Computer Engineering Commons^™

Real-Time Illumination Capture And Rendering On Mobile Devices, Snehal Padhye, James A. Ferwerda

Frameless

We present our efforts to develop methods for rendering 3D objects on mobile devices using real-world dynamic illumination from the user’s environment. To achieve this, we use the front and back cameras on the mobile device to estimate the light distribution in the environment in real time. We then create a dynamic illumination map and render the object at interactive rates in a browser on the device using a web-based graphics API. This project achieves one of the goals of our related work on realistic visualization of virtual objects: to make virtual objects appear to be situated within the scene …

Mixed Reality Prototype Device Showcase: Using Smart Glasses To Enhance Language Access, Aaron Parker, Connor Switenky, Roshan Mathew, Wendy Dannels

Frameless

While the use of American Sign Language (ASL) interpreters and real-time captioning significantly increases deaf individuals’ ability to participate in a wide variety of functions, there are some limitations. The major problem is that current ways of providing communication and information access to deaf people require them to split their attention between the visual focus of the specific content and the interpreter/captioning display. At any given point in time, deaf people are forced to decide what explanation to miss with subsequent effects on topic comprehension and perceptions of the interaction and environments.

Novel 360-Degree Camera, Ian Gauger, Andrew Kurtz, Zakariya Niazi

Frameless

Circle Optics is developing novel technology for low-parallax, real time, panoramic image capture using an integrated array of multiple adjacent polygonal-edged cameras. This technology can be optimized and deployed for a variety of markets, including cinematic VR. Circle Optics’ existing prototype, Hydra Alpha, will be demonstrated.

Warehouse And Logistics: Smart Picking With Vuzix Smart Glasses, Elise Hemink

Frameless

Vuzix is an industry leader in augmented reality (AR) technology. We provide innovative products to an array of industries, a few being defense, security, enterprise, and consumers. Our AR technology provides a perfect balance of engagement in the digital and real worlds thanks to their innovative optics, AI apps and 5G capability.

Creating A Virtual Reality Experience In Service To A Non-Profit Agency, Frank Deese, Susan Lakin, Isabelle Anderson

Frameless

In the summer of 2018, RIT Professors Susan Lakin and Frank Deese discussed with the principal officers of the Society for the Protection and Care of Children (SPCC) in Rochester how the new technology of Virtual Reality might be used to not only impart information to viewers, but generate empathy for those receiving services from the organization as well as those performing those services. Their ultimate goal was to create an experience that could be viewed with VR headsets at fundraising events and on a website using low-cost Google Cardboard.

Exploring The Psychological Consequences Of Distances In Virtual Reality, Gary D. Jacobs

Frameless

This presentation will examine common concepts of traveling between formalized spaces inside virtual reality (VR) experiences.

The common method for traveling in virtual reality is to click on an area or trigger and be transported to that location. These “teleportations”, however, remove the notion of distances from our virtual worlds. This is akin to a magic wand that eliminates the consequences of travel in VR. Often heralded as a boon for the virtual worlds we can create, wherein we can travel to far away lands without lag in time and without effort on the part of the participant. We posit …

Vr Sound Mapping: Make Sound Accessible For Dhh People In Virtual Reality Environments, Ziming Li, Roshan Peiris

Frameless

In-game audio plays an important role in enhancing the sense of reality and immersion in the gaming experience. In many games, sounds are also used to provide notifications and clues which are essential to the gameplay. However, in this case, the DHH (deaf and hard of hearing) players may fail to access the information conveyed by sounds, which degrades their gaming experience (Jain et al. 2021).

A Deep Reinforcement Learning Approach With Prioritized Experience Replay And Importance Factor For Makespan Minimization In Manufacturing, Jose Napoleon Martinez

LSU Doctoral Dissertations

In this research, we investigated the application of deep reinforcement learning (DRL) to a common manufacturing scheduling optimization problem, max makespan minimization. In this application, tasks are scheduled to undergo processing in identical processing units (for instance, identical machines, machining centers, or cells). The optimization goal is to assign the jobs to be scheduled to units to minimize the maximum processing time (i.e., makespan) on any unit.

Machine learning methods have the potential to "learn" structures in the distribution of job times that could lead to improved optimization performance and time over traditional optimization methods, as well as to adapt …

Development Of A Compliant Gripper Driven By 3 Dof Soft Robot, Derek M. Price Ii, Ricardo Ramirez, Pt Angel Tran

Symposium of Student Scholars

Industrial robots are moving toward automation, which makes it increasingly necessary to replace the functions traditionally performed by humans with robotics. Pick and place operation is a prime example of such automation. Robots that pick up and place objects mimic the human action of picking an object up and placing it in a targeted location. It has led to the development of robotic end-effectors that have a human-like feel. Grippers can be articulated in various ways depending on their application area and well-defined desired tasks. As compliant and soft links deflect more under the same load than their rigid body …

