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IEEE Princeton Section Sarnoff Symposium

作者： Shuangquan Wang K. Raghukumar A. Abdi J. Wallace M. Jensen New Jersey Center for Wireless Telecommunications and Center for Communications and Signal Processing Research Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Marine Physical Laboratory Scripps Institution of Oceanography University of California San Diego San Diego CA USA Department of Electrical and Computer Engineering Brigham Young University Provo UT USA

Accurate modeling of indoor multiple-input multiple-output (MIMO) channels is an important prerequisite for multi-antenna system design. In this paper, a new model for indoor MIMO channels is proposed, and a closed-form expression for the spatio-temporal cross-correlation function between any two subchannels is derived. This new analytical correlation expression includes many key physical parameters of interest such as mean angle-of-departure at the transmitter and mean angle-of-arrival at the receiver, the associated angle spreads, the distance between transmitter and receiver, etc. in a compact form. Comparison of this model with channel correlations and capacity, using the collected indoor MIMO data, exhibits the utility of the model.

关键词： MIMO Mathematical model Scattering Radio transmitters Underwater communication signal processing Marine technology Laboratories Closed-form solution Channel capacity

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Power spectral density estimation and tracking nonstationary pressure signals based on Kalman filtering

Power spectral density estimation and tracking nonstationary...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： M. Aboy J. McNames O.W. Marquez R. Hornero T. Thong B. Goldstein Signal Theory and Communications Dept ETSI-Telecomunicacion University of Vigo Spain Biomedical Signal Processing Laboratory Electrical and Computer Engineering Portland State University Portland USA Signal Theory and Communications Department ETSI-Telecomunicación University of Valladolid Spain Biomedical Engineering Department Oregon Health and Sciences University Portland USA Pediatrics Department Oregon Health and Sciences University Portland USA

We describe an algorithm to estimate and track slow changes in power spectral density (PSD) of nonstationary pressure signals. The algorithm is based on a Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary pressure signals than classical nonparametric methodologies, and does not assume a piecewise stationary model of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation and tracking of short records of simulated pressure waveforms. This algorithm is intended for applications were the PSD must be estimated and tracked during short transient periods, possibly after clinical interventions.

关键词： Kalman filters signal resolution signal processing Frequency estimation signal processing algorithms signal analysis Filtering Biomedical engineering Autocorrelation Transient analysis

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Multiresolution automatic segmentation of T1-weighted brain MR images

Multiresolution automatic segmentation of T1-weighted brain ...

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IEEE International Symposium on Biomedical Imaging

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

Automatic segmentation of brain tissues is crucial to many medical imaging applications. We use a multi-resolution analysis and a power transform to extend the well-known Gaussian mixture model expectation maximization based algorithm for segmentation of white matter, gray matter, and cerebrospinal fluid from T1-weighted magnetic resonance images (MRI) of the brain. Experimental results with near 4000 synthetic and real images are included. The results illustrate that the proposed method outperforms six existing methods.

关键词： Image resolution Image segmentation Biomedical imaging Multiresolution analysis Image analysis Magnetic analysis Algorithm design and analysis Brain modeling Magnetic liquids Magnetic resonance

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MRSI brain tumor characterization using wavelet and wavelet packets feature spaces and artificial neural networks

MRSI brain tumor characterization using wavelet and wavelet ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Yazdan-Shahmorad H. Soltanian-Zadeh R.A. Zoroofi Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran Radiology Department Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive technique for assessing biochemical fingerprint of tissue composition. The need to differentiate between normal and abnormal tissues and determine type of abnormality before biopsy or surgery motivated development and application of MRSI. There are several technical reasons that make the brain easier than other organs to be examined with MRSI. This work presents our proposed methods and results for the analysis of the brain spectra of patients with three tumor types (malignant glioma, astrocytoma, and oligodendroglioma). After extracting features from MRSI data using wavelet and wavelet packets, we use artificial neural networks to determine the abnormal spectra and the type of abnormality. We evaluated the proposed methods using clinical and simulated MRSI data and biopsy results. The MRSI analysis results were correct 97% of the time when classifying the spectra of the clinical MRSI data into normal tissue, tumor, and radiation necrosis. They were correct 72% and 83% of the time when determining tumor types using the clinical and simulated MRSI data, respectively.

关键词： Wavelet packets Neoplasms Artificial neural networks Biopsy Magnetic resonance Spectroscopy Magnetic resonance imaging Fingerprint recognition Surgery Cancer

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Corrections to “Design of Two-Dimensional Recursive Filters Using Genetic Algorithms”

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IEEE Transactions on Circuits and Systems I: Regular Papers 2004年第12期51卷 2516-2516页

作者： N.E. Mastorakis I.F. Gonos M.N.S. Swamy Department of Computer Science Hellenic Naval Academy Military Institutions of University Education Piraeus Greece High Voltage Laboratory of Athens 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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A guard interval based frequency offset compensation scheme for MC-CDMA up-link 3

A guard interval based frequency offset compensation scheme ...

