Recent Patents on Computer Science

ISSN: 1874-4796 - Volume 1, 2008

Upcoming Articles


Recent Patents on Genetic Programming
Michael O’Neill and Anthony Brabazon
[Abstract]


Biomedical Text Data Mining: Recent Patents
Colleen E. Crangle
[Abstract]


Towards AQM Cooperation for Congestion Avoidance in DiffServ/MPLS Networks
Yassine Hadjadj-Aoul
[Abstract]


Multi-users Quantum Key Distribution Via Wavelength Routers in an Optical Network
P.P. Yupapin and S. Mitatha
[Abstract]


Rough Fuzzy Set in Incomplete Fuzzy Information System Based on Similarity Dominance Relation
Xibei Yang, Lihua Wei, Dongjun Yu and Jingyu Yang
[Abstract]



Abstracts

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Recent Patents on Genetic Programming
Michael O’Neill and Anthony Brabazon

Genetic Programming is a form of Natural Computing which adopts principles from neo-Darwinian evolution to automatically solve problems. It is a model induction method in that both the structure and parameters of the solution are explored simultaneously. Genetic Programming is a particularly interesting method as it is claimed to be an invention machine, producing solutions to problems that are competitive and in some cases superior to those produced by human experts. Its best solutions have become patentable inventions in their own right. In this article we overview some of the recent patents relating to Genetic Programming over the past three years. In light of the number and diversity of patent applications during this period it is clear that Genetic Programming is a vibrant field of research, which is having a significant impact on real-world applications, and is demonstrating clear commercial potential.


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Biomedical Text Data Mining: Recent Patents
Colleen E. Crangle

Biomedical information continues to grow beyond the capacity of scientists to capture and use all that is produced. Much of this information is presented in scientific journal articles and expressed in natural language. Biomedical text data mining is concerned with automated methods for analyzing the content of these documents and discovering and extracting the knowledge in them. Numerical data mining has long been used to uncover patterns in numerical data and make predictions based on those patterns. Text data mining builds on the success of numerical data mining but presents additional challenges. This article examines text data mining for biomedical text, paying particular attention to the complexities of natural language that must be taken into account and to the role of biomedical knowledge sources. Using this perspective, recent patents for data mining specific to biomedical text are discussed and expected future patent activity is appraised.


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Towards AQM Cooperation for Congestion Avoidance in DiffServ/MPLS Networks
Yassine Hadjadj-Aoul

DiffServ over MPLS networks is a widely accepted approach to considerably improve network’s ability to support delay-sensitive applications such as voice over IP. In such networks, over-provisioning and careful admission control techniques are still needed, although insufficient to eliminate completely the congestion. To prevent congested paths, these networks use, currently, the preemption policy that induces waste of resources and excessive rerouting, which are triggered only after packets dropping. Besides, the increased end-to-end loss rates and delays, experienced by a service, are mostly due to one or few congested switches along the Label Switched Path "LSP" while the other routers are in relaxed conditions. In this paper, we tackle these issues by extending the traditional local management of congestions (isolated Active Queue Management “AQM”) into a cooperative process involving all switches along the service path at network operator's scale. Going from AQM limitation, we propose, a network self-managing framework that dynamically re-adjusts switches' parameters throughout the LSP. In this way, most of the prospective congestions impact is absorbed through balancing the routers aggressiveness without reconsidering other traffic engineering strategies (e.g., rerouting decision). Otherwise, the proposed framework allows rerouting at the point where congestion would most likely occur, which permits minimizing both packets loss and excessive rerouting. While considering QoS guarantees along a given service path, network loss ratio is reduced. This obviously allows network operators to further exploit theirs underlying resources by accepting more QoS-enabled services.


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Multi-users Quantum Key Distribution Via Wavelength Routers in an Optical Network
P.P. Yupapin and S. Mitatha

We propose a new system of a continuous variable quantum key distribution via a wavelength router in the optical networks. A large bandwidth signal is generated by a soliton pulse propagating within the micro ring resonator, which is allowed to form the continuous wavelength with large tunable channel capacity. Two forms of soliton pulses are generated and localized, i.e. temporal and spatial solitons. The required information can be transmitted via the spatial soliton while the continuous variable quantum key distribution is formed by using the temporal one. This is formed by using an optical add/drop multiplexer incorporated in the optical network, where the localized soliton pulses are available for add/drop signals to/from the optical network. The high security and capacity information can be performed.


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Rough Fuzzy Set in Incomplete Fuzzy Information System Based on Similarity Dominance Relation
Xibei Yang, Lihua Wei, Dongjun Yu and Jingyu Yang

The purpose of this paper is to introduce the concept of dominance-based rough set into the incomplete fuzzy decision system. In such information system, all unknown values are considered as lost, the similarity dominance relation is then used to construct information granules. The lower and upper rough fuzzy approximations in terms of the similarity dominance relation are presented, from which one can derive all “at least” and “at most” decision rules from the incomplete fuzzy decision system. Moreover, to obtain the optimal decision rules, we propose two types of knowledge reductions, relative lower and upper approximate reducts. These two reducts are minimal subsets of the condition attributes, which preserve the lower and upper approximate memberships for an object respectively. Some numerical examples are employed to substantiate the conceptual arguments and related patents are also reviewed in the paper.