DARPA’s Processor Reconfiguration for Wideband Sensor Systems (PROWESS) program aims to develop high-throughput, streaming-data processors that reconfigure in real time to detect and characterize novel signals. Through processors that self-reconfigure within 50 nanoseconds, PROWESS will enable “just-in-time” synthesis of processing pipelines in uncertain environments. PROWESS will allow future receivers to optimize performance to both measured spectrum conditions and the needs of cognitive RF decision logic.

Gun violence and the reckless use of firearms have been a pervasive and growing problem in the United States, especially in low-income communities and communities of underrepresented minorities. The problem has only become exacerbated by increasingly permissive gun control laws, easy access to firearms, and an increasing mistrust of law enforcement that often inhibits reporting by the community.

The Future Airborne Capability Environment (FACE®) Standard is an Open Architecture approach to creating reusable, interoperable software for DoD Platforms (https://www.opengroup.org/face). The FACE Consortium was formed in 2010 to define an open avionics environment for all military airborne platform types. Vanderbilt has been an Academia team member supporting the FACE standard development effort since its inception in 2009 assisting in the formative concepts and continues to provide key technical

This project aims to develop a new Science of Design for societal-scale Cyber- Physical Systems (CPS).

The Cyber-Physical Systems Virtual Organization (CPS-VO) was founded by NSF in 2010 to: (i)facilitate and foster interaction and exchanges among CPS PIs and their teams; (ii) enable sharing of artifacts and knowledge generated by the projects with the broader engineering and scientific communities; and (iii) facilitate and foster collaboration and information exchange between CPS researchers and industry. During the last five years, the CPS-VO has become the focal point of the CPS community in the US and it has played a significant role in catalyzing CPS research world-wide.

Transportation accounts for 28% of the total energy use in the United States and as such, it is responsible for immense environmental impact, including urban air pollution and greenhouse gas emissions, and may pose a severe threat to energy security. As we encourage mode shift from personal vehicles to public transit, it is important to consider that public transit systems still require substantial amounts of energy; for example, public bus transit services in the U.S. are responsible for at least 19.7 million metric tons of CO2 emission annually.

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service.

The rapid evolution of data-driven analytics, Internet of things (IoT) and cyber-physical systems (CPS) are fueling a growing set of Smart and Connected Communities (SCC) applications, including for smart transportation and smart energy. However, the deployment of such technological solutions without proper security mechanisms makes them susceptible to data integrity and privacy attacks, as observed in a large number of recent incidents. If not addressed properly, such attacks will not only cripple SCC operations but also influence the extent to which customers are willing to share data.

Anomaly detection, prognostication and automated mitigation are very critical for data center management. Most of these approaches can be divided into two categories - model-based and data-driven. While model-based techniques rely on physics guided models that can explain and predict the expected progression of parameters such as temperature and voltage in electronics, the data-driven approach is suitable for complex scenarios where a suitable physics based model is unavailable. The data-driven approaches can be further divided into supervised and unsupervised methods.