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IEEE Workshop on Signal processing Advances in Wireless Communications (SPAWC)

作者： J. F. Schmidt J. E. Cousseau R. Wichman F. H. Gregorio CONICET-Department of Electrical and Computer Engineering Universidad Nacional del Sur Bahia Blanca Argentina Signal Processing Laboratory Hut Helsinki University of Technology Finland

We present a novel channel estimator for fading channels using basis function approximations and Kalman filtering. The proposal results in a low complexity estimator that is robust to the shape of the channel Doppler spectrum and allows different Doppler spectra for each channel coefficient in a frequency-selective scenario. The estimator is based on the description of channel variations via truncated discrete cosine transform (DCT). We show that DCT can be approximated by a set of linear narrow-band passband filters that can be inserted into a Kalman filter formulation that track a set of decoupled parameters. This estimator attains close to perfect channel knowledge performance with only 1% training symbols in a data frame with Doppler frequencies up to 0.8 times the frame frequency.

关键词： Discrete cosine transforms Frequency estimation Function approximation Fading Kalman filters Filtering Proposals Robustness Shape Narrowband

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CHESAPEAKE BAY WATER QUALITY MONITORING USING SATELLITE IMAGERY

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International Journal of High Speed Electronics and Systems 2007年第4期17卷 681-688页

作者： BHARATH RAMAKRISHNA CHEIN-I CHANG BRUCE TROU JERRY HENQEMIHLE Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA Microtel LLC 111 Centerway Greenbelt MD 20770 USA

The Chesapeake Bay is a valued ecological, economic, recreational, cultural and scenic resource. The Bay watershed States and the District of Columbia, in conjunction with the EPA Chesapeake Bay Program, have worked and teamed together over the past 20 years to protect and restore the Bay ecosystem. A key component of this effort is water quality and habitat monitoring to assess the impact of management actions and natural processes, and evaluate habitat parameters on living resources such as submerged aquatic vegetation (SAV), oysters, and fisheries. Using aerial and satellite remote sensing imagery has become a practical and effective means of monitoring water quality in a timely manner. Of particular interest in evaluation of water clarity are several initiative measures. Specifically, Secchi-Disk Transparency (SDT) and Chlorophyll a (Chl- a ) have been widely accepted as critical indicators of water quality and their reliable estimation using satellite imagery provides a cost effective and speedy means for water quality monitoring. Work done at Water Resources Center, University of Minnesota has demonstrated the feasibility of performing regional assessment of lake water quality using LANDSAT image data. This paper investigates an approach similar to their work but uses a different type of satellite imagery, EO-1 ALI imagery where the SDT and Chl- a are also used as indicators to estimate water quality for the Chesapeake Bay and DC area (Potomac River). In doing so, three major issues are investigated, which are (1) the study site that is an open Bay area, not a self-contained lake; (2) investigation of applicability of equations that are used to specify the SDT and Chl- a to our Bay area study; (3) the use of a different type of satellite imagery for water quality monitoring. This paper develops techniques to address these three issues and presents preliminary experiments which show encouraging results.

关键词： Chesapeake Bay EO-1 ALI Imagery Secchi Depth (SDT) Chl- a

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on Image processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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Low SNR Capacity of Fading Channels -MIMO and Delay Spread

Low SNR Capacity of Fading Channels -MIMO and Delay Spread

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IEEE International Symposium on Information Theory

作者： Vignesh Sethuraman Ligong Wang Bruce Hajek Amos Lapidoth Qualcomm Inc. Campbell CA USA ETH Zurich Signal and Information Processing Laboratory Switzerland Department of Electrical and Computer Engineering and the Coordinated Science Laboratory University of Illinois Urbana-Champaign USA

ISBN: (纸本)9781424413973

Discrete-time Rayleigh fading multiple-input multiple-output (MIMO) channels are considered, with no channel state information at the transmitter and receiver. The fading is assumed to be correlated in time and independent from antenna to antenna. Peak and average transmit power constraints are imposed, either on the sum over antennas, or on each individual antenna. In both cases, an upper bound and an asymptotic lower bound, as the signal-to-noise ratio approaches zero, on the channel capacity are presented. The limit of normalized capacity is identified under the sum power constraints, and, for a subclass of channels, for individual power constraints. These results carry over to a SISO channel with delay spread (i.e. frequency selective fading).

