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• Canonical problem: localization and tracking Networking sensor networks Synchronization and localization Sensor tasking and control Sensor network database Sensor network platforms and tools Application and future direction.
• (source: Nielsen Book Data)

Information processing in sensor networks is a rapidly emerging area of computer science and electrical engineering research. Because of advances in micro-sensors, wireless networking and embedded processing, ad hoc networks of sensor are becoming increasingly available for commercial, military, and homeland security applications. Examples include monitoring (e.g., traffic, habitat, security), industrail sensing and diagnostics (e.g., factory, appliances), infrastructures (i.e., power grid, water distribution, waste disposal) and battle awareness (e.g., multi-target tracking). This book introduces practitioners to the fundamental issues and technology constraints concerning various aspects of sensor networks such as information organization, querying, routing, and self-organization using concrete examples and does so by using concrete examples from current research and implementation efforts.
(source: Nielsen Book Data)

• Canonical problem: localization and tracking Networking sensor networks Synchronization and localization Sensor tasking and control Sensor network database Sensor network platforms and tools Application and future direction.
• (source: Nielsen Book Data)

Information processing in sensor networks is a rapidly emerging area of computer science and electrical engineering research. Because of advances in micro-sensors, wireless networking and embedded processing, ad hoc networks of sensor are becoming increasingly available for commercial, military, and homeland security applications. Examples include monitoring (e.g., traffic, habitat, security), industrail sensing and diagnostics (e.g., factory, appliances), infrastructures (i.e., power grid, water distribution, waste disposal) and battle awareness (e.g., multi-target tracking). This book introduces practitioners to the fundamental issues and technology constraints concerning various aspects of sensor networks such as information organization, querying, routing, and self-organization using concrete examples and does so by using concrete examples from current research and implementation efforts.
(source: Nielsen Book Data)

• Canonical problem: localization and tracking Networking sensor networks Synchronization and localization Sensor tasking and control Sensor network database Sensor network platforms and tools Application and future direction.
• (source: Nielsen Book Data)

Information processing in sensor networks is a rapidly emerging area of computer science and electrical engineering research. Because of advances in micro-sensors, wireless networking and embedded processing, ad hoc networks of sensor are becoming increasingly available for commercial, military, and homeland security applications. Examples include monitoring (e.g., traffic, habitat, security), industrail sensing and diagnostics (e.g., factory, appliances), infrastructures (i.e., power grid, water distribution, waste disposal) and battle awareness (e.g., multi-target tracking). This book introduces practitioners to the fundamental issues and technology constraints concerning various aspects of sensor networks such as information organization, querying, routing, and self-organization using concrete examples and does so by using concrete examples from current research and implementation efforts.
(source: Nielsen Book Data)

The Earth Mover's Distance (EMD) between two finite distributions of weight is proportional to the minimum amount of work required to transform one distribution into the other. Current content-based retrieval work in the Stanford Vision Laboratory uses the EMD as a common framework for measuring image similarity with respect to color, texture, and shape content. In this report, we present some fast to compute lower bounds on the EMD which may allow a system to avoid exact, more expensive EMD computations during query processing. The effectiveness of the lower bounds is tested in a color-based retrieval system. In addition to the lower bound work, we also show how to compute the EMD under translation. In this problem, the points in one distribution are free to translate, and the goal is to find a translation that minimizes the EMD to the other distribution.

This book constitutes the refereed proceedings of the Second International Workshop on Information Processing in Sensor Networks, IPSN 2003, held in Palo Alto, CA, USA, in April 2003. The 23 revised full papers and 21 revised poster papers presented were carefully reviewed and selected from 73 submissions. Among the topics addressed are wireless sensor networks, query processing, decentralized sensor platforms, distributed databases, distributed group management, sensor network design, collaborative signal processing, adhoc sensor networks, distributed algorithms, distributed sensor network control, sensor network resource management, data service middleware, random sensor networks, mobile agents, target tracking, sensor network protocols, large scale sensor networks, and multicast.
(source: Nielsen Book Data)

We introduce a metric between two distributions that we call the Earth Mover's Distance (EMD). The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense. We show that the EMD has attractive properties for content-based image retrieval. The most important one, as we show, is that it matches perceptual similarity better than other distances used for image retrieval. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.

Abstract: "The Earth Mover's Distance (EMD) between two finite distributions of weight is proportional to the minimum amount of work required to transform one distribution into the other. Current content-based retrieval work in the Stanford Vision Laboratory uses the EMD as a common framework for measuring image similarity with respect to color, texture, and shape content. In this report, we present some fast to compute lower bounds on the EMD which may allow a system to avoid exact, more expensive EMD computations during query processing. The effectiveness of the lower bounds is tested in a color-based retrieval system. In addition to the lower bound work, we also show how to compute the EMD under translation. In this problem, the points in one distribution are free to translate, and the goal is to find a translation that minimizes the EMD to the other distribution."