Edge Computing, Embedded Systems
Digital twin group signs two liaison agreements
The Digital Twin Consortium is developing AI and IoT enabling technologies and propelling them across manufacturing.
Manufacturer, university collaborating on extended reality, IIoT projects
Bosch announced a collaboration with Carnegie Mellon University (CMU) to further research in spatial computing to design and demonstrate an architecture for extended reality (XR) applications in the Industrial Internet of Things (IIoT) context.
Machine learning can lead to cleaner water
A Louisiana State University-Penn State research team is using machine learning (ML) to developer a smarter approach to ionic separations which could improve water treatment, resource recovery and energy production.
Two schools receive NSF funding for AI research
Indiana University will be a principal organization in two new National Science Foundation Artificial Intelligence Research Institutes.
AI, controls and the future of maintenance
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.
Augmented reality for testing nuclear components
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.
Finite-state machine for embedded systems
Get help for finite-state machine programming for embedded systems using C programming language.
How sea slugs can create better artificial intelligence systems
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.
Robot control system for complex applications
The MIRAI controller enables robots more precise and complex performances
Scaling machine learning for the manufacturing masses
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.
