• Projects

  • A Virtual Crisis Information Sharing and Situational Awareness Platform for Collaborative Disaster Response:
    The overall goal of this effort is to work with homeland security and emergency management community to develop techniques to improve resilience and quality of service of information sharing platforms for crisis response. Existing incident management systems rely on underlying internet communication infrastructure for exchanging data and the underlying internet offers very limited quality of service guarantees, especially in situations such as crisis response where first responders and incident commanders are trying to exchange time critical information related to search and rescue operations and containing an emergency situation. The project team is working towards meeting the following objectives to accomplish the goal. (1) Design high-availability solution to support reliable communication with command-and-control centers, (2) Deploy required hardware and software infrastructure at UL Lafayette to demonstrate proof-of-concept, (3) Software implementation to demonstrate high definition video transmission & redundant communications, and (4) Demonstrate the project to key stakeholders that includes public safety and emergency management community. People involved: Mr. Clay Rives, Dr. Mohsen Amini, Dr. Michael Dunaway, Ms. Matin Hosseini, Ms. Sana Tafleen ,Ms. Claire Maxell
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  • NSF Center for Visual and Decision Informatics
    Informatics: Dr. Raju Gottumukkala is the Site Director and co-PI for the National Science Foundation (NSF) Center for Visual and Decision Informatics (CVDI) brings a consortium of researchers and students across multiple universities to advance research and innovation in big data with respect to Internet of Things – specifically how large scale multi-dimensional datasets are analyzed and interpreted using advanced data mining, and visual and perceptual techniques for decision makers. The core strengths of CVDI are in the area of visual analytics, predictive analytics, interactive visualization, and data summarization methods. CVDI is part of NSF industry/university cooperative research program (Ref: http://www.nsf.gov/eng/iip/iucrc/about.jsp) that promotes high-quality industry relevant research and direct technology transfer of ideas, research results and technology to U.S. industry. CVDI received more than $5.1 million in funding since 2012 from industry members, the National Science Foundation and Tekes, a Finnish Funding Agency for Innovation. In its first four years, CVDI completed 25 research projects that developed next-generation technologies that help industry members improve the process of analyzing and interpreting large volumes of data.
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  • Real-time influenza forecasting model
    Seasonal influenza forecasting is a challenging problem, given how the virus strain changes every year, and the environmental factors. Researchers at the Center for Visual and Decision Informatics (CVDI) developed a novel big data based real-time seasonal influenza forecasting technique projects titled “Visual Analytic Approaches for Mining Large-Scale Dynamic Graphs”. This novel influenza forecasting model is a two-stage vectorized time series model that captures the influence of local environmental weather conditions (based on frequent associations between the flu severity and weather conditions). The impacts of environmental conditions and spatiotemporal flu spread characteristics are then integrated into a vectorized time series model to forecast future flu occurrences. CVDI’s influenza forecasting model outperforms existing influenza prediction models. It can be used to forecast patients visiting emergency departments for influenza type illness. This model was nominated as a major technologyy breakthrough by the CVDI Industry Advisory Board People involved: Mr. Siva Venna, Mr. Amirhossein Taveni
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  • Digital Security Modules for Electric Vehicles in Smart Grid
    This is a real-time traffic and fuel prediction system for managing fuel related emergencies for regional evacuation. The system is operational during major evacuations and sponsored by the Department of Energy through the Louisiana’s Department of Natural Resources (funding: $800K). The system uses evacuee behavior data, and data streams from evacuation traffic sensors and fuel monitoring sensors deployed in gas stations along evacuation highways to People involved: Dr. Paul Darby, Mr. Rizwan Merchant, Mr. Andrew Roche, Ms. Camile Chagron
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  • Virtual Business Emergency Operations Center:
    The overall vision of the V-BEOC is to develop a national-scale effective PPP (Public Private Partnership) information sharing communication and coordination platform between IoT devices, people and legacy IT systems for homeland security and emergency management acommunity. Specifically, V-BEOC Portal provides the ability for the emergency management community to seamlessly work with the private sector to inform businesses of impending events, share operational information, distribute situational awareness reports, and leverage private sector resources. Please refer beta.vbeoc.org, www.labeoc.org and www.greatercincinnatir2r.org/ for more information. People involved: Dr. Michael Dunaway, Mr. Clay Rives, Dr. Ramesh Kolluru, Ms. Tina Mouton, Mr. Marchus Shannon
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  • Visual Analytics Sandbox for handling High Volume Data Streams
    This effort funded by NSF will build state-of-the-art visual analytics sandbox to handle extremely fast analytics and facilitate knowledge discovery on data streams arriving from IoT. The system addresses several limitations of existing business intelligence tools in terms of improving the visual analytics performance, through novel data modeling, visualization, graph sampling, and human computer interaction components. This system would be available for use on collaborative research projects related to IoT.
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  • intelligent Levee (iLevee)
    iLevee An intelligent levee real-time surveillance dashboard system that presents the health of the levee using data captured from several thousands of sensors. The project is sponsored by the Louisiana’s Office of Coastal Protection and Restoration Authority and Geocomp Corporation (funding $320K).
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  • Evacuation Traffic & Fuel Demand Prediction Model for the State of Louisiana
    This is a real-time traffic and fuel prediction system for managing fuel related emergencies for regional evacuation. The system is operational during major evacuations and sponsored by the Department of Energy through the Louisiana’s Department of Natural Resources (funding: $800K). The system uses evacuee behavior data, and data streams from evacuation traffic sensors and fuel monitoring sensors deployed in gas stations along evacuation highways to
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