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KAIST sends out Music and Bio-Signs of Professor Kwon Ji-yong, a.k.a. G-Dragon, into Space to Pulsate through Universe and Resonate among Stars
KAIST (President Kwang-Hyung Lee) announced on the 10th of April that it successfully promoted the world’s first ‘Space Sound Source Transmission Project’ based on media art at the KAIST Space Research Institute on April 9th through collaboration between Professor Jinjoon Lee of the Graduate School of Culture Technology, a world-renowned media artist, and the global K-Pop artist, G-Dragon.
This project was proposed as part of the ‘AI Entertech Research Center’ being promoted by KAIST and Galaxy Corporation. It is a project to transmit the message and sound of G-Dragon (real name, Kwon Ji-yong), a singer/song writer affiliated with Galaxy Corporation and a visiting professor in the Department of Mechanical Engineering at KAIST, to space for the first time in the world.
This is a convergence project that combines science, technology, art, and popular music, and is a new form of ‘space culture content’ experiment that connects KAIST’s cutting-edge space technology, Professor Jinjoon Lee’s media art work, and G-Dragon’s voice and sound source containing his latest digital single, "HOME SWEET HOME".
< Photo 1. Professor Jinjoon Lee's Open Your Eyes Project "Iris"'s imagery projected on the 13m space antenna at the Space Research Institute >
This collaboration was planned with the theme of ‘emotional signals that expand the inner universe of humans to the outer universe.’ The image of G-Dragon’s iris was augmented through AI as a window into soul symbolizing his uniqueness and identity, and the new song “Home Sweet Home” was combined as an audio message containing the vibration of that emotion.
This was actually transmitted into space using a next-generation small satellite developed by KAIST Space Research Institute, completing a symbolic performance in which an individual’s inner universe is transmitted to outer space.
Professor Jinjoon Lee’s cinematic media art work “Iris” was unveiled at the site. This work was screened in the world’s first projection mapping method* on KAIST Space Research Institute’s 13m space antenna. This video was created using generative artificial intelligence (AI) technology based on the image of G-Dragon's iris, and combined with sound using the data of the sounds of Emile Bell rings – the bell that holds a thousand years of history, it presented an emotional art experience that transcends time and space.
*Projection Mapping: A technology that projects light and images onto actual structures to create visual changes, and is a method of expression that artistically reinterprets space.
This work is one of the major research achievements of KAIST TX Lab and Professor Lee based on new media technology based on biometric data such as iris, heartbeat, and brain waves.
Professor Jinjoon Lee said, "The iris is a symbol that reflects inner emotions and identity, so much so that it is called the 'mirror of the soul,' and this work sought to express 'the infinite universe seen from the inside of humanity' through G-Dragon's gaze."
< Photo 2. (From left) Professor Jinjoon Lee of the Graduate School of Culture Technology and G-Dragon (Visiting Professor Kwon Ji-yong of the Department of Mechanical Engineering) >
He continued, "The universe is a realm of technology as well as a stage for imagination and emotion, and I look forward to an encounter with the unknown through a new attempt to speak of art in the language of science including AI and imagine science in the form of art." “G-Dragon’s voice and music have now begun their journey to space,” said Yong-ho Choi, Galaxy Corporation’s Chief Happiness Officer (CHO). “This project is an act of leaving music as a legacy for humanity, while also having an important meaning of attempting to communicate with space.” He added, “This is a pioneering step to introduce human culture to space, and it will remain as a monumental performance that opens a new chapter in the history of music comparable to the Beatles.”
Galaxy Corporation is leading the future entertainment technology industry through its collaboration with KAIST, and was recently selected as the only entertainment technology company in a private meeting with Microsoft CEO Nadella. In particular, it is promoting the globalization of AI entertainment technology, receiving praise as a “pioneer of imagination” for new forms of AI entertainment content, including the AI contents for the deceased.
< Photo 3. Photo of G-Dragon's Home Sweet Home being sent into the space via Professor Jinjoon Lee's Space Sound Source Transmission Project >
Through this project, KAIST Space Research Institute presented new possibilities for utilizing satellite technology, and showed a model for science to connect with society in a more popular way.
KAIST President Kwang-Hyung Lee said, “KAIST is a place that always supports new imaginations and challenges,” and added, “We will continue to strive to continue creative research that no one has ever thought of, like this project that combines science, technology, and art.”
In the meantime, Galaxy Corporation, the agency of G-Dragon’s Professor Kwon Ji-yong, is an AI entertainment company that presents a new paradigm based on IP, media, tech, and entertainment convergence technology.
2025.04.10 View 1084 -
Ultralight advanced material developed by KAIST and U of Toronto
< (From left) Professor Seunghwa Ryu of KAIST Department of Mechanical Engineering, Professor Tobin Filleter of the University of Toronto, Dr. Jinwook Yeo of KAIST, and Dr. Peter Serles of the University of Toronto >
Recently, in advanced industries such as automobiles, aerospace, and mobility, there has been increasing demand for materials that achieve weight reduction while maintaining excellent mechanical properties. An international joint research team has developed an ultralight, high-strength material utilizing nanostructures, presenting the potential for various industrial applications through customized design in the future.
KAIST (represented by President Kwang Hyung Lee) announced on the 18th of February that a research team led by Professor Seunghwa Ryu from the Department of Mechanical Engineering, in collaboration with Professor Tobin Filleter from the University of Toronto, has developed a nano-lattice structure that maximizes lightweight properties while maintaining high stiffness and strength.
In this study, the research team optimized the beam shape of the lattice structure to maintain its lightweight characteristics while maximizing stiffness and strength.
Particularly, using a multi-objective Bayesian optimization algorithm*, the team conducted an optimal design process that simultaneously considers tensile and shear stiffness improvement and weight reduction. They demonstrated that the optimal lattice structure could be predicted and designed with significantly less data (about 400 data points) compared to conventional methods.
*Multi-objective Bayesian optimization algorithm: A method that finds the optimal solution while considering multiple objectives simultaneously. It efficiently collects data and predicts results even under conditions of uncertainty.
< Figure 1. Multi-objective Bayesian optimization for generative design of carbon nanolattices with high compressive stiffness and strength at low density. The upper is the illustration of process workflow. The lower part shows top four MBO CFCC geometries with their 2D Bézier curves. (The optimized structure is predicted and designed with much less data (approximately 400) than the conventional method >
Furthermore, to maximize the effect where mechanical properties improve as size decreases at the nanoscale, the research team utilized pyrolytic carbon* material to implement an ultralight, high-strength, high-stiffness nano-lattice structure.
