Embedded Computing Design

One of the toughest challenges with solving predictive maintenance, enabled by AI, is that there is usually no data available for training the system. Modern equipment may not break down for 5 years or more so there is insufficient data when the product is first launched on when and how it will break down. But AI systems using traditional methods such as Deep Learning can only operate when sufficiently large volumes of data are available for training the system and hence, they are incapable of addressing this problem.

One of the most impactful areas for businesses to deploy AI is in digital signage. AI-driven digital can deliver personalized content, respond instantly to changing conditions, and make every interaction more meaningful. A challenge that comes with deploying this kind of AI-powered digital signage is ensuring the content is flexible enough to adapt to different display sizes or settings, or to simplify the content so it can fit any display.

AI is moving from the cloud to the edge. In product after product, we see ever more intelligence being brought to the edge, and to the devices that reside there.

Hello Embedded Engineers, Developers and Makers! Welcome to In Case You Missed it: Embedded Insights, the weekly news show all about Embedded technologies and solutions from Embedded Computing Design.

Autonomous vehicle capability has been advancing in the open for more than a decade, and now it sits at the center of the shift toward AI-defined vehicles.

In warehousing, manufacturing, robotics retail and other supply chain delivery arenas, one of the factors slowing down crucial delivery times is an apparent shortage of agents to move products, but Pattern AI says this isn’t the case. Instead, it’s idle clustering of agents that leads to what seems like a shortage.

Hello Embedded Engineers, Developers and Makers! Welcome to In Case You Missed it: Embedded Insights, the weekly news show all about Embedded technologies and solutions from Embedded Computing Design.

What do the manufacturing, robotics, transportation, video, city safety, and security industries all have in common? They are searching for more efficiencies in the process of real-time object detection and dynamic tracking. The answer to that search could be discriminative AI for many companies.

The restaurant and hospitality industry has always been dynamic, but today’s environment is especially unforgiving. Rising commercial real estate costs, which often increase by 8–15% year over year in major cities, combined with escalating labor expenses and talent shortages, make profitability more challenging than ever. Despite extended operating hours, most restaurants still rely on a narrow peak window, typically accounting for less than 25 percent of the day, which contributes 60 to 70 percent of their revenue. During those hours, maintaining fast and consistent service becomes mission critical.

Hello Embedded Engineers, Developers and Makers! Welcome to In Case You Missed it: Embedded Insights, the weekly news show all about Embedded technologies and solutions from Embedded Computing Design.

Server-like features being available at the edge of an enterprise’s network seems like a pipe dream, but if it were achievable, many industries would benefit greatly. In healthcare, manufacturing & robotics, retail, video, smart cities, public safety, and security, in particular, such feature availability in a compact form factor could be transformative in some cases and mission-critical for others.

Companies struggle to process enormous and diverse data efficiently, often leading to delays and higher cost due to complex needs and limited AI computing power. This is increasingly obvious and problematic at the Edge, where the form factors can limit performance, storage, and battery.

Hello, developers! Welcome to DevKit Weekly, the unboxing show from Embedded Computing Design where we give you a first look at boards, SOMs, COMs, and especially Development Kits from the biggest and best manufacturers in the Embedded Computing industry.

There are many industries seeking to deploy real-time, rugged edge AI computing power, particularly those that require low latency and precise results. Manufacturing, robotics, machine vision, Smart City, and Safety and Security applications require edge processing solutions, and companies are beginning to make investments to meet these needs.

Hello Embedded Engineers, Developers and Makers! Welcome to In Case You Missed it: Embedded Insights, the weekly news show all about Embedded technologies and solutions from Embedded Computing Design.

In the fast-evolving world of software-defined vehicles (SDVs), cybersecurity has become as critical as performance and safety. The latest SOAFEE Blueprint from VicOne, developed as part of the SOAFEE community, introduces a new model for in-vehicle defense that moves intelligence from the cloud to the edge, powered by adaptive AI.

Hello Embedded Engineers, Developers and Makers! Welcome to In Case You Missed it: Embedded Insights, the weekly news show all about Embedded technologies and solutions from Embedded Computing Design.

The world of High-Performance Computing, or HPC, is all about power, efficiency, expandability, and flexibility. As AI applications become a regular part of any embedded system, whether at the edge or in the cloud, these attributes will become increasingly important.

The Edge of the Network is increasingly becoming the hub for data processing and analysis. However, to ensure the Edge intelligence and computing power is effective, it must be supported by safety, security, reliability, and resiliency.

The Smart Transportation space is one of the most rapidly evolving markets right now. It is implementing AI, energy-efficient compute, IoT, and other next-generation technologies to enable rapid and intelligent growth.