Techniques for mapping extended Kalman filters onto linear arrays of programmable cells designed for real-time applications are described. First, a general method for mapping a standard (nonsquare root) Kalman filter,...
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Techniques for mapping extended Kalman filters onto linear arrays of programmable cells designed for real-time applications are described. First, a general method for mapping a standard (nonsquare root) Kalman filter, where the columns of the covariance matrix are updated in parallel, is introduced. Next, a general method for mapping a factorized (square root) filter, where fast Givens rotations are used to triangularize the prematrix and where rotations of the rows of the prematrix are performed in parallel, is introduced. These mappings are used to implement an extended Kalman filter commonly used in target tracking applications on the Warp computer. The Warp is a commercially available linear array of 10 or more programmable cells connected to an MC68020-based workstation. The Warp implementation of the standard Kalman filter running on 8 Warp cells achieves a measured speedup of 7 over the same filter running on a single cell. The Warp implementation of the factorized filter running on 10 Warp cells achieves a measured speedup of 2.< >
A discussion is presented of obstacles on the path toward more sophisticated silicon compilers. An examination is also made of the gap between the ultimate compilers and those now coming into use and the uncertainty o...
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A discussion is presented of obstacles on the path toward more sophisticated silicon compilers. An examination is also made of the gap between the ultimate compilers and those now coming into use and the uncertainty over exactly what a silicon compiler is, how compilers work, the various architectural options, and the use of the expert-system approach to design.
An algorithm for computing the aspect graph for polyhedral objects is described. The aspects graph is a representation of three-dimensional objects by a set of two-dimensional views. The set of viewpoints on the Gauss...
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An algorithm for computing the aspect graph for polyhedral objects is described. The aspects graph is a representation of three-dimensional objects by a set of two-dimensional views. The set of viewpoints on the Gaussian sphere is partitioned into regions such that in each region the qualitative structure of the line drawing remains the same. At the boundaries between adjacent regions are the accidental viewpoints where the structure for the line drawing changes. It is shown that for polyhedral objects there are two fundamental visual events: (1) the projections of an edge and a vertex coincide; and (2) the projections of three nonadjacent edges intersect at a point. The geometry of the object is reflected in the locus of the accidental viewpoints. The algorithm computes the partition together with a representative view for each region of the partition.
A video decoder with an efficient block-level-pipeline controller scheme for MPEG-2 MP@ML is presented in this paper. The architecture in most of the reported literature for MPEG-2 MP@ML video uses a 64-bit bus and a ...
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A video decoder with an efficient block-level-pipeline controller scheme for MPEG-2 MP@ML is presented in this paper. The architecture in most of the reported literature for MPEG-2 MP@ML video uses a 64-bit bus and a complex bus arbitration scheme to communicate with external DRAM, display, and incoming FIFO. Our design imposes a certain order in the DRAM access by various processing units instead of allowing any processing unit within the decoder to request bus access arbitrarily. This efficient DRAM accessing order allows us to reduce bus width from 64 bits to 32 bits, without significantly increasing embedded buffer sizes, and still meet the requirements for MPEG-2 MP@ML real-time decoding. The bus arbitration algorithm is also simple, allowing for a less complex controller design.
作者:
MACINNES, CIBM CORP
DIV GEN TECHNOLE FISHKILL FACILHOPEWELL JUNCTIONNY 12533
In the analysis of large electrical circuits, encompassing thousands of nodes and circuit elements, sparse matrix techniques are essential to the efficient solution of the resulting sparse linear systems. The maintena...
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In the analysis of large electrical circuits, encompassing thousands of nodes and circuit elements, sparse matrix techniques are essential to the efficient solution of the resulting sparse linear systems. The maintenance of sparsity during Gaussian elimination is of paramount importance. In an attempt to preserve sparsity during Gaussian elimination, circuit analysis programs such as ASTAP [1] and AS/X [9] permute rows and columns of the circuit matrix using the Markowitz reordering technique [4];this is done prior to the execution of the analysisalgorithm. Since row and/or column pivoting during the analysis would tend to destroy the initial sparsity-preserving arrangement of rows and columns, this is not allowed at, possibly, the expense of solution accuracy. A technique, termed small pivot perturbation (SPP), is presented which can improved the accuracy of Gaussian elimination in those cases where small pivot magnitudes are encountered during the Gaussian elimination procedure, thus avoiding an inaccurate solution and allowing the analysis to continue. The SPP technique is applicable to either time-domain or frequency-domain analysis and is especially useful with circuit equation formulations where "perfect cancellation" can occur during Gaussian elimination, such as in some implementations of the modified nodal formulation [2], [3].
In this paper, some new qualitative properties of discrete-time neural networks based on the ''brain-state-in-a-box'' model are presented. These properties concern both the characterization of equilibr...
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In this paper, some new qualitative properties of discrete-time neural networks based on the ''brain-state-in-a-box'' model are presented. These properties concern both the characterization of equilibrium points and the global dynamical behavior, Next, the analysis results are used as guidelines in developing an efficient synthesis procedure for networks that function as associative memories, A constrained designalgorithm is presented that gives completely stable dynamical neural networks sharing some interesting features. It is guaranteed the absence of nonbinary stable equilibria, that is stable states with nonsaturated components, It is guaranteed that in close proximity (Hamming distance one) of the stored patterns there is no other binary equilibrium point, Moreover, the presented method allows one to optimize a design parameter that controls the size of the attraction basins of the stored vectors and the accuracy needed in a digital realization of the network, Some design examples and computer simulations are presented to demonstrate the effectiveness of the proposed method.
A list Viterbi decoding algorithm (LVA) produces a rank ordered list of the L globally best candidates after a trellis search. Here, we present two such algorithms, (i) a parallel LVA that simultaneously produces the ...
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A list Viterbi decoding algorithm (LVA) produces a rank ordered list of the L globally best candidates after a trellis search. Here, we present two such algorithms, (i) a parallel LVA that simultaneously produces the L best candidates and (ii) a serial LVA that iteratively produces the k(th) best candidate based on knowledge of the previously found k-1 best paths. The application of LVA to a concatenated communication system consisting of an inner convolutional code and an outer error detecting code is considered in detail. analysis as well as simulation results show that significant improvement in error performance is obtained when the inner decoder, which is conventionally based on the Viterbi algorithm (VA), is replaced by the LVA. An improvement of up to 3 dB is obtained for the additive white Gaussian noise (AWGN) channel due to an increase in the minimum Euclidean distance. Ever larger gains are obtained for the Rayleigh fading channel due to an increase in the time diversity. It is also shown that a 10% improvement in throughput is obtained along with significantly reduced probability of a decoding failure for a hybrid FEC/ARQ scheme with the inner code being a rate compatible punctured convolutional (RCPC) code.
This paper describes a new method of data reduction and classification in a multidimensional symptom space for diagnostic aid of congenital heart diseases. The algorithm developed here is to reduce interactively a mul...
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This paper describes a new method of data reduction and classification in a multidimensional symptom space for diagnostic aid of congenital heart diseases. The algorithm developed here is to reduce interactively a multidimensional symptom space to sectorial regions representing each disease in a semicircle using the modified constellation graph method. This method enables us to classify patients using the angle in the semicircle as a single classifying parameter with an accuracy of about 90%, that is, with little overlapping between disease sectors. Comparing this method with conventional factor analysis, we have found the former far more effective than the latter for disease region separation.
The applications of multivariate Edgeworth series and higher-order statistics to the discrete-time detection of a known constant signal in multivariate non-Gaussian noise are considered. A technique to derive suboptim...
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The applications of multivariate Edgeworth series and higher-order statistics to the discrete-time detection of a known constant signal in multivariate non-Gaussian noise are considered. A technique to derive suboptimum detectors from the Neyman-Pearson optimum and locally optimum detectors is described. A numerical algorithm based on knowledge of the noise cumulants is presented in order to analyze the finite-sample size performance of the suboptimum detectors. As an example, the performance of the detectors as compared with the linear detector in multivariate Gaussian-Gaussian mixture noise is presented via receiver operating characteristic curves. Numerical results indicate that the suboptimum detectors, when exploiting knowledge of the dependence structure of the noise, can have very good performance with respect to the linear detector.
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