14410544
Fluid modeling of pollution proliferation in P2P networks
P2P systems are highly vulnerable to pollution attacks in which attackers inject multiple versions of corrupted content into the system, which is then further proliferated by unsuspecting users. However, to our knowledge, there are no closed-form solutions that describe this phenomenon, nor are there models that describe how the injection of multiple versions of corrupted content impacts a clients' ability to receive a valid copy. In this paper we develop a suite of fluid models that model pollution proliferation in P2P systems. These fluid models lead to systems of non-linear differential equations. We obtain closed-form solutions for the differential equations; for the remaining models, we efficiently solve the differential equations numerically. The models capture a variety of user behaviors, including propensity for popular versions, abandonment after repeated failure to obtain a good version, freeloading, and local version blacklisting. Our analysis reveals intelligent strategies for attackers as well as strategies for clients seeking to recover non-polluted content within large-scale P2P networks.
8733679
Stochastic Fluid Theory for P2P Streaming Systems
We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.
14392689
Composition trust bindings in pervasive computing service composition
In pervasive computing, devices or peers may implement or compose services using services from other devices or peers, and may use components from various sources. A composition trust binding is a prescriptive set of rules which defines the combination of allowable components for a particular service or application. Composition trust bindings can be used to protect both the service invocation path as well as the content handling path. The subsidiary relationships addressed by a composition trust binding are typically transparent today, but represent potential security exposure in pervasive computing systems because the subsidiary services or components may have security vulnerabilities. We define the composition trust binding and illustrate its use in the context of rights management and distributed search in personal content publishing. We compare this approach to existing authentication and authorization methods in service composition
17081981
Matching vehicles under large pose transformations using approximate 3D models and piecewise MRF model
We propose a robust object recognition method based on approximate 3D models that can effectively match objects under large viewpoint changes and partial occlusion. The specific problem we solve is: given two views of an object, determine if the views are for the same or different object. Our domain of interest is vehicles, but the approach can be generalized to other man-made rigid objects. A key contribution of our approach is the use of approximate models with locally and globally constrained rendering to determine matching objects. We utilize a compact set of 3D models to provide geometry constraints and transfer appearance features for object matching across disparate viewpoints. The closest model from the set, together with its poses with respect to the data, is used to render an object both at pixel (local) level and region/part (global) level. Especially, symmetry and semantic part ownership are used to extrapolate appearance information. A piecewise Markov Random Field (MRF) model is employed to combine observations obtained from local pixel and global region level. Belief Propagation (BP) with reduced memory requirement is employed to solve the MRF model effectively. No training is required, and a realistic object image in a disparate viewpoint can be obtained from as few as just one image. Experimental results on vehicle data from multiple sensor platforms demonstrate the efficacy of our method.
12166917
Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects
This paper proposes a novel approach for multi-view multi-pose object detection using discriminative shapebased exemplars. The key idea underlying this method is motivated by numerous previous observations that manually clustering multi-view multi-pose training data into different categories and then combining the separately trained two-class classifiers greatly improved the detection performance. A novel computational framework is proposed to unify different processes of categorization, training individual classifier for each intra-class category, and training a strong classifier combining the individual classifiers. The individual processes employ a single objective function that is optimized using two nested AdaBoost loops. The outer AdaBoost loop is used to select discriminative exemplars and the inner AdaBoost is used to select discriminative features on the selected exemplars. The proposed approach replaces the manual time-consuming process of exemplar selection as well as addresses the problem of labeling ambiguity inherent in this process. Also, our approach fully complies with the standard AdaBoost-based object detection framework in terms of real-time implementation. Experiments on multi-view multi-pose people and vehicle data demonstrate the efficacy of the proposed approach.
62017650
VideoTrek: A vision system for a tag-along robot
We present a system that combines multiple visual navigation techniques to achieve GPS-denied, non-line-of-sight SLAM capability for heterogeneous platforms. Our approach builds on several layers of vision algorithms, including sparse frame-to-frame structure from motion (visual odometry), a Kalman filter for fusion with inertial measurement unit (IMU) data and a distributed visual landmark matching capability with geometric consistency verification. We apply these techniques to implement a tag-along robot, where a human operator leads the way and a robot autonomously follows. We show results for a real-time implementation of such a system with real field constraints on CPU power and network resources.
23462768
A method for deep etching in dielectric films for 3D-chip and photonics applications
Abstract In this paper, RIE of thick undoped silicate glass (USG) films for various applications is described. Though moderate etch rates (∼0.4 μm/min) were achieved for USG film, process development was quite challenging due to stringent requirements of deep USG etching (>6 μm) and vertical profile. Alternatively, etch process with higher etch rate (1.0 μm/min) was also evaluated for profile angle and results were found not comparable to low etch rate process. However, low etch rate process was found to have tool limitations and an alternate method called ‘discrete etching’ was proposed and successfully verified. Promising results were obtained with etch depth (6 μm) and vertical profile (∼89.50°) in USG as well as SiON films. Based on the results discrete etching method was found to have provided advantage of unlimited capability for deep etching.
8291379
Video Flashlights: Real Time Rendering of Multiple Videosfor Immersive Model Visualization
Videos and 3D models have traditionally existed in separate worlds and as distinct representations. Although texture maps for 3D models have been traditionally derived from multiple still images, real-time mapping of live videos as textures on 3D models has not been attempted. This paper presents a system for rendering multiple live videos in real-time over a 3D model as a novel and demonstrative application of the power of commodity graphics hardware. The system, metaphorically called the Video Flashlight system, "illuminates" a static 3D model with live video textures from static and moving cameras in the same way as a flashlight (torch) illuminates an environment. The Video Flashlight system is also an augmented reality solution for security and monitoring systems that deploy numerous cameras to monitor a large scale campus or an urban site. Current video monitoring systems are highly limited in providing global awareness since they typically display numerous camera videos on a grid of 2D displays. In contrast, the Video Flashlight system exploits the real-time rendering capabilities of current graphics hardware and renders live videos from various parts of an environment co-registered with the model. The user gets a global view of the model and is also able to visualize the dynamic videos simultaneously in the context of the model. In particular, the location of pixels and objects seen in the videos are precisely overlaid on the model while the user navigates through the model. The paper presents an overview of the system, details of the real-time rendering and demonstrates the efficacy of the augmented reality application.