The Army Research Office has awarded a MURI grant to Ness Shroff and collaborators from Penn State, Harvard, Duke and the University of British Columbia. This project, led by Penn State, aims at understanding how data centric organization of sensor networks can enable efficient data fusion of spatial-temporal events in urban environments. This has become a critically important problem given the monitoring and sensing needs in the military's fight against global terrorism and the Department of Defense's use of network centric warfare.
NSF is supporting Bruce Weide and Harvey Friedman (Mathematics) on a new project, Logical support for verification. This collaboration among logicians and software engineering researchers also involves Jeremy Avigad from Carnegie Mellon University and Murali Sitaraman from Clemson University. The team will undertake a number of specific projects in mathematical and software verification that are considered key to the Reusable Software Research Group's vision of addressing the 'verifying compiler' grand challenge.
Xiaodong Zhang leads a collaborative NSF NeTS-NOSS grant entitled LeapNet: self-adaptable all terrain sensor networks. He and his collaborators, Li Xiao, Matt Mutka, and Ning Xi from Michigan State University, will address algorithmic and system issues for sensors to be deployed in the areas of difficult terrain and natural obstacles, where radio signals can be partially or fully blocked.
NSF has awarded OSU a Human and Social Dynamics award entitled Using machine learning to model the interplay of production dynamics and perception dynamics in phonological acquisition led by Mary Beckman (Linguistics) and Eric Fosler-Lussier. The collaborative award, along with researchers at the Universities of Wisconsin and Minnesota, will adapt acoustic modeling techniques for robust Automatic Speech Recognition (ASR) to a large, multi-language database of adult and child speech recordings, in order to explore how cognitive representations relevant to speech production and perception in any given speech community come to be internalized by normally developing children.
Ron Li (Mapping and GIS Laboratory) and DeLiang Wang (CSE and Perception and Neurodynamics Laboratory) have been awarded a National Geospatial Agency University Research Initiatives (NURI) grant to support a project that uses both biologically and geometrically inspired methods for automatic target recognition from multispectral/hyperspectral, multi-scale and multiplatform images. This project intends to develop a system that quickly analyzes and extracts information from remote sensing images covering large areas.
Ness Shroff and researchers from the University of Illinois, Urbana Champaign, Purdue, Princeton, and UT Austin have recently received a 1.2 million dollar grant from NSF to develop a scientific foundation for designing network architectures. The project aims to develop a rigorous analytic framework for designing such architectures by building on the PI's recent successes in understanding protocols as optimizers and layering as mathematical decompositions.
Ness Shroff and Prasun Sinha have received a NSF NeTS-NOSS grant to investigate energy efficiency in sensor networks entitled Energy-efficient distributed sensor network control: Theory to implementation. Energy is a critical component in the emerging area of sensor networks, and its efficient use could lead to significant improvements in the lifetime, quality of service, security, and cost of these networks. The aim of this project is to develop high-performance, cross-layer control mechanisms for sensor networks that are simple, distributed, and robust. This is a joint project with Prof. Lin of Purdue University.
Prasun Sinha has received a NSF NeTS-NOSS award entitled Doing More with Less: Tracking Movements Using a Sparse Sensor Network. This collaborative project with Santosh Kumar ('06 CSE) of the University of Memphis, proposes to establish a strong foundation for all large scale movement tracking applications and address the key systems issues faced in such applications. This project proposes a novel model of coverage called Trap Coverage that can be used for systematic deployment of sparse sensor networks, while ensuring frequent tracking of movements of interest. The proposed Trap Coverage model allows for holes of bounded size in the deployment, leading to substantial savings in total number of sensors required to provide coverage.
NSF has awarded Srinivasan Parthasarathy a Small Grant for Exploratory Research (SGER) entitled An Event Based Framework for Analyzing Dynamic Interaction Data. The main scientific outcome or intellectual merit of this research will include the ability to extract, analyze and understand key features of such dynamic interaction networks in the context of end applications drawn from clinical and social settings.
Donna Byron will collaborate with Joy Hanna of Oberlin College Department of Psychology on a newly awarded NSF project entitled Establishing and Breaking Conceptual Pacts with Dialog Partners. The project will apply recent psycholinguistic results to investigate how conversations between people and computer-generated characters can be made more efficient.