A reconfiguration is developed for 2-dimensional mesh architectures and applied to a fault T tolerant cellular architecture. The reconfiguration is accomplished by adding communications paths to each cell which can be enabled by means of transistor switches controlled by decoding the contents of a register containing the relative position of faulty cells. This enables faulty cells to be bypassed and operations of cells in the same row east of the faulty cell to be shifted one cell to the east and a spare cell included in the active pattern. A modified s-value algorithm is also developed which enables a cell to determine the size of a square pattern that may be centered on that cell. / Master of Science

Sensor arrays are used in many applications where their ability to localize signal sources is essential. For many applications, it is necessary to estimate the direction-of-arrival (DOA) of target sources. Although there are many DOA estimation methods available, most of them are valid only for narrowband signals where time delay can be approximated as a phase shift. This thesis focuses on DOA estimation algorithms for wideband sources. Specifically, this thesis proposes the pruned fast beamformer which can reduce the number of computations of Delay-and-Sum (DS) beamforming by using a multi-resolution structure. For high resolution methods, signal subspace methods are required. Most of the subspace techniques for wideband signals decompose the received wideband signals into several bands of narrowband signals through bandpass filtering. Then, there are two different ways of processing decomposed signals. The incoherent methods process each band independently by a given narrowband method and average the results. The coherent methods attempt to modulate the signals in each band so that they can be combined coherently. In this thesis, a new DOA estimator, which is called TOPS, is developed to avoid disadvantages of both the incoherent and the coherent methods. The new method which can be categorized as a non-coherent method is tested and compared with other methods. It exhibits many desirable features for a number of applications where the sources are wideband such as acoustic direction finding.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

Graduate

Thesis (Ph. D.)--Ohio University, June, 1990. / Title from PDF t.p.

Thesis (Ph. D.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 18, 2006) Vita. Includes bibliographical references.

Thesis (M.S. in Biomedical Engineering)--Vanderbilt University, Aug. 2007. / Title from title screen. Includes bibliographical references.

by Anthony J. Weiss, Alan S. Willsky and Bernard C. Levy. / Cover title. / Includes bibliographical references. / Supported by a Rothschild Fellowship. Supported in part by the National Science Foundation. ECS-8312921 Supported in part by the Army Research Office. DAAG-84-K-0005 DAAL 03-86-K-0171

Eigenstructure based techniques have been studied extensively in the last decade to estimate the number and locations of incoming radiating sources using a passive sensor array. One of the early limitations was the computational load involved in arriving at the eigendecompositions. The introduction of VLSI circuits and parallel processors however, has reduced the cost of computation A tremendously. As a consequence, we study eigendecomposition algorithms with highly parallel and A localized data flow, in order to take advantage of VLSI capabilities. This dissertation presents a fast Recursive/Iterative Toeplitz (Hermitian) Eigenspace (RITE) algorithm, and its extension to the generalized strongly regular eigendecomposition situation (C-RITE). Both procedures exhibit highly parallel structures, and their applicability to fast passive array processing is emphasized. The algorithms compute recursively in increasing order, the complete (generalized) eigendecompositions of the successive subproblems contained in the maximum size one. At each order, a number of independent, structurally identical, non-linear problems is solved in parallel. The (generalized) eigenvalues are found by quadratically convergent iterative search techniques. Two different search methods, a restricted Newton approach and a rational approximation based technique are considered. The eigenvectors are found by solving Toeplitz systems efficiently. The multiple minimum (generalized) eigenvalue case and the case of a cluster of small (generalized) eigenvalues are treated also. Eigenpair residual norms and orthonormality norms in comparison with IMSL library routines, indicate good performance and stability behavior for increasing dimensions for both the RITE and C-RITE algorithms. Application of the procedures to the Direction Of Arrival (DOA) identification problem, using the MUSIC algorithm, is presented. The order-recursive properties of RITE and C-RITE permit estimation of angles for all intermediate orders imbedded in the original problem, facilitating the earliest possible estimation of the number and location of radiating sources. The detection algorithm based on RITE or C-RITE can then stop, thereby minimizing the overall computational load to that corresponding to the smallest order for which angle of arrival estimation is indicated to be reliable. Some extensions of the RITE procedure to Hermitian (non-Toeplitz) matrices are presented. This corresponds in the array processing context to correlation matrices estimated from non-linear arrays or incoming signals with non-stationary characteristics. A first—order perturbation approach and two Subspace Iteration (SI) methods are investigated. The RITE decomposition of the Toeplitzsized (diagonally averaged) matrix is used as a starting point. Results show that the SI based techniques lead to good approximation of the eigen-information, with the rate of convergence depending upon the SNR ar1d the angle difference between incoming sources, the convergence being faster than starting the SI method from an arbitrary initial matrix. / Ph. D.

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