Dejan Raskovic, Ph.D. - US grants

The next generation of wireless is in distributed sensor networks. Applications in this area are a rapidly growing research area. The project will investigate possibilities for coordinated action to improve the energy efficiency in distributed sensor network on all levels, from software to component design to system integration. The overall goal and impact of this will be the decrease in the cost of sensor network, allowing wider, more capable deployments for generalized information gathering and surveillance. A test-bed environment for energy-efficiency and performance evaluation of reconfigurable hierarchical networks of sensors will be established at the Center for Nanosensor Technology at the University of Alaska Fairbanks. Another anticipated benefit of such intelligent surveillance sensor networks is the extension of the same principles to everyday applications of distributed sensor networks.

This project from an EPSCoR state, developing a hardware-in-the-loop simulator (HILS) for mechatronic systems including robotics and hybrid electric vehicles, services 2 on-going projects and two new ones.
Remotely controllable PUMA 560 Robot,
Modeling and Simulation of HEV's,
Remotely Controllable HILS for direct drive robot configuration, and
Remotely Controllable HILS for geared robot configuration.
The projects involve the remote control of an open architecture robot. PUMA, a staple learning robot, services introductory robotics. The project allows and encourages conducting different remote experiments.

Broader Impact: The platform services the wider Alaska community allowing hands-on work in courses that are currently limited to only theory, thus providing strong outreach educational opportunities.

Accurate knowledge of sea ice thickness over large scales is crucial for understanding the current and future states of the Arctic ice cover, and for near- and long-term predictions of Arctic marine environments. With the Arctic ice pack undergoing a major transition from perennial to seasonal ice, ice thickness - more so than ice extent - is a key variable describing the state and evolution of the ice-ocean system. However, methods of observing sea ice thickness at regional or basin scales with sufficient accuracy and resolution to capture growth and melt processes, detect hazards, or assess habitat quality are lacking. This project will develop an Airborne electromagnetic (AEM) snow radar system capable of being integrated into long-range Unmanned Aerial Systems (UAS). This will allow acquisition of basin-scale ice thickness and snow depth data as part of a network for Arctic observations that addresses information needs of researchers, local communities and industry. This MRI development project will contribute to NSF's Navigating the New Arctic Big Idea.

AEM methods offer a novel means of measuring sea ice thickness over the full range of thicknesses found in the Polar Regions. By remotely sensing the positions of the upper and lower surfaces of the ice cover, AEM measurements typically achieve an accuracy of better than 10% of the total thickness, with less sensitivity to uncertainties in snow cover or sea surface topography. Development and commissioning of the Long-range Airborne Snow and Sea Ice Thickness Observing System (LASSITOS) will also provide opportunities for education and training, including capstone projects for the University of Alaska Fairbanks' new minor in aeronautical engineering and student involvement in comprehensive calibration/validation field activities. LASSITOS is expected to generate interest among native students from coastal villages in northern Alaska, who represent another key stakeholder group for sea ice information. The leader of this project is an early-career researcher.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.