Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters.

Ocean Big Data (OBD) is an emerging area of research that benefits ocean environmental monitoring, offshore exploration, disaster prevention, and military surveillance. It is now affordable for oil and gas companies, fishing industry, militaries, and marine researchers to deploy physical undersea sensor systems to obtain strategic advantages. However, these sensing activities are scattered, isolated, and often follow the traditional "deploy, wait, retrieve, and post-process" routine.

The aim of this collaborative project is to increase the efficiency of utility scale solar arrays using sensors, machine learning and signal processing methods to detect faults and optimize power. New cyber-computing strategies, that rely on sensor data and imaging methods to predict solar panel shading, are used to improve efficiency. A programmable 18kW testbed that consists of 104 panels equipped with sensors, actuators and cameras is used to validate all theoretical results and test new approaches for using solar analytics to optimize power generation.

This project develops a theory of accountability that encompasses both control and computing systems. A unified theory of accountability in Cyber-Physical Systems (CPS) can be built on a foundation of causal information flow analysis, a well-established set of methods for computer security. Information flow properties model how inputs of a system affect its outputs.

This research investigates a cyber-physical framework for scalable, long-term monitoring and maintenance of civil infrastructures. With growth of the world economy and its population, there has been an ever increasing dependency on larger and more complex networks of civil infrastructure as evident in the billions of dollars spent by the federal, state and local governments to either upgrade or repair transportation systems or utilities. Despite these large expenditures, the nation continues to suffer staggering consequences from infrastructural decay.

Proof assistants are a programming technique for writing trustworthy software, in which the programmer writes not only the program code but also a mathematical proof of the code's correctness. An automated proof checker then either verifies that the code is correct or shows where the proof is wrong, thus empowering the programmer to fix incorrect assumptions. This project focuses on the goal of software assurance for autonomous vehicles (AVs), which are complex cyber-physical systems, such as multi-robot teams, that move in the world and interact with one another.

One of the challenges toward achieving the vision of smart cities is improving the state of the underground infrastructure. For example, large US cities have thousands of miles of aging water mains, resulting in hundreds of breaks every year, and a large percentage of water consumption that is unaccounted for. The goal of this project is to develop models and methods to generate, analyze, and share data on underground infrastructure systems, such as water, gas, electricity , and sewer networks.

Two current trends promise to revolutionize the safety, reliability, and energy-efficiency of future automotive transportation: (i) wireless connectivity of vehicles to each other, to smart infrastructure, and to other mobile devices, and (ii) autonomy, ranging from driver assistance to full self-driving autonomy. Connected autonomous vehicles (CAVs) are cyber-physical systems with increasingly complex software algorithms in control of a physical vehicle moving in uncertain real-world environments.

The primary objective of this research is to develop a new computing paradigm for wirelessly powered Internet-of-things (IoT) based devices and enhance their computational capabilities by more than an order of magnitude. The proposed research brings new opportunities to emerging applications such as computational RFIDs (Radio Frequency Identifiers), bio-implantable devices, and structural/environmental monitoring. The results of this research will contribute to the development of smarter IoT devices that are beyond the boundaries of traditional cyber-physical systems.

The security of every vehicle on the road is necessary to ensure the safety of every person on or near roadways, whether a motorist, bicyclist, or pedestrian. Features such as infotainment, telematics, and driver assistance greatly increase the complexity of vehicles: top-of-the-line cars contain over 200 computers and 100 million lines of software code. With rising complexity comes rising costs to ensure safety and security.