Abstract
Three-dimensional (3D) objects reconstruction using just bi-dimensional (2D) images has been a major research topic in Computer Vision. However, it is still a hard problem to address, when automation, speed and precision are required and/or the objects have complex shapes or image properties. In this paper, we compare two Active Computer Vision methods frequently used for the 3D reconstruction of objects from image sequences, acquired with a single off-the-shelf CCD camera: Structure From Motion (SFM) and Generalized Voxel Coloring (GVC). SFM recovers the 3D shape of an object based on the relative motion involved, while VC is a volumetric method that uses photo-consistency measures to build the required 3D model. Both methods considered do not impose any kind of restrictions on the relative motion involved. © Springer Science+Business Media B.V. 2009.

Abstract
Three-dimensional (3D) objects reconstruction using just bi-dimensional (2D) images has been a major research topic in Computer Vision. However, it is still a hard problem to address, when automation, speed and precision are required and/or the objects have complex shapes or image properties. In this paper, we compare two Active Computer Vision methods frequently used for the 3D reconstruction of objects from image sequences, acquired with a single off-the-shelf CCD camera: Structure From Motion (SFM) and Generalized Voxel Coloring (GVC). SFM recovers the 3D shape of an object based on the relative motion involved, while VC is a volumetric method that uses photo-consistency measures to build the required 3D model. Both methods considered do not impose any kind of restrictions on the relative motion involved.

Abstract
Consider a wireless sensor network (WSN) where a broadcast from a sensor node does not reach all sensor nodes in the network; such networks are often called multihop networks. Sensor nodes take individual sensor readings, however, in many cases, it is relevant to compute aggregated quantities of these readings. In fact, the minimum and maximum of all sensor readings at an instant are often interesting because they indicate abnormal behavior, for example if the maximum temperature is very high then it may be that a fire has broken out. In this context, we propose an algorithm for computing the min or max of sensor readings in a multihop network. This algorithm has the particularly interesting property of having a time complexity that does not depend on the number of sensor nodes; only the network diameter and the range of the value domain of sensor readings matter. © 2009 pringer Science+Business Media B.V.

Abstract
This paper addresses sensor network applications which need to obtain an accurate image of physical phenomena and do so with a high sampling rate in both time and space. We present a fast and scalable approach for obtaining an approximate representation of all sensor readings at high sampling rate for quickly reacting to critical events in a physical environment This approach is an improvement on previous work in that after the new approach has undergone a startup phase then the new approach can use a very small sampling period.

Abstract
WiDom is a previously proposed prioritized medium access control protocol for wireless channels. We present a modification to this protocol in order to improve its reliability. This modification has similarities with cooperative relaying schemes, but, in our protocol, all nodes can relay a carrier wave. The preliminary evaluation shows that, under transmission errors, a significant reduction on the number of failed tournaments can be achieved.

Abstract
We focus on large-scale and dense deeply embedded systems where, due to the large amount of information generated by all nodes, even simple aggregate computations such as the minimum value (MIN) of the sensor readings become notoriously expensive to obtain. Recent research has exploited a dominance-based medium access control (MAC) protocol, the CAN bits, for computing aggregated quantities in wired systems. For example, MIN can be computed efficiently and an interpolation function which approximates sensor data in an. area can be obtained efficiently as well. Dominance-based MAC protocols have recently been proposed for wireless channels and these protocols can be expected to be used for achieving highly scalable aggregate computations in. wireless systems. But no experimental demonstration is currently available in the research literature. In this paper we demonstrate that highly scalable aggregate computations in wireless networks are possible. We do so by (i) building a new wireless hardware platform with appropriate characteristics for making dominance-based MAC protocols efficient, (ii) implementing dominance-based MAC protocols on this platform, (iii) implementing distributed algorithms for aggregate computations (MIN, MAX, Interpolation) using the new implementation of the dominance-based MAC protocol and (iv) performing experiments to prove that such highly scalable aggregate computations in wireless networks are possible.