The percolation of a broadcast in a multihop radio network modeled by a spatial Poisson process is studied. The effect of station density and transmission radius on the extent of broadcast percolation is examined. For...
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The percolation of a broadcast in a multihop radio network modeled by a spatial Poisson process is studied. The effect of station density and transmission radius on the extent of broadcast percolation is examined. For broadcast percolation in one spatial dimension, analytical expressions for the average extent of percolation are derived. A model for two-dimensional spatial percolation is presented along with related simulation results. The results suggest that in optimizing transmission radius as a function of communication performance measures, the choice of radius may be bounded from below by the need to maintain a desired network connectivity. The connectivity constraint can be relaxed to some extent in certain non-Poisson spatial models.< >
A serial interfacing scheme in which several embedded memories share the built-in, self-test circuit is presented. For external testing, this approach requires only two serial pins for access to the data path. There i...
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A serial interfacing scheme in which several embedded memories share the built-in, self-test circuit is presented. For external testing, this approach requires only two serial pins for access to the data path. There is considerable savings in routing area, and fewer external pins are needed to test random-access memories with wide words, such as those in application-specific integrated circuits for telecommunications. Even though the method uses serial access to the memory, a test pattern is applied every clock cycle because the memory itself shifts the test data. The method has been adapted to four common algorithms. In implementations of built-in self-test circuitry on several product chips, the area overhead was found to be acceptable.
Conventional gradient-type adaptive filters use the fixed convergence factor\\muwhich is normally chosen to be the same for all the filter parameters. In this paper, we propose to use individual convergence factors wh...
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Conventional gradient-type adaptive filters use the fixed convergence factor\\muwhich is normally chosen to be the same for all the filter parameters. In this paper, we propose to use individual convergence factors which are optimally tailored to adapt individual filter parameters. Furthermore, we propose to adjust the individual convergence factors in real time so that their values are kept optimum for a new set of input variables. We call this approach "individual" adaptation as opposed to the conventional fixed "group" adaptation using the same fixed\\mufor all the filter parameters. Computer simulation results show that the individual adaptation approach yields much better filters than the conventional fixed group adaptation approach. Optimization of individual time-varying convergence factors leads to a constraint which may be satisfied by several different algorithms. We propose and investigate here two algorithms satisfying the above constraint: individual adaptation (IA) and homogeneous adaptation (HA). The HA algorithm turns out to have the general form as some well known gradient algorithms that normalize the step size which were previously obtained either intuitively or using involved derivations. Both IA and HA are shown to provide much better performance than the conventional "group" adaptation. However, for several simulations, IA provides better performance than HA, at the expense of increased computation.
The analysis of residual harmonics requires combining linear and nonlinear analysis into a computer algorithm. The mathematical techniques are to apply state space methods to solve the nonlinear loads such as converte...
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The analysis of residual harmonics requires combining linear and nonlinear analysis into a computer algorithm. The mathematical techniques are to apply state space methods to solve the nonlinear loads such as converters in a time domain format, analyze the resulting waveform with a Fourier analysis to convert the data to the frequency domain, and then solve the linear portion of the circuit with a linear algebra method, preferably utilizing sparsity techniques to speed the solution. The key model in a power system is that for the three-phase transformer. It is of consequence to note that the phase angle of any harmonic is a function both of the fundamental and of the particular harmonic.
This paper is the second of a two-part study summarizing the results of a research program on the survivability analysis of command and control communication (C^{3}) systems. In the first part, we consider the formula...
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This paper is the second of a two-part study summarizing the results of a research program on the survivability analysis of command and control communication (C^analysis) systems. In the first part, we consider the formulation of realistic survivability models and criteria and the fundamental physical problems affecting survivability analysis. In this paper, we first discuss algorithms for deterministic analysis of C 3 systems to identify bottlenecks and more vulnerable elements in network structure. The problems encountered in probabilistic analysis techniques are then discussed. Finally, an efficient, practical probabilistic survivability analysis procedure is given and an example of its use is presented.
The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications, The segmentation algorithm, Iterative Mutually Optimum Region Merging...
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The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications, The segmentation algorithm, Iterative Mutually Optimum Region Merging (IMORM), is presented and used to partition images into elements that are thereafter classified by Linear Canonical Discriminant analysis and a Maximum Likelihood allocation rule. This per-segment approach results in much higher accuracy than the conventional per-pixel approach. Furthermore. separability matrices indicate that many land cover categories cannot be correctly defined by per-pixel statistics.
Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives, to facilitate movement identification, modeling, and learn...
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Movement primitive segmentation enables long sequences of human movement observation data to be segmented into smaller components, termed movement primitives, to facilitate movement identification, modeling, and learning. It has been applied to exercise monitoring, gesture recognition, humanmachine interaction, and robot imitation learning. This paper proposes a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application-specific requirements, algorithm mechanics, and validation techniques. The framework is applied to human motion segmentation methods by grouping them into online, semionline, and offline approaches. Among the online approaches, distance-based methods provide the best performance, while stochastic dynamic models work best in the semionline and offline settings. However, most algorithms to date are tested with small datasets, and algorithm generalization across participants and to movement changes remains largely untested.
A lossless data compression and decompression (LDCD) algorithm based on the notion of textual substitution has been implemented in silicon using a linear systolic array architecture. This algorithm employs a model whe...
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A lossless data compression and decompression (LDCD) algorithm based on the notion of textual substitution has been implemented in silicon using a linear systolic array architecture. This algorithm employs a model where the encoder and decoder each have a finite amount of memory which is referred to as the dictionary. Compression is achieved by finding matches between the dictionary and the input data stream whereby a substitution is made in the data stream by an index referencing the corresponding dictionary entry. The LDCD system is built using 30 application-specific integrated circuits (ASIC's) each containing 126 identical processing elements (PE's) which perform both the encoding and decoding function at clock rates up to 20 MHz.
An algorithm for segmenting images of 3-D scenes is presented. From an input color image, the algorithm determines the number of materials in the scene and labels each pixel according to the corresponding material. Th...
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An algorithm for segmenting images of 3-D scenes is presented. From an input color image, the algorithm determines the number of materials in the scene and labels each pixel according to the corresponding material. This segmentation is useful for many visual tasks including 3-D inspection and 3-D object recognition. The segmentation algorithm is based on a detailed analysis of the physics underlying color image formation and may be applied to images of a wide range of materials and surface textures. An initial edge detection on the intensity image is used to guide the segmentation process and to ensure accurate localization of region boundaries. The algorithm is based on the computation of local image features and can be mapped effectively onto high performance parallel hardware. Issues related to illumination and sensors are addressed. Experimental results obtained for several images are presented.
Five programs for efficient computation of DFT of real-valued data are analyzed with respect to their operation counts vis-a-vis run times on weak and powerful floating-point processors. The results help dispose of th...
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Five programs for efficient computation of DFT of real-valued data are analyzed with respect to their operation counts vis-a-vis run times on weak and powerful floating-point processors. The results help dispose of the claims of superiority of FHT over corresponding real-valued FFT. The Bruun algorithm turns out to be a ''best'' performer.
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