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Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

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We present an automatic vehicle detection system for aerial surveillance in this paper. In this system, we escape from the stereotype and existing frameworks of vehicle detection in aerial surveillance, which are either region based or sliding window based. We design a pixelwise classification method for vehicle detection. The novelty lies in the fact that, […]

Abrupt Motion Tracking Via Intensively Adaptive Markov-Chain Monte Carlo Sampling

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The robust tracking of abrupt motion is a challenging task in computer vision due to its large motion uncertainty. While various particle filters and conventional Markov-chain Monte Carlo (MCMC) methods have been proposed for visual tracking, these methods often suffer from the well-known local-trap problem or from poor convergence rate. In this paper, we propose […]

Exploiting Excess Capacity to Improve Robustness of WDM Mesh Networks

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Motivated by recent emerging systems that can leverage partially correct packets in wireless networks, this paper proposes the novel concept of error estimating coding (EEC). Without correcting the errors in the packet, EEC enables the receiver of the packet to estimate the packet’s bit error rate, which is perhaps the most important meta-information of a […]

Latency Equalization as a New Network Service Primitive

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Multiparty interactive network applications such as teleconferencing, network gaming, and online trading are gaining popularity. In addition to end-to-end latency bounds, these applications require that the delay difference among multiple clients of the service is minimized for a good interactive experience. We propose a Latency EQualization (LEQ) service, which equalizes the perceived latency for all […]

Efficient Error Estimating Coding: Feasibility and Applications

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Motivated by recent emerging systems that can leverage partially correct packets in wireless networks, this paper proposes the novel concept of error estimating coding (EEC). Without correcting the errors in the packet, EEC enables the receiver of the packet to estimate the packet’s bit error rate, which is perhaps the most important meta-information of a […]

Analyzing the Performance of Greedy Maximal Scheduling via Local Pooling and Graph Theory

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Efficient operation of wireless networks and switches requires using simple (and in some cases distributed) scheduling algorithms. In general, simple greedy algorithms (known as Greedy Maximal Scheduling, or GMS) are guaranteed to achieve only a fraction of the maximum possible throughput (e.g., 50% throughput in switches). However, it was recently shown that in networks in […]

Adaptive Opportunistic Routing for Wireless Ad Hoc Networks

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A distributed adaptive opportunistic routing scheme for multihop wireless ad hoc networks is proposed. The proposed scheme utilizes a reinforcement learning framework to opportunistically route the packets even in the absence of reliable knowledge about channel statistics and network model. This scheme is shown to be optimal with respect to an expected average per-packet reward […]

Abnormally Malicious Autonomous Systems and Their Internet Connectivity

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While many attacks are distributed across botnets, investigators and network operators have recently identified malicious networks through high profile autonomous system (AS) depeerings and network shutdowns. In this paper, we explore whether some ASs indeed are safe havens for malicious activity. We look for ISPs and ASs that exhibit disproportionately high malicious behavior using 10 […]

A Greedy Link Scheduler for Wireless Networks With Gaussian Multiple-Access and Broadcast Channels

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Information-theoretic broadcast channels (BCs) and multiple-access channels (MACs) enable a single node to transmit data simultaneously to multiple nodes, and multiple nodes to transmit data simultaneously to a single node, respectively. In this paper, we address the problem of link scheduling in multihop wireless networks containing nodes with BC and MAC capabilities. We first propose […]

TSCAN: A Content Anatomy Approach to Temporal Topic Summarization

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A topic is defined as a seminal event or activity along with all directly related events and activities. It is represented by a chronological sequence of documents published by different authors on the Internet. In this study, we define a task called topic anatomy, which summarizes and associates the core parts of a topic temporally […]