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This poster presents a real-time decentralized temperature control scheme via Heating Ventilation and Air Conditioning (HVAC) systems for energy efficient buildings, which balances user comfort and energy saving. Firstly, we introduce a thermal dynamic model of building systems and its approximation. Then a steady-state optimization problem is formulated, which aims to minimize the aggregate deviation between zone temperatures and their set points, as well as the building energy consumption.

Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete

The discrete prolate spheroidal sequences (DPSS's) provide an efficient representation for signals that are perfectly timelimited and nearly bandlimited. Unfortunately, because of the high computational complexity of projecting onto the DPSS basis -- also known as the Slepian basis -- this representation is often overlooked in favor of the fast Fourier transform (FFT). In this presentation, we show that there exist fast constructions for computing approximate projections onto the leading Slepian basis elements.

Energy storage systems are becoming a key component in smart grids with increasing renewable penetration. Storage technologies feature diverse capacity, charging, and response specifications. Investment and degradation costs may require charging batteries at multiple timescales, potentially matching the control periods at which grids are dispatched. To this end, a microgrid equipped with slow- and fast-responding batteries is considered here. Energy management decisions are taken at two stages.

Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the
flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our