*Pyrolytic carbon: A carbon material obtained by decomposing organic substances at high temperatures. It has excellent heat resistance and strength, making it widely used in industries such as semiconductor equipment coatings and artificial joint coatings, where it must withstand high temperatures without deformation.
For this, the team applied two-photon polymerization (2PP) technology* to precisely fabricate complex nano-lattice structures, and mechanical performance evaluations confirmed that the developed structure simultaneously possesses strength comparable to steel and the lightness of Styrofoam.
*Two-photon polymerization (2PP) technology: An advanced optical manufacturing technique based on the principle that polymerization occurs only when two photons of a specific wavelength are absorbed simultaneously.
Additionally, the research team demonstrated that multi-focus two-photon polymerization (multi-focus 2PP) technology enables the fabrication of millimeter-scale structures while maintaining nanoscale precision.
Professor Seunghwa Ryu explained, "This technology innovatively solves the stress concentration issue, which has been a limitation of conventional design methods, through three-dimensional nano-lattice structures, achieving both ultralight weight and high strength in material development."
< Figure 2. FESEM image of the fabricated nano-lattice structure and (bottom right) the macroscopic nanolattice resting on a bubble >
He further emphasized, "By integrating data-driven optimal design with precision 3D printing technology, this development not only meets the demand for lightweight materials in the aerospace and automotive industries but also opens possibilities for various industrial applications through customized design."
This study was led by Dr. Peter Serles of the Department of Mechanical & Industrial Engineering at University of Toronto and Dr. Jinwook Yeo from KAIST as co-first authors, with Professor Seunghwa Ryu and Professor Tobin Filleter as corresponding authors.
The research was published on January 23, 2025 in the international journal Advanced Materials (Paper title: “Ultrahigh Specific Strength by Bayesian Optimization of Lightweight Carbon Nanolattices”).
DOI: https://doi.org/10.1002/adma.202410651
This research was supported by the Multiphase Materials Innovation Manufacturing Research Center (an ERC program) funded by the Ministry of Science and ICT, the M3DT (Medical Device Digital Development Tool) project funded by the Ministry of Food and Drug Safety, and the KAIST International Collaboration Program.
2025.02.18 View 2287 -
KAIST Develops AI-Driven Performance Prediction Model to Advance Space Electric Propulsion Technology
< (From left) PhD candidate Youngho Kim, Professor Wonho Choe, and PhD candidate Jaehong Park from the Department of Nuclear and Quantum Engineering >
Hall thrusters, a key space technology for missions like SpaceX's Starlink constellation and NASA's Psyche asteroid mission, are high-efficiency electric propulsion devices using plasma technology*. The KAIST research team announced that the AI-designed Hall thruster developed for CubeSats will be installed on the KAIST-Hall Effect Rocket Orbiter (K-HERO) CubeSat to demonstrate its in-orbit performance during the fourth launch of the Korean Launch Vehicle called Nuri rocket (KSLV-2) scheduled for November this year.
*Plasma is one of the four states of matter, where gases are heated to high energies, causing them to separate into charged ions and electrons. Plasma is used not only in space electric propulsion but also in semiconductor manufacturing, display processes, and sterilization devices.
On February 3rd, the research team from the KAIST Department of Nuclear and Quantum Engineering’s Electric Propulsion Laboratory, led by Professor Wonho Choe, announced the development of an AI-based technique to accurately predict the performance of Hall thrusters, the engines of satellites and space probes.
Hall thrusters provide high fuel efficiency, requiring minimal propellant to achieve significant acceleration of spacecrafts or satellites while producing substantial thrust relative to power consumption. Due to these advantages, Hall thrusters are widely used in various space missions, including the formation flight of satellite constellations, deorbiting maneuvers for space debris mitigation, and deep space missions such as asteroid exploration.
As the space industry continues to grow during the NewSpace era, the demand for Hall thrusters suited to diverse missions is increasing. To rapidly develop highly efficient, mission-optimized Hall thrusters, it is essential to predict thruster performance accurately from the design phase.
However, conventional methods have limitations, as they struggle to handle the complex plasma phenomena within Hall thrusters or are only applicable under specific conditions, leading to lower prediction accuracy.
The research team developed an AI-based performance prediction technique with high accuracy, significantly reducing the time and cost associated with the iterative design, fabrication, and testing of thrusters. Since 2003, Professor Wonho Choe’s team has been leading research on electric propulsion development in Korea. The team applied a neural network ensemble model to predict thruster performance using 18,000 Hall thruster training data points generated from their in-house numerical simulation tool.
The in-house numerical simulation tool, developed to model plasma physics and thrust performance, played a crucial role in providing high-quality training data. The simulation’s accuracy was validated through comparisons with experimental data from ten KAIST in-house Hall thrusters, with an average prediction error of less than 10%.
< Figure 1. This research has been selected as the cover article for the March 2025 issue (Volume 7, Issue 3) of the AI interdisciplinary journal, Advanced Intelligent Systems. >
The trained neural network ensemble model acts as a digital twin, accurately predicting the Hall thruster performance within seconds based on thruster design variables.
Notably, it offers detailed analyses of performance parameters such as thrust and discharge current, accounting for Hall thruster design variables like propellant flow rate and magnetic field—factors that are challenging to evaluate using traditional scaling laws.
This AI model demonstrated an average prediction error of less than 5% for the in-house 700 W and 1 kW KAIST Hall thrusters and less than 9% for a 5 kW high-power Hall thruster developed by the University of Michigan and the U.S. Air Force Research Laboratory. This confirms the broad applicability of the AI prediction method across different power levels of Hall thrusters.
Professor Wonho Choe stated, “The AI-based prediction technique developed by our team is highly accurate and is already being utilized in the analysis of thrust performance and the development of highly efficient, low-power Hall thrusters for satellites and spacecraft. This AI approach can also be applied beyond Hall thrusters to various industries, including semiconductor manufacturing, surface processing, and coating, through ion beam sources.”
< Figure 2. The AI-based prediction technique developed by the research team accurately predicts thrust performance based on design variables, making it highly valuable for the development of high-efficiency Hall thrusters. The neural network ensemble processes design variables, such as channel geometry and magnetic field information, and outputs key performance metrics like thrust and prediction accuracy, enabling efficient thruster design and performance analysis. >
Additionally, Professor Choe mentioned, “The CubeSat Hall thruster, developed using the AI technique in collaboration with our lab startup—Cosmo Bee, an electric propulsion company—will be tested in orbit this November aboard the K-HERO 3U (30 x 10 x 10 cm) CubeSat, scheduled for launch on the fourth flight of the KSLV-2 Nuri rocket.”
