Computer Science

- Professor Jaeyoun Hwang’s team at DGIST, in collaboration with Dabeeo Inc., has developed a deep learning technology for object detection using aerial and satellite images. - The technology is anticipated to find applications in various fields, including defense, autonomous driving vehicles, and medical imaging.

- Prof. Sang Hyun Park of the Department of Robotics and Mechatronics Engineering has developed a logical anomaly detection technology that utilizes segmentation results through artificial intelligence (AI). - The research results were published by the Association for the Advancement of Artificial Intelligence (AAAI), the premier AI conference, in February 2024.

- A team led by DGIST Professor Jeongho Kwak proposes VisionScaling algorithm, enhancing user experience in 6G vision services while conserving energy in mobile devices - Efficient technology development anticipated to enhance user experience in various vision services of the upcoming 6G era (AR/XR, autonomous driving, etc.)

Recreating the action of opening doors in the virtual world requires engineering ways in which to provide the equivalent haptic feedback and steer users away from walls in the real world. A research group has done just this; developing RedirectedDoors+, which employs door robots and rotation to create a more realistic experience.

Researchers at Tohoku university have addressed one of the key challenges of electromagnetic waves in the terahertz frequency range by developing a new type of tuneable filter for signals in the terahertz wave band.

A group of Tohoku University researchers has developed a theoretical model for a high-performance spin wave reservoir computing (RC) that utilizes spintronics technology. The breakthrough moves scientists closer to realizing energy-efficient, nanoscale computing with unparalleled computational power.

A novel method for studying genes in testicular cells of living animals could lead to breakthroughs in male contraception and fertility treatments.

Analysis of materials can be done quicker and with less expertise with the help of proven machine learning techniques established in biomedical fields.

Experimental analysis and computer simulations reveal how woven fabric composite materials are deformed by heat.

GPT-4 shows promise as an aid to chemistry researchers, yet its limitations reveal the need for further improvements.

MozzHub, a user-friendly dengue hotspot detector, could significantly assist the management and control of dengue.

Researchers have developed a proof-of-concept system that allows robotic experiments to run without any human intervention.

Researchers have developed an AI-driven system that can design novel molecules with any desired properties and suggest methods to create them using readily available materials.

Researchers have combined machine learning with robotic process automation to speed up and simplify a time-consuming process.

Lingnan University held a Chinese New Year media reception today (27 February) at its Tuen Mun campus, where Prof S. Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University, together with Prof Xin Yao, Vice-President (Research and Innovation), who joined Lingnan in January; Prof Sam Kwong Tak-wu, Associate Vice-President (Strategy Research); Prof Lau Chi-pang, Associate Vice-President (Academic Affairs and External Relations); and Ms Margaret Cheung Wai-fong, Registrar, shared the latest developments of the University with the media.

To strengthen collaborative ties with prestigious institutions on the mainland, Lingnan University recently welcomed a delegation from Zhejiang University. The delegation, consisting of 31 outstanding students participating in the Morningside Cultural China Scholars Programme, along with three accompanying teachers, engaged with Prof S. Joe Qin, President and Wai Kee Kau Chair Professor of Data Science of Lingnan University, who also delivered a keynote address titled "The Emerging Era of Data Science and AI".

AI records rare bird footage, Lighting up IoT and smart cities, Crystal language designs new materials, Detecting toxins with mobile phones, and Dragonfly wings for better aerodynamics. Plus join us for SciCom coffee. Read all in the latest Editor's Choice.

Yokohama National University scientists are working towards creating a better tomorrow by addressing diverse challenges, from snow algae and tropical cyclones to AI cyberthreats, and much more.

A trained AI system learns to design cellular materials with specific target properties for a wide range of potential uses, including tissue engineering and energy storage.

We may not think about it while doing it, but our nervous system is directing our bones, joints, muscles, tendons, and more to move as efficiently as possible at varying speeds. Replicating this in robots is notoriously difficult. But now, Tohoku University researchers have created a model that makes this possible, thanks in large part to an innovative algorithm.

Non-Heisenberg-type approximant crystals have many interesting properties and are intriguing for researchers of condensed matter physics. However, their magnetic phase diagrams, which are crucial for realizing their potential, remain completely unknown. Now, for the first time, a team of researchers has constructed the magnetic phase diagram of a non-Heisenberg Tsai-type 1/1 gold-gallium-terbium approximant crystal. This development marks a significant step forward for quasicrystal research and for the realization of magnetic refrigerators and spintronic devices.

7000 forever chemicals (PFAS) and human PPARa binding properties predicted using AI technology

The Council of Lingnan University approved the Search Committee’s recommendation to appoint Prof Yao Xin as Vice-President (Research and Innovation) and concurrently Tong Tin Sun Chair Professor of Machine Learning, effective from 22 January 2024, for a term of five years. Prof Yao is also appointed as Tong Tin Sun Chair Professor of Machine Learning and will oversee and steer the development of research, innovation and knowledge transfer at the University, and provide leadership for the research institutes and centres with the Associate Vice-President (Strategic Research). He will oversee the School of Graduate Studies and the Office of Research and Knowledge Transfer directly.

What actually happens is much weirder, and may help us understand more about quantum mechanics

Prof Sam Kwong Tak-wu, Associate Vice-President (Strategic Research) and Chair Professor of Computational Intelligence of Lingnan University, co-authored the research paper that has received the Best Paper, Chester W. Sall Memorial Awards 2024 from the Institute of Electrical and Electronics Engineers (IEEE), the world's largest technical professional organisation dedicated to advancing technology for the benefit of humanity. This honour was published in their monthly peer-reviewed scientific journal “IEEE Transactions on Consumer Electronics”. Only three Best Paper Awards are given each year, and the competition is extremely fierce.

A research team led by Lingnan University's School of Interdisciplinary Studies Assistant Professor Xiao Hang recently devised a “crystal language” that enables artificial intelligence (AI) to create new materials with desired properties (Figure 1). This groundbreaking discovery holds the potential for creating the next revolutionary semiconductor material that makes solar energy abundant and affordable, and opens possibilities for the development of novel superconductors that effortlessly transport clean electricity with zero losses. The findings of this breakthrough research have been published in the prestigious academic journal Nature Communications, which is also the first time Lingnan University scholar published a lead-author paper on the esteemed Nature series.

Researchers have developed a high-speed, high-sensitivity terahertz-wave detector operating at room temperature, paving the way for advancements in the development of next generation 6G/7G technology.

- Professor Sung-hoon Lim’s team at DGIST has developed a metric learning technology that increases 3D object estimation performance, a key self-driving technology - Commercialization of self-driving is expected due to the reduced self-driving framework installation cost

- Professor Sunghoon Im's team at DGIST has developed depth-sensing technology that can be reliably used in diverse environments by analyzing the relationship between the neural network structure and distance sensing in autonomous driving. - The new technology is expected to contribute to the development of a wide range of AI fields, such as autonomous driving, which must be performed safely in various urban settings and weather conditions

A research team at Osaka Metropolitan University has fabricated a gallium nitride (GaN) transistor using diamond, which of all natural materials has the highest thermal conductivity on earth, as a substrate, and they succeeded in increasing heat dissipation by more than two times compared with conventional transistors. The transistor is expected to be useful not only in the fields of 5G communication base stations, weather radar, and satellite communications, but also in microwave heating and plasma processing.