DiffServ over MPLS networks is a widely accepted approach to considerably improve networks 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 operators 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.
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.
A spatial information system (SIS) is critical to the hosting, querying, and analyzing of spatial data sets. The increasing availability of three-dimensional (3D) data (e.g. from aerial and terrestrial laser scanning) and the desire to use such data in large geo-spatial platforms have been dual drivers in the evolution of integrated SISs. Within this context, recent patents demonstrate efforts to handle large data sets, especially complex point clouds. While the development of feature-rich geo-systems has been well documented, the implementation of support for 3D capabilities is only now being addressed. This paper documents the underlying technologies implemented for the support for 3D features in SISs. Examples include ESRIs ArcGIS geo-database with its support for two-and-a-half dimensions (2.5D) in its Digital Elevation Model (DEM) and Triangular Irregular Network (TIN), the more recent development of the Terrain feature class, and support for 3D objects and buildings with its multi-patch feature class. Recent patents and research advances aim to extract DEMs and TINs automatically from point cloud data. In this context, various data structuring innovations are presented including both commercial and open source alternatives.
This paper surveys the developments of the last 10 years in the area of vision based target tracking for autonomous vehicles navigation. First, the motivations and existing applications of using vision based target tracking for autonomous vehicles navigation are discussed in the introduction section . It can be concluded that it is very necessary to develop robust visual target tracking based navigation algorithms for the broad applications of autonomous vehicles . Then this paper reviews the recent techniques in vision based target tracking for the applications of autonomous land vehicle navigation. Next the increasing trends of using data fusion for visual target tracking  based autonomous vehicles navigation are discussed. It is clear that through data fusion the tracking performance is improved and becomes more robust. Based on the reviews, the remaining research challenges are summarized and future research directions are investigated.
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.
There are several shortcomings with the traditional token based authentication using magnetic strip card with PIN/password protection such as easy to duplicate and from forgotten to stolen password etc. Fingerprint authentication is more secure and is a convenient replacement of PIN/password. With the advancement in the microprocessor technology for token such as smartcard and fingerprint authentication technology, token based fingerprint authentication becomes a viable alternative to replace the existing magnetic strip card with PIN/password protection. In this paper, an overview of the token-based fingerprint authentication will be given and a review of the recent publications and patents will be presented.
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.
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.
Humans are equipped with “space-variant” vision, i.e. a concentration of photoreceptors, retinal ganglion cells and other visual resources at the central fovea, and a sparser coverage of other regions within a wide 180 degree field of view. If the entire visual field was equipped with foveal ganglion cell resolution, then the brain would have to cope with approximately 350 times more visual information. We will review the relatively small number of existing hardware implementations and patents involving space variant vision. Space-variant vision is challenging to implement, because it comes along with distorted image representations, complicating standard geometry-based processing. Recent learning algorithms for feature detection and transformations are more flexible and may cope with foveated images. Foveated vision requires an active vision system: ballistic eye-movements termed “saccades” frequently move the fovea to points of interest in the visual field. The metric of saccades is adjustable, and the resolution increase at the fovea may play a role in supplying the feedback to the system. Furthermore, saccades are related to visual space perception and embodied vision.