This research was published online in Advanced Intelligent Systems on December 25, 2024 with PhD candidate Jaehong Park as the first author and was selected as the journal’s cover article, highlighting its innovation.
< Figure 3. Image of the 150 W low-power Hall thruster for small and micro satellites, developed in collaboration with Cosmo Bee and the KAIST team. The thruster will be tested in orbit on the K-HERO CubeSat during the KSLV-2 Nuri rocket’s fourth launch in Q4 2025. >
This research was supported by the National Research Foundation of Korea’s Space Pioneer Program (200mN High Thrust Electric Propulsion System Development).
(Paper Title: Predicting Performance of Hall Effect Ion Source Using Machine Learning, DOI: https://doi.org/10.1002/aisy.202400555 )
< Figure 4. Graphs of the predicted thrust and discharge current of KAIST’s 700 W Hall thruster using the AI model (HallNN). The left image shows the Hall thruster operating in KAIST Electric Propulsion Laboratory’s vacuum chamber, while the center and right graphs present the prediction results for thrust and discharge current based on anode mass flow rate. The red lines represent AI predictions, and the blue dots represent experimental results, with a prediction error of less than 5%. >
2025.02.03 View 3216 -
A Way for Smartwatches to Detect Depression Risks Devised by KAIST and U of Michigan Researchers
- A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches
- It has the potential to be used as a medical technology to replace the economically burdensome fMRI measurement test
- It is expected to expand the scope of digital health data analysis
The CORONA virus pandemic also brought about a pandemic of mental illness. Approximately one billion people worldwide suffer from various psychiatric conditions. Korea is one of more serious cases, with approximately 1.8 million patients exhibiting depression and anxiety disorders, and the total number of patients with clinical mental diseases has increased by 37% in five years to approximately 4.65 million. A joint research team from Korea and the US has developed a technology that uses biometric data collected through wearable devices to predict tomorrow's mood and, further, to predict the possibility of developing symptoms of depression.
< Figure 1. Schematic diagram of the research results. Based on the biometric data collected by a smartwatch, a mathematical algorithm that solves the inverse problem to estimate the brain's circadian phase and sleep stages has been developed. This algorithm can estimate the degrees of circadian disruption, and these estimates can be used as the digital biomarkers to predict depression risks. >
KAIST (President Kwang Hyung Lee) announced on the 15th of January that the research team under Professor Dae Wook Kim from the Department of Brain and Cognitive Sciences and the team under Professor Daniel B. Forger from the Department of Mathematics at the University of Michigan in the United States have developed a technology to predict symptoms of depression such as sleep disorders, depression, loss of appetite, overeating, and decreased concentration in shift workers from the activity and heart rate data collected from smartwatches.
According to WHO, a promising new treatment direction for mental illness focuses on the sleep and circadian timekeeping system located in the hypothalamus of the brain, which directly affect impulsivity, emotional responses, decision-making, and overall mood.
However, in order to measure endogenous circadian rhythms and sleep states, blood or saliva must be drawn every 30 minutes throughout the night to measure changes in the concentration of the melatonin hormone in our bodies and polysomnography (PSG) must be performed. As such treatments requires hospitalization and most psychiatric patients only visit for outpatient treatment, there has been no significant progress in developing treatment methods that take these two factors into account. In addition, the cost of the PSG test, which is approximately $1000, leaves mental health treatment considering sleep and circadian rhythms out of reach for the socially disadvantaged.
The solution to overcome these problems is to employ wearable devices for the easier collection of biometric data such as heart rate, body temperature, and activity level in real time without spatial constraints. However, current wearable devices have the limitation of providing only indirect information on biomarkers required by medical staff, such as the phase of the circadian clock.
The joint research team developed a filtering technology that accurately estimates the phase of the circadian clock, which changes daily, such as heart rate and activity time series data collected from a smartwatch. This is an implementation of a digital twin that precisely describes the circadian rhythm in the brain, and it can be used to estimate circadian rhythm disruption.
< Figure 2. The suprachiasmatic nucleus located in the hypothalamus of the brain is the central biological clock that regulates the 24-hour physiological rhythm and plays a key role in maintaining the body’s circadian rhythm. If the phase of this biological clock is disrupted, it affects various parts of the brain, which can cause psychiatric conditions such as depression. >
The possibility of using the digital twin of this circadian clock to predict the symptoms of depression was verified through collaboration with the research team of Professor Srijan Sen of the Michigan Neuroscience Institute and Professor Amy Bohnert of the Department of Psychiatry of the University of Michigan.
The collaborative research team conducted a large-scale prospective cohort study involving approximately 800 shift workers and showed that the circadian rhythm disruption digital biomarker estimated through the technology can predict tomorrow's mood as well as six symptoms, including sleep problems, appetite changes, decreased concentration, and suicidal thoughts, which are representative symptoms of depression.
< Figure 3. The circadian rhythm of hormones such as melatonin regulates various physiological functions and behaviors such as heart rate and activity level. These physiological and behavioral signals can be measured in daily life through wearable devices. In order to estimate the body’s circadian rhythm inversely based on the measured biometric signals, a mathematical algorithm is needed. This algorithm plays a key role in accurately identifying the characteristics of circadian rhythms by extracting hidden physiological patterns from biosignals. >
Professor Dae Wook Kim said, "It is very meaningful to be able to conduct research that provides a clue for ways to apply wearable biometric data using mathematics that have not previously been utilized for actual disease management." He added, "We expect that this research will be able to present continuous and non-invasive mental health monitoring technology. This is expected to present a new paradigm for mental health care. By resolving some of the major problems socially disadvantaged people may face in current treatment practices, they may be able to take more active steps when experiencing symptoms of depression, such as seeking counsel before things get out of hand."
< Figure 4. A mathematical algorithm was devised to circumvent the problems of estimating the phase of the brain's biological clock and sleep stages inversely from the biodata collected by a smartwatch. This algorithm can estimate the degree of daily circadian rhythm disruption, and this estimate can be used as a digital biomarker to predict depression symptoms. >
The results of this study, in which Professor Dae Wook Kim of the Department of Brain and Cognitive Sciences at KAIST participated as the joint first author and corresponding author, were published in the online version of the international academic journal npj Digital Medicine on December 5, 2024. (Paper title: The real-world association between digital markers of circadian disruption and mental health risks) DOI: 10.1038/s41746-024-01348-6
This study was conducted with the support of the KAIST's Research Support Program for New Faculty Members, the US National Science Foundation, the US National Institutes of Health, and the US Army Research Institute MURI Program.
