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Curtiss-Wright’s new modular electromechanical train-tilting stabilization solution lowers life-cycle costs while improving passenger comfort.

To exploit the potential benefits of new technologies such as artificial intelligence (AI), robotics, video analysis, Internet of Things (IoT), augmented reality and virtual reality, and other innovative technologies on mobile platforms at the tactical edge, Department of Defense (DoD) communications programs must effectively deploy advanced IT infrastructure, connectivity, and compute resources.

Size, weight, power consumption, and cooling technologies today block quantum computing from the embedded world, but new generations should yield advances in artificial intelligence and computer security.

Efficient execution is key to real-time mission-critical operating systems software, yet new demands are emerging such as safety-critical operations for avionics and other life-critical applications.

In the defense industry, AI is on the path to becoming a loyal companion to the warfighter and manufacturers alike. As technology progresses, so does the range of defense capabilities that AI could not only supplement but eventually manage.

In support of the Australian Army’s Project Land 400 Phase 2 Combat Reconnaissance Vehicle (CRV), Curtiss-Wright Defense Solutions’ Switzerland-based Drive Technology business is supplying its modular Turret Drive Servo System (TDSS) and hand controller technologies to Rheinmetall Landsysteme.

The latest generation of unmanned vehicles operating on land, in the air, and at sea no longer simply are remotely operated. These advanced systems have built-in intelligence to learn from their experiences and make their own decisions.

Industry experts say open standards are helping drive development in small-form-factor battlefield and aerospace hardware like system-on-chip and single-board computers.

The COTS Confidential Roundtable gathers experts from the defense electronics industry – from major prime contractors to defense component suppliers.

Which is better for machine-learning applications: 2nd Generation Intel Xeon Scalable Processors or Nvidia’s T4? A head-to-head comparison helps explain whether a CPU or a GPU is best for your high-performance ML solution.

New GPGPU modules expand Curtiss-Wright’s family of high-performance embedded computing (HPEC) processors for artificial intelligence (AI) applications.

The use of artificial intelligence (AI) based machine learning technologies in autonomous vehicles is on the rise.