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 additive manufacturing systems, especially metal, bring revolutionary capabilities and have the potential to reduce supply chain issues and enable new designs through unique layer-by-layer fabrication capabilities, 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.
FTL developed “DADTMA”, Distributed Acquisition Digital Twin Maintenance Architecture, is a software solution for accelerating data acquisition and management of maintenance data in the field and for analyzing maintenance and inspection data for a specific aircraft or fleet over time in a Digital Twin framework.
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.
FODHAT was developed in collaboration with Northrop Grumman, and uses neural network-enhanced 2D and 3D imaging for autonomous inspection guidance during manufacturing.
HemoSight is a novel, robust, high-throughput, low-sample-volume device that measures blood clot contraction dynamics, provides portability and ease-of-use, and facilitates rapid decision-making, while integrating seamlessly into existing clinical and research settings.
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 (Structural Damage and Repair Inferencing Tool), is an automated inventory, inspection, and repair planning software tool for condition monitoring of assets such as piers, bridges, roads, tunnels, dams, and other structures. SDARIT employs custom AI algorithms for detecting and identifying structural elements and damage caused by wear, seismic effects, fatigue, battle damage, and other environmental factors.
FTL Labs’ SHR3DR (Sub-Seafloor High Resolution 3D Reconstruction) is a
hardware and software solution for the automatic detection of problems such as voids forming in the subterranean environments beneath Navy waterfront facilities.
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.
FTL developed “TRAIDMA”, Transit Reliability and AI-Driven Maintenance Architecture, as a predictive maintenance software platform built upon its proven DADTMA fleet maintenance tool. TRAIDMA enables transit agencies and fleet operators to optimize maintenance activity by leveraging advanced predictive modeling and intuitive data visualization.
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.
