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.

Direct digital manufacturing (DDM) is the practice of creating a physical part directly from a computer-aided design (CAD) model with minimal process planning. DDM is typically applied in additive manufacturing (AM) processes, which offer rapid implementation and the ability to produce complex geometry. However, the treatment of subtractive manufacturing (SM) in the context of DDM is still lacking. SM is a decades- old process that is responsible for the production of many parts in the world today. SM and AM have distinct advantages and disadvantages.

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.

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.

Assistive machines, like robot arms mounted on powered wheelchairs, promote independence and ability in those with severe motor impairments. Users of these assistive robots can teleoperate their devices to perform activities of daily living like eating and self care. However, as these machines become more capable, they often also become more complex. Traditional teleoperation interfaces cover only a portion of the control space, and it is necessary to switch between di erent control modes to access the full control space.