WORKING DRAFT: The broader impacts of this I-Corps Hub: Mid-Atlantic are to develop programs and resources that will accelerate the translation of fundamental deep technology research to practical applications (particularly in engineering disciplines) that will increase US economic competitiveness and improve the well-being of society . Hub activities will result in increased partnerships between academia, industry, and others in the Mid-Atlantic Region and further on a national scale. The Mid-Atlantic Hub will actively work to engage the participation of underrepresented populations and persons with disabilities, as well as individuals from diverse economic regions and backgrounds, in all activities and programs. The Mid-Atlantic-Hub research will investigate utilization of business and industry mentors, the best practices in delivering I-Corps training, effectiveness of programs to engage researchers from underrepresented populations and various methods of interaction to guide researchers exploring commercialization of STEM technologies. The training and materials developed by the Mid-Atlantic-Hub, lessons learned, and the research findings will be shared and disseminated widely across the Mid-Atlantic region and nationally. This Mid-Atlantic Hub will deliver I-Corps training programs at research universities throughout the District of Columbia, Maryland, North Carolina, Pennsylvania, and Virginia and will centralize, standardize and grow the pool of certified national and regional instructor pool based on NSF I-Corps best practices. The Mid-Atlantic-Hub will train teaching team from Affiliate Universities, investigate methods of assessing I-Corps team composition and alternative models of mentoring. The Mid-Atlantic Hub will explore creative and potentially transformative concepts and widely disseminate findings, lessons learned, new content and training materials across the region and the National Innovation Network (NIN). The Mid-Atlantic Hub will build and coordinate a growing and robust pipeline of teams from across the region for the National I-Corps Team program. It will provide additional support and nurturing for those teams to continue to advance after completing the team program. This project will enable the Mid-Atlantic Hub to reach and influence researchers across the region guiding them on how to translate their inventions and discoveries into products, services, and organizations that benefit society and the marketplace.

Data-Enabled, Interdisciplinary Research Traineeships in the Science and Engineering of Atomic Structure (SEAS) are proposed at the intersection of materials research, statistics, mathematics, and education. The vision of the SEAS traineeship program is to train a new generation of interdisciplinary, data-driven physical scientists who can develop and apply advanced statistical methods to the data being generated from cutting-edge scattering and imaging experiments; SEAS specifically addresses the NRT priority area of Data-Enabled Science and Engineering (DESE). SEAS graduates will be prepared to develop new ways to analyze data (including ����������������Big Data���������������) coming from a new and evolving generation of atomically sensitive instruments, including modern synchrotron and free electron laser X-ray sources, reactor and spallation neutron sources, and state-of-the-art electron microscopes. SEAS trainees and the tools they create (e.g., algorithms, software) are urgently needed in the field of materials science and physics, where instrumentation for materials research has evolved significantly faster than the ability to properly analyze the data. The effort integrates advanced instrumentation, data analysis, and computational tools, consistent with the NSF Strategic Plan and contributes directly and indirectly to the national Materials Genome Initiative (MGI), a multi-agency initiative spearheaded by the White House that advances the U.S. economy by enabling faster deployment of new materials. The proposed SEAS traineeships are the result of a two-year planning effort at NC State to bring together the materials science and statistics communities, including hosting of two interdisciplinary workshops and seeding interdisciplinary collaborative research projects (sponsored by the Kenan Institute for Engineering, Technology and Science and the Eastman Chemical Company - University Engagement Fund). Under SEAS, NC State will collaborate with staff scientists from national user facilities to pilot a new graduate training model for interdisciplinary traineeships in this national priority area. The effort will leverage several programs and initiatives at NC State, including the Analytical Instrumentation Facility (AIF, Director: J. Jones), Laboratory for Analytic Sciences (LAS, PI: Wilson), Statistical and Applied Mathematical Sciences Initiative (SAMSI, former A/Director: Smith), Data Sciences Initiative (DSI, Founding Director: Vouk), Research Triangle Nanotechnology Network (RTNN, Director: J. Jones), Center for Dielectrics and Piezoelectrics (CDP, Director: Dickey), and the Data-Driven Science cluster (Coordinator: Wilson).

Since 1995, the Triangle Computer Science Distinguished Lecturer Series (TCSDLS) has been hosting influential university researchers and industry leaders from computer-related fields as speakers at the three universities within the Research Triangle Area. The lecturer series, sponsored by the Army Research Office (ARO), is organized and administered by the Computer Science departments at Duke University, NC State University, and the University of North Carolina at Chapel Hill. This proposal argues for continuation, for an additional 3 years, of this highly successful lecturer series which is being led by Duke University.

The proposed work examines novel services built on top of public cloud infrastructure to enable cost-effective high-performance computing. We will explore the on-demand, elastic, and configurable features of cloud computing to complement the traditional supercomputer/cluster platforms. If successful, this research will result in tools that adaptively aggregate, configure, and re-configure cloud resources for different HPC needs, with the purpose of offering low-cost R&D environments for scalable parallel applications.

