Mobile Augmented Reality Applications (MARA)  at UNT

This research develops the science needed to enhance mobile augmented reality applications with (a) Spatial Analysis at UNT (b) Situational awareness, (c) Navigation, (d) Evacuation, and (e) Emergency response. The projects will advance discovery of visualization techniques to permit mobile applications to enhance the viewing of the physical world, while promoting contextualized 3D visualizations, spatial knowledge acquisition and cognitive mapping thereby enhancing situational awareness. The mobile AR application uses existing permanent features such as room numbers and signage in the building as markers to display the floor plan of the building and show navigational directions to the exit. Through the visualization of integrated geographic information systems and real-time data analysis, our mobile AR application provides the current location of the person, the number of exits, and user-specific personalized evacuation routes. The Mobile AR application provides information to support effective decision-making during emergencies for both building occupants and emergency responders.A range of use cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full-scale architectural form visualization.

Data Science and Data Visualization

Data analysis and crime data visualization offer a powerful approach to unraveling the complex dynamics of criminal behavior. Analyzing crime data involves a multifaceted study of different dimensions of crime. This involves examining the types of crimes committed, their frequency, and distribution in different geographic areas. By analyzing these factors, we can find patterns and hot spots that can show areas of criminal activity. The projects include: 1) Analysis of Crime 2) Common links between COVID-19 data and crime data in Baltimore. 3) COVID-19 Data Visualization, 4) Crime Data in Baltimore Visualization, 5) Scientific Data Visualization , 6) Data Analytics: Improve the Quality of Life in Urban Areas.

Active Shooter Response and Training

The goal of this NSF funded project is to developed and evaluate a collaborative immersive environment in VR for active shooter response for BSU campus. Immersive collaborative virtual reality environment also offers a unique way for training in the emergencies for campus safety. The contribution lies in our approach to combining computer simulated agents (AI agents) and user-controlled autonomous agents in a collaborative virtual environment for conducting emergency response training for civilians and security personnel's. The immersive collaborative VR environment offers a unique method for training in emergencies for campus safety.

HoloLens Applications for Building Evacuation and Crime Analysis

Early hands-on experiences with the Microsoft HoloLens augmented/mixed reality device have given promising results for building evacuation & crime analysis applications. A range of use cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full-scale architectural form visualization. We present how the mixed reality technology can provide spatial contextualized 3D visualization that promotes knowledge acquisition and support cognitive mapping.

Multiā€User Virtual Reality (MUVR)

MUVR environments for emergency evacuation drills are developed that include: Subway evacuationairplane evacuation, school bus evacuationVR citynight club disaster evacuationbuilding evacuation, and university campus evacuationOur developed applications show an immersive collaborative virtual reality environment for performing virtual evacuation drills using head displays. Immersive collaborative virtual reality environment offers a unique way for training for emergencies situations. The participant can enter the collaborative virtual reality environment setup on the cloud and participate in the evacuation drill or a tour which leads to considerable cost advantages over large scale real life exercises.

Augmented Reality with Hololens: Building Evacuation

Early hands-on experiences with the Microsoft Hololens augmented/mixed reality device have given promising results for building evacuation application. A range of use cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full scale architectural form visualization. The Hololens is a remarkable tool for moving from traditional visualization of 3D objects on a 2D screen, to fully experiential 3D visualizations embedded in the real world. Our Hololens application gives a visual representation of a computer science building in 3D space, allowing people to see where exits are in the building. It also gives path to the various exits; shortest path to the exist as well as directions to safe zone.

Human Centric Cyber Situation Awareness and Data Visualization

The goal of this project is to explore ways to visualize the Cyber Situational Awareness capability of an enterprise to the next level by developing holistic human centric situational awareness approaches into new systems that can achieve self-awareness. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. The humans are more adept at inferring meaning from graphical objects, links and associations in a data element. The project aims to use virtual reality techniques to visualize the XML data through the use of a Force Directed Node Graph in 3D which renders and updates in real-time. It can be used to visualize computer networks for cyber-attacks.

Virtual Reality Instructional (VRI) modules for Teaching Complex Topics and for Improving Quality of Care and Patient Safety

The goal of this research work is to develop virtual reality instructional (VRI) modules for Teaching, Health Care, Training, and Manufacturing. The projects include 1) create instructional course curriculum modules with more inquiry based problem-solving activities and hand-on experiences based on Gaming and Virtual Reality for teaching complex topics, 2) train integrated care team members to engage patients from vulnerable populations safely and efficiently. 3) development of training modules geared for COVID-19 testing.

Multi-Agent System (MAS)

Two MAS and models are developed and evaluated namely AvatarSim and AvatarSim2. AvatarSim was developed in Java and AvatarSim2 was developed in C# language. The AvatarSim model comprises of three models which are: a) Geometrical Model, b) Social Force Model, and c) Fuzzy behavioral Model. AvatarSim2 model further combines genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. The result of this simulation was very promising as we are able to observe the agents use GA and NN to learn how to find the various exits.

Harnessing the Data Revolution (HDR): Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas

The goal of this NSF funded project is to develop a team-based data science corps program for undergraduate students from Computer Science, Information Systems, and Business integrating both academic training as well as hands-on experience through real-world data science projects. This project is a collaborative effort with the University of Maryland Baltimore County as the coordinating as well as an implementing organization, and the University of Baltimore, Towson University, and Bowie State University as implementing organizations. This project focuses on the city of Baltimore as an exemplar for other cities in the US and across the globe. The project team will collaborate with a number of communities in the city of Baltimore to integrate real-world data science projects into classroom instruction in data science.