The field of deep learning and neural networks is leaping ahead very quickly. The difficulty is in finding ever-growing applicability to the myriad of real-world complex analysis and automation challenges. FTL works to apply cutting edge AI approaches to unique situations and mission specific cases.
Since FTL has been developing real-time algorithms for sensing and analysis applications, machine learning and neural networks have progressed from academic anomalies, to exotic approaches for supercomputing, to veritable swiss-army-knife libraries applicable to a staggering variety of applications.
Starting from classifiers for analyzing images based on thousands of training images, FTL is now applying machine learning, neural networks, and AI to many problems in image analysis, 3D spatial awareness, situational prediction, smart sensing, and automation.
The links below provide summaries of projects in which FTL has applied machine learning to achieve cutting-edge system performance to solve real-world problems
While AM systems, especially metal AM, bring revolutionary capabilities and have the potential to reduce supply chain issues and enable new designs through unique layer-by-layer fabrication capabilities, AM technologies currently suffer from defects that exist within the components. Defects such as porosity, inclusions, large-scale voids, and chemical inconsistencies can inhibit the functional performance of a part and reduce confidence in designing parts for AM.
Unmanned Surface Vessels (USV’s) are anticipated to play an important role in future Naval systems. These vary from very small systems deployed by hand from boats, to very large vessels, as large as a manned battleship, but operated entirely remotely.
DADTMA (Data Analysis and Decision Tool for Mission Assurance) is a strategic risk assessment tool designed for the Department of Defense. It enables rapid evaluation of mission-critical systems by integrating data sources, visualizing risk factors, and supporting data-driven decision-making at the operational level.
This contamination detection system was developed to identify and analyze fluid-fluid interactions in critical aerospace systems. Using advanced sensors and automated diagnostics, it enables early detection of cross-contamination events to prevent system failure and ensure mission continuity.
FTL has current and active machine vision programs for the Navy and Air Force, most notably FTL’s FODHAT (FOD Or Defect Hazard Analysis Twin) machine vision and inspection systems.
MaxSpot is a predictive analytics platform developed to enhance aircraft maintenance planning for the U.S. Air Force. By analyzing historical and real-time data, it identifies high-risk components and optimizes inspection schedules to reduce downtime and improve fleet readiness.
OilEye is a real-time fuel monitoring system developed to detect contamination and performance issues in military aircraft. It combines sensor data with predictive analytics to enhance maintenance efficiency and reduce the risk of in-flight engine failures.
PlaySurface and STEM Table are interactive learning platforms developed to promote STEM education through hands-on digital engagement. Designed for use in classrooms and museums, they combine touch-sensitive displays with educational software to enhance collaborative learning experiences.
SDARIT (Secure Distributed AR Integration Toolkit) is a framework for deploying augmented reality solutions in secure, networked environments. Designed for defense applications, it enables real-time collaboration and visualization while maintaining strict cybersecurity and data integrity standards.
SHR3DR is a compact 3D scanning and reconstruction system developed for field use in defense and engineering applications. It captures high-resolution spatial data to enable rapid modeling, reverse engineering, and integration into digital twin environments.
Sponsored by the Defense Health Agency, FTL’s SPARO (Sensorized Prosthetic Alignment Read-Out) provides clinicians and researchers a tool that enables quantitative measurements of prosthetic alignment and the effects it has on the rest of the body’s motions and dynamics through whole body gait and movement analysis.
VAME (Virtual Assessment and Modeling Environment) is a simulation platform designed to support digital twin development for defense systems. It enables virtual prototyping, behavioral analysis, and performance optimization across a range of operational scenarios.
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