2025.01.20 View 4012 -
KAIST Secures Core Technology for Ultra-High-Resolution Image Sensors
A joint research team from Korea and the United States has developed next-generation, high-resolution image sensor technology with higher power efficiency and a smaller size compared to existing sensors. Notably, they have secured foundational technology for ultra-high-resolution shortwave infrared (SWIR) image sensors, an area currently dominated by Sony, paving the way for future market entry.
KAIST (represented by President Kwang Hyung Lee) announced on the 20th of November that a research team led by Professor SangHyeon Kim from the School of Electrical Engineering, in collaboration with Inha University and Yale University in the U.S., has developed an ultra-thin broadband photodiode (PD), marking a significant breakthrough in high-performance image sensor technology.
This research drastically improves the trade-off between the absorption layer thickness and quantum efficiency found in conventional photodiode technology. Specifically, it achieved high quantum efficiency of over 70% even in an absorption layer thinner than one micrometer (μm), reducing the thickness of the absorption layer by approximately 70% compared to existing technologies.
A thinner absorption layer simplifies pixel processing, allowing for higher resolution and smoother carrier diffusion, which is advantageous for light carrier acquisition while also reducing the cost. However, a fundamental issue with thinner absorption layers is the reduced absorption of long-wavelength light.
< Figure 1. Schematic diagram of the InGaAs photodiode image sensor integrated on the Guided-Mode Resonance (GMR) structure proposed in this study (left), a photograph of the fabricated wafer, and a scanning electron microscope (SEM) image of the periodic patterns (right) >
The research team introduced a guided-mode resonance (GMR) structure* that enables high-efficiency light absorption across a wide spectral range from 400 nanometers (nm) to 1,700 nanometers (nm). This wavelength range includes not only visible light but also light the SWIR region, making it valuable for various industrial applications.
*Guided-Mode Resonance (GMR) Structure: A concept used in electromagnetics, a phenomenon in which a specific (light) wave resonates (forming a strong electric/magnetic field) at a specific wavelength. Since energy is maximized under these conditions, it has been used to increase antenna or radar efficiency.
The improved performance in the SWIR region is expected to play a significant role in developing next-generation image sensors with increasingly high resolutions. The GMR structure, in particular, holds potential for further enhancing resolution and other performance metrics through hybrid integration and monolithic 3D integration with complementary metal-oxide-semiconductor (CMOS)-based readout integrated circuits (ROIC).
< Figure 2. Benchmark for state-of-the-art InGaAs-based SWIR pixels with simulated EQE lines as a function of TAL variation. Performance is maintained while reducing the absorption layer thickness from 2.1 micrometers or more to 1 micrometer or less while reducing it by 50% to 70% >
The research team has significantly enhanced international competitiveness in low-power devices and ultra-high-resolution imaging technology, opening up possibilities for applications in digital cameras, security systems, medical and industrial image sensors, as well as future ultra-high-resolution sensors for autonomous driving, aerospace, and satellite observation.
Professor Sang Hyun Kim, the lead researcher, commented, “This research demonstrates that significantly higher performance than existing technologies can be achieved even with ultra-thin absorption layers.”
< Figure 3. Top optical microscope image and cross-sectional scanning electron microscope image of the InGaAs photodiode image sensor fabricated on the GMR structure (left). Improved quantum efficiency performance of the ultra-thin image sensor (red) fabricated with the technology proposed in this study (right) >
The results of this research were published on 15th of November, in the prestigious international journal Light: Science & Applications (JCR 2.9%, IF=20.6), with Professor Dae-Myung Geum of Inha University (formerly a KAIST postdoctoral researcher) and Dr. Jinha Lim (currently a postdoctoral researcher at Yale University) as co-first authors. (Paper title: “Highly-efficient (>70%) and Wide-spectral (400 nm -1700 nm) sub-micron-thick InGaAs photodiodes for future high-resolution image sensors”)
This study was supported by the National Research Foundation of Korea.
2024.11.22 View 3196 -
KAIST Develops Janus-like Metasurface Technology that Acts According to the Direction of Light
Metasurface technology is an advanced optical technology that is thinner, lighter, and capable of precisely controlling light through nanometer-sized artificial structures compared to conventional technologies. KAIST researchers have overcome the limitations of existing metasurface technologies and successfully designed a Janus metasurface capable of perfectly controlling asymmetric light transmission. By applying this technology, they also proposed an innovative method to significantly enhance security by only decoding information under specific conditions.
KAIST (represented by President Kwang Hyung Lee) announced on the 15th of October that a research team led by Professor Jonghwa Shin from the Department of Materials Science and Engineering had developed a Janus metasurface capable of perfectly controlling asymmetric light transmission.
Asymmetric properties, which react differently depending on the direction, play a crucial role in various fields of science and engineering. The Janus metasurface developed by the research team implements an optical system capable of performing different functions in both directions.
Like the Roman god Janus with two faces, this metasurface shows entirely different optical responses depending on the direction of incoming light, effectively operating two independent optical systems with a single device (for example, a metasurface that acts as a magnifying lens in one direction and as a polarized camera in the other). In other words, by using this technology, it's possible to operate two different optical systems (e.g., a lens and a hologram) depending on the direction of the light.
This achievement addresses a challenge that existing metasurface technologies had not resolved. Conventional metasurface technology had limitations in selectively controlling the three properties of light—intensity, phase, and polarization—based on the direction of incidence.
The research team proposed a solution based on mathematical and physical principles, and succeeded in experimentally implementing different vector holograms in both directions. Through this achievement, they showcased a complete asymmetric light transmission control technology.
< Figure 1. Schematics of a device featuring asymmetric transmission. a) Device operating as a magnifying lens for back-side illumination. b) Device operating as a polarization camera for front-side illumination. >
Additionally, the research team developed a new optical encryption technology based on this metasurface technology. By using the Janus metasurface, they implemented a vector hologram that generates different images depending on the direction and polarization state of incoming light, showcasing an optical encryption system that significantly enhances security by allowing information to be decoded only under specific conditions.
This technology is expected to serve as a next-generation security solution, applicable in various fields such as quantum communication and secure data transmission.
