Articles related to Kontron
January 09, 2018
TOULON, France. A 6U VPX dual eight-core Intel Xeon D processor blade computer made by embedded computing and Internet of Things company Kontron has been successfully integrated and tested in a radar application by defense electronics corporation Thales.
February 03, 2017
Electronic warfare (EW) systems are among the most challenging embedded systems to design and deploy. Not only do they require voracious amounts of signal processing, they also require more mundane server-style processing (for signal library maintenance, data logging, etc.) and are often packaged in extremely size, weight, and power (SWaP)-constrained environments such as under wing pods. As a result, advanced EW systems can benefit from consolidating workloads on a single machine with the means to efficiently execute these two very different processing problems using parallel virtual machine (VM) execution. Modern commercial off-the-shelf (COTS) 3U VPX boards based on Intel server-class processors are a compelling option for these sorts of systems.
November 29, 2016
With new platforms scarce in today?s budget-constrained defense environment, more funding emphasis has been placed on upgrading the existing electronics suites in avionics, radar, shipboard, and other military applications, which means solving problems such as reducing the heat generated by modern processors in size, weight, and power (SWaP)-constrained environments and managing long-term life cycle costs. In this Q & A with Andy Mason, Head of the Technology Platforms Group at Kontron?s Avionics, Transportation, and Defense division, he discusses the thermal management challenge in embedded computing upgrades and how open architectures and a building block approach that leverages commercial-off-the-shelf (COTS) can help manage it across multiple platforms while keeping development costs down. Edited excerpts follow.
October 31, 2016
The long-sought era of machine learning in finally at hand. The potential benefit to the warfighter of deep learning techniques both enormous and profound. With defense systems trending towards greater application autonomy, deep learning techniques too complex to implement with more traditional processing technologies can now help to significantly drive advancements in on-platform processing of streaming signal or image data. These techniques are proving useful for pattern recognition tasks such as natural language processing and image feature detection, producing highly reliable autonomous decisions based on huge data sets.
July 04, 2015
Defense OEMs must find a way to cost-effectively meet mounting data throughput and processing needs with commercial off-the-shelf (COTS) platforms in smaller form factors. Application-ready systems that have been ruggedized for reliability in extreme military settings instill confidence with high availability by effectively addressing the significant power densities generated at the board, chassis, and system levels.
December 10, 2014
High-performance embedded computing (HPEC) is moving from the data center into the field of combat. Mainstream HPEC building blocks can reduce costs for these applications while providing ultrafast backplane speeds in a much smaller footprint and easily accommodating new proof-of-concept requirements.
September 12, 2014