Georgia Tech police department is using a proprietary system to manage over 700 cameras on campus. The current approach requires administrators to click on each camera pin on the map and watch the stream to find out useful information, or wade through backup camera videos to track incidents. This process requires a lot of human labor and is not scalable. We use fog computing infrastructure to provide real-time object tracking, with car tracking across space and time as a concrete use-case. 

This CPS project is driven by largescale online multimodal transit feeds to achieve equilibrium in demand and supply relationship in transportation CPS systems through cyber-control.

The project is developing novel architectures for control and diagnosis of complex cyber-­‐physical  systems  subject to stringent performance  requirements  in  terms  of safety, resilience, and adaptivity. These ever-­‐increasing demands necessitate the use of    formal    model-­‐based    approaches    to    synthesize    provably-­‐correct    feedback controllers.

Design of cyber-physical systems today relies on executable models. Designers develop models, simulate them, nd defects, and improve their designs before the system is built, thus greatly reducing the design costs. However, current model-based design methods lack support for model libraries (creating and exchanging models as "black boxes"), tool interoperability (allowing models to be co-simulated by multiple tools), and multi-view modeling (allowing to combine models that "live in different worlds", for instance, a control-logic model with an energy-consumption model).

The artificial pancreas (AP) is a set of increasingly sophisticated devices and algorithms that will automate the delivery of insulin to patients with type-1 diabetes. While the AP concept promises to alleviate the burden posed by the self-management of blood glucose levels, it also poses significant risks arising from a combination of external disturbances such as patient meals, physical activity, sensor errors, network delays and physiological variations.

Our proposal’s main objective is to realize cyber-physical platform and principles for (i) interrogating global modalities of intracellular transport with causative factors isolated at the single-molecule scale and (ii) realizing efficient and robust infrastructure for transporting micron/molecular scale cargo using distributed strategies We are realizing in-vitro, a transport network with roadways formed by microtubules where motorproteins, kinesin and dynein, will form vehicles ferrying cargo.

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.

Performance monitoring data (e.g., measurements, logs, events) are becoming increasingly accessible and abundant (in terms of cost and availability) in modern distributed complex systems such as computer systems and networks, integrated buildings, industrial systems, transportation networks and power-grids. With efficient exploration of such data, health monitoring, diagnosis and prognosis can be greatly improved from the current state-of-the-art.