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3rd IEEE International Symposium on signal processing and Information Technology, ISSPIT 2003

作者： Mashury Ali, H. Communications and Signal Processing Group Department of Electrical and Computer Engineering Curtin University of Technology GPO Box U1987 PerthWA6845 Australia

ISBN: (纸本)0780382927

In this paper, a new frequency offset compensation scheme for up link of multicarrier code-division multiple access (MC-CDMA) systems is proposed. The proposed scheme exploits guard interval (GI) information embedded in MC-CDMA symbol blocks and is an extension of the work in F. Daffara and A. Chouly (May 1993). Simulation results show that the new scheme results in improved performance in MC-CDMA uplink. © 2003 IEEE.

关键词： Wireless sensor networks

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signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing

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Journal of VLSI signal processing systems for signal, image and video technology 2003年第3期35卷 229-243页

作者： Andrade, Lucio Manolakos, Elias S. Electrical and Computer Engineering Department Communications and Digital Signal Processing (CDSP) Center for Research and Graduate Studies Northeastern University Boston USA

Accurate identification of a DNA sequence depends on the ability to precisely track the time varying signal baseline in all parts of the electrophoretic trace. We propose a statistical learning formulation of the signal background estimation problem that can be solved using an Expectation-Maximization type algorithm. We also present an alternative method for estimating the background level of a signal in small size windows based on a recursive histogram computation. Both background estimation algorithms introduced here can be combined with regression methods in order to track slow and fast baseline changes occurring in different regions of a DNA chromatogram. Accurate baseline tracking improves cluster separation and thus contributes to the reduction in classification errors when the Bayesian EM (BEM) base-calling system, developed in our group (Pereira et al., Discrete Applied Mathematics, 2000), is employed to decide how many bases are “hidden” in every base-call event pattern extracted from the chromatogram.

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A continuous tracking algorithm for long-term memory motion estimation [video coding]

A continuous tracking algorithm for long-term memory motion ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： C. Duanmu M.O. Ahmad M.N.S. Swamy Center for Signal Processing and Communications Department of Electrical & Computer Engineering Concordia University Montreal QUE Canada

It is known that long-term memory motion estimation can bring a significant coding gain to a video coding system. In this paper, a fast algorithm for long-term memory motion estimation is presented. The proposed algorithm attempts to track the direction of the best long-term motion vector in a frame-by-frame manner from the most recent reference frame to the oldest reference frame. Nine direction patterns for the direction tracking are used and the search locations of the long-term memory motion estimation are adaptively selected according to the chosen direction pattern. Simulation results demonstrate that the proposed algorithms can speed up long-term motion estimation by more than 40 times.

关键词： Tracking Motion estimation Video coding signal processing algorithms Video sequences Video signal processing MPEG 4 Standard Discrete cosine transforms

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8-bit partial sums of 16 luminance values for fast block motion estimation

8-bit partial sums of 16 luminance values for fast block mot...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： C.J. Duanmu M.O. Ahmad M.N.S. Swamy Center for Signal Processing and Communications Department of Electrical & Computer Engineering Concordia University Montreal QUE Canada

Fast block motion estimation algorithms are needed for real-time implementations of video coding standards due to the high computational complexity of the full-search algorithm for block motion estimation. In this paper, an algorithm using 8-bit partial sums of 16 luminance values for a fast block motion estimation is proposed. The technique of using the partial sums is employed to reduce the computational complexity of not only the full-search algorithm but also some of the fast block motion estimation algorithms while maintaining their accuracy. Furthermore, it is shown that the byte-type data-parallelism on an SIMD architecture can be utilized to access and process these partial sums concurrently to accelerate the process of motion estimation. Simulation results are presented to demonstrate that the use of the partial sums can accelerate the execution of the full-search, three-step search, and four-step search algorithms on an SIMD architecture significantly.

关键词： Motion estimation signal processing algorithms Video coding Computational complexity Acceleration computer architecture Video signal processing Communication standards MPEG 4 Standard Video sequences

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A fast three-step search algorithm by the utilization of multilevel vector partial sums

A fast three-step search algorithm by the utilization of mul...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： C.J. Duanmu M.O. Ahmad M.N.S. Swamy Center for Signal Processing and Communications Department of Electrical & Computer Engineering Concordia University Montreal QUE Canada

Due to the high computational requirement of the full-search algorithm for block motion estimation, fast block motion estimation algorithms are needed for real-time implementations of the video coding standards. Recently, a three-step search algorithm for block motion estimation has been proposed in the literature. In this paper, a fast three-step search algorithm is proposed to further reduce the computational complexity of the three-step search algorithm with no loss of accuracy. By using a multilevel vector partial sums and lower bounds in the proposed algorithm, a large number of possible candidate motion vectors are discarded while still retaining the optimal motion vector of the three-step search algorithm. It is shown that not all the levels of partial sums and lower bounds are needed. A method to select these vector partial sums and lower bounds are also presented. Simulations of the proposed algorithm are carried out for various benchmark video sequences and the results demonstrate that the new algorithm can reduce the computational complexity of the three-step search algorithm by 20 to 60 percent with no loss of accuracy.

关键词： Motion estimation signal processing algorithms Video coding Computational complexity Decoding Video sequences Entropy Encoding Video codecs Video signal processing

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