Zobrazeno 1 - 10
of 19
pro vyhledávání: '"D. Hemkumar"'
Publikováno v:
Peer-to-Peer Networking and Applications. 14:1650-1665
Continuous publication of statistics collected from various location-based applications may compromise users’ privacy as the statistics could be procured from users’ private data. Differential Privacy (DP) is a new privacy notion that offers a st
Publikováno v:
Engineering Science and Technology, an International Journal. 23:1291-1300
The rapid growth in the usage of location-based services has resulted in extensive research on users’ trajectory data publishing. But, a key concern here is a potential breach of user privacy through various linkage attacks by an efficient adversar
Akademický článek
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Autor:
K. Vinaykumar, D. Hemkumar
Publikováno v:
2012 International Conference on Computing Sciences.
In this project we study the one of the most important performance in aggregate congestion control is fairness, i.e. the equal use of resources. The objective of Aggregate TCP congestion management is to achieve the fair sharing of the bottleneck ban
Publikováno v:
SIMULATION. 61:151-159
The use of a programming model which extends naturally from the underlying hardware, greatly eases the design and implementation of simulators, especially for those systems that resemble the hardware in the paradigm of computation. Given the characte
Publikováno v:
[Proceedings] 1992 IEEE International Symposium on Circuits and Systems.
A systolic algorithm for the SVD (singular value decomposition) of arbitrary complex matrices based on the cyclic Jacobi method with parallel ordering is presented. A novel two-step, two-sided unitary transformation scheme, tailored to the use of COR
Publikováno v:
IEEE Symposium on Computer Arithmetic
A two-sided unitary transformation (Q transformation) structured to permit integrated evaluation and application using CORDIC primitives is introduced. The Q transformation is shown to be useful as an atomic operation in parallel arrays for computing
Publikováno v:
Scopus-Elsevier
The singular value decomposition (SVD) is an important matrix factorization used in a variety of applications. The SVD exhibits better numerical stability due to the insensitivity to ill-conditioning or rank deficiency of matrices. However, the SVD i
Conference
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Akademický článek
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