关键词： Channel capacity Fading Delay Rayleigh channels MIMO Transmitting antennas Channel state information Transmitters Upper bound Signal to noise ratio

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3D model search and retrieval using the spherical trace transform

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Eurasip Journal on Advances in Signal processing 2007年 2007卷

作者： Zarpalas, Dimitrios Daras, Petros Axenopoulos, Apostolos Tzovaras, Dimitrios Strintzis, Michael G. Information Processing Laboratory Electrical and Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki 54006 Greece Informatics and Telematics Institute 1st km Thermi-Panorama Road Thermi-Thessaloniki 57001 Greece

This paper presents a novel methodology for content-based search and retrieval of 3D objects. After proper positioning of the 3D objects using translation and scaling, a set of functionals is applied to the 3D model producing a new domain of concentric spheres. In this new domain, a new set of functionals is applied,resulting in a descriptor vector which is completely rotation invariant and thus suitable for 3D model matching. Further, weights are assigned to each descriptor, so as to significantly improve the retrieval results. Experiments on two different databases of 3D objects are performed so as to evaluate the proposed method in comparison with those most commonly cited in the literature. The experimental results show that the proposed method is superior in terms of precision versus recall and can be used for 3D model search and retrieval in a highly efficient manner.

关键词： Content based retrieval

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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

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IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424406715;1424406714

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.

关键词： Neoplasms Image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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Ensemble Based Data Fusion from Parietal Region Event Related Potentials for Early Diagnosis of Alzheimer's Disease

Ensemble Based Data Fusion from Parietal Region Event Relate...

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International Joint Conference on Neural Networks (IJCNN)

作者： Brian A. Balut Matthew T. Karnick Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.

关键词： Alzheimer's disease Enterprise resource planning Hospitals Electrodes Senior citizens Biomarkers Protocols Delay Neural networks USA Councils

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Document Image Mosaicing with Mobile Phones

Document Image Mosaicing with Mobile Phones

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International Conference on Image Analysis and processing

作者： Jari Hannuksela Pekka Sangi Janne Heikkila Xu Liu David Doermann Machine Vision Group Infortech Oulu Department of Electrical and Information Engineering University of Oulu Finland Language and Media Processing Laboratory Institute for Advanced Computer Studies University of Maryland College Park USA

This paper presents a novel user interaction concept for document image scanning with mobile phones. A high resolution mosaic image is constructed in two main stages. Firstly, online camera motion estimation is applied to the phone to assist the user to capture small image patches of the document page. Automatic image stitching process with the help of estimated device motion is carried out to reconstruct the full view of the document. Experiments on document images captured and processed with mosaicing software clearly show the feasibility of the approach.

关键词： Mobile handsets Motion estimation Cameras Image resolution Mice Optical character recognition software Solid modeling Motion measurement Image motion analysis Machine vision

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A first-order mechanical device to model traumatized craniovascular biodynamics

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Journal of Medical Devices, Transactions of the ASME 2007年第1期1卷 89-95页

作者： Kohles, Sean S. Mangan, Ryan W. Stan, Edward McNames, James Kohles Bioengineering Portland OR 97214-5135 United States Department of Mechanical and Materials Engineering Portland State University Portland OR 97207-0751 United States Department of Electrical and Computer Engineering Portland State University Portland OR 97207-0751 United States Biomedical Signal Processing Laboratory Department of Electrical and Computer Engineering Portland State University Portland OR 97207-0751 United States Department of Surgery Oregon Health and Science University Portland OR 97239-3098 United States

Mathematical models currently exist that explore the physiology of normal and traumatized intracranial function. Mechanical models are used to assess harsh environments that may potentially cause head injuries. However, few mechanical models are designed to study the adaptive physiologic response to traumatic brain injury. We describe a first-order physical model designed and fabricated to elucidate the complex biomechanical factors associated with dynamic intracranial physiology. The uni-directional flow device can be used to study interactions between the cranium, brain tissue, cerebrospinal fluid, vasculature, blood, and the heart. Solid and fluid materials were selected to simulate key properties of the cranial system. Total constituent volumes (solid and fluid) and volumetric flow (650 ml/min) represent adult human physiology, and the lengths of the individual segments along the flow-path are in accord with Poiseuille's equation. The physical model includes a mechanism to simulate autoregulatory vessel dynamics. Intracranial pressures were measured at multiple locations throughout the model during simulations with and without post-injury brain tissue swelling. Two scenarios were modeled for both cases: Applications of vasodilation/constriction and changes in the head of bed position. Statistical results indicate that all independent variables had significant influence over fluid pressures measured throughout the model (p < 0.0001) including the vasoconstriction mechanism (p=0.0255). The physical model represents a first-order design realization that helps to establish a link between mathematical and mechanical models. Future designs will provide further insight into traumatic head injury and provide a framework for unifying the knowledge gained from mathematical models, injury mechanics, clinical observations, and the response to therapies. Copyright © 2007 by ASME.

关键词： Biomechanics

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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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