Growing demands on our civil infrastructure have heightened the need for smart structural components and systems whose behavior and performance can be controlled under a variety of loading scenarios such as high winds and earthquakes. However, due to the sheer size, scale and cost of most civil engineering structures, design and testing of such smart structures needs to be conducted using a hybrid cyber-physical approach where the infrastructure system in question, for example a bridge, is studied by coupling a small number of physical components with a numerical model of the rest of the structure. Undoubtedly, the success of such a hybrid approach, especially for dynamic real-time applications, hinges on effective integration of the cyber and physical components of the system. This project provides the essential building blocks and a computational integration platform to enable real-time hybrid testing of civil engineering structures. Design and development of physical components, multi-level numerical models, and real-time control algorithms will be conducted at Purdue University. Washington University will provide an adaptive, configurable concurrency platform and communication mechanisms that meet the strict scheduling constraints of real-time cyber-physical systems. The two institutions will collaboratively design a prototype system and conduct extensive testing to validate the integration of the various components and evaluate system performance. Specifications, software, benchmarks, and data developed during the course of this project will be made freely available to the cyber-physical research community. In addition to directly advancing the state-of-the-art in real-time hybrid testing, this research will also impact the areas of avionics, automotive design, smart grids for distributed power transmission and similar applications in other domains.

Growing demands on our civil infrastructure have heightened the need for smart structural components and systems whose behavior and performance can be controlled under a variety of loading scenarios such as high winds and earthquakes. However, due to the sheer size, scale and cost of most civil engineering structures, design and testing of such smart structures needs to be conducted using a hybrid cyber-physical approach where the infrastructure system in question, for example a bridge, is studied by coupling a small number of physical components with a numerical model of the rest of the structure. Undoubtedly, the success of such a hybrid approach, especially for dynamic real-time applications, hinges on effective integration of the cyber and physical components of the system. This project provides the essential building blocks and a computational integration platform to enable real-time hybrid testing of civil engineering structures. Design and development of physical components, multi-level numerical models, and real-time control algorithms will be conducted at Purdue University. Washington University will provide an adaptive, configurable concurrency platform and communication mechanisms that meet the strict scheduling constraints of real-time cyber-physical systems. The two institutions will collaboratively design a prototype system and conduct extensive testing to validate the integration of the various components and evaluate system performance. Specifications, software, benchmarks, and data developed during the course of this project will be made freely available to the cyber-physical research community. In addition to directly advancing the state-of-the-art in real-time hybrid testing, this research will also impact the areas of avionics, automotive design, smart grids for distributed power transmission and similar applications in other domains.

This project will construct a wireless network of animal-borne embedded devices that will be deployed and tested in a biologically-relevant application. The networked devices will provide not only geo-location data, but also execute cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. This project comprises three concurrent and interdependent research themes. The first is the investigation of methods to design and analyze the performance of distributed algorithms that implement autonomous decisions at the mobile agents, subject to communication and computational constraints. The second will pursue data-driven fundamental research on the modeling of animal group motion and will promote a formal understanding of the mechanisms of social interaction. The third is centered on the investigation of methods for hardware integration to build distributed networks of embedded devices that are capable of executing the newly developed algorithms, subject to power and weight constraints. The results and experience gained in this project will guide the development of effective autonomous systems for the monitoring and protection of endangered species. This project will create undergraduate and graduate research opportunities at all participating institutions, expanding on an existing collaboration between the University of Maryland, Princeton University, and the National Geographic Society. There is the potential for using wide-reaching media resources to disseminate the results of this project to a broad audience. This may contribute to attracting more students to engineering and science.

This project will construct a wireless network of animal-borne embedded devices that will be deployed and tested in a biologically-relevant application. The networked devices will provide not only geo-location data, but also execute cooperative strategies that save battery-life by selectively recording bandwidth-intensive audio and high-definition video footage of occurrences of animal group behavior of interest, such as predation. This project comprises three concurrent and interdependent research themes. The first is the investigation of methods to design and analyze the performance of distributed algorithms that implement autonomous decisions at the mobile agents, subject to communication and computational constraints. The second will pursue data-driven fundamental research on the modeling of animal group motion and will promote a formal understanding of the mechanisms of social interaction. The third is centered on the investigation of methods for hardware integration to build distributed networks of embedded devices that are capable of executing the newly developed algorithms, subject to power and weight constraints. The results and experience gained in this project will guide the development of effective autonomous systems for the monitoring and protection of endangered species. This project will create undergraduate and graduate research opportunities at all participating institutions, expanding on an existing collaboration between the University of Maryland, Princeton University, and the National Geographic Society. There is the potential for using wide-reaching media resources to disseminate the results of this project to a broad audience. This may contribute to attracting more students to engineering and science.