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KAIST & LG U+ Team Up for Quantum Computing Solution for Ultra-Space 6G Satellite Networking
KAIST quantum computer scientists have optimized ultra-space 6G Low-Earth Orbit (LEO) satellite networking, finding the shortest path to transfer data from a city to another place via multi-satellite hops. The research team led by Professor June-Koo Kevin Rhee and Professor Dongsu Han in partnership with LG U+ verified the possibility of ultra-performance and precision communication with satellite networks using D-Wave, the first commercialized quantum computer. Satellite network optimization has remained challenging since the network needs to be reconfigured whenever satellites approach other satellites within the connection range in a three-dimensional space. Moreover, LEO satellites orbiting at 200~2000 km above the Earth change their positions dynamically, whereas Geo-Stationary Orbit (GSO) satellites do not change their positions. Thus, LEO satellite network optimization needs to be solved in real time. The research groups formulated the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and managed to solve the problem, incorporating the connectivity and link distance limits as the constraints. The proposed optimization algorithm is reported to be much more efficient in terms of hop counts and path length than previously reported studies using classical solutions. These results verify that a satellite network can provide ultra-performance (over 1Gbps user-perceived speed), and ultra-precision (less than 5ms end-to-end latency) network services, which are comparable to terrestrial communication. Once QUBO is applied, “ultra-space networking” is expected to be realized with 6G. Researchers said that an ultra-space network provides communication services for an object moving at up to 10 km altitude with an extreme speed (~ 1000 km/h). Optimized LEO satellite networks can provide 6G communication services to currently unavailable areas such as air flights and deserts. Professor Rhee, who is also the CEO of Qunova Computing, noted, “Collaboration with LG U+ was meaningful as we were able to find an industrial application for a quantum computer. We look forward to more quantum application research on real problems such as in communications, drug and material discovery, logistics, and fintech industries.”
Decoding Brain Signals to Control a Robotic Arm
Advanced brain-machine interface system successfully interprets arm movement directions from neural signals in the brain Researchers have developed a mind-reading system for decoding neural signals from the brain during arm movement. The method, described in the journal Applied Soft Computing, can be used by a person to control a robotic arm through a brain-machine interface (BMI). A BMI is a device that translates nerve signals into commands to control a machine, such as a computer or a robotic limb. There are two main techniques for monitoring neural signals in BMIs: electroencephalography (EEG) and electrocorticography (ECoG). The EEG exhibits signals from electrodes on the surface of the scalp and is widely employed because it is non-invasive, relatively cheap, safe and easy to use. However, the EEG has low spatial resolution and detects irrelevant neural signals, which makes it difficult to interpret the intentions of individuals from the EEG. On the other hand, the ECoG is an invasive method that involves placing electrodes directly on the surface of the cerebral cortex below the scalp. Compared with the EEG, the ECoG can monitor neural signals with much higher spatial resolution and less background noise. However, this technique has several drawbacks. “The ECoG is primarily used to find potential sources of epileptic seizures, meaning the electrodes are placed in different locations for different patients and may not be in the optimal regions of the brain for detecting sensory and movement signals,” explained Professor Jaeseung Jeong, a brain scientist at KAIST. “This inconsistency makes it difficult to decode brain signals to predict movements.” To overcome these problems, Professor Jeong’s team developed a new method for decoding ECoG neural signals during arm movement. The system is based on a machine-learning system for analysing and predicting neural signals called an ‘echo-state network’ and a mathematical probability model called the Gaussian distribution. In the study, the researchers recorded ECoG signals from four individuals with epilepsy while they were performing a reach-and-grasp task. Because the ECoG electrodes were placed according to the potential sources of each patient’s epileptic seizures, only 22% to 44% of the electrodes were located in the regions of the brain responsible for controlling movement. During the movement task, the participants were given visual cues, either by placing a real tennis ball in front of them, or via a virtual reality headset showing a clip of a human arm reaching forward in first-person view. They were asked to reach forward, grasp an object, then