Furthermore, the ultra-thin structure of the metasurface is expected to significantly reduce the volume and weight of traditional optical devices, contributing greatly to the miniaturization and lightweight design of next-generation devices.
< Figure 2. Experimental demonstration of Janus vectorial holograms. With front illuminations, vector images of the butterfly and the grasshopper are created, and with the back-side illuminations, vector images of the ladybug and the beetle are created. >
Professor Jonghwa Shin from the Department of Materials Science and Engineering at KAIST stated, "This research has enabled the complete asymmetric transmission control of light’s intensity, phase, and polarization, which has been a long-standing challenge in optics. It has opened up the possibility of developing various applied optical devices." He added, "We plan to continue developing optical devices that can be applied to various fields such as augmented reality (AR), holographic displays, and LiDAR systems for autonomous vehicles, utilizing the full potential of metasurface technology."
This research, in which Hyeonhee Kim (a doctoral student in the Department of Materials Science and Engineering at KAIST) and Joonkyo Jung participated as co-first authors, was published online in the international journal Advanced Materials and is scheduled to be published in the October 31 issue. (Title of the paper: "Bidirectional Vectorial Holography Using Bi-Layer Metasurfaces and Its Application to Optical Encryption")
The research was supported by the Nano Materials Technology Development Program and the Mid-Career Researcher Program of the National Research Foundation of Korea.
2024.10.15 View 2682 -
KAIST Develops Stretchable Displays Featuring 25% Expansion Without Image Distortion
Stretchable displays, praised for their spatial efficiency, design flexibility, and human-like flexibility, are seen as the next generation of display technology. A team of Korean researchers has developed a stretchable display that can expand by 25% while maintaining clear image quality without distortion. It can also stretch and contract up to 5,000 times at 15% expansion without any performance degradation, making it the first deformation-free stretchable display with a negative Poisson's ratio* developed in Korea.
*Poisson’s ratio of -1: A ratio where both width and length stretch equally, expressed as a negative value. A positive Poisson's ratio represents the ratio where horizontal stretching leads to vertical contraction, which is the case for most materials.
KAIST (represented by President Kwang-Hyung Lee) announced on the 20th of August that a research team led by Professor Byeong-Soo Bae of the Department of Materials Science and Engineering (Director of the Wearable Platform Materials Technology Center) , in collaboration with the Korea Institute of Machinery & Materials (President Seoghyeon Ryu), successfully developed a stretchable display substrate that suppresses image distortion through omnidirectional stretchability.
Currently, most stretchable displays are made with highly elastic elastomer* materials, but these materials possess a positive Poisson's ratio, causing unavoidable image distortion when the display is stretched.
*Elastomer: A polymer with elasticity similar to rubber.
To address this, the introduction of auxetic* meta-structures has been gaining attention. Unlike conventional materials, auxetic structures have a unique 'negative Poisson's ratio,' expanding in all directions when stretched in just one direction. However, traditional auxetic structures contain many empty spaces, limiting their stability and usability in display substrates.
*Auxetic structure: A special geometric structure that exhibits a negative Poisson's ratio.
To tackle the issue of image distortion, Professor Bae's research team developed a method to create a seamless surface for the auxetic meta-structure, achieving the ideal negative Poisson's ratio of -1 and overcoming the biggest challenge in auxetic meta-structures.
To overcome the second issue of elastic modulus*, the team inserted a textile made of glass fiber bundles with a diameter of just 25 micrometers (a quarter of the thickness of human hair) into the elastomer material. They then filled the empty spaces with the same elastomer, creating a flat and stable integrated film without gaps.
*Elastic Modulus: The ratio that indicates the extent of deformation when force is applied to a material. A higher elastic modulus means that the material is less likely to deform under force.
The research team theoretically identified that the difference in elasticity between the auxetic structure and the elastomer material directly influences the negative Poisson's ratio and successfully achieved an elasticity difference of over 230,000 times, producing a film with a Poisson's ratio of -1, the theoretical limit.
< Figure 2. Deformation of S-AUX film. a) Configurations and visualized principal strain distribution of the optimized S-AUX film at various strain rates. b) Biaxial stretching image. While pristine elastomer shrinks in the directions that were not stretched, S-AUX film developed in this study expands in all directions simultaneously while maintaining its original shape. >
Professor Byeong-Soo Bae, who led the study, explained, "Preventing image distortion using auxetic structures in stretchable displays is a core technology, but it has faced challenges due to the many empty spaces in the surface, making it difficult to use as a substrate. This research outcome is expected to significantly accelerate commercialization through high-resolution, distortion-free stretchable display applications that utilize the entire surface."
This study, co-authored by Dr. Yung Lee from KAIST’s Department of Materials Science and Engineering and Dr. Bongkyun Jang from the Korea Institute of Machinery & Materials, was published on August 20th in the international journal Nature Communications under the title "A seamless auxetic substrate with a negative Poisson's ratio of –1".
The research was supported by the Wearable Platform Materials Technology Center at KAIST, the Korea Institute of Machinery & Materials, and LG Display.
< Figure 3. Structural configuration of the distortion-free display components on the S-AUX film and a contour image of a micro-LED chip transferred onto the S-AUX film. >
< Figure 4. Schematic illustrations and photographic images of the S-AUX film-based image: distortion-free display in its stretched state and released state. >
2024.09.20 View 4192 -
KAIST Employs Image-recognition AI to Determine Battery Composition and Conditions
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.
KAIST (President Kwang-Hyung Lee) announced on July 2nd that Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with the Electronics and Telecommunications Research Institute (ETRI) and Drexel University in the United States, has developed a method to predict the major elemental composition and charge-discharge state of NCM cathode materials with 99.6% accuracy using convolutional neural networks (CNN)*.
*Convolutional Neural Network (CNN): A type of multi-layer, feed-forward, artificial neural network used for analyzing visual images.
The research team noted that while scanning electron microscopy (SEM) is used in semiconductor manufacturing to inspect wafer defects, it is rarely used in battery inspections. SEM is used for batteries to analyze the size of particles only at research sites, and reliability is predicted from the broken particles and the shape of the breakage in the case of deteriorated battery materials.
The research team decided that it would be groundbreaking if an automated SEM can be used in the process of battery production, just like in the semiconductor manufacturing, to inspect the surface of the cathode material to determine whether it was synthesized according to the desired composition and that the lifespan would be reliable, thereby reducing the defect rate.
