, we estimate probability density functions (pdf) iteratively. The proposed method minimizes an entropy-based energy function using quasi-Newton algorithm. To improve the results, we use analytical derivatives. Sample results are given for the segmentation of putamen, thalamus and caudate illustrating the impact of coupling on the accuracy of the results.

关键词： Biomedical imaging image segmentation Shape Couplings Brain Magnetic resonance Magnetic resonance imaging Principal component analysis Probability density function Iterative algorithms

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