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.

Machining is a fundamental processing capability critical to production of end-user goods and systems, as well as components used in virtually every industrial process. Machine tool programming to support these processes is critical for production and cost estimation. Unfortunately, currently available automated process planning methods constrain the process along geometrically simple trajectories due to computational constraints, this at the sacrifice of final processing efficiency.

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.

Explanation of Demonstration: Traffic waves will arise in the absence of bottlenecks as a result of human driving behavior alone. We conduct an experiment on a ring-road track to demonstrate the ability of a single autonomous vehicle in a flow of human-piloted vehicles to dampen these waves. In this demonstration, we presented a virtual reality video of the experiment.

Vehicle systems, being either ground/air/water vehicles, require hundreds/thousands of battery cells to meet their power and energy needs. In this project, we aim to develop comprehensive management solutions of such large-scale batteries with the joint consideration of peak power, operation time, and battery life for vehicle electrification.

The prevalence of battery-powered systems such as electric vehicles, smartphones, and IoT devices has made batteries crucial to everyone's daily life and business. Battery health, however, degrades over time, not only decreasing system reliability such as unexpected system shutoffs, but also causing overheating/gassing which, in turn, increases safety risks such as thermal runaway or even battery fire/explosion. To address these problems, we must monitor, prognose, and optimize battery health throughout the physical system life.

Battery-free sensors are annually attached to billions of items including pharmaceutical drugs, clothes, and manufacturing parts. The fundamental challenge with these sensors is that they are only reliable at short distances. As a result, today’s systems for communicating with and localizing battery-free sensors are crippled by the limited range. This research proposes a cyber-physical system architecture that can overcome this challenge to enable sensing, communicating with, and localizing these sensors at an unprecedented scale.

Stroke is a leading cause of long-term disability, with 80% of survivors having locomotor impairments. Individuals after stroke typically present with hemiparetic gait, characterized as slow, asymmetric, and inefficient. Our lab has been developing soft wearable cyber-physical sytems, called soft exosuits, that interface with paretic limb after stroke through soft and conformal textile-based structures to assist hemiparetic walking. Soft exosuits transmit mechanical power to human body joints via interaction between functional textiles worn on the body and Bowden cable retraction.