< Figure 1. Example images of true cases and their grad-CAM overlays from the best trained network. >
The researchers trained a CNN-based AI applicable to autonomous vehicles to learn the surface images of battery materials, enabling it to predict the major elemental composition and charge-discharge cycle states of the cathode materials. They found that while the method could accurately predict the composition of materials with additives, it had lower accuracy for predicting charge-discharge states. The team plans to further train the AI with various battery material morphologies produced through different processes and ultimately use it for inspecting the compositional uniformity and predicting the lifespan of next-generation batteries.
Professor Joshua C. Agar, one of the collaborating researchers of the project from the Department of Mechanical Engineering and Mechanics of Drexel University, said, "In the future, artificial intelligence is expected to be applied not only to battery materials but also to various dynamic processes in functional materials synthesis, clean energy generation in fusion, and understanding foundations of particles and the universe."
Professor Seungbum Hong from KAIST, who led the research, stated, "This research is significant as it is the first in the world to develop an AI-based methodology that can quickly and accurately predict the major elemental composition and the state of the battery from the structural data of micron-scale SEM images. The methodology developed in this study for identifying the composition and state of battery materials based on microscopic images is expected to play a crucial role in improving the performance and quality of battery materials in the future."
< Figure 2. Accuracies of CNN Model predictions on SEM images of NCM cathode materials with additives under various conditions. >
This research was conducted by KAIST’s Materials Science and Engineering Department graduates Dr. Jimin Oh and Dr. Jiwon Yeom, the co-first authors, in collaboration with Professor Josh Agar and Dr. Kwang Man Kim from ETRI. It was supported by the National Research Foundation of Korea, the KAIST Global Singularity project, and international collaboration with the US research team. The results were published in the international journal npj Computational Materials on May 4. (Paper Title: “Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images”)
2024.07.02 View 4744 -
KAIST appoints K-Pop Star “G-Dragon” Kwon Ji-yong as a visiting professor
KAIST (President Kwang Hyung Lee) announced on June 5th that its Department of Mechanical Engineering has appointed Kwon Ji-yong, a K-Pop star better known as G-Dragon, as a visiting professor. The appointment was officiated at the “Innovate Korea 2024” event held at the KAIST Sports Complex in the morning of the 5th.
This appointment was promoted to expand the global competitiveness of Korean culture by applying the latest scientific technology developed from KAIST to K-content and the cultural industry.
An official from Galaxy Corporation, Mr. Kwon’s agency, said, “Through this appointment, we expect to contribute to market innovation and to the global spread and growth of K-culture through research and development of ‘entertech’*.”
* Entertech: A compound word of ‘entertainment’ and ‘technology’ refering to an industry that creates new added value by combining ICT with intellectual property rights (IP) and contents, which are the core values of entertainment.
The newly appointed Visiting Professor Kwon Ji-yong is scheduled to give a special lecture on leadership to undergraduate and graduate students. The purpose is to share his experience and life as a world-class artist to provide KAIST students with vision and insight into the world, as well as the courage and inspiration to pioneer within their own fields of expertise.
In addition, cultural events are planned to be held to help facilitate communication with KAIST members and provide artistic experiences necessary to grow into creative and convergent talents.
Joint research that applies KAIST's various technologies to art and cultural contents will also be pursued. ‘(temporarily named) KAIST-Galaxy Corporation Entertech Research Center’ will be established within the Department of Mechanical Engineering, conducting ▴research on Digital Twin technology for Korean Wave artists, starting with G-Dragon himself ▴convergent research on science and technology encompassing artificial intelligence, robots, and metaverse and K-Culture and ▴research on the development of differentiated avatars of artists using the latest technologies such as volumetrics, motion capture, and haptics.
Galaxy Corporation produces highly talked about televised shows including 'Physical: 100 Season 2', which achieved the feat of ranking first in the Netflix’s ‘Global Top 10 Non-English TV Shows’ category, as well as 'Street Woman Fighter', '2 Days & 1 Night', 'Let's Play Soccer: Mungchyeoya Chanda' and 'Mr. Trot 2', and expanded its IP (intellectual property rights) to all aspects of entertainment, from broadcasting to the music industry by recruiting G-Dragon in December last year.
It is an 'entertech' company that is pioneering a new content market by combining IP with the latest digital technologies such as metaverse, avatar, and artificial intelligence (AI). Based on these achievements, it recently signed a partnership with a global investment bank (IB). Through global investment, the company is soon to be the first entertech startup to become a unicorn (corporate value of 1 trillion won).
Kwon Ji-yong said, “It is an honor to be a visiting professor at KAIST, where countless scientific geniuses come out of. I am hoping the best of science and technology experts and my expertise in the entertainment business will come together and a great synergy, a ‘big bang’ will be ensue as a result.
“More and more musicians are working with artificial intelligence and these cutting-edge technologies enable more diverse forms of creative work,” he continued. He also expressed his specific wish. “Through the AI avatar developed jointly by Galaxy Corporation and KAIST, I wish to communicate more closely with my fans around the world that I cannot meet so often otherwise.”
President Kwang-Hyung Lee said, “Considering that KAIST is a university that has always explored new things and pioneered unknown territories since its establishment, I believe that Kwon Ji-yong also shares KAIST's DNA in that he is a leader and pioneer who has attained world-class achievements in the world of culture and arts.”
President Lee continued expressing his expectations saying, “Kwon Ji-yong will not only contribute to taking KAIST’s science and technology to spread and grow K-culture on the global stage, through his activities but also in sharing his experience and spirit as the trend leader of the world, he will be an asset to the members of KAIST which aims to become a top-tier university.”
The period of Kwon Ji-yong’s professorship is two years from the 4th of this month to June of 2026. In addition, Professor Kwon Ji-yong has been appointed as KAIST’s global ambassador to help strengthen KAIST's international presence.
Professor Kwon Ji-yong, who began his career as G-Dragon, has established himself as an icon symbolizing Korean pop culture over 18 years as a member and the leader of the K-Pop idol group 'Big Bang', which debuted in 2006 and gained global popularity. While being recognized for his outstanding ability in all aspects of his music, from writing lyrics, composing, and producing to performing, he has also been active in fashion, becoming the first Asian man to be selected as a global ambassador for Chanel in 2016, spreading Korean music and fashion to the world.
In June, 2017, he released his 3rd solo album 'Kwon Ji Yong' on USB instead of the existing CD format, for the first time in Korea. He also attended CES, the world's largest home appliance and IT exhibition held in Las Vegas, USA in January of this year, extending his strides as a 'tech-tainer'.
