The military and aerospace market maintains an insatiable appetite for smaller, lighter, and cheaper. Recent technological breakthroughs in SWaP-C reduction has yielded the introduction of USFF mission computer and networking systems.
Introducing your essential guide to all things MOSA. This white paper explores the MOSA directive, its significance for defense technology, and MOSA-supporting standards like SOSA, CMOSS, VICTORY, GVA, FACE, and OMS/UCI.
This white paper was presented at the 2021 NDIA Ground Vehicle Systems Engineering and Technology Symposium, Vehicle Electronics Architectures Technical Session, August 10-12, 2021.
The increased data from, and desire for more ground control over, onboard data acquisition and recording systems has led to significant interest in bi-directional, Ethernet-like telemetry protocols. The Telemetry Network Standard (TmNS), was recently released in IRIG 106-19 for exactly...
A recent online cybersecurity expert discussion explored emerging cybersecurity vulnerabilities and the challenges faced by the defense and aerospace industries in identifying and mitigating attacks. This white paper from Wind River summarizes the ideas and key points that were covered...
Tensor cores are indispensable for performing the types of calculations needed for artificial intelligence (AI) and machine learning. The role of AI and machine learning in defense applications is on the rise, making tensor cores critical for defense.
Tactical data link (TDL) communication brings critical visibility to the battlefield by providing warfighters easily accessible, sharable, and precise location and identification information of everyone in the combat zone which enables them to make better tactical decisions.
This white paper discusses different touch screen technologies and why now is the right time for aerospace and defense organizations to evolve to smartphone like touch screens.
The key to increasing situational awareness for today's ground combat vehicles is in designing a video management system that reduces latency from end to end.
Like humans, machines learn from experience. They make observations from inputs of images, text, or other data, and then look for patterns. After the machine runs through the mathematical layers, it learns to make better decisions based on the examples it was given.