Iot Clusters Platform For Data Collection, Analysis, And Visualization Use Case, Soin Abdoul Kassif Baba M Traore

Symposium of Student Scholars

Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season's unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino's clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part …

Analysis Of The Internal Delays Of Various Wireless Technologies For Autonomous Vehicle Applications, Luanne Seaman

Symposium of Student Scholars

Analysis of the Internal Delays of Various Wireless Technologies for Autonomous Vehicle Applications

Poster Presentation Undergraduate Student: Luanne Seaman

Research Mentor: Dr. Billy Kihei

Autonomous vehicles use VANETs (vehicular ad-hoc networks) to communicate with the world around them. Despite the simple premise, VANETs rely on a variety of wireless technologies depending on range, internal delay, urgency of information, and other factors. Therefore, VANET algorithms will need to weigh these elements accordingly. Through this research, I hope to contribute reliable measurements of internal delay to reduce the algorithms’ complexity. To calculate internal delay, I connected two Arduinos to a STM32 Nucleo …

A Brief Literature Review For Machine Learning In Autonomous Robotic Navigation, Jake Biddy, Jeremy Evert

Student Research

Machine learning is becoming very popular in many technological aspects worldwide, including robotic applications. One of the unique aspects of using machine learning in robotics is that it no longer requires the user to program every situation. The robotic application will be able to learn and adapt from its mistakes. In most situations, robotics using machine learning is designed to fulfill a task better than a human could, and with the machine learning aspect, it can function at the highest level of efficiency and quality. However, creating a machine learning program requires extensive coding and programming knowledge that can be …

Computational Analysis Of The Synthesis Of Hydrogels Using The Diels Alder Reaction, Avery Boley

Honors Theses

No abstract provided.

Putin And Putnam: Interpreting Russian Military Activity Through A Three Player, Two-Level Game, Nathan M. Colvin

Modeling, Simulation and Visualization Student Capstone Conference

Is Vladimir Putin a bad strategist, perhaps irrational? Previous military activity by Russia, such as the annexation of Crimea of 2014, yielded limited international gains, at a significant economic and reputational cost. Yet as the 2022 invasion of Ukraine shows, Putin is willing to commit military power, despite the cost of sanctions and other possible retaliation. This three-player simultaneous game, originally created in June, 2021, demonstrates how domestic and international considerations of President Vladimir Putin might lead to otherwise unpredictable Russian military behavior. In this extended version of Robert Putnam’s “two-level game,” President Putin rationally uses the international venue as …

Deapsecure Computational Training For Cybersecurity: Third-Year Improvements And Impacts, Bahador Dodge, Jacob Strother, Rosby Asiamah, Karina Arcaute, Wirawan Purwanto, Masha Sosonkina, Hongyi Wu

Modeling, Simulation and Visualization Student Capstone Conference

The Data-Enabled Advanced Training Program for Cybersecurity Research and Education (DeapSECURE) was introduced in 2018 as a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. By its third year, DeapSECURE, like many other educational endeavors, experienced abrupt changes brought by the COVID-19 pandemic. The training had to be retooled to adapt to fully online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, …

Real-Time External Labeling For Interactive Visualization In Virtual Environments, Shan Liu, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

A real-time external labeling algorithm has been developed to explore the potential for applying annotation and visualization to virtual reality environments, which manages label placement in the projections of virtual 3D models on the view plane. The approach intends to place labels with visual constraints, such as no overlapping, intersections, and occlusions, close proximity to the model parts, by adjusting external annotations' positions concerning available space in the view plane. This algorithm is based on the projected model's contour and adapts to camera viewpoint changes within interactive frame rates. It solves the visibility problem of annotations and operates in real-time …

Multi-Modality Breast Mri Segmentation Using Nn-Unet For Preoperative Planning Of Robotic Surgery Navigation, Motaz Alqaoud, John Plemmons Md, Eric Feliberti Md, Facs, Krishnanand Kaipa, Siqin Dong, Gabor Fichtinger, Yimming Xiao, Michel Audette

Modeling, Simulation and Visualization Student Capstone Conference

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling …

Rapid Development Of Advanced Virtual Labs For In-Person And Online Education, Yiyang Li, Pauline Delacruz, Yuzhong Shen

Modeling, Simulation and Visualization Student Capstone Conference

This abstract discusses methodologies and preliminary findings on rapid development of advanced virtual labs using modeling and simulation for in-person and online education, including rapid generation of virtual environment, integration of state-of-the-art industry leading software tools, advanced software design techniques that enables large scale software reuse, and innovative user interface design that facilitate the configuration and use of virtual labs by instructors and students. The latest design and development of the virtual lab for electronic circuits is presented.

Future Wireless Networking Experiments Escaping Simulations, Sachin Sharma, Saish Urumkar, Gianluca Fontanesi, Byrav Ramamurthy, Avishek Nag

Articles

In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …