Videophone services over IP (Internet protocol) will become key services in the next-generation network (NGN). To provide a high-quality service for users, designing and managing the quality of experience (QoE) appropriately is extremely important. To do this, developing an objective quality assessment method that estimates subjective quality by using quality parameters of the videophone system is desirable. We propose a parametric-planning model for assessing video quality affected by coding and packet loss. The results indicated quality estimation accuracy was sufficient for practical use because the estimation errors of our model were equivalent to the statistical reliability of subjective assessment. Therefore, our model could be applied to effective design of video quality for videophone applications and networks. As an example of using our model, we show video-quality planning of videophone services.

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Rapidly spreading 3D shape applications have led to the development of content-based 3D shape retrieval research. In this paper, we propose a new retrieval method using Spherical Healpix. Spherical Healpix is a new framework for efficient discretization and fast analysis or synthesis of functions defined on the sphere. We analyzed the construction process of this structure and defined a new Spherical Healpix Extent Function. We then analyzed this Spherical Healpix Extent Function using an inverse-construction process from the sphere to the Euclidean plane. We transformed the result of inverse-construction to the frequency domain using a 2D Fourier transform, instead of spherical harmonics, a well-known tool in spherical analysis. We obtained the low-frequency component in the frequency domain by using a Butterworth low-pass filter. The power spectrum of the low frequency component can be used as the feature vector to describe a 3D shape. This descriptor is extracted in the canonical coordinate frame; that is, each 3D-model is first normalized. We have examined this method on the Konstanz Shape Benchmark and SHREC data set, and confirmed its efficiency. We also compared this method with other methods on the same Konstanz Shape Benchmark and SHREC data set and evaluated the shape retrieval performance.

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A document clusteringmethod for time series documents produces a sequence of clustering results over time. Analyzing the contents and trends in a long sequence of clustering results is a hard and tedious task since there are too many number of clusters. In this paper, we propose a framework to find clusters of users' topics of interest and evolution patterns called transition patterns involving the topics. A cluster in a clustering result may continue to appear in or move to another cluster, branch into more than one cluster, merge with other clusters to form one cluster, or disappear in the adjacent clustering result. This research aims at providing users facilities to retrieve specific transition patterns in the clustering results. For this purpose, we propose a query language for time series document clustering results and an approach to query processing. The first experimental results on TDT2 corpus clustering results are presented.

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RCAN [1] is a novel multi-ring content addressable peer-to-peer system. RCAN was proposed in the aim of improving the routing performance of CAN [4] overlays while minimizing the maintenance overhead during nodes churn in large networks. The key idea of RCAN is to equip each node with few long-links towards some distant nodes. Long-links are clockwise directed and wrap around to form small rings along each dimension. The number of rings and their sizes self-adjust as nodes join and leave the systems. RCAN is a pure P2P design, where all nodes assume the same responsibility. Unlike some existing P2P overlays, RCAN is self-organizing and does not assume any a-priori fixed limits for the network size or the routing state per node. Each node auto-adapts its routing state to cope with network changes.
We present in this paper an extensive study of the routing performance of RCAN under uniform and non-uniform data distributions. Experimental results show that in an overlay of n nodes, a node maintains a routing state of O(log n) long-links in average, and is able to reach any other nodes within O(log n) routing hops even in the presence of non-uniform space partitioning. Using simulation we demonstrate the full scalability and efficiency of our design and its advantages over other existing methods.

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Local dominance has been shown to improve significantly the overall performance of multiobjective evolution-ary algorithms (MOEAs) on combinatorial optimization problems. This work proposes the control of dominance area of solutions in local dominance MOEAs to enhance Pareto selection aiming to find solutions with high convergence and diversity properties. We control the expansion or contraction of the dominance area of solutions and analyze its effects on the search performance of a local dominance MOEA using 0/1 multiobjective knapsack problems. We show that convergence can be significantly improved while keeping a good distribution of solutions along the whole true Pareto front by using the local dominance MOEA with expansion of dominance area of solutions. We also show that dominance can be applied within very small neighborhoods by controlling the dominance area of solutions, which reduces significantly the computational cost of the local dominance MOEA.

