Abstract-This paper presents an online controller for tracking power-budgets in multicore processors using dynamic voltage-frequency scaling. The proposed control law comprises an integral controller whose gain is adjusted online based on the derivative of the power-frequency relationship. The control law is designed to achieve rapid settling time, and its tracking property is formally proven. Importantly, the controller design does not require off-line analysis of application workloads making it feasible for emerging heterogeneous and asymmetric multicore processors. Simulation results are presented for controlling power dissipation in multiple cores of an asymmetric multicore processor. Each core is i) equipped with the controller, ii) assigned a power budget, and iii) operates independently in tracking to its power budget. We use a cycle-level multicore simulator driven by traces from SPEC2006 benchmarks demonstrating that the proposed algorithm achieves a faster settling time than examples of a static setting of the controller gain.

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Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance.