This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility

This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review

Networked sensors-those that coordinate amongst them-selves to achieve a larger sensing task-will revolutionize information gathering and processing both in urban environments and in inhospitable terrain. The sheer numbers of these sensors and the expected dynamics in these environments present

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grouping of textual or partially textual entities. Document retrieval, categorization, routing, filtering, and clustering, as well as natural language processing tasks such as tagging, word sense disambiguation, and some aspects of understanding can be formulated as text classification. As the amount

To realize the full potential of mobile crowd sensing, techniques are needed to deal with un-certainty in participant locations and trajectories. We propose a novel model for spatial task assignment in mobile crowd sensing that uses a dynamic and adaptive data driven scheme to as-sign moving

This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Other than in the form of a simple

control. We reformulated the manipulator con-trol problem as direct control of manipulator motion in oper-ational space—the space in which the task is originally described—rather than as control of the task’s corresponding joint space motion obtained only after geometric and kine

. Develops reciprocity and cooperation among students. 3. Uses active learning techniques. 4. Gives prompt feedback. 5. Emphasizes time on task. 6. Communicates high expectations. 7. Respects diverse talents and ways of learning. We can do it ourselves-- with a little bit of help.... A Focus for Improvement

-tensions. 0 For graphs embeddable in low-dimensional spaces with a small distortion, we can find low-diameter decompositions (in the sense of [4] and [34]). The parameters of the decomposition depend only on the dimension and the distortion and not on the size of the graph. 0 In graphs embedded this way