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In this paper we describe a multi-camera traffic monitoring system relying on the concept of probability fusion maps (PFM) to detect vehicles in a traffic scene. In the PFM, traffic images from multiple cameras are inverse perspective-mapped and registered onto a common reference frame, combining the multiple camera information to reduce the impact of occlusions. Although the unconstrained perspective projection is non-invertible, imposing the condition that the image points be co-planar allows the relaxation of this constraint. In images of road scenes, the road surface is locally planar, and as such can be inversely mapped. We show that computing the perspective undistortion by finding 4 matching points between a camera image and an ideal non-perspectively distorted image, a system of linear equations can be solved that corresponds to applying the rotation, translation, and re-projection onto the common reference plane without needing calibrated cameras. We show that this method yields good results in the detection of vehicles for subsequent tracking and monitoring.

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In this paper, we present a Conditional Random Fields (CRFs) framework for the Clause Splitting problem. We adapt the CRFs model to this problem in order to use very large sets of arbitrary, overlapping and non-independent features. We also extend N-best list by using the Joint-CRFs (Shi and Wang 2007). In addition, we propose the use of rich linguistic information along with a new bottom-up dynamic algorithm for decoding to split a sentence into clauses. The experiments show that our results are competitive with the state-of-the art results.

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This paper reviews three hybrid cognitive architectures (Soar, ACT-R, and CoJACK) and how they can support including models of emotions. There remain problems creating models in these architectures, which is a research and engineering problem. Thus, the term cognitive science engineering is introduced as an area that would support making models easier to create, understand, and re-use.

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We propose a hose bandwidth allocation method to achieve a minimum throughput assurance (MTA) service for the hose model. Although the hose model, which has been proposed as a novel VPN service model for provider provisioned virtual private networks (PPVPNs), has been proven to be effective for network resource efficiency and configuration complexity, there has been no consideration of a mechanism to assure quality of service (QoS) in the hose model. The basic idea of our method is to gather available bandwidth information from inside a network and use it to divide the available bandwidth into hoses on each bottleneck link. We evaluate and clarify our method through computer simulation runs. The simulation results show that our method can achieve an MTA service in the hose model.

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Quality of Service (QoS) routing in Mobile Ad hoc NETworks (MANETs) is hard to achieve because the network topology tends to change constantly in a dynamic network. In this paper, we propose an effective QoS routing scheme to satisfy the required service demand and adapt to the dynamic changes in network resources. This novel approach of Application-Level QoS Routing Scheme (ALRS) comprises three significant features: (1) Estimation of consumed bandwidth on each node by using 2-hop neighbors' traffic information, (2) Route construction combined with admission control on each application session with (1), and (3) Route maintenance based on (1) and (2). By using our proposed ALRS scheme the availability of bandwidth is greatly improved for high-load communications, such as multimedia applications. By computer simulation we confirmed that ALRS increases the delivery ratio by up to 50% and decreases delay by less than one-fourth of the existing traditional method.

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In this paper, we propose a new solution for route optimization that can be applied to heterogeneous mobile IP networks that support client-based and network-based IP mobility management. The proposed solution aims to satisfy important requirements such as an efficient network resources utilization, an improvement of the end-to-end quality of service, and the capability to monitor user traffic for policy control and charging. In the proposed solution, the management of the route optimization context is performed by the mobility anchor, while the IP packet processing for route optimization is performed by the nodes located around the edge of the networks. We provide a comparative evaluation of the proposed solution and existing solutions in terms of: 1) the ability to maintain route optimization when the mobile node moves across a boundary between network domains where different types of IP mobility management are employed, 2) the range of application, 3) the latency required to continue route optimization upon handover, and 4) the amount of signaling. The evaluation results show that our proposed solution best satisfies the route optimization requirements for the heterogeneous mobile IP networks that we consider in this paper.

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The mobile Internet has become increasingly visible in everyday life. As mobile Internet penetration leverages content business opportunities, it is crucial to identify methodologies to fit mobile-specific demands. Regularity is one of the important measures to capture easy-come, easy-go mobile users. It is known that users with multiple visits per day with a long interval in between have a higher likelihood of revisiting in the following month than other users. The author proposes a 3+1bit method to incorporate this empirical law in order to cope with the two major mobile restrictions: distributed server environments and large data streams. The proposed method can be performed in a one-path manner with 32-bit word boundary-awareness for memory compaction. Experimental results show that the method is promising for identifying revisiting users under mobile-specific constraints.

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