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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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Data-Dependent Superimposed Training for Noncoherent Channels

Data-Dependent Superimposed Training for Noncoherent Channel...

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Asilomar Conference on signals, Systems & computers

作者： Jingnong Yang Douglas B. Williams John M. Cioffi Amicus Wireless Technology Sunnyvale CA USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Electrical Engineering University of Stanford Stanford CA USA

This paper focuses on noncoherent coding design for fast fading channels, where the receiver does not have reliable channel state information. Unitary space-time codes and training based schemes have been proposed historically. We propose a data-dependent superimposed training scheme to improve the performance of training based codes. The transmitter is equipped with multiple training sequences and dynamically selects a training sequence for each data sequence to minimize channel estimation error. The set of training sequences are optimized to minimize pairwise error probability between codewords.

关键词： Fading Transmitters Channel estimation Transmitting antennas Paper technology image coding Space time codes Decoding Additive white noise AWGN

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Single image Super-Resolution Based on Support Vector Regression

Single Image Super-Resolution Based on Support Vector Regres...

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International Joint Conference on Neural Networks (IJCNN)

作者： Dalong Li Steven Simske Russell M. Mersereau Digital Printing and Imaging Lab Hewlett Packard Laboratories Fort Collins CO USA Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR ... 详细信息

关键词： image resolution signal resolution Deconvolution Interpolation Low pass filters Discrete wavelet transforms PSNR High-resolution imaging Filtering Discrete cosine transforms

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Time-frequency analysis of heart rate variability for neonatal seizure detection

Eurasip Journal on Advances in Signal Processing

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Eurasip Journal on Advances in signal processing 2007年 2007卷

作者： Malarvili, M.B. Mesbah, Mostefa Boashash, Boualem Perinatal Research Centre School of Medicine University of Queensland Herston QLD 4029 Australia Signal Processing Research Center Department of Electrical and Computer Engineering College of Engineering P.O. Box 27272 Sharjah United Arab Emirates

There are a number of automatic techniques available for detecting epileptic seizures using solely electroencephalogram (EEG), which has been the primary diagnosis tool in newborns. The electrocardiogram (ECG) has been much neglected in automatic seizure detection. Changes in heart rate and ECG rhythm were previously linked to seizure in case of adult humans and animals. However, little is known about heart rate variability (HRV) changes in human neonate during seizure. In this paper, we assess the suitability of HRV as a tool for seizure detection in newborns. The features of HRV in the low-frequency band (LF: 0.03-0.07 Hz), mid-frequency band (MF: 0.07-0.15 Hz), and high-frequency band (HF: 0.15-0.6 Hz) have been obtained by means of the time-frequency distribution (TFD). Results of ongoing time-frequency (TF) research are presented. Based on our preliminary results, the first conditional moment of HRV which is the mean/central frequency in the LF band and the variance in the HF band can be used as a good feature to discriminate the newborn seizure from the nonseizure.

关键词： signal detection

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DCT Local Adaptive Filtering of images Corrupted by Fluctuative Noise with a Priori Unknown Statistical Properties

DCT Local Adaptive Filtering of Images Corrupted by Fluctuat...

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International Kharkov Symposium on Physics and engineering of Microwaves, Millimeter, and Submillimeter Waves

作者： Vladimir V. Lukin Dmitriy V. Fevralev Alexander A. Zelensky Oleksiy B. Pogrebnyak Alexander V. Kildishev Dept of Transmitters Receivers and Signal Processing Kharkov National Aerospace University KhAI Kharkiv Ukraine Instituto Politecnico Nacional Mexicali Mexico School of Electrical and Computer Engineering and Brick Nanotechnology Center Purdue University West Lafayette IN USA

A DCT based locally adaptive filter has been proposed and applied for processing of real and imaginary components of bispectrum for solving a problem of unknown signal shape (waveform) reconstruction. The bispectrum is a 2-D complex function and in its real and imaginary components a strongly non-stationary noise is present. Thus, the filter designed can be also effectively applied to processing different kind of images for which non-stationary noise with a priori unknown local statistical properties is present. The modifications of DCT based filters have been successfully used for removal of pure multiplicative, speckle and film-grain noise.

关键词： Discrete cosine transforms Adaptive filters Ultrasonic imaging Electronic mail Statistics Circuit noise Noise reduction Radar imaging Filtering Transmitters

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Control of nonlinear systems with a linear state-feedback controller and a modified neural network tuned by genetic algorithm

Control of nonlinear systems with a linear state-feedback co...

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Congress on Evolutionary Computation

作者： H.K. Lam S.H. Ling H.H.C. Iu C.W. Yeung F.H.F. Leung Division of Engineering King''s College London UK School of Electrical Electronic and Computer Engg University of Western Australia WA Australia Center for Signal Processing Department of Electronic and Information Engg Hong Kong Polytechnic University Hung Hom China

This paper presents the control of nonlinear systems with a neural network. In the proposed neural network, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. By using a genetic algorithm with arithmetic crossover and non-uniform mutation, the parameters of the proposed neural network can be tuned. Application examples are given to illustrate the merits of the proposed neural network.

关键词： Nonlinear control systems Control systems Nonlinear systems Neural networks Genetic algorithms Evolutionary computation

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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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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Conjugate Distributed Arithmetic Adaptive FIR Filters and their Hardware Implementation

Conjugate Distributed Arithmetic Adaptive FIR Filters and th...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Walter Huang Venkatesh Krishnan David V. Anderson Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Adaptive filtering constitutes an important class of DSP algorithms employed in several hand held mobile devices for applications such as echo cancellation, signal de-noising, and channel equalization. In this paper, a new hardware architecture using conjugate distributed arithmetic (CDA) which is suitable for high throughput hardware implementations of LMS adaptive filters is presented. Unlike a traditional distributed arithmetic (DA) implementation where all possible combination sums of the filter coefficients are stored in a look-up-table (LUT), in the CDA architecture, all possible combination sums of the input signal samples are stored in the LUT and updated at the arrival of every sample using an efficient update procedure. We describe the design of CDA adaptive filters and show that practical implementations of CDA adaptive filters have very high throughput relative to multiply and accumulate architectures. We also show that CDA adaptive filters have a potential area and power consumption advantage over DSP microprocessor architectures for a given throughput.

关键词： Arithmetic Finite impulse response filter Hardware Adaptive filters Throughput Digital signal processing Table lookup Filtering algorithms Echo cancellers signal denoising

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Iterative Detection of Linear Objects in GPR and Seismic images

Iterative Detection of Linear Objects in GPR and Seismic Ima...

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Sensor Array and Multichannel signal processing Workshop

作者： A.C. Gurbuz J.H. McClellan Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

An iterative technique for detecting linear features in 2D images based on the Radon transform is developed. The algorithm is suitable for processing ground penetrating radar (GPR) and seismic images to find underground pipes and tunnels. Detection is performed in the Radon transform space. The length and width of the linear object area are estimated and the corresponding portion of the image is removed at each iteration. The algorithm has the advantage of detecting very weak linear objects, which are barely detected by existing detection algorithms, in Radon transform space. The proposed algorithm is tested on both simulated and experimental data measured from a laboratory scale model area. Results show successful detection of strong and weak linear objects

关键词： Object detection Ground penetrating radar Radar detection Space technology Iterative algorithms Detection algorithms Testing Laboratories signal processing image processing

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