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Tomographic Measurement of Dielectric Tensors
Dielectric tensor tomography allows the direct measurement of the 3D dielectric tensors of optically anisotropic structures
A research team reported the direct measurement of dielectric tensors of anisotropic structures including the spatial variations of principal refractive indices and directors. The group also demonstrated quantitative tomographic measurements of various nematic liquid-crystal structures and their fast 3D nonequilibrium dynamics using a 3D label-free tomographic method. The method was described in Nature Materials.
Light-matter interactions are described by the dielectric tensor. Despite their importance in basic science and applications, it has not been possible to measure 3D dielectric tensors directly. The main challenge was due to the vectorial nature of light scattering from a 3D anisotropic structure. Previous approaches only addressed 3D anisotropic information indirectly and were limited to two-dimensional, qualitative, strict sample conditions or assumptions.
The research team developed a method enabling the tomographic reconstruction of 3D dielectric tensors without any preparation or assumptions. A sample is illuminated with a laser beam with various angles and circularly polarization states. Then, the light fields scattered from a sample are holographically measured and converted into vectorial diffraction components. Finally, by inversely solving a vectorial wave equation, the 3D dielectric tensor is reconstructed.
Professor YongKeun Park said, “There were a greater number of unknowns in direct measuring than with the conventional approach. We applied our approach to measure additional holographic images by slightly tilting the incident angle.”
He said that the slightly tilted illumination provides an additional orthogonal polarization, which makes the underdetermined problem become the determined problem. “Although scattered fields are dependent on the illumination angle, the Fourier differentiation theorem enables the extraction of the same dielectric tensor for the slightly tilted illumination,” Professor Park added.
His team’s method was validated by reconstructing well-known liquid crystal (LC) structures, including the twisted nematic, hybrid aligned nematic, radial, and bipolar configurations. Furthermore, the research team demonstrated the experimental measurements of the non-equilibrium dynamics of annihilating, nucleating, and merging LC droplets, and the LC polymer network with repeating 3D topological defects.
“This is the first experimental measurement of non-equilibrium dynamics and 3D topological defects in LC structures in a label-free manner. Our method enables the exploration of inaccessible nematic structures and interactions in non-equilibrium dynamics,” first author Dr. Seungwoo Shin explained.
-PublicationSeungwoo Shin, Jonghee Eun, Sang Seok Lee, Changjae Lee, Herve Hugonnet, Dong Ki Yoon, Shin-Hyun Kim, Jongwoo Jeong, YongKeun Park, “Tomographic Measurement ofDielectric Tensors at Optical Frequency,” Nature Materials March 02, 2022 (https://doi.org/10/1038/s41563-022-01202-8)
-ProfileProfessor YongKeun ParkBiomedical Optics Laboratory (http://bmol.kaist.ac.kr)Department of PhysicsCollege of Natural SciencesKAIST
2022.03.22 View 8987 -
CXL-Based Memory Disaggregation Technology Opens Up a New Direction for Big Data Solution Frameworks
A KAIST team’s compute express link (CXL) provides new insights on memory disaggregation and ensures direct access and high-performance capabilities
A team from the Computer Architecture and Memory Systems Laboratory (CAMEL) at KAIST presented a new compute express link (CXL) solution whose directly accessible, and high-performance memory disaggregation opens new directions for big data memory processing. Professor Myoungsoo Jung said the team’s technology significantly improves performance compared to existing remote direct memory access (RDMA)-based memory disaggregation.
CXL is a peripheral component interconnect-express (PCIe)-based new dynamic multi-protocol made for efficiently utilizing memory devices and accelerators. Many enterprise data centers and memory vendors are paying attention to it as the next-generation multi-protocol for the era of big data.
Emerging big data applications such as machine learning, graph analytics, and in-memory databases require large memory capacities. However, scaling out the memory capacity via a prior memory interface like double data rate (DDR) is limited by the number of the central processing units (CPUs) and memory controllers. Therefore, memory disaggregation, which allows connecting a host to another host’s memory or memory nodes, has appeared.
RDMA is a way that a host can directly access another host’s memory via InfiniBand, the commonly used network protocol in data centers. Nowadays, most existing memory disaggregation technologies employ RDMA to get a large memory capacity. As a result, a host can share another host’s memory by transferring the data between local and remote memory.
