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Plug-meters benefit many grid and building-level energy management applications like automated load control and load scheduling. However, installing and maintaining large and/orlong term deployments of such meters requires assignment and updating of the identity (labels) of electrical loads connected to them. Although the literature on electricity disaggregation and appliance identification is extensive, there is no consensus on the generalizability of the proposed solutions, especially with respect to the features that are extracted from voltage and current measurements.

This talk focuses on dynamic inference of hidden, time-varying, and sparse social networks that facilitate the diffusion of information (e.g., viral news, or spread of consumer habits). The key novelty in this work is recognizing that the dynamics of hidden networks are sometimes driven by switching behavior between discrete (and finite) states. The advocated model tacitly accounts for switching dynamics, and a novel efficient tracking algorithm that leverages proximal gradient methods is developed.

In the presentation, I will present our research on how to analyze the likelihood of cyber data attacks to power systems.

Inherent unipolar orthogonal frequency division multiplexing (OFDM) schemes have a reduced spectral efficiency (SE) when compared with direct current (DC)-biased optical OFDM (DCO-OFDM). The concept of superposition modulation was proposed as a solution to the SE loss of unipolar orthogonal frequency division multiplexing (U-OFDM) and the power efficiency loss of pulse-amplitude-modulated discrete multitone modulation (PAM-DMT).