2024.06.05 View 15752 -
Team KAIST placed among top two at MBZIRC Maritime Grand Challenge
Representing Korean Robotics at Sea: KAIST’s 26-month strife rewarded
Team KAIST placed among top two at MBZIRC Maritime Grand Challenge
- Team KAIST, composed of students from the labs of Professor Jinwhan Kim of the Department of Mechanical Engineering and Professor Hyunchul Shim of the School of Electrical and Engineering, came through the challenge as the first runner-up winning the prize money totaling up to $650,000 (KRW 860 million).
- Successfully led the autonomous collaboration of unmanned aerial and maritime vehicles using cutting-edge robotics and AI technology through to the final round of the competition held in Abu Dhabi from January 10 to February 6, 2024.
KAIST (President Kwang-Hyung Lee), reported on the 8th that Team KAIST, led by students from the labs of Professor Jinwhan Kim of the Department of Mechanical Engineering and Professor Hyunchul Shim of the School of Electrical Engineering, with Pablo Aviation as a partner, won a total prize money of $650,000 (KRW 860 million) at the Maritime Grand Challenge by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), finishing first runner-up.
This competition, which is the largest ever robotics competition held over water, is sponsored by the government of the United Arab Emirates and organized by ASPIRE, an organization under the Abu Dhabi Ministry of Science, with a total prize money of $3 million.
In the competition, which started at the end of 2021, 52 teams from around the world participated and five teams were selected to go on to the finals in February 2023 after going through the first and second stages of screening. The final round was held from January 10 to February 6, 2024, using actual unmanned ships and drones in a secluded sea area of 10 km2 off the coast of Abu Dhabi, the capital of the United Arab Emirates. A total of 18 KAIST students and Professor Jinwhan Kim and Professor Hyunchul Shim took part in this competition at the location at Abu Dhabi.
Team KAIST will receive $500,000 in prize money for taking second place in the final, and the team’s prize money totals up to $650,000 including $150,000 that was as special midterm award for finalists.
The final mission scenario is to find the target vessel on the run carrying illegal cargoes among many ships moving within the GPS-disabled marine surface, and inspect the deck for two different types of stolen cargo to recover them using the aerial vehicle to bring the small cargo and the robot manipulator topped on an unmanned ship to retrieve the larger one. The true aim of the mission is to complete it through autonomous collaboration of the unmanned ship and the aerial vehicle without human intervention throughout the entire mission process. In particular, since GPS cannot be used in this competition due to regulations, Professor Jinwhan Kim's research team developed autonomous operation techniques for unmanned ships, including searching and navigating methods using maritime radar, and Professor Hyunchul Shim's research team developed video-based navigation and a technology to combine a small autonomous robot with a drone.
The final mission is to retrieve cargo on board a ship fleeing at sea through autonomous collaboration between unmanned ships and unmanned aerial vehicles without human intervention. The overall mission consists the first stage of conducting the inspection to find the target ship among several ships moving at sea and the second stage of conducting the intervention mission to retrieve the cargoes on the deck of the ship. Each team was given a total of three opportunities, and the team that completed the highest-level mission in the shortest time during the three attempts received the highest score.
In the first attempt, KAIST was the only team to succeed in the first stage search mission, but the competition began in earnest as the Croatian team also completed the first stage mission in the second attempt. As the competition schedule was delayed due to strong winds and high waves that continued for several days, the organizers decided to hold the finals with the three teams, including the Team KAIST and the team from Croatia’s the University of Zagreb, which completed the first stage of the mission, and Team Fly-Eagle, a team of researcher from China and UAE that partially completed the first stage. The three teams were given the chance to proceed to the finals and try for the third attempt, and in the final competition, the Croatian team won, KAIST took the second place, and the combined team of UAE-China combined team took the third place. The final prize to be given for the winning team is set at $2 million with $500,000 for the runner-up team, and $250,000 for the third-place.
Professor Jinwhan Kim of the Department of Mechanical Engineering, who served as the advisor for Team KAIST, said, “I would like to express my gratitude and congratulations to the students who put in a huge academic and physical efforts in preparing for the competition over the past two years. I feel rewarded because, regardless of the results, every bit of efforts put into this up to this point will become the base of their confidence and a valuable asset in their growth into a great researcher.” Sol Han, a doctoral student in mechanical engineering who served as the team leader, said, “I am disappointed of how narrowly we missed out on winning at the end, but I am satisfied with the significance of the output we’ve got and I am grateful to the team members who worked hard together for that.”
HD Hyundai, Rainbow Robotics, Avikus, and FIMS also participated as sponsors for Team KAIST's campaign.
2024.02.09 View 10000 -
North Korea and Beyond: AI-Powered Satellite Analysis Reveals the Unseen Economic Landscape of Underdeveloped Nations
- A joint research team in computer science, economics, and geography has developed an artificial intelligence (AI) technology to measure grid-level economic development within six-square-kilometer regions.
- This AI technology is applicable in regions with limited statistical data (e.g., North Korea), supporting international efforts to propose policies for economic growth and poverty reduction in underdeveloped countries.
- The research team plans to make this technology freely available for use to contribute to the United Nations' Sustainable Development Goals (SDGs).
The United Nations reports that more than 700 million people are in extreme poverty, earning less than two dollars a day. However, an accurate assessment of poverty remains a global challenge. For example, 53 countries have not conducted agricultural surveys in the past 15 years, and 17 countries have not published a population census. To fill this data gap, new technologies are being explored to estimate poverty using alternative sources such as street views, aerial photos, and satellite images.
The paper published in Nature Communications demonstrates how artificial intelligence (AI) can help analyze economic conditions from daytime satellite imagery. This new technology can even apply to the least developed countries - such as North Korea - that do not have reliable statistical data for typical machine learning training.
The researchers used Sentinel-2 satellite images from the European Space Agency (ESA) that are publicly available. They split these images into small six-square-kilometer grids. At this zoom level, visual information such as buildings, roads, and greenery can be used to quantify economic indicators. As a result, the team obtained the first ever fine-grained economic map of regions like North Korea. The same algorithm was applied to other underdeveloped countries in Asia: North Korea, Nepal, Laos, Myanmar, Bangladesh, and Cambodia (see Image 1).
