University of Minnesota Twin Cities researchers have developed a more accurate, less invasive technology that allows amputees to move a robotic arm using their brain signals instead of their muscles. Many current commercial prosthetic limbs use a cable and harness system that is controlled by the shoulders or chest, and more advanced limbs use sensors to pick up on subtle muscle movements in a patient's existing limb above the device. But, both options can be cumbersome, unintuitive, and take months of practice for amputees to learn how to move them. Researchers in the University's Department of Biomedical Engineering, with the help of industry collaborators, have created a small, implantable device that attaches to the peripheral nerve in a person's arm. When combined with an artificial intelligence computer and a robotic arm, the device can read and interpret brain signals, allowing upper limb amputees to control the arm using only their thoughts. The researchers' most recent paper is published in the Journal of Neural Engineering, a peer-reviewed scientific journal for the interdisciplinary field of neural engineering.

Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The study was funded by the National Science Foundation (NSF). The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs.

IMAGE: A new machine-learning method developed by researchers at the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey will provide more accurate stream and river temperature predictions, even when... view more Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The study was funded by the National Science Foundation (NSF). The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs.

Machine learning algorithms do a lot for us every day--send unwanted email to our spam folder, warn us if our car is about to back into something, and give us recommendations on what TV show to watch next. Now, we are increasingly using these same algorithms to make environmental predictions for us. A team of researchers from the University of Minnesota, University of Pittsburgh, and U.S. Geological Survey recently published a new study on predicting flow and temperature in river networks in the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining (SDM21) proceedings. The research demonstrates a new machine learning method where the algorithm is "taught" the rules of the physical world in order to make better predictions and steer the algorithm toward physically meaningful relationships between inputs and outputs. The study presents a model that can make more accurate river and stream temperature predictions, even when little data is available, which is the case in most rivers and streams.

Unmanned aerial vehicles (UAV's) or better called drones, are becoming quite popular. But, with its growth a very few know its proper application and operation. Not everyone knows how they're allowed to use them. For this reason, the Minnesota Department of Transportation is helping people learn about drones. Various people learnt about the technology and there were also flights simulations and Competitive drone racing.