By linking the transportation network to the communication grid, connected vehicle (CV) technology facilitates communication among vehicles, the infrastructure, and other road users. This connectivity is enabled through the Dedicated Short Range Communication (DSRC) technology, and creates a synergistic effect that paints a more comprehensive picture of the transportation network than could be observed by each individual road user.

This Cyber-Physical Systems (CPS) project supports research to enable the automated monitoring of building and infrastructure construction projects. The purpose of construction monitoring is to provide developers, contractors, subcontractors, and tradesmen with the information they need to easily and quickly make project control decisions. These decisions have a direct impact on the overall efficiency of a construction project. Given that construction is a $800 billion industry, gains in efficiency could lead to enormous cost savings, benefiting both the U.S. economy and society.

This project is focused on achieving persistent autonomy that advances the ability of autonomous underwater vehicles (AUVs) to perform long-term missions. The approach includes efficient mission planning, energy routing scheduling, adaptable docking system, and low-infrastructure platforms. The goal is to provide practical solutions by lowering deployment and operating costs, while also increasing efficiency, endurance and persistence. 

Autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to wirelessly share visual information with other nearby vehicles.

This project aims to create a Holonic Multiagent System (HMAS) architecture to support transactive energy market of “active consumers” engaged in buying and selling electricity (locally generated from resources such as rooftop solar photovoltaic) in response to real time electricity pricing.  The architecture should require little change to the existing investment in power distribution systems while allowing for the dynamic, adaptive control required to integrate active consumers with current and future combinations of high-variability distributed power sources, such as PV gener

The objective of this research is to develop new forms of tool-supported safety analyses for next-generation integrated medical systems that are based on the concept of medical application platforms (MAP). A MAP is a safety- and security-critical real-time computing platform for (a) integrating heterogeneous devices, medical IT systems, and information displays via a communication infrastructure and (b) hosting application programs (”apps”) that provide medical utility via the ability to both acquire information from and update/control integrated devices, IT systems, and displays.

This project is a component of a larger effort is to develop the foundations of modeling, synthesis and development of verified medical device software and systems from verified closed-loop models of the device and organ(s). This research spans both implantable medical devices such as cardiac pacemakers and physiological control systems such as drug infusion pumps which have multiple networked medical systems.

Although deep learning has been successfully implemented in processing images and video data, it still has limited applications in investigating physical processes for smart process calibration and control. Collaborating with HP research scientists, this study demonstrates how engineering-informed deep learning can assist to understand the powder bed fusion in 3D printing processes. The studies show promising model performance in both prediction quality (e.g., accuracy) and the computational cost.

Abstract: Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-­‐physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world.