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National Instruments (NI)

11500 N Mopac Expwy
Austin, TX 78759-3504
https://www.ni.com/en-us.html
National Instruments (NI)
Articles related to National Instruments (NI)
Radar/EW

Key Considerations for Radar Test - Whitepaper

July 25, 2019

The operating environment and requirements for military radars are changing rapidly, and pushing the complexity of these systems to new extremes. Use the resources in this guide to help you address... ...

A.I.

Artificial Intelligence in Software Defined SIGINT Systems - Whitepaper

July 23, 2019

Learn about artificial intelligence (AI) and deep learning (DL) in signals intelligence (SIGINT) systems and see how to apply them with commercial off-the-shelf (COTS) software defined radio (SDR). ...

Comms

SkySafe Defeats Drone Threats with Open-Source SDR - Whitepaper

June 05, 2019

As with any disruptive technology, drone operation often attracts bad actors looking to exploit the capability for less-than-noble objectives. How do you keep pace with technological evolution and... ...

Radar/EW

Empowering Future-Proof Test Organizations Through Standardization - Whitepaper

April 24, 2019

Using the same technology and tools across the development cycle and across applications and job sites can be the difference between thriving with a sustainable competitive advantage and struggling... ...

Radar/EW

Keep pace with a standardized development process - Blog

February 28, 2019
Standardization has been an aspirational objective in test organizations for decades. In 1961, D.B. Dobson and L.L. Wolff of Radio Corporation of America (RCA) published a paper, ?Standardization of Electronic Test Equipment.? The paper presented the principles, criteria, and techniques used in the investigation and prototyping of multipurpose missile system test equipment.
A.I.

AI and military RF systems - Story

November 15, 2018
Advances in artificial intelligence (AI) are enabling significant leaps in science and technology, including the fields of digital signal processing (DSP) and radio frequency (RF) systems. Methods nominally labeled as "AI" have been applied to radio systems for decades, but always with the goal of optimizing the control plane of a hand-engineered system (e.g., "smart radios" or "cognitive radios"). Using a class of AI known as deep learning, we are now able to learn entirely new systems by processing sample data which can provide greater sensitivity, better performance, and reduced power consumption and processing requirements as compared to traditional approaches. The effect of this breakthrough methodology will be significant, and as with many new technologies, we will first see its use and impact in military systems.