Irvine, California. Telit Cinterion announced the verification of its 5G FN980 and FN990 series data cards. It features an NVIDIA Ampere architecture GPU utilizing 8-core Arm Cortex-A78AE CPUs.

CES 2023. During CES 2023, AAEON demonstrated its AI edge solutions driven by the NVIDIA Jetson Orin system-on-module (SoM), the BOXER-8652AI and the BOXER-8651AI. The NVIDIA Jetson Orin Nano will power the BOXER-8621AI while in the near future we will see the release of the BOXER-8646AI.

CES 2023. Aetina showed up to CES 2023 and unveiled its DeviceEdge, a NVIDIA Jetson-based series of embedded platforms, driven by NVIDIA Jetson Orin NX and Orin Nano system-on-modules (SoMs). The  Aetina series consists of the AIB-SO21/31 & AIB-SN31/41, AIB-MO22/32 & AIB-MN32/42, AIE-KO21/31 & AIE-KN31/41, and AIE-KO22/32 & AIE-KN32/42.

The third article in our metaverse series focuses on conceptual and metadata models that enable interoperability among connected digital twins in an industrial metaverse.

The concept of digital twins, taken to its ultimate potential, can create digital representations of nearly everything. Yet, much of the value of digital twins depends on the ability of distributed, heterogeneous systems to interoperate.

Munich, Germany. Advantech, a leading provider of industrial computing platforms, is pleased to announce the release of the AIMB-288E industrial motherboard with 12th Gen Intel® Core™ Desktop Processor and NVIDIA Quadro T1000 GPU. AIMB-288E leverages an ultra-slender design to deliver outstanding computing power and superior graphics performance to visual computing in edge intelligence. It supports three independent displays (2 x DisplayPort & 1 x eDP with up to 4K resolution) to present precise visualization during diverse inspection tasks. In addition, AIMB-288E benefits from Advantech’s strict design process, thermal technology, and IEC-60068-2 compliance — attributes that ensure stability during long-term operation. This combination of features and capabilities make AIMB-288E an excellent choice for AI development and deployment in applications requiring a compact, slim form factor design.

Built from scratch by Southern Methodist University students in Dallas, a DIY, aka do-it-yourself, supercomputer using NVIDIA’s Jetson Nano modules is helping provide the hands-on experience students may not receive with a standard supercomputer, which is often too large for a typical university learning environment.

On this episode of Embedded Insiders, we’re joined by Paul Butcher, Senior Software Engineer at AdaCore, to discuss how AI can make fuzz testing even more robust through the integration of techniques like symbolic execution and input-to-state correspondence that optimize test data sets against scenarios a system might encounter in the real world