• Dr. Carolyn Beck
  • University of Illinois at Urbana-Champaign
  • 104D Surge Building
  • 4:00 p.m.
  • Faculty Host: Dr. Mazen Farhood

In this talk we present a computational framework for solving a large class of dynamic clustering and coverage control problems, ranging from those that arise in deployment of mobile sensor networks to identification of ensemble spike trains in neuronal data. This framework provides the ability to identify natural clusters in an underlying dynamic data set, and allows us to address inherent trade-offs such as those between cluster resolution and computational cost. More specifically, we define the problem of minimizing an instantaneous coverage metric as an optimization problem using a Maximum Entropy Principle formulation, constructed specifically for the dynamic setting. Locating cluster centers and tracking their associated dynamics is cast as a control design problem that ensures the algorithm achieves progressively better coverage with time.