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This article explores the significant advancements in AI-driven MEMS sensors, focusing on STMicroelectronics' (ST) cutting-edge technologies, their applications, and the ecosystem supporting these innovations.
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The Guide to Understanding the Technologies Defining the Next Phase of Edge AI
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As augmented smart glasses technology edges closer to mainstream adoption, engineers face challenges around display performance. Namely, many seek to achieve higher resolution without driving up power consumption and device bulk.
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TAGGED WITH machine learning
Nakahira received a Young Investigator Award from the Japan Science and Technology Agency for developing a machine that builds autonomous systems.
Princeton researchers have developed a new tool to speed the discovery of advanced materials known as metal organic frameworks, or MOFs.
EPFL roboticists have shown that when a modular robot shares power, sensing, and communication resources among its individual units, it is significantly more resistant to failure than traditional robotic systems, where the breakdown of one element often means a loss of functionality.
MIT researchers' DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
TAGGED WITH computer vision
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Matroid builds no-code computer-vision detectors that can spot everything from microscopic material defects to real-time safety hazards on a factory floor.
In large-scale warehousing and distribution operations, conveyor belts are an essential infrastructure that must operate with near-zero downtime to ensure the timely delivery of products. The presence of loose or foreign items on a conveyor belt can pose a serious risk to these operations.
In this post, we'll walk through how to evaluate that progress using the same metrics our platform provides automatically, so you can build detectors that get smarter, sharper, and more reliable over time.
Latest Posts
This article explores the significant advancements in AI-driven MEMS sensors, focusing on STMicroelectronics' (ST) cutting-edge technologies, their applications, and the ecosystem supporting these innovations.
As augmented smart glasses technology edges closer to mainstream adoption, engineers face challenges around display performance. Namely, many seek to achieve higher resolution without driving up power consumption and device bulk.
A Review of Presentations from CDFAM Computational Design Symposium Series, Past and Future
8 minutes read
Wevolver spoke with John Weil, Vice President of IoT and Edge AI Processor Business at Synaptics, to understand the challenges of bringing AI to the edge, how the Synaptics Astra™AI Native platform enables cognitive computing in real-world devices, and what the future holds for edge intelligence.
Generative artificial intelligence (GenAI) suffers from several types of biases that reflect human failings. How can we avoid being tricked?
4 minutes read
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In addition to training future players, the technology could expand the capabilities of other humanoid robots, such as for search and rescue.
EPFL researchers have discovered key 'units' in large AI models that seem to be important for language, mirroring the brain's language system. When these specific units were turned off, the models got much worse at language tasks.
4 minutes read
New type of "state-space model" leverages principles of harmonic oscillators.
webinar | JUN 05 - JUN 05, 2025
Join MVTec to explore how advanced machine vision software is revolutionizing semiconductor wafer inspection with live demos, not slides—boosting precision, quality, and efficiency.
Explore how advanced computer vision technology transforms quality control in metal manufacturing and why it represents the industry's future.
"IntersectionZoo," a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
A new approach could enable intuitive robotic helpers for household, workplace, and warehouse settings.
The Fraunhofer Institute for Factory Operation and Automation IFF (Fraunhofer IFF) is working to overcome challenges to e-waste disposal.
