An AC computing methodology is proposed for integration in wirelessly powered devices such as RF tags and sensor nodes. Contrary to traditional platforms that integrate DC-powered computational logic along with the rectification and regulation stages, in the proposed approach, the harvested RF signal is directly used to power the data processing circuitry by leveraging charge-recycling and adiabatic circuit theory.

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet.

Robust Linear Temporal Logic (rLTL) was crafted to incorporate the notion of robustness into Linear-time Temporal Logic specifications. Robustness is ubiquitous in control systems and translates the intuitive notion that “small” violations of environment assumptions should only lead to “small” violations of system guarantees. This notion was formalized in the logic rLTL via 5 different truth values and it led to an increase in the time complexity of the associated model checking problem.

Explanation of Demonstration: We will demonstrate wearable ultrasound imaging sensors for intuitively controlling assistive devices such as prosthetic hands and exoskeletons.

This project is focused on developing data analytics and decision-making techniques for early detection and mitigation of soybean diseases via fusing data from ground robots, UAVs and satellites. We aim to collect RGB and hyperspectral image data for soybean diseases from research farms at Iowa State and via collaboration with the Iowa Soybean Association and the NASA Jet Propulsion Lab (for satellite data).

Overview. The fundamental challenge in cyber-physical systems is the confluence of distinct scientific and engineering models, methods, and tools for cyber and physical systems. Cyber systems are primarily about processing information, formally modeled as patterns of bits. Physical systems are primarily about structure and dynamics, the evolution of the state of the system in time. There are certainly connections between these models, methods, and tools. For example, cyber methods may be used to build simulations of physical systems, and physical systems (e.g.

Explanation of Demonstration: We will demonstrate how our layered framework of sensors, models, land-use information and citizens can be used to understand community-scale differences in pollution levels. The demonstrations, in the form of videos, will illustrate, how local policies on fireworks can dramatically affect pollution levels and how such a framework can act as an early warning for smoke from a nearby wildfire.