ENG: The physical laws of everyday water flow were established two centuries ago. However, scientists today struggle to simulate disrupted water flow virtually, e.g., when a hand or object alters its flow. Now, a research team from Tohoku University has harnessed the power of deep reinforcement learning to replicate the flow of water when disturbed. Replicating this agitated liquid motion, as it is known, allowed them to recreate water flow in real time based on only a small amount of data from real water. The technology opens up the possibility for virtual reality interactions involving water.
Category: Engineering
High-tech pavement markers support autonomous driving in tough conditions, remote areas
ENG: Self-driving electric vehicles still face steep hills on the road to reliability. Researchers from the Department of Energy’s Oak Ridge National Laboratory (ORNL) and Western Michigan University (WMU) are working together to drive solutions from outside the car: sensors and processing embedded in road infrastructure. Working with partners, ORNL engineers are placing low-powered sensors in the reflective raised pavement markers that are already used to help drivers identify lanes.
A system to keep cloud-based gamers in sync
ENG: Cloud gaming, which involves playing a video game remotely from the cloud, witnessed unprecedented growth during the lockdowns and gaming hardware shortages that occurred during the heart of the Covid-19 pandemic. Today, the burgeoning industry encompasses a $6 billion global market and more than 23 million players worldwide.
However, interdevice synchronization remains a persistent problem in cloud gaming and the broader field of networking. In cloud gaming, video, audio, and haptic feedback are streamed from one central source to multiple devices, such as a player’s screen and controller, which typically operate on separate networks. These networks aren’t synchronized, leading to a lag between these two separate streams. A player might see something happen on the screen and then hear it on their controller half a second later.
Reinforcement learning allows robots to locate and track objects underwater
ENG: A team led by the Institut de Ciències del Mar in Barcelona in collaboration with the Monterey Bay Aquarium Research Institute in Califòrnia, the Universitat Politècnica de Catalunya and the Universitat de Girona, proves for the first time that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.
