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What’s New in MIPI Security: MIPI CCISE and Security for Debug

This blog post was originally published at MIPI Alliance’s website. It is reprinted here with the permission of MIPI Alliance. As the need for security becomes increasingly more critical, MIPI Alliance has continued to broaden its portfolio of standardized solutions, adding two more specifications in late 2025, and continuing work on significant updates to the MIPI Camera […]

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Production-Ready, Full-Stack Edge AI Solutions Turn Microchip’s MCUs and MPUs Into Catalysts for Intelligent Real-Time Decision-Making

Chandler, Ariz., February 10, 2026 — A major next step for artificial intelligence (AI) and machine learning (ML) innovation is moving ML models from the cloud to the edge for real-time inferencing and decision-making applications in today’s industrial, automotive, data center and consumer Internet of Things (IoT) networks. Microchip Technology (Nasdaq: MCHP) has extended its edge AI offering

Production-Ready, Full-Stack Edge AI Solutions Turn Microchip’s MCUs and MPUs Into Catalysts for Intelligent Real-Time Decision-Making Read More +

Accelerating next-generation automotive designs with the TDA5 Virtualizer™ Development Kit

This blog post was originally published at Texas Instruments’ website. It is reprinted here with the permission of Texas Instruments. Introduction Continuous innovation in high-performance, power-efficient systems-on-a-chip (SoCs) is enabling safer, smarter and more autonomous driving experiences in even more vehicles. As another big step forward, Texas Instruments and Synopsys developed a Virtualizer Development Kit™ (VDK) for the

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Into the Omniverse: OpenUSD and NVIDIA Halos Accelerate Safety for Robotaxis, Physical AI Systems

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. NVIDIA Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse. New NVIDIA safety

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Enhancing Images: Adaptive Shadow Correction Using OpenCV

This blog post was originally published at OpenCV’s website. It is reprinted here with the permission of OpenCV. Imagine capturing the perfect landscape photo on a sunny day, only to find harsh shadows obscuring key details and distorting colors. Similarly, in computer vision projects, shadows can interfere with object detection algorithms, leading to inaccurate results.

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Production Software Meets Production Hardware: Jetson Provisioning Now Available with Avocado OS

This blog post was originally published at Peridio’s website. It is reprinted here with the permission of Peridio. The gap between robotics prototypes and production deployments has always been an infrastructure problem disguised as a hardware problem. Teams build incredible computer vision models and robotic control systems on NVIDIA Jetson developer kits, only to hit

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Robotics Builders Forum offers Hardware, Know-How and Networking to Developers

On February 25, 2026 from 8:30 am to 5:30 pm ET, Advantech, Qualcomm, Arrow, in partnership with D3 Embedded, Edge Impulse, and the Pittsburgh Robotics Network will present Robotics Builders Forum, an in-person conference for engineers and product teams. Qualcomm and D3 Embedded are members of the Edge AI and Vision Alliance, while Edge Impulse

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OpenMV’s Latest: Firmware v4.8.1, Multi-sensor Vision, Faster Debug, and What’s Next

OpenMV kicked off 2026 with a substantial software update and a clearer look at where the platform is headed next. The headline is OpenMV Firmware v4.8.1 paired with OpenMV IDE v4.8.1, which adds multi-sensor capabilities, expands event-camera support, and lays the groundwork for a major debugging and connectivity upgrade coming with firmware v5. If you’re

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On-Device LLMs in 2026: What Changed, What Matters, What’s Next

In On-Device LLMs: State of the Union, 2026, Vikas Chandra and Raghuraman Krishnamoorthi explain why running LLMs on phones has moved from novelty to practical engineering, and why the biggest breakthroughs came not from faster chips but from rethinking how models are built, trained, compressed, and deployed. Why run LLMs locally? Four reasons: latency (cloud

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Faster Sensor Simulation for Robotics Training with Machine Learning Surrogates

This article was originally published at Analog Devices’ website. It is reprinted here with the permission of Analog Devices. Training robots in the physical world is slow, expensive, and difficult to scale. Roboticists developing AI policies depend on high quality data—especially for complex tasks like picking up flexible objects or navigating cluttered environments. These tasks rely

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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