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Electronic Systems and Signals Research Laboratory

J. A. O'Sullivan Research Projects

Joseph A. O'Sullivan Research Projects

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Signal Processing for Advanced Storage Media

Data storage systems have relied primarily on designs based on storing data on tracks. On magnetic media, input data are encoded and stored as flux reversals on tracks, with decoding being based on standard algorithms such as the Viterbi algorithm. As data densities increase, fundamental limits for recording on tracks are approached, and alternative data storage technologies must be considered. We describe several approaches for the design of encoding and decoding for two-dimensional data storage systems that have two-dimensional (2D) intersymbol interference. This interference invalidates assumptions in the Viterbi and related algorithms necessitating novel decoding strategies. Our approach to the problem is two-fold: (1) to use existing equalization methods and combine them with error-correction coding to enhance system performance, (2) perform joint equalization and decoding using message-passing algorithms. Simulations demonstrate the potential for such systems. (more)

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CT Imaging in the Presence of Foreign Metal Bodies

Standard image reconstruction techniques for X-ray computed tomography (CT) often do not yield clinically useful images in the presence of highly attenuating materials. The observed image degradation is caused by phenomena such as aliasing, noise, scatter, and beam hardening, among others. In our work, we adopt a stochastic model for the measured data that accounts for the above phenomena. Images are reconstructed by maximizing the data loglikelihood using an alternating minimization algorithm we developed. We reported that our method successfully reduces image artifacts in several experiments on simulated data when compared to the standard technique of filtered backprojection (FBP). Our present research focus is on efficient and fast implementation, processing of real data collected by clinical scanners, as well as extending our method to volume (3-D) reconstruction. (more)
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Information Theory: Alternating Minimization Algorithms
Information Theory: Information Value Decomposition
Information Theory: Information Hiding
Information-Theoretic Imaging
Automatic Target Recognition for Synthetic Aperture Radar Data
Image Science: Spiral CT Imaging
Imaging Science: Performance Bounds
Imaging Science: Hyperspectral Imaging
Magnetic Recording

Professor O'Sullivan's research interests include information theory, estimation theory, and imaging science, with applications in object recognition, tomographic imaging, magnetic recording, radar, and formal languages. Current research projects include: modeling and performance analysis of target recognition, orientation estimation, and tracking using high resolution radar data; spiral CT imaging in the presence of known high density attenuators; physics-based capacity bounds for magnetic media; derivation and analysis of alternating minimization algorithms; information-theoretic analysis of steganography; and systems integration issues in magnetic information systems.

Information Theory: Alternating Minimization Algorithms.

Much research over the past several years has been devoted to a characterization of several types of alternating minimization algorithms. Several alternating minimization algorithms have been presented using a unified treatment, including expectation-maximization algorithms, Blahut-Arimoto algorithms, Blahut's rate-distrotion algorithm, and generalized iterative scaling. The unified view of these algorithms has led to new algorithms that are useful in spiral CT imaging in the presence of high density attenuators, hyperspectral imaging, and in the general derivation and computation of the information value decomposition. In addition, these ideas are being applied to iterative decoding algorithms for maximum likelihood sequence estimation in communications problems.
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Information Theory: Information Value Decomposition.

A new paradigm for the analysis of positive matrices and multidimensional positive-valued functions is being explored. An algorithm has been derived and called the information value decomposition. One conference submission has resulted, with papers expected shortly. The algorithm approximates an arbitrary positive-valued matrix by a lower rank-positive valued matrix. The lower rank approximation is closest to the original matrix in the sense that it minimizes the I-divergence between the original matrix and the approximation. It has been applied to hyperspectral imaging with some success, with plans for continued research.
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Information Theory: Information Hiding.

There have been several methods proposed in the literature to hide information within host data sets. Some of the work goes under the names steganography, (digital) watermarking, fingerprinting, and traitor tracing. Prof. O'Sullivan and Pierre Moulin have presented a new way to analyze these systems in a common framework and has derived fundamental bounds on the performance of such systems. Each system is characterized in communications terms as being designed to send information to an ultimate arbiter, with side information in the form of a key. The rates achievable in this communication have been characterized in several important cases.
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Information-Theoretic Imaging.

There is a growing recognition in the research community of the role of information theory in imaging problems. Several activities within the information theory community and within the imaging and automatic target recognition communities exemplify this. These include the establishment of the Center for Imaging Science, a paper within the special issue of the IEEE Transactions on Information Theory commemorating fifty years of information theory, an Information Theory Workshop on Detection, Estimation, Classification, and Imaging, and a special issue of the IEEE Transactions on Information Theory on Information-Theoretic Imaging. Prof. O'Sullivan has played a role in each of these activities and plans to continue to work on developing and applying new ideas in this area.
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Imaging Science: Automatic Target Recognition for Synthetic Aperture Radar Data.

Comprehensive computational and analytical studies of ATR algorithms for SAR data are being pursued. A new class of ATR algorithms based on conditionally Gaussian models has been derived. This new class of algorithms is being compared to exisiting algorithms over a wide range of parameters to yeild performance-complexity tradeoffs. For essentially all operating conditions studied to date, the new algorithms perform achieve better performance for each complexity.
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Imaging Science: Spiral CT Imaging.

Prof. O'Sullivan is a member of CT Visualization and Quantification Team, working with B. Whiting and J. Blaine from the Electronic Radiology Laboratory at the Washington University School of Medicine, with J. Williamson from Radiation Oncology, and D. L. Snyder from ESSRL. The team is developing a set of code to simulate CT data and to implement new and known image reconstruction algorithms, in support of multiple clinical efforts. In radiation oncology, the goal is to estimate the position and orientation of a known high density attenuator and to estimate the attenuation function in the vicinity of the known attenuator. In this case, the attenuator is a radiation brachytherapy applicator. The theory is applicable to arbitrary known objects.
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Imaging Science: Performance Bounds.

The performance os ATR systems is being analyzed and bounded using several techniques. Working with graduate student N. A. Schmid, the performance degradation from using estimated parameters in ATR systems is by quantified in both the limit as the number of training samples is large and in the limit as the number is small. Improved ATR algorithms are being derived based on the peaking phenomenon in classification problems.
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Imaging Science: Hyperspectral Imaging.

Prof. O'Sullivan has been working with Profs. D. R. Fuhrmann, D. L. Snyder, and W. H. Smith to put together a hyperspectral imaging team. Papers have appeared over the past year that represent the initial results from the collaboration. The goals of the research efforts include the characterization of hyperspectral imaging sensor data, the derivation of algorithms and performance bounds for estimating components in the models (including endmembers), and applications to automatic target recognition, topographic engineering, and earth science.
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Magnetic Recording.

Prof. O'Sullivan has a long-standing collaboration with R. S. Indeck and M. W. Muller in magnetic recording. The work has progressed from the micromagnetic modeling of magnetic media through the derivation of capacity bounds for magnetic media to the design of systems based on parameters that capture medium noise characteristics. Systems integration issues are beign pursued that included further studies related to capacity bounds for magnetic recording systems.
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J. A. O'Sullivan Home Page

Edited June 11, 2004

Washington University in St. Louis     School of Engineering     Deptartment of Electrical and Systems Engineering