Instrumenting the physical world through large networks of wireless sensor nodes, particularly for applications like environmental monitoring of water and soil, requires that these nodes be very small, light, untethered and unobtrusive. The problem of localization, i.e., determining where a given

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations

Abstract—Measurements of received signal strength (RSS) on wireless links provide position information in various localization systems, including multilateration-based and fingerprint-based positioning systems, and device-free localization systems. Existing localization schemes assume a fixed

Abstract: This chapter explores the features and advantages of kernel-based local-ization. Kernel methods simplify received signal strength (RSS)-based localization by providing a means to learn the complicated relationship between RSS measurement vector and position. We discuss their use in self

of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination

Abstract — We propose a secure localization scheme for sensor networks based on the received signal strength (RSS) ranging techniques. Our scheme enables the network authority to obtain locations of sensor nodes in the presence of an attacker. The proposed scheme uses a small number of anchor nodes

for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof

A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each

that researchers are currently resolving. The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections. This article also points out the open research issues and intends to spark new interests and developments

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial