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.
The second article in our metaverse series focuses on defining the industrial metaverse, its building blocks, parallels with model-based systems engineering (MBSE), and examples of how modeling and simulation are being used to empower digital twin ecosystems.
Software & OS
The development world is changing, and fast. So fast that multi-disciplinary engineering teams can no longer rely on waterfall development models and hope to keep up with the constant updates and revisions required by modern electronic systems and software.
Embedded Toolbox with Opal Kelly: Program A Machine Vision FPGA Platform Completely with APIs - VideoOctober 22, 2022
Today’s vision systems are becoming so complex they're often leveraged in simultaneous object detection, robotic control, and other functions on the same piece of equipment. This means machine vision controllers must integrate high-bandwidth memory, high-performance processing, and the flexibility to support the constant march of AI model trainings and optimizations over time.
Industrial Metaverse Blueprints, Part 1: Definitions & Requirements for the Most Impactful Economic Outcomes, Part 1 - StoryOctober 01, 2022
Gartner defines the Metaverse as “a collective virtual open space created by the convergence of virtually enhanced physical and digital reality. It’s physically persistent and provides enhanced immersive experiences.” IDC defines the metaverse as an evolution of today’s internet that leverages mobile devices, augmented and virtual reality headsets and next-generation networks to create persistent and continuous user experiences with a strong sense of presence.
Neither are inaccurate. However, neither accurately portrays the depth, scale, and potential of the metaverse beyond its current, nascent form or its value beyond basic human entertainment as a solution to some of the world’s most pressing, complex problems.
Indeed, the metaverse represents a massive paradigm shift that promises to redefine how we approach solving problems through technology. The earliest and biggest opportunities to apply its concepts lie in industry, where leading organizations are already leveraging the building blocks of the metaverse – whether they know it or not – in the deployment of digital twins that will one day form systems of systems that comprise entire virtual worlds.
This is the first article in a series covering the industrial metaverse and digital twin technologies, applications, current industry activities, and the potential for standards. We encourage you to follow this series, comment, and ask questions. Participation and collaboration are critical as we embark on what may be the most significant inflection point in the history of technology.
To reduce latency, network utilization, and cost, many IoT deployments now store and analyze data at or near the edge node. But “distributed” can be a bad thing when it comes to data, particularly if it means information gets trapped in silos across a network.
So what happens when you inevitably need it?
Espressif Systems’ ESP32 MCU is now supported by the open-source Luos containerization platform, which enables lightweight microservices to run on embedded systems. Luos’ leverages a simple API to dynamically link one or more microservices on the same hardware target, multiple hardware targets, a host PC, or the cloud, which modularizes and simplifies IoT management and updates.
Industry wants AI. Industry also has to deal with harsh environments and limited resources, which keep ruggedness and power-efficiency at the top of the design criteria whether a platform is artificially intelligent or not. And at that intersection lies AAEON’s* de next-TGU8 SBC.
Everyone knows encryption is a cornerstone of network security, but at this point very few understand what a problem it is.
According to ETSI, we’re all about to find out.
Have you ever taken apart your Amazon Echo? There’s a lot in there, including an embedded applications processor, MEMS microphones, WiFi and Bluetooth connectivity, an AWS cloud backend, and a ton of sophisticated automatic speech recognition (ASR) and natural language processing (NLP) software. When you’re ready to put it back together, or better yet want to build your own, you’ll probably be interested in the* SmartCow Apollo Audio/Visual AI Engineering Kit.
Bluetooth has permeated the audio market, from speakers to earbuds to hearing aids. As consumers, we now have some pretty clear expectations of Bluetooth-enabled audio products, like high-fidelity, decent range, and lengthy battery life.
Here’s what you’ll need if you want to build an industrial IoT sensor node:
- The intelligence to convert, filter, and in some cases analyze, analog signals at the edge
- Enough efficiency to run for long periods on minimal resources
- Some amount of connectivity
- And because of that, local security
Oh yeah, you’ll probably want to start thinking about compliance with safety standards like IEC 61508, too.
The previous four parts of Make Any Sensor a Smart Sensor with PICMG IoT.1 discussed the importance of industrial sensors to intelligent automation environments and a path forward that empowers anyone, regardless of technical ability, to build smart sensors of their own. Over the next several installments, the series pivots to the beneficiaries of smart sensor data – smart effecters that translate data intelligence into real-world action.
It’s the kit you’ve all been waiting for! Welcome to Dev Kit Weekly, where this week we’ll be reviewing and raffling the Jetson AGX Orin Developer Kit from NVIDIA.
Imagine a police body cam. We’ve all seen, or, at least heard of those, right? Now, for first responders, what if those on-body systems were artificial intelligence-enabled and cloud- connected, capable of detecting and tracking faces, live streaming to support services, and saving footage into secure cloud storage. Things would certainly be more … transparent.
I recently came across an 8-bit PIC MCU data sheet that predates 1982. That means it also predates Microchip as a company (incorporated in 1989), making it a remnant of bygone General Instruments days.
Everyone has had a conversation with that experienced engineer or tenured member of the technical staff who’s said, “IoT is nothing new. I’ve been doing that for [insert number of decades], we just called it [insert connected embedded/M2M/other term implying networked equipment].” It’s eye roll-inducing. And it’s wrong.
Debug & Test
Protocol analysis isn’t going out of style anytime soon, and neither are the oscilloscopes you need to perform it. In fact, they’re modernizing.