6014524
Physically-based animation of volumetric objects
The paper presents a voxel-based animation technique which employs either a mass-spring model or a finite element model. Two volumetric objects, a voxelized chair and a scanned muscle, are used as case studies with the two different models. The mass-spring model is used to show an animation sequence of a falling and bouncing chair: wireframe display and volume rendering are used to display a real-time animation of the process. In addition, a real-time simulation is carried out by the finite element method (FEM) of a voxel-based multi-resolution muscle mesh. Two techniques, a direct integration and a simplified modal analysis method are discussed in the context of applying FEM for muscle deformation. Local deformation optimization with modal analysis for higher resolution muscle volumetric animation, which allows accurate prediction of muscle deformation changes, has been used. Physiological muscle force has been considered and a biomechanically-based 3D FEM muscle model has been implemented. Realistic animations have been produced based on the FEM simulation with various graphics techniques.
887532
A Suite of Schemes for User-Level Network Diagnosis without Infrastructure
It is highly desirable and important for end users, with no special privileges, identify and pinpoint faults inside the network that degrade the performance of their applications. However, existing tools are inaccurate to infer the link-level loss rates and have large diagnosis granularity (in terms of the number of hops). To address these problems, we propose a suite of user-level diagnosis approaches in two categories: (1) only need to be deployed at the source and (2) deployed at both source and destination. For the former, we propose two fragmentation aided diagnosis approaches (FAD), Algebraic FAD and Opportunistic FAD, which uses IP fragmentation to enable accurate link-level loss rate inference. For the latter category, we propose Striped Probe Analysis (SPA) which significantly improves the diagnosis granularity over those of the source-only approaches. Internet experiments are applied to evaluate each individual schemes (including an improved version of the state-of-the-art tool, Tulip [1]) and various hybrid approaches. The results indicate that our approaches dramatically outperform existing work (especially for diagnosis granularity) and provide not only the best performance but also smooth tradeoff among deployment requirement, diagnosis accuracy and granularity.
166109
End-to-End Inference of Router Packet Forwarding Priority
Packet forwarding prioritization (PFP) in routers is one of the mechanisms commonly available to network administrators. PFP can have a significant impact on the performance of applications, the accuracy of measurement tools' results and the effectiveness of network troubleshooting procedures. Despite their potential impact, no information on PFP settings is readily available to end users. In this paper, we present an end-to-end approach for packet forwarding priority inference and its associated tool, POPI. This is the first attempt to infer router packet-forwarding priority through end-to-end measurement. Our POPI tool enables users to discover such network policies through the monitoring and rank classification of loss rates for different packet types. We validated our approach via statistical analysis, simulation, and wide-area experimentation in PlanetLab. As part of our wide-area experiments, we employed POPI to analyze 156 random paths across 162 PlanetLab nodes. We discovered 15 paths flagged with multiple priorities, 13 of which were further validated through hop-by-hop loss rates measurements. In addition, we surveyed all related network operators and received responses for about half of them confirming our inferences.
10897641
Reversible sketches for efficient and accurate change detection over network data streams
Traffic anomalies such as failures and attacks are increasing in frequency and severity, and thus identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows and looks for heavy changes in traffic patterns (<i>e.g.</i>, volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is not scalable. The recently proposed sketch-based schemes [14] are among the very few that can detect heavy changes and anomalies over massive data streams at network traffic speeds. However, sketches do not preserve the key (<i>e.g.</i>, source IP address) of the flows. Hence, even if anomalies are detected, it is difficult to infer the culprit flows, making it a big practical hurdle for online deployment. Meanwhile, the number of keys is too large to record.
 To address this challenge, we propose efficient <i>reversible hashing</i> algorithms to infer the keys of culprit flows from sketches without storing any explicit key information. No extra memory or memory accesses are needed for recording the streaming data. Meanwhile, the heavy change detection daemon runs in the background with space complexity and computational time sublinear to the key space size. This short paper describes the conceptual framework of the reversible sketches, as well as some initial approaches for implementation. See [23] for the optimized algorithms in details. comment We further apply various emph IP-mangling algorithms and emph bucket classification methods to reduce the false positives and false negatives. Evaluated with netflow traffic traces of a large edge router, we demonstrate that the reverse hashing can quickly infer the keys of culprit flows even for many changes with high accuracy.