Although RDMA-based memory disaggregation provides a large memory capacity to a host, two critical problems exist. First, scaling out the memory still needs an extra CPU to be added. Since passive memory such as dynamic random-access memory (DRAM), cannot operate by itself, it should be controlled by the CPU. Second, redundant data copies and software fabric interventions for RDMA-based memory disaggregation cause longer access latency. For example, remote memory access latency in RDMA-based memory disaggregation is multiple orders of magnitude longer than local memory access.
To address these issues, Professor Jung’s team developed the CXL-based memory disaggregation framework, including CXL-enabled customized CPUs, CXL devices, CXL switches, and CXL-aware operating system modules. The team’s CXL device is a pure passive and directly accessible memory node that contains multiple DRAM dual inline memory modules (DIMMs) and a CXL memory controller. Since the CXL memory controller supports the memory in the CXL device, a host can utilize the memory node without processor or software intervention. The team’s CXL switch enables scaling out a host’s memory capacity by hierarchically connecting multiple CXL devices to the CXL switch allowing more than hundreds of devices. Atop the switches and devices, the team’s CXL-enabled operating system removes redundant data copy and protocol conversion exhibited by conventional RDMA, which can significantly decrease access latency to the memory nodes.
In a test comparing loading 64B (cacheline) data from memory pooling devices, CXL-based memory disaggregation showed 8.2 times higher data load performance than RDMA-based memory disaggregation and even similar performance to local DRAM memory. In the team’s evaluations for a big data benchmark such as a machine learning-based test, CXL-based memory disaggregation technology also showed a maximum of 3.7 times higher performance than prior RDMA-based memory disaggregation technologies.
“Escaping from the conventional RDMA-based memory disaggregation, our CXL-based memory disaggregation framework can provide high scalability and performance for diverse datacenters and cloud service infrastructures,” said Professor Jung. He went on to stress, “Our CXL-based memory disaggregation research will bring about a new paradigm for memory solutions that will lead the era of big data.”
-Profile: Professor Myoungsoo Jung Computer Architecture and Memory Systems Laboratory (CAMEL)http://camelab.org School of Electrical EngineeringKAIST
2022.03.16 View 23519 -
'Fingerprint' Machine Learning Technique Identifies Different Bacteria in Seconds
A synergistic combination of surface-enhanced Raman spectroscopy and deep learning serves as an effective platform for separation-free detection of bacteria in arbitrary media
Bacterial identification can take hours and often longer, precious time when diagnosing infections and selecting appropriate treatments. There may be a quicker, more accurate process according to researchers at KAIST. By teaching a deep learning algorithm to identify the “fingerprint” spectra of the molecular components of various bacteria, the researchers could classify various bacteria in different media with accuracies of up to 98%.
Their results were made available online on Jan. 18 in Biosensors and Bioelectronics, ahead of publication in the journal’s April issue.
Bacteria-induced illnesses, those caused by direct bacterial infection or by exposure to bacterial toxins, can induce painful symptoms and even lead to death, so the rapid detection of bacteria is crucial to prevent the intake of contaminated foods and to diagnose infections from clinical samples, such as urine. “By using surface-enhanced Raman spectroscopy (SERS) analysis boosted with a newly proposed deep learning model, we demonstrated a markedly simple, fast, and effective route to classify the signals of two common bacteria and their resident media without any separation procedures,” said Professor Sungho Jo from the School of Computing.
Raman spectroscopy sends light through a sample to see how it scatters. The results reveal structural information about the sample — the spectral fingerprint — allowing researchers to identify its molecules. The surface-enhanced version places sample cells on noble metal nanostructures that help amplify the sample’s signals.
However, it is challenging to obtain consistent and clear spectra of bacteria due to numerous overlapping peak sources, such as proteins in cell walls. “Moreover, strong signals of surrounding media are also enhanced to overwhelm target signals, requiring time-consuming and tedious bacterial separation steps,” said Professor Yeon Sik Jung from the Department of Materials Science and Engineering.
To parse through the noisy signals, the researchers implemented an artificial intelligence method called deep learning that can hierarchically extract certain features of the spectral information to classify data. They specifically designed their model, named the dual-branch wide-kernel network (DualWKNet), to efficiently learn the correlation between spectral features. Such an ability is critical for analyzing one-dimensional spectral data, according to Professor Jo.
