Edge Computing, Embedded Systems
Artificial intelligence (AI) techniques are helping control and maintenance effectiveness, and an application demonstration shows how process simulation and machine teaching builds AI-based controllers to resolve industry problems, as shown in an Oct. 21 Virtual Training Week course.
A machine learning platform developed by University of Michigan researchers detects and quantifies radiation-induced defects instantaneously and could be extended to interpret other microscopy data.
Get help for finite-state machine programming for embedded systems using C programming language.
Researchers have discovered quantum material for artificial intelligence (AI) systems could mimic basic forms of learning found in the sea slug, which could help create better hardware.
The MIRAI controller enables robots more precise and complex performances
Enabling access to machine learning algorithms in easy-to-use advanced analytics applications accelerates insights in process data. A specialty chemical manufacturer used ML tools to predict over 90% of quality deviations and save more than $500,000 per year in quality downgrades.
3D digital twins and augmented reality (AR) can help manufacturers better understand places that are often dangerous for a human to access.
A Purdue university survey found Indiana manufacturers are willing to use artificial intelligence (AI) and data-driven initiatives to improve their facilities, but actually getting there remains a hurdle for many.
The AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) and AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), will each receive a total of $20 million over five years from the National Science Foundation (NSF). See video.
Recent $30 million growth round led by Zeev Ventures and Insight Partners, with participation from Spider Capital and UpWest