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New cyber technologies are providing profound opportunities for network-wide autonomy in critical infrastructures, but also introducing new complexities in infrastructure operations. At its essence, achieving network-wide autonomy requires coordinating sensing and control resources across a dynamical network to achieve resilient operations in the face of disruptions.

Greenhouse gases emitted by the manufacturing of building materials makes up 11% of global emissions. While the building industry has made significant progress toward eliminating carbon from building operations, material emissions have been flying under the climate radar. The urgency of reducing global carbon emissions in the next decade puts building materials on center stage – these emissions have an impact on our environment before materials even arrive to the jobsite.

This dissertation considers various machine-learning-based signal processing architectures for equalization and detection of two- and three-dimensional magnetic recording signals for hard disk drives (HDDs). Recording in multiple dimensions on magnetic hard drives has been a challenge in the HDD industry. The objective of reading approaches for magnetic recording is to detect the highest density of information possible with an acceptable error rate.