Abstract: Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Abstract: Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.

Abstract: Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Abstract: Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Abstract: Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.

Abstract: Various embodiments of a cyber-physical system for providing cloud prediction for photovoltaic array control are disclosed herein.

Abstract: The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

Abstract: The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

Abstract: A system and a method for detecting events in time-series data are disclosed, wherein the time-series data represent atomic activities sensed by sensors in an environment, and wherein each atomic activity includes a time and a location at which the each atomic activity is sensed, comprising the steps of: mapping a specified event to a Petri net (PN), wherein the specified event is a spatio-temporal pattern of the atomic activities, wherein the spatio-temporal pattern is based only on the time and the location of the atomic activities, such that a spatio-temporal sequence of the atomic activities forms a primitive activity, and the spatio-temporal pattern includes primitive activities and constraints on the primitive activities, wherein the constraints are sequential and/or concurrent; and detecting, in the time-series data, a sensed event corresponding the specified event mapped to the PN to produce a result, wherein the detecting is performed by a processor.