return their hand and release the object, while wearing motion sensors on their wrists and fingers. In a second task, they were instructed to imagine reaching forward without moving their arms. The researchers monitored the signals from the ECoG electrodes during real and imaginary arm movements, and tested whether the new system could predict the direction of this movement from the neural signals. They found that the novel decoder successfully classified arm movements in 24 directions in three-dimensional space, both in the real and virtual tasks, and that the results were at least five times more accurate than chance. They also used a computer simulation to show that the novel ECoG decoder could control the movements of a robotic arm. Overall, the results suggest that the new machine learning-based BCI system successfully used ECoG signals to interpret the direction of the intended movements. The next steps will be to improve the accuracy and efficiency of the decoder. In the future, it could be used in a real-time BMI device to help people with movement or sensory impairments. This research was supported by the KAIST Global Singularity Research Program of 2021, Brain Research Program of the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning, and the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education. -PublicationHoon-Hee Kim, Jaeseung Jeong, “An electrocorticographic decoder for arm movement for brain-machine interface using an echo state network and Gaussian readout,” Applied SoftComputing online December 31, 2021 (doi.org/10.1016/j.asoc.2021.108393) -ProfileProfessor Jaeseung JeongDepartment of Bio and Brain EngineeringCollege of EngineeringKAIST
Professor Naehyuck Jang was Appointed Technical Program Chair of the Design Automation Conference
Professor Naehyuck Jang of the Electrical Engineering Department at KAIST was appointed as the technical program chair of the Design Automation Conference (DAC). He is the first Asian to serve the conference as the chair. At next year’s conference, he will select 150 program committee members and supervise the selection process of 1,000 papers. Founded in 1964, DAC encompasses research related to automation of semiconductor processes, which usually involve billions of transistors. More than seven thousand people and 150 companies from all around the world participate, of which only the top 20% of the submitted papers are selected. It is the most prestigious conference in the field of semiconductor automation. The Design Automation Conference also introduces optimization and automation of design processes of systems, hardware security, automobiles, and the Internet of things. Professor Jang specializes in low power system designs. As an ACM Distinguished Scientist, Professor Jang was elected as the chairman after contributing to this year’s program committee by reforming the process of selection of papers. Professor Jang said, “This year’s conference represents a departure, where we move from the field of traditional semiconductors to the optimization of embedded system, the Internet of things, and security. He added that “we want to create a paper selection process that can propose the future of design automation.” The 53rd annual DAC will take place at the Austin Convention Center in Texas in June 2016.
KAIST Ph.D Mihyun Jang Employed as Professor at Technische Universitat Graz
A Ph.D purely from Korea has been employed as a professor at Technische Universitat Graz. This is the news of Prof.Mihyun Kang (39) who has graduated from KAIST’s mathematics department. Prof.Kang has transferred on January 2012. KAIST explained that “it’s the first time for a mathematics Ph.D from Korea has been employed abroad.” Technische Universitat Graz of Australia is ranked the top third university within the country. It is a global university with 1,700 students from 78 different countries out of its 11,000 students. Prof. Kang researched mainly theories of combination including random graphing theories, analytical combination theories, and probabilistic combination theories. She has been employed as a lifetime professor through open recruitment where she competed with others through academic debates and interviews. Technische Universitat Graz valued Prof. Kang’s research highly made her the department head of the ‘Optimization and Discrete Mathematics department’ to create an environment where she could continuously research. Prof. Kang graduated from Jeju university majoring math educations and did her graduate studies in KAIST. She is a purely ‘Korean’ Ph.D. After her studies, she worked for Germany’s Humboldt University and Freie Universitat Berlin. In 2007, she was able to be employed as a professor in Germany, and in 2008, she was chosen as a Heisenberg fellow. Prof. Kang who had her research achievements recognized in Germany and Austria was also offered seat as professor in Ludwig Masximilan University of Germany and Alpenadria University in Austria, but chose Technische Universitat Graz.
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