With the growing world population and diminishing agricultural lands, it becomes imperative to maximize crop yield by protecting crop health and mitigating against pests and diseases. Though there are decades-old practices still in place, there is also growing adoption of so-called precision agriculture solutions, which employ emerging technologies in sensing, automation, and analytics in daily farmland operations.

The goal of this research is to enable a broad spectrum of programmers to successfully create apps for distributed computing systems including smart and connected communities, or for systems that require tight coordination or synchronization of time. Creating an application for, say, a smart intersection necessitates gathering information from multiple sources, e.g., cameras, traffic sensors, and passing vehicles; performing distributed computation; and then triggering some action, such as a warning.

This NSF CPS CAREER project studies the hardware/software co-design of sub-millisecond machine learning control for high-rate dynamic systems with non-stationary inputs that change the system?s state (i.e., damage). Such systems include combustion processes in jet engines, vehicle structures during crashes, and active blast mitigation structures. The novelty of the approach taken in this project is to co-design the control systems with the computing hardware they will run on to constrain system latency to within 1 millisecond.

The interactions of light with objects in a scene are often complex. An image --- which only captures 2D spatial variations --- is poorly equipped to unravel these interactions and infer properties of a scene including its shape, reflectance, and its composition. This is especially true for scenes that have sharp reflections, refractions, and volumetric scattering. This research models interactions of light with scenes using light rays and their transformations.

Wearable sensors show much promise for medical, sports, defense, emergency, and consumer applications, but are currently limited to obtrusive implementations. Akin to the evolution of cell phones that evolved from foot-long prototypes to recent smart devices, next-generation wearables are envisioned to be seamlessly embedded in fabrics. This CAREER project aims to understand the unique challenges of operating such textile sensors ?in-the-wild? and to empower their reliable operation via closed-loop interaction among fabrics, electronics, and humans.

Cognitive workload refers to the level of mental effort put forth by an individual in response to a cognitive task. Unfortunately, no technology currently exists that can monitor an individual?s levels of cognitive workload in real-world environments using a seamless, reliable, and low-cost approach. We propose to fill this gap by using a novel magnetocardiography (MCG) system worn upon the subject?s chest to allow the sensor to collect the magnetic fields that are naturally emanated by the heart and associated with brain activity.

Increases in temperatures and drought duration and intensity due to climate change, together with the expansion of wildlife-urban interfaces, has dramatically increased the frequency and intensity of forest fires, and has had devastating effects on lives, property, and the environment. To address this challenge, this project?s goal is to design a network of airborne drones and wireless sensors that can aid in initial wildfire localization and mapping, near-term prediction of fire progression, and providing communications support for firefighting personnel on the ground.

Today, operators of cellular networks and electricity grids measure large volumes of data, which can provide rich insights into city-wide mobility and congestion patterns. Sharing such real-time societal trends with independent, external entities, such as a taxi fleet operator, can enhance city-scale resource allocation and control tasks, such as electric taxi routing and battery storage optimization. However, the owner of a rich time series and an external control authority must communicate across a data boundary, which limits the scope and volume of data they can share.

The proposed decentralized/distributed control and optimization for the critical cyber-physical networked infrastructures (CPNI) will improve the robustness, security and resiliency of the electric distribution grid, which directly impacts the life of citizens and national economy.

This award will support a conference for the development of a roadmap for the control community that identifies societal-scale challenges and research paths for addressing them. The roadmap will seek to identify key societal drivers, emerging technological trends, and outline methodologies that squarely address the fundamental challenges by embracing these drivers and leveraging advances in enabling technologies.