10399949
Quantifying Network Denial of Service: A Location Service Case Study
Network Denial of Service (DoS) attacks are increasing in frequency, severity and sophistication, making it desirable to measure the resilience of systems to DoS attacks. In this paper, we propose a simulation-based methodology and apply it to attacks on object location services such as DNS. Our results allow us to contrast the DoS resilience of three distinct architectures for object location.
61999607
Towards unbiased end-to-end network diagnosis
Internet fault diagnosis is extremely important for end users, overlay network service providers (like Akamai [1]) and even Internet service providers (ISPs). However, because link-level properties cannot be uniquely determined from end-to-end measurements, the accuracy of existing statistical diagnosis approaches is subject to uncertainty from statistical assumptions about the network. In this paper, we propose a novel Least-biased End-to-end Network Diagnosis (in short, LEND) system for inferring link-level properties like loss rate. We define a minimal identifiable link sequence (MILS) as a link sequence of minimal length whose properties can be uniquely identified from end-to-end measurements. We also design efficient algorithms to find all the MILSes and infer their loss rates for diagnosis. Our LEND system works for any network topology and for both directed and undirected properties, and incrementally adapts to network topology and property changes. It gives highly accurate estimates of the loss rates of MILSes, as indicated by both extensive simulations and Internet experiments. Furthermore, we demonstrate that such diagnosis can be achieved with fine granularity and in near real-time even for reasonably large overlay networks. Finally, LEND can supplement existing statistical inference approaches and provide smooth tradeoff between diagnosis accuracy and granularity.
20353418
Perspectives on Service-Oriented Computing and Service-Oriented System Engineering
The service-oriented computing (SOC) paradigm leads the innovation of today's computing system and software development, and it can transform the human society into a new republic of all-to-all connected world. There are a number of interesting and innovative research challenges involved in achieving this vision of service-oriented system engineering (SOSE). This paper highlights some of these significant research opportunities and also the need for enhanced education in this emerging area
129842
BLAS: an efficient XPath processing system
We present BLAS, a Bi-LAbeling based System, for efficiently processing complex XPath queries over XML data. BLAS uses P-labeling to process queries involving consecutive child axes, and D-labeling to process queries involving descendant axes traversal. The XML data is stored in labeled form, and indexed to optimize descendent axis traversals. Three algorithms are presented for translating complex XPath queries to SQL expressions, and two alternate query engines are provided. Experimental results demonstrate that the BLAS system has a substantial performance improvement compared to traditional XPath processing using D-labeling.
206473788
Booth's law of word frequency
Booth’s (1967) law is widely used to model the incidence of low frequency words in English text. Fedorowicz (198 1, 1982a, 1982b) used equation (3) below to study automated bibliographic systems. Pao (1978) used equation (4) to derive a transition point in automatic text analysis. The present communication confirms this usage, corrects a deficiency in the original derivation, and makes the results more general. Booth’s results are based on Zipf’s (1949) well-known relationship between the probability of occurrence, p(r), and the corresponding rank, r, of a word where
14315679
Hierarchical MRF model for model-based multi-object tracking
To track multiple objects, top-down (model-based methods) and bottom-up (multi-layer analysis) methods have been proposed separately. A hierarchical MRF model is proposed to integrate these two trends into a MAP framework for tracking non-rigid objects such as human hands or faces. Parametric models of color, shape and frame difference for both foreground and background are given. Dynamic constraints are used to update the observation models and present an initial segmentation for the new frame. A novel hierarchical MRF model is proposed to efficiently refine the segmentation based on local smoothness constraints. The algorithm does not need to initialize and can detect new moving objects and track them. It can also handle the stopped objects because of the utilization of spatial-temporal constraints. Promising results are reported.
5638961
Blind Detection and Compensation of Camera Lens Geometric Distortions
Author(s): Ma, Lili; Chen, YangQuan; Moore, Kevin L | Abstract: This paper presents a blind detection and compensation technique for camera lens geometric distortions. The lens distortion introduces higher-order correlations in the frequency domain and in turn it can be detected using higher-order spectral analysis tools without assuming any specific calibration target. The existing blind lens distortion removal method only considered a single-coefficient radial distortion model. In this paper, two coefficients are considered to model approximately the geometric distortion. All the models considered have analytical closed-form inverse formulae.