Fundamental calculus on generalized stochastically bounded bursty traffic for communication networks

Since many applications and networks do not require or provide deterministic service guarantees, stochastic service guarantee analysis is becoming increasingly important and has attracted a lot of research attention in recent years. For this, several stochastic versions of deterministic traffic models have been proposed in the literature. Unlike previous stochastic models that are based on the traffic amount property of an input process, we present another stochastic model, generalized Stochastically Bounded Burstiness (gSBB), which is based on the virtual backlog property of the input process. We show the advantages of this approach. We study the superposition of gSBB traffic, and set up the input–output relation. Under various service disciplines, we characterize the output process for each source and investigate probabilistic upper bound on delay. Finally, we introduce a stochastic ordering monotonicity property of gSBB. With this property, we show that many well-known traffic models can be readily represented using the proposed gSBB model. These results set up the basis for a network calculus for gSBB traffic.     

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.     

Design of a Scalable Multicast Scheme With an Application-Network Cross-Layer Approach

This paper develops an efficient and scalable multicast scheme for high-quality multimedia distribution. The traditional IP multicast, a pure network-layer solution, is bandwidth efficient in data delivery but not scalable in managing the multicast tree. The more recent overlay multicast establishes the data-dissemination structure at the application layer; however, it induces redundant traffic at the network layer. We propose an application-oriented multicast (AOM) protocol, which exploits the application-network cross-layer design. With AOM, each packet carries explicit destinations information, instead of an implicit group address, to facilitate the multicast data delivery; each router leverages the unicast IP routing table to determine necessary multicast copies and next-hop interfaces. In our design, all the multicast membership and addressing information traversing the network is encoded with bloom filters for low storage and bandwidth overhead. We theoretically prove that the AOM service model is loop-free and incurs no redundant traffic. The false positive performance of the bloom filter implementation is also analyzed. Moreover, we show that the AOM protocol is a generic design, applicable for both intra-domain and inter-domain scenarios with either symmetric or asymmetric routing.     

Practical Interdomain Routing Security

This article reviews risks and vulnerabilities in interdomain routing and best practices that can have near-term benefits for routing security. It includes examples of routing failures and common attacks on routers, and countermeasures to reduce router vulnerabilities.     

Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation

Skid-steered mobile robots are widely used because of their simple mechanism and high reliability. Understanding the kinematics and dynamics of such a robotic platform is, however, challenging due to the complex wheel/ground interactions and kinematic constraints. In this paper, we develop a kinematic modeling scheme to analyze the skid-steered mobile robot. Based on the analysis of the kinematics of the skid-steered mobile robot, we reveal the underlying geometric and kinematic relationships between the wheel slips and locations of the instantaneous rotation centers. As an application example, we also present how to utilize the modeling and analysis for robot positioning and wheel slip estimation using only low-cost strapdown inertial measurement units. The robot positioning and wheel slip-estimation scheme is based on an extended Kalman filter (EKF) design that incorporates the kinematic constraints for accuracy enhancement. The performance of the EKF-based positioning and wheel slip-estimation scheme are also presented. The estimation methodology is tested and validated experimentally on a robotic test bed.     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

Tracking control for switched linear systems with time‐delay: a state‐dependent switching method

Tracking control for switched linear systems with time‐delay is investigated in this paper. Based on the state‐dependent switching method, sufficient conditions for the solvability of the tracking control problem are given. We use single Lyapunov function technique and a typical hysteresis switching law to design a tracking control law such that the H_∞ model reference tracking performance is satisfied. The controller design problem can be solved efficiently by using linear matrices inequalities. Since convex combination techniques are used to derive the delay independent criteria, some subsystems are allowed to be unstable. It is highly desirable that a non‐switched time‐delay system can not earn such property. Simulation example shows the feasibility and validity of the switching control law. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

uWave: Accelerometer-based personalized gesture recognition and its applications

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures. We evaluate uWave using a large gesture library with over 4000 samples for eight gesture patterns collected from eight users over one month. uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. We also present applications of uWave in gesture-based user authentication and interaction with 3D mobile user interfaces. In particular, we report a series of user studies that evaluates the feasibility and usability of lightweight user authentication. Our evaluation shows both the strength and limitations of gesture-based user authentication.     

Semplore: A scalable IR approach to search the Web of Data

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.