In this paper, we consider the problems of co-biconnectivity and strong co-connectivity, i.e., computing the biconnected components and the strongly connected components of the complement of a given graph. We describe...
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In this paper, we consider the problems of co-biconnectivity and strong co-connectivity, i.e., computing the biconnected components and the strongly connected components of the complement of a given graph. We describe simple sequential algorithms for these problems, which work on the input graph and not on its complement, and which for a graph on n vertices and m edges both run in optimal O(n + in) time. Our algorithms are not data structure-based and they employ neither breadth-first-search nor depth-first-search. Unlike previous linear co-biconnectivity and strong co-connectivity sequential algorithms, both algorithms admit efficient parallelization. The co-biconnectivity algorithm can be parallelized resulting in an optimal parallel algorithm that runs in O(log(2) n) time using O((n + m)/log(2)- n) processors. The strong co-connectivity algorithm can also be parallelized to yield an O(log(2) n)-time and O(m(1.188)/ log n)-processor solution. As a byproduct, we obtain a simple optimal O(log n)-time parallel co-connectivity algorithm. Our results show that, in a parallel process environment, the problems of computing the biconnected components and the strongly connected components can be solved with better time-processor complexity on the complement of a graph rather than on the graph itself. (c) 2007 Elsevier B.V. All rights reserved.
Deep learning (DL) computation offloading is commonly adopted to enable the use of computation-intensive DL techniques on resource-constrained devices. However, sending private user data to an external server raises a...
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An attempt has been made to model a production engine control module. Both extended Kalman filter (EKF) algorithm and iterated extended Kalman filter (IEKF) algorithm are used in the construction of the model. The res...
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ISBN:
(纸本)0769528759
An attempt has been made to model a production engine control module. Both extended Kalman filter (EKF) algorithm and iterated extended Kalman filter (IEKF) algorithm are used in the construction of the model. The results shows the model trained by both algorithms can produce accurate results with RMS errors in a range of 2-3%, while iterated extended Kalman filter algorithm outperforms the extended Kalman filter algorithm.
Factorisation of probability trees is a useful tool for inference in Bayesian networks. Prob abilistic potentials some of whose parts are proportional can be decomposed as a product of smaller trees. Some algorithms, ...
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ISBN:
(纸本)8086742148
Factorisation of probability trees is a useful tool for inference in Bayesian networks. Prob abilistic potentials some of whose parts are proportional can be decomposed as a product of smaller trees. Some algorithms, like lazy propagation, can take advantage of this fact. Also, the factorisation can be used as a tool for approximating inference, if the decompo sition is carried out even if the proportionality is not completely reached. In this paper we propose the use of approximate factorisation as a means of controlling the approximation level in a dynamic importance sampling algorithm.
This paper demonstrates implementation of some CDMA system compatible antenna array algorithms namely LMS and space code correlator (SCC) on an FPGA. A comparative study of FPGA and DSP implementation issues such as a...
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ISBN:
(纸本)9781424403974
This paper demonstrates implementation of some CDMA system compatible antenna array algorithms namely LMS and space code correlator (SCC) on an FPGA. A comparative study of FPGA and DSP implementation issues such as architecture complexity and weight vector computation time is also given. The implementations of the algorithms are performed on Xilinx Virtex 11 Pro FPGA and Texas Instruments (TI) TMS320C67x floating-point DSP platforms. For the signal modeling, cdma2000 reverse link signal model is considered for uniform linear array topology and varying multipath propagation conditions. Results show that the both algorithms, which were implemented on Xilinx XC2VP4 and TMS320C6713 DSP, provides weight vector computation time smaller than 10 ms period.
The proceedings contain 5 papers. The topics discussed include: design of dependable personal networks;dynamic reconfiguration of middleware for ubiquitous computing;efficient decision making algorithms for adaptive a...
ISBN:
(纸本)9781605586465
The proceedings contain 5 papers. The topics discussed include: design of dependable personal networks;dynamic reconfiguration of middleware for ubiquitous computing;efficient decision making algorithms for adaptive applications;iCAAS: interoperable and configurable architecture for accessing sensor networks;and MobiThin management framework: design and evaluation.
Data cubes is the base of OLAP and OLAM. The traditional algorithms can transfer only one relation table to data cubes at the same time and have no choice but compute the data cubes one by one for relation set compose...
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Range reduction is widely used in the element function library and high performance computing applications. A new rang reduction algorithm and an improved one based on the Payne and Hanek Algorithm are proposed. Using...
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ISBN:
(纸本)9781424455379
Range reduction is widely used in the element function library and high performance computing applications. A new rang reduction algorithm and an improved one based on the Payne and Hanek Algorithm are proposed. Using the 64-bit integer arithmetic unit, all the intermediate computation is optimized for its Parallelism, table size and throughput, and the improved one base on it debases the complexity, and doesn't increase the table size using a pre-computed technology. The performance of the new algorithms gets above 50% improvement on the loongson2f platform through the experiment.
We improve the quality of cryptographically privacy-preserving genome-wide association studies by correctly handling population stratification-the inherent genetic difference of patient groups, e.g., people with diffe...
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We improve the quality of cryptographically privacy-preserving genome-wide association studies by correctly handling population stratification-the inherent genetic difference of patient groups, e.g., people with different ancestries. Our approach is to use principal component analysis to reduce the dimensionality of the problem so that we get less spurious correlations between traits of interest and certain positions in the genome. While this approach is commonplace in practical genomic analysis, it has not been used within a privacy-preserving setting. In this paper, we use cryptographically secure multi-party computation to tackle principal component analysis, and present an implementation and experimental results showing the performance of the approach.
A new exact algorithm for computing answer sets of logic programs is presented and analyzed. The algorithm takes a logic program in Kernel normal form as an input and computes its answer sets by reducing the problem t...
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A new exact algorithm for computing answer sets of logic programs is presented and analyzed. The algorithm takes a logic program in Kernel normal form as an input and computes its answer sets by reducing the problem to a suitable version of graph colorability. Even though worst-case complexity is exponential, thanks to a straightforward formulation we can prove that the algorithm works in time O*(1.6181n), which is asymptotically better than the trivial bound O*(2n) of the brute force algorithms. We argue that this new algorithm represents a significant progress in terms of worst-case time complexity over traditional branch-and-bound algorithms.
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