Current technological developments and application-driven demands are bringing us closer to the realization of autonomous multirobot systems performing increasingly complex missio...
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Phase-decoupled methods for code generation are the state of the art in compilers for standard processors but generally produce code of poor quality for irregular target architect...
— We consider a network of rechargeable sensors, deployed redundantly in a random sensing environment, and address the problem of how sensor nodes should be activated dynamically...
The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...