“Despite having interfering signals or noise from the media, which make the general shapes of different bacterial spectra and their residing media signals look similar, high classification accuracies of bacterial types and their media were achieved,” Professor Jo said, explaining that DualWKNet allowed the team to identify key peaks in each class that were almost indiscernible in individual spectra, enhancing the classification accuracies. “Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time.”
The researchers plan to use their platform to study more bacteria and media types, using the information to build a training data library of various bacterial types in additional media to reduce the collection and detection times for new samples.
“We developed a meaningful universal platform for rapid bacterial detection with the collaboration between SERS and deep learning,” Professor Jo said. “We hope to extend the use of our deep learning-based SERS analysis platform to detect numerous types of bacteria in additional media that are important for food or clinical analysis, such as blood.”
The National R&D Program, through a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, supported this research.
-PublicationEojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon Park, Hanhwi Jang, Yeon Sik Jung, Sungho Jo, “Separation-free bacterial identification in arbitrary media via deepneural network-based SERS analysis,” Biosensors and Bioelectronics online January 18, 2022 (doi.org/10.1016/j.bios.2022.113991)
-ProfileProfessor Yeon Sik JungDepartment of Materials Science and EngineeringKAIST
Professor Sungho JoSchool of ComputingKAIST
2022.03.04 View 22602 -
KAA Recognizes 4 Distinguished Alumni of the Year
The KAIST Alumni Association (KAA) recognized four distinguished alumni of the year during a ceremony on February 25 in Seoul. The four Distinguished Alumni Awardees are Distinguished Professor Sukbok Chang from the KAIST Department of Chemistry, Hyunshil Ahn, head of the AI Economy Institute and an editorial writer at The Korea Economic Daily, CEO Hwan-ho Sung of PSTech, and President Hark Kyu Park of Samsung Electronics.
Distinguished Professor Sukbok Chang who received his MS from the Department of Chemistry in 1985 has been a pioneer in the novel field of ‘carbon-hydrogen bond activation reactions’. He has significantly contributed to raising Korea’s international reputation in natural sciences and received the Kyungam Academic Award in 2013, the 14th Korea Science Award in 2015, the 1st Science and Technology Prize of Korea Toray in 2018, and the Best Scientist/Engineer Award Korea in 2019. Furthermore, he was named as a Highly Cited Researcher who ranked in the top 1% of citations by field and publication year in the Web of Science citation index for seven consecutive years from 2015 to 2021, demonstrating his leadership as a global scholar.
Hyunshil Ahn, a graduate of the School of Business and Technology Management with an MS in 1985 and a PhD in 1987, was appointed as the first head of the AI Economy Institute when The Korea Economic Daily was the first Korean media outlet to establish an AI economy lab. He has contributed to creating new roles for the press and media in the 4th industrial revolution, and added to the popularization of AI technology through regulation reform and consulting on industrial policies.
PSTech CEO Hwan-ho Sung is a graduate of the School of Electrical Engineering where he received an MS in 1988 and a PhD in EMBA in 2008. He has run the electronics company PSTech for over 20 years and successfully localized the production of power equipment, which previously depended on foreign technology. His development of the world’s first power equipment that can be applied to new industries including semiconductors and displays was recognized through this award.
Samsung Electronics President Hark Kyu Park graduated from the School of Business and Technology Management with an MS in 1986. He not only enhanced Korea’s national competitiveness by expanding the semiconductor industry, but also established contract-based semiconductor departments at Korean universities including KAIST, Sungkyunkwan University, Yonsei University, and Postech, and semiconductor track courses at KAIST, Sogang University, Seoul National University, and Postech to nurture professional talents. He also led the national semiconductor coexistence system by leading private sector-government-academia collaborations to strengthen competence in semiconductors, and continues to make unconditional investments in strong small businesses.
KAA President Chilhee Chung said, “Thanks to our alumni contributing at the highest levels of our society, the name of our alma mater shines brighter. As role models for our younger alumni, I hope greater honours will follow our awardees in the future.”
2022.03.03 View 8483 -
SM CEP Soo-Man Lee to Teach at the KAIST School of Computing
The Founder and Chief Executive Producer of SM Entertainment Soo-Man Lee was appointed as a distinguished visiting professor in the KAIST School of Computing. His three-year term starts on March 1.