Jamming resistance is crucial for applications where reliable wireless communication is required, such as rescue missions and military applications. Spread spectrum techniques such as Frequency Hopping (FH) and Direct Sequence Spread Spectrum (DSSS) have been used as countermeasures against jamming attacks. However, these anti-jamming techniques require that senders and receivers share a secret key to communicate with each other, and thus are vulnerable to insider attacks where the adversary has access to the secret key. The objective of this project is to develop a suite of techniques to defend against insider jammers in DSSS and FH based wireless communication systems. We will develop novel and efficient insider-jamming-resistant techniques for both DSSS- and FH-based wireless communication systems. Our proposed research consists of two thrusts. The first thrust is to develop novel spreading/despreading techniques, called DSD-DSSS (which stands for DSSS based on Delayed Seed Disclosure), to enhance DSSS-based wireless communication to defend against insider jamming threats, while the second thrust is to develop a new approach, called USD-FH (which stands for FH based on Uncoordinated Seed Disclosure), to enable sender and receivers using FH to communicate without pre-establishing any common secret hopping pattern. A key property of our new approaches is that they do not depend on any secret shared by the sender and receivers. Our solution has the potential to significantly enhance the anti-jamming capability of today?s wireless communication systems.

Simulating realistic mobility patterns of mobile devices is important for the performance study of mobile networks because deploying a real testbed of mobile networks is extremely difficult, and furthermore, even with such a testbed, constructing repeatable performance experiments using mobile devices is not trivial. Humans are a big factor in simulating mobile networks as most mobile nodes or devices (cell phones, PDAs and cars) are attached to or driven by humans. Emulating the realistic mobility patterns of humans can enhance the realism of simulation-based performance evaluation of human-driven mobile networks. Our NSF-funded research that ends this year has studied the patterns of human mobility using GPS traces of over 100 volunteers from five different sites including university campuses, New York City, Disney World, and State Fair. This research has revealed many important fundamental statistical properties of human mobility, namely heavy-tail flight distributions, self-similar dispersion of visit points, and least-action principle for trip planning. Most of all, it finds that people tend to optimize their trips in a way to minimize their discomfort or cost of trips (e.g., distance). No existing mobility models explicitly represent all of these properties. Our results are very encouraging and the proposed research will extend the work well beyond what has been accomplished so far. . We will perform a measurement study tracking the mobility of 100 or 200 students in a campus simultaneously, and analyze the mobility patterns associated with geo-physical and social contexts of participants including social networks, interactions, spatio-temporal correlations, and meetings. . We will cast the problem of mobility modeling as an optimization problem borrowing techniques from AI and Robotics which will make it easy to incorporate the statistical properties of mobility patterns commonly arising from group mobility traces. The realism of our models in expressing human mobility will surpass any existing human mobility models. . We will develop new routing protocols leveraging the researched statistical properties found in real traces to optimize delivery performance. The end products of the proposed research is (a) a new human mobility model that is capable of realistically expressing mobility patterns arising from reaction to social and geo-physical contexts, (b) their implementation in network simulators such as NS-2/3 and GloMoSim, (c) mobility traces that contain both trajectories of people in a university campus and contact times, (d) new efficient routing protocols for mobile networks

This is a pilot internship program between NCSU and Cisco for 4 undergraduate students to learn through working part-time on real life problems for Cisco with the hope that this pilot program can grow and develop into a long term working relationship. Specifically, NCSU students will participate in Cisco Software Application Support plus Upgrades (SASU) projects and/or conduct research for SASU. This will be done with an understanding that the interns are students, and as such are learning and being trained with the training coming from both the Cisco (for SASU-specific skills), and NCSU (through the undergraduate program they are enrolled in) in general relevant skills.

In partnership with industry and faculty from across the country, this project will develop a transformative approach to developing the communication abilities (writing, speaking, teaming, and reading) of Computer Science and Software Engineering students. We will integrate communication instruction and activities throughout the curriculum in ways that enhance rather than replace their learning technical content and that supports development of computational thinking abilities of the students. We will implement the approach at two institutions. By creating concepts and resources that can be adapted by all CS and SE programs, this project also has the potential to increase higher education's ability nationwide to meet industry need for CS and SE graduates with much better communication abilities than, on average, is the case today. In addition, by using the concepts and resources developed in this project, CS and SE programs will be able to increase their graduates' mastery of technical content and computational thinking.

In this project, we aim at developing a new indoor localization technique relying on low-frequency radio that can penetrate indoor obstacles (or detour obstacles by diffraction in the shortest path) by its long wave characteristics. The smartphone running this system would be able to identify its position by measuring straight-line distances from a few radio transmission towers deployed in a city scale (or in a district scale). Straight-line distances that have not been affected by indoor obstacles would be able to provide a three-dimensional position including floor information and position information in the floor (e.g., store information in a shopping complex).

This program will establish a national Secure Open Systems Institute (SOSI), located on North Carolina State's premier Centennial Campus that will be a global center for Open Systems security research and development.