Incomplete LU factorization is a valuable preconditioning approach for sparse iterative solvers. An "ideal" but inefficient preconditioner for the iterative solution of Ax = b is A-1 itself. This paper descr...
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Incomplete LU factorization is a valuable preconditioning approach for sparse iterative solvers. An "ideal" but inefficient preconditioner for the iterative solution of Ax = b is A-1 itself. This paper describes a preconditioner based on sparse approximations to partitioned representations of A-1, in addition to the results of implementation of the proposed method in a shared memory parallel environment. The partitioned inverses are normally somewhat sparse. Their sparsity can be enhanced with suitable ordering and partitioning algorithms. Sparse approximations to these partitioned inverse representations can be obtained either by discarding selected nonzero entries of these inverses or by precluding the creation of some inversion fills. Experimental results indicate that the use of these partitioned incomplete inverses as preconditioners results in excellent highly parallel preconditioners.
Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge dete...
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Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.
Distributed Processing Models (DPM) such as the Grid, cluster computing and parallel processors are now more available, and affordable, to simple users. This availability permits the execution of extremely CPU demandi...
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Distributed Processing Models (DPM) such as the Grid, cluster computing and parallel processors are now more available, and affordable, to simple users. This availability permits the execution of extremely CPU demanding applications. Digital Signal Processing (DSP) is an area that benefits from such advances. DSP is used in a wide area of commercial and scientific applications such as computer graphics, simulation, media copyright protection and image processing. DPM for DSP is technically and economically viable. Promising applications that will be explored in this work include coding, rendering, copyright protection that is, highly demanding applications in terms of CPU load. Specific cases of heavy load mathematical transformations for large media objects are discussed. A proposal for incorporating the watermarking procedure in the Grid architecture is also briefly presented.
This paper presents an examination of the optical response of LED in applied electrical signals. LED's optical response is important to examine in order to use as cheap solution for electrical-to-optical conversio...
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This paper presents an examination of the optical response of LED in applied electrical signals. LED's optical response is important to examine in order to use as cheap solution for electrical-to-optical conversion in moderate and short distances optical communication networks. The evaluation parameter of the response is the optical-to-electrical current ratio T(ω). This ratio represents the possible maximum bit rate for data transmission using LED.
In this paper is presented a channel management scheme that gives priority to Low Moving Terminals in Tetra Networks. The proposed scheme is based on a two-layer cellular architecture. The lower layer is based on a mi...
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In this paper is presented a channel management scheme that gives priority to Low Moving Terminals in Tetra Networks. The proposed scheme is based on a two-layer cellular architecture. The lower layer is based on a microcellular solution, for absorbing the traffic loads of both the handoff and new calls of the Low Moving Terminals (LMT). The higher layer is based on a macro-cell umbrella solution, for absorbing the traffic load of the handoff and new calls of the Fast Moving Terminals (FMT). The simulation results show that assigning the optimum number of channels in each layer, the QoS of the LMT is optimized having the minimum negative effect on the QoS of the HMT.
This paper reports on the evaluation of automatic prosodic phrase break assignment. We utilize two tree-structured predictors, the commonly used CART and a C4.5, to predict break placement from sequences of easily to ...
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This paper reports on the evaluation of automatic prosodic phrase break assignment. We utilize two tree-structured predictors, the commonly used CART and a C4.5, to predict break placement from sequences of easily to extract shallow textual features. We are experimenting with two 500-utterance prosodic corpora developed by two Greek universities that originate from different domains in order to focus on the differences in prediction between generic and limited domain datasets. The evaluation shows that while the limited dataset achieves better accuracy than the generic one in the CART case, this difference is lowered with the introduction of C4.5. Minor breaks proved to be the most difficult class to predict in CART case, while we achieved a 50% improvement with C4.5.
We consider the NP-complete problem of deciding whether an input graph on n vertices has k vertex-disjoint copies of a fixed graph H. For H = K3 (the triangle) we give an O(22klogk+1.869k n2) algorithm, and for genera...
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Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance ed...
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ISBN:
(纸本)1604238216
Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.
Several widely used methods have been proposed for fusing high resolution panchromatic data and lower resolution multi-channel data. However, many of these methods fail to maintain spectral consistency of the fused hi...
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