Re-Architecting ARCortex's Unity-Based AR Platform

ARCortex has developed a Unity-based platform that integrates various APIs and SDKs to provide a wide range of AR capabilities, including cross-platform support, multi-user functionality, tracking, and environment reconstruction. However, the current platform is aging, leading to performance, maintenance, and scalability challenges. The goal of this project is to re-architect the platform using newer technologies such as dynamic package loading, OpenXR standardization, and incorporating Large Language Models (LLMs) and Machine Learning (ML). Additionally, the project aims to address security concerns, move processing to the cloud, and make the platform production-ready for the AR development market. The ultimate objective is to enable the quick creation of complex, multi-user, georeferenced AR applications with minimal coding through a user-friendly studio interface.

Cybersecurity Compliance and Risk Assessment for AR Solutions

ARCortex, a company specializing in Augmented Reality (AR) solutions, aims to compete for government programs and access Controlled Unclassified Information (CUI). To achieve this, the company must comply with stringent cybersecurity policies such as the Cybersecurity Maturity Model Certification (CMMC) and NIST 800-171. This project involves assessing the current cybersecurity posture of ARCortex and developing a comprehensive plan and roadmap to implement necessary fixes. The assessment will include a detailed review of the company's general IT environment and a focused analysis on the unique cybersecurity risks associated with AR devices like Head-Mounted Displays (HMDs) and drones. Urgent recommendations should be identified and implemented promptly to mitigate immediate risks.

Real-Time AR Smoke Simulation in Unity

ARCortex seeks to develop a cutting-edge algorithm or process to render realistic smoke effects in an augmented reality (AR) display. The project involves creating a simulation that can be integrated into Unity using C# and is compatible with both Android and iOS mobile platforms. The primary goal is to achieve a realistic representation of smoke within a 3D model of a room or tunnel, ensuring that the smoke fits the geometry of the environment and propagates in a lifelike manner. The smoke should appear denser when viewed over longer distances, mimicking real-world behavior. The process should aim for real-time performance, though a small amount of preliminary processing is acceptable if necessary. Techniques such as GPU acceleration, AI, particle effects, neural radiance fields (NeRFs), and Gaussian splatting or any other relevant techniques can be utilized to achieve the desired effect. The final solution should allow the smoke to start at a specific point within the 3D model and propagate realistically from any viewpoint.

Drone-Based IoT Sensor Localization and Optimal Positioning

ARCortex has developed a drone equipped with multiple antennae capable of receiving data from IoT sensors on the ground. The primary objective of this project is to evaluate the drone's ability to map or triangulate the locations of these IoT emitters using the signals received by each antenna. The project will involve understanding the limitations and specifications required to achieve accurate localization. Additionally, the project aims to determine the optimal positions for the drone to both receive data from each IoT sensor effectively and maintain a strong communication link with the Ground Control Station (GCS) collecting the data. This project will allow learners to apply their knowledge in signal processing, drone navigation, and IoT systems. Key tasks include: - Analyzing the signal data received by the drone's antennae. - Developing algorithms for triangulating the positions of IoT sensors. - Identifying limitations and necessary specifications for accurate localization. - Determining optimal drone positions for data reception and GCS communication.