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Recent channel measurement campaigns have revealed that at high carrier frequencies (10GHz and above) MIMO channels exhibit sparsity structure, i.e., a few dominant propagation paths and, in some cases, it can also reduce to a single path, yet very directive, wireless channel. In this work, we leverage the sparsity feature into the development of sparse-MIMO channel estimator and show, how an adaptive l1-optimization method can greatly improve the estimation error.

This work examines the charging power allocation and beam selection problem for distributed estimation in wireless passive sensor networks, where the sensors are charged over the air by RF-enabled energy sources. A two-phase replenishment and transmission cycle is considered. In the replenishment phase, each wireless charger emits power over the air through a carefully selected beam and power to replenish the wireless sensors.

This work proposes a learning-based energy management policy that takes into consideration the trade-off between the depth-of-discharge (DoD) and the lifetime of batteries. The impact of DoD on the energy management policy is often neglected in the past due to the inability to model its effect on the marginal cost per battery usage. In this work, a novel battery cost evaluation method that takes into consideration the DoD of each battery usage is proposed, and is utilized to devise the day-ahead energy management policy using reinforcement learning and linear value-function approximations.

A robust, efficient video analytic tool is presented for detection and estimation of driver’s head position and orientation in the SHRP 2 NDS dataset. It is shown that by exploiting the temporal correlations between successive frames in a video, challenging conditions such as lighting variations, and lower resolutions, may be mitigated effectively. The Viola Jones frontal face detector provides initial results. Then, statistical inference methods for spurious faces elimination and missing driver’s head detection are developed.

The scalability of applications is a key requirement to improving performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) increases programmer productivity to implement hybrid workflows that scale to multi-GPU systems.

In this paper, we focus on the problem of joint load scheduling and generation management to better match supply and demand. We formulate an optimization problem to jointly minimize the generation cost and discomfort cost of the users subject to the voltage and power balance equations for the equivalent circuit of the power system. The optimal power flow (OPF) equations are solved using semidefinite programming (SDP) relaxation technique. In our system model, we assume that users can exploit renewable energy resources (RERs).