By embedding sensors in mobile devices, it is possible to exploit the ubiquitous presence of these devices to construct applications for large-scale sensing and monitoring of environmental phenomena. To this end, we present Environmental Tomography, a novel approach in which mobile devices participate in the collection of aggregate sensor readings along roads or sidewalks, and these aggregates are used to reconstruct an estimate of the contaminant distribution throughout a region. We demonstrate how our data collection process preserves user location privacy and is robust to sensor and location reading errors. We also show how the estimation process can be formulated as a convex optimization problem that incorporates the physical dynamics of the phenomenon of interest. We study the performance of Environmental Tomography using various road network layouts and realistic models of pollution. Results indicate that estimates generated from path aggregates are of comparable accuracy to estimates generated from significantly greater numbers of individual sensor readings.

The ubiquitous nature of mobile phones, which are location-aware devices, presents a unique platform for large-scale computing applications. In particular, if mobile phones are coupled with sensors, they can be used for detection and monitoring of environmental phenomena such as pollution and radiation. In this demonstration, we present environmental tomography, a system for ubiquitous environmental sensing with mobile devices. Aggregate sensor measurements are collected by the devices along fixed paths such as roads, and these aggregates are used to reconstruct an estimate of the distribution of the underlying physical phenomenon. Our system is robust to the dynamic characteristics of mobile networks and also preserves the privacy of mobile user locations. We demonstrate a prototype that generates estimate distributions from user specified data collection paths and underlying data distributions. The accuracy of the reconstructed distributions is illustrated both numerically and graphically.

We investigate the use of large-scale Automated Highway Systems, also called one-dimensional vehicle platoons, as a practical traffic mitigation solution. We review recent theoretical results related to vehicle platooning and discuss their implications on the scalability and safety of large-scale pl

A consistent development of general moment invariants of affine transformations for two-dimensional image functions is presented. Based on this development, a new general moment-invariants/attributed-graph (MIAG) method is presented for the identification of three-dimensional objects from a single observed image using a model-matching approach. The three-dimensional location and orientation parameters of the object are also obtained as a byproduct of the matching procedure. The scheme presented allows the observed object to be partially Occluded. For identification purposes, a three-dimensional object is represented by an attributed graph describing the geometrical structure and shape of the surface bounding the object. In such a description, two-dimensional general moment invariants of the rigid planar patches (RPP) constituting the object faces are used as attributes or feature vectors which are invariant under three-dimensional motion. With this representation, the identification problem becomes a subgraph isomorphism problem between the observed image and a library model. An algorithm is presented for this matching process, and the results are illustrated by computer simulations.

We present new algorithms for rapid identification and three-dimensional (3D) attitude determination of a solid object from a single image, using a model matching approach. Our scheme allows the observed object to be partially occluded. The object, as well as the model to which it is matched are represented by the 3D surface constituting their boundaries. We assume that these surfaces consist of flat faces, i.e. the object and the model are (not necessarily convex) polyhedra. We represent each by an attributed graph. The nodes of the graph denote faces on the surface and edges indicate the adjacency of faces. Attributes on the nodes are features invariant to 3D motion made up of 2D moment invariants. With this representation the recognition problem becomes a subgraph matching problem between the image of the observed object and the stored models. We present an algorithm for the matching process, furthermore the exact attitude is obtained as a byproduct of the matching procedure.