Deep learning for virtual reality; quantum circuits; autonomous shuttles.
Deep-learning-based virtual reality tool
Given that future systems which enable people to interact with virtual environments will require computers to interpret the human hand’s nearly endless variety and complexity of changing motions and joint angles, Purdue University researchers have created a convolutional neural network-based system that is capable of deep learning.
They reminded that in virtual and augmented reality, the user wears a headset that displays the virtual environment as video and images, and where augmented reality allows the user to see the real world as well as the virtual world and to interact with both, virtual reality completely immerses the user in the artificial environment. In both cases the systems must be able to see and interpret what the user’s hands are doing. If a user’s hands can’t interact with the virtual world, they can’t do anything; that’s why the hands are so important.
The new DeepHand system mimics the human brain in order to understand the hand’s nearly endless complexity of joint angles and contortions.
DeepHand uses a depth-sensing camera to capture the user’s hand, and specialized algorithms then interpret hand motions. This is called a spatial user interface because the users interfaces with the computer in space instead of on a touch screen or keyboard. It was trained with a database of 2.5 million hand poses and configurations.
Building circuits for quantum computers
Thanks to Penn State University researchers, the era of quantum computers is one step closer with the creation of a new way to pack a lot more quantum computing power into a much smaller space and with much greater control than ever before.
This advance, using a 3D array of atoms in quantum states called quantum bits — or qubits — was made by David S. Weiss, professor of physics at Penn State University, and three students on his lab team.
“Our result is one of the many important developments that still are needed on the way to achieving quantum computers that will be useful for doing computations that are impossible to do today, with applications in cryptography for electronic data security and other computing-intensive fields,” he said.
The new technique uses both laser light and microwaves to precisely control the switching of selected individual qubits from one quantum state to another without altering the states of the other atoms in the cubic array, and demonstrates the potential use of atoms as the building blocks of circuits in future quantum computers, the team noted.
They explained their invention allows for the arrangement and precise control of the qubits, which are necessary for doing calculations in a quantum computer.
While Weiss’s team plans to continue developing further. This is also expected to be useful to scientists pursuing other approaches to building a quantum computer, including those based on other atoms, on ions, or on atom-like systems in 1 or 2 dimensions.
Autonomous shuttles start giving rides
Autonomous shuttles made history in Switzerland last week when they begin carrying passengers in the historical district of Sion, the largest city in the Canton of Valais. Two vehicles were scheduled to wend their way along the edge of town and through the pedestrian area, with stops to include the Place de la Planta and the Place du Midi. This pilot project, part of the Mobility Lab Sion Valais initiative, is an opportunity for EPFL researchers to test and improve their traffic and fleet-management algorithms.
The smart vehicles will be run by PostBus, Switzerland’s leading public bus operator. They will carry up to 11 passengers at a time, at a maximum speed of 20 kilometers per hour. They will be monitored and controlled by a remote operator using a software program developed by the EPFL startup BestMile. For reasons of safety and security, an attendant will be onboard during this groundbreaking test phase. The shuttles will be free of charge, and a set schedule will be announced once the test phase has been completed.