The key feature of their research model is the "human-machine collaborative approach," which lets researchers combine human input with AI predictions for areas with scarce data. In this research, ten human experts compared satellite images and judged the economic conditions in the area, with the AI learning from this human data and giving economic scores to each image. The results showed that the Human-AI collaborative approach outperformed machine-only learning algorithms.
< Image 1. Nightlight satellite images of North Korea (Top-left: Background photo provided by NASA's Earth Observatory). South Korea appears brightly lit compared to North Korea, which is mostly dark except for Pyongyang. In contrast, the model developed by the research team uses daytime satellite imagery to predict more detailed economic predictions for North Korea (top-right) and five Asian countries (Bottom: Background photo from Google Earth). >
The research was led by an interdisciplinary team of computer scientists, economists, and a geographer from KAIST & IBS (Donghyun Ahn, Meeyoung Cha, Jihee Kim), Sogang University (Hyunjoo Yang), HKUST (Sangyoon Park), and NUS (Jeasurk Yang). Dr Charles Axelsson, Associate Editor at Nature Communications, handled this paper during the peer review process at the journal.
The research team found that the scores showed a strong correlation with traditional socio-economic metrics such as population density, employment, and number of businesses. This demonstrates the wide applicability and scalability of the approach, particularly in data-scarce countries. Furthermore, the model's strength lies in its ability to detect annual changes in economic conditions at a more detailed geospatial level without using any survey data (see Image 2).
< Image 2. Differences in satellite imagery and economic scores in North Korea between 2016 and 2019. Significant development was found in the Wonsan Kalma area (top), one of the tourist development zones, but no changes were observed in the Wiwon Industrial Development Zone (bottom). (Background photo: Sentinel-2 satellite imagery provided by the European Space Agency (ESA)). >
This model would be especially valuable for rapidly monitoring the progress of Sustainable Development Goals such as reducing poverty and promoting more equitable and sustainable growth on an international scale. The model can also be adapted to measure various social and environmental indicators. For example, it can be trained to identify regions with high vulnerability to climate change and disasters to provide timely guidance on disaster relief efforts.
As an example, the researchers explored how North Korea changed before and after the United Nations sanctions against the country. By applying the model to satellite images of North Korea both in 2016 and in 2019, the researchers discovered three key trends in the country's economic development between 2016 and 2019. First, economic growth in North Korea became more concentrated in Pyongyang and major cities, exacerbating the urban-rural divide. Second, satellite imagery revealed significant changes in areas designated for tourism and economic development, such as new building construction and other meaningful alterations. Third, traditional industrial and export development zones showed relatively minor changes.
Meeyoung Cha, a data scientist in the team explained, "This is an important interdisciplinary effort to address global challenges like poverty. We plan to apply our AI algorithm to other international issues, such as monitoring carbon emissions, disaster damage detection, and the impact of climate change."
An economist on the research team, Jihee Kim, commented that this approach would enable detailed examinations of economic conditions in the developing world at a low cost, reducing data disparities between developed and developing nations. She further emphasized that this is most essential because many public policies require economic measurements to achieve their goals, whether they are for growth, equality, or sustainability.
The research team has made the source code publicly available via GitHub and plans to continue improving the technology, applying it to new satellite images updated annually. The results of this study, with Ph.D. candidate Donghyun Ahn at KAIST and Ph.D. candidate Jeasurk Yang at NUS as joint first authors, were published in Nature Communications under the title "A human-machine collaborative approach measures economic development using satellite imagery."
< Photos of the main authors. 1. Donghyun Ahn, PhD candidate at KAIST School of Computing 2. Jeasurk Yang, PhD candidate at the Department of Geography of National University of Singapore 3. Meeyoung Cha, Professor of KAIST School of Computing and CI at IBS 4. Jihee Kim, Professor of KAIST School of Business and Technology Management 5. Sangyoon Park, Professor of the Division of Social Science at Hong Kong University of Science and Technology 6. Hyunjoo Yang, Professor of the Department of Economics at Sogang University >
2023.12.07 View 6988 -
KAIST builds a high-resolution 3D holographic sensor using a single mask
Holographic cameras can provide more realistic images than ordinary cameras thanks to their ability to acquire 3D information about objects. However, existing holographic cameras use interferometers that measure the wavelength and refraction of light through the interference of light waves, which makes them complex and sensitive to their surrounding environment.
On August 23, a KAIST research team led by Professor YongKeun Park from the Department of Physics announced a new leap forward in 3D holographic imaging sensor technology.
The team proposed an innovative holographic camera technology that does not use complex interferometry. Instead, it uses a mask to precisely measure the phase information of light and reconstruct the 3D information of an object with higher accuracy.
< Figure 1. Structure and principle of the proposed holographic camera. The amplitude and phase information of light scattered from a holographic camera can be measured. >
The team used a mask that fulfills certain mathematical conditions and incorporated it into an ordinary camera, and the light scattered from a laser is measured through the mask and analyzed using a computer. This does not require a complex interferometer and allows the phase information of light to be collected through a simplified optical system. With this technique, the mask that is placed between the two lenses and behind an object plays an important role. The mask selectively filters specific parts of light,, and the intensity of the light passing through the lens can be measured using an ordinary commercial camera. This technique combines the image data received from the camera with the unique pattern received from the mask and reconstructs an object’s precise 3D information using an algorithm.
This method allows a high-resolution 3D image of an object to be captured in any position. In practical situations, one can construct a laser-based holographic 3D image sensor by adding a mask with a simple design to a general image sensor. This makes the design and construction of the optical system much easier. In particular, this novel technology can capture high-resolution holographic images of objects moving at high speeds, which widens its potential field of application.
< Figure 2. A moving doll captured by a conventional camera and the proposed holographic camera. When taking a picture without focusing on the object, only a blurred image of the doll can be obtained from a general camera, but the proposed holographic camera can restore the blurred image of the doll into a clear image. >
The results of this study, conducted by Dr. Jeonghun Oh from the KAIST Department of Physics as the first author, were published in Nature Communications on August 12 under the title, "Non-interferometric stand-alone single-shot holographic camera using reciprocal diffractive imaging".
Dr. Oh said, “The holographic camera module we are suggesting can be built by adding a filter to an ordinary camera, which would allow even non-experts to handle it easily in everyday life if it were to be commercialized.” He added, “In particular, it is a promising candidate with the potential to replace existing remote sensing technologies.”
This research was supported by the National Research Foundation’s Leader Research Project, the Korean Ministry of Science and ICT’s Core Hologram Technology Support Project, and the Nano and Material Technology Development Project.
2023.09.05 View 6738