This work presents a residential load simulation framework that allows the user to test the effectiveness of peak reduction, peak shifting, and valley filling load management strategies on a representative residential load prior to physical smart sensor and load control deployment. The simulation methodology uses household occupancy, appliance time-of-use, and...
In this work, we study network coding technique, its relation to random matrices, and their applications to communication systems. The dissertation consists of three main contributions. First, we propose efficient algorithms for data synchronization via a broadcast channel using random network coding. Second, we study the resiliency of network coding...
In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...