Carnegie Mellon University (CMU)

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Carnegie Mellon University (CMU) articles

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Collaboration focuses on next-generation machine learning

Collaboration focuses on next-generation machine learning
It has been announced by Honeywell that they are entering a strategic collaboration with Carnegie Mellon University to advance robotics technology for distribution centres. The initiative brings together Honeywell Intelligrated, a division of Honeywell Safety and Productivity Solutions, and Carnegie Mellon’s National Robotics Engineering Center in Pittsburgh, Pennsylvania.
20th December 2018

Sensors and AI: the dream team

Sensors and AI: the dream team
You don’t have to attach sensors to everything. With machine learning, a new suite of only nine different sensors can recognise dozens of various phenomena. Ubiquitous sensors seem almost synonymous with the Internet of Things (IoT), but some Carnegie Mellon University researchers say ubiquitous sensing - with a single, general purpose sensor for each room - may be better. Author: Hermann Straubinger
17th August 2017

DoC: latest paradigm for big data computing

DoC: latest paradigm for big data computing
Diana Marculescu and Radu Marculescu have been awarded an NSF grant to develop a paradigm for Big Data computing. Specifically, this project focuses on a Datacenter-on-a-Chip (DoC) design consisting of thousands of cores that can run compute- and data-intensive applications more efficiently compared to existing platforms. Currently, data centers (DC) and high performance computing clusters are dominated by power, thermal, and area constraints.
12th September 2016


Electroadhesive clutch substitutes conventional ones in robotics

Electroadhesive clutch substitutes conventional ones in robotics
When Steve Collins first envisioned the electroadhesive clutch, he made a prototype with a sandwich bag and a couple of pieces of aluminum foil from his kitchen. Since creating that makeshift prototype, he and his research team have developed a sophisticated, functional device that can be used in exoskeletons that compensate for a person's disability or enhance their athletic performance.
8th July 2016

Fall-prevention sensors enhance senior care

Fall-prevention sensors enhance senior care
Carnegie Mellon University's College of Engineering conducted a survey on falls among the elderly, and discovered that Americans are very worried about their elderly parent falling—and that this worry leads to action. Every 13 seconds, an older adult is treated in the emergency room for a fall. Every 20 minutes, an older adult dies from a fall-related trauma.
26th May 2016

Robot's in-hand eye maps surroundings & determines location

Robot's in-hand eye maps surroundings & determines location
Before a robot arm can reach into a tight space or pick up a delicate object, the robot needs to know precisely where its hand is. Researchers at Carnegie Mellon University’s Robotics Institute have shown that a camera attached to the robot’s hand can rapidly create a 3D model of its environment and also locate the hand within that 3D world.
18th May 2016

Turning the entire lower arm into a touchpad

Turning the entire lower arm into a touchpad
Since the advent of smartwatches, technologists have been looking to expand interactions beyond the confines of the small watch face. New technology developed by the Human-Computer Interaction Institute’s (HCII) Future Interfaces Group at Carnegie Mellon University suggests turning the entire lower arm into a touchpad.
17th May 2016

Internet on a chip: energy-efficient multicore chips

Internet on a chip: energy-efficient multicore chips
In their recent paper, "Wireless NoC for VFI-enabled multicore chip design: performance evaluation and design trade-offs", researchers from Carnegie Mellon's Department of Electrical and Computer Engineering and Washington State University identify a new approach for enabling energy-efficient multicore systems.
30th March 2016

Robotically driven system could reduce cost of drug discovery

Researchers from Carnegie Mellon University have created the first robotically driven experimentation system to determine the effects of a large number of drugs on many proteins, reducing the number of necessary experiments by 70%. The model, presented in the journal eLife, uses an approach that could lead to accurate predictions of the interactions between novel drugs and their targets, helping to reduce the cost of drug discovery.
11th February 2016


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