KAIST and the SM Entertainment signed an MOU on joint research on the metaverse last year and Lee’s appointment is the extension of their mutual collaborations in fields where technologies converge and will encourage innovative advancements in engineering technology and the entertainment industry.
Lee, who completed a graduate program in computer science at California State University Northridge will give special leadership lectures for both undergraduate and graduate students, and will participate in metaverse-related research as a consultant.
In particular, Professor Lee will participate in joint research with the tentatively named Metaverse Institute affiliated with the KAIST Institute for Artificial Intelligence. The institute will help SM Entertainment stay ahead of the global metaverse market by using the avatars of celebrities, and lend itself to raising the already strong brand power of the K-pop leader.
Professor Lee said, “I am grateful that KAIST, the very cradle of Korea’s science and technology, has given me the opportunity to meet its students as a visiting professor. We will lead the metaverse world, in which Korea is emerging as a market leader, with the excellent contents and technology unique to our country, and work together to lead the future global entertainment market.”
President Kwang-Hyung Lee said, “The ability to expand our limitless creativity in the metaverse is indispensable for us as we adapt to this new era. We hope that the vision and creative insights of Executive Producer Lee, which have allowed him to look ahead into the future of the entertainment contents market, will have a positive and fresh impact on the members of KAIST.”
The global influence and reputation of Executive Producer Lee has been well established through his various awards. He was the first Korean to be listed on Variety500 for five consecutive years from 2017 to 2021. He was also the first Korean awardee of the Asia Game Changer Awards in 2016, the first cultural figure to receive the YoungSan Diplomacy Award in 2017, the only Korean to be listed on the 2020 Billboard Impact List, and he has also received the K-pop Contribution Award at the 10th Gaon Chart Music Awards. He recently introduced Play2Create (P2C), a new interactive and creative culture in which re-creation can be enjoyed like a game using IP, and is leading the establishment of the P2C ecosystem.
2022.03.03 View 6503 -
Scientist Discover How Circadian Rhythm Can Be Both Strong and Flexible
Study reveals that master and slave oscillators function via different molecular mechanisms
From tiny fruit flies to human beings, all animals on Earth maintain their daily rhythms based on their internal circadian clock. The circadian clock enables organisms to undergo rhythmic changes in behavior and physiology based on a 24-hour circadian cycle. For example, our own biological clock tells our brain to release melatonin, a sleep-inducing hormone, at night time.
The discovery of the molecular mechanism of the circadian clock was bestowed the Nobel Prize in Physiology or Medicine 2017. From what we know, no one centralized clock is responsible for our circadian cycles. Instead, it operates in a hierarchical network where there are “master pacemaker” and “slave oscillator”.
The master pacemaker receives various input signals from the environment such as light. The master then drives the slave oscillator that regulates various outputs such as sleep, feeding, and metabolism. Despite the different roles of the pacemaker neurons, they are known to share common molecular mechanisms that are well conserved in all lifeforms. For example, interlocked systems of multiple transcriptional-translational feedback loops (TTFLs) composed of core clock proteins have been deeply studied in fruit flies.
However, there is still much that we need to learn about our own biological clock. The hierarchically-organized nature of master and slave clock neurons leads to a prevailing belief that they share an identical molecular clockwork. At the same time, the different roles they serve in regulating bodily rhythms also raise the question of whether they might function under different molecular clockworks.
Research team led by Professor Kim Jae Kyoung from the Department of Mathematical Sciences, a chief investigator at the Biomedical Mathematics Group at the Institute for Basic Science, used a combination of mathematical and experimental approaches using fruit flies to answer this question. The team found that the master clock and the slave clock operate via different molecular mechanisms.
In both master and slave neurons of fruit flies, a circadian rhythm-related protein called PER is produced and degraded at different rates depending on the time of the day. Previously, the team found that the master clock neuron (sLNvs) and the slave clock neuron (DN1ps) have different profiles of PER in wild-type and Clk-Δ mutant Drosophila. This hinted that there might be a potential difference in molecular clockworks between the master and slave clock neurons.
However, due to the complexity of the molecular clockwork, it was challenging to identify the source of such differences. Thus, the team developed a mathematical model describing the molecular clockworks of the master and slave clocks. Then, all possible molecular differences between the master and slave clock neurons were systematically investigated by using computer simulations. The model predicted that PER is more efficiently produced and then rapidly degraded in the master clock compared to the slave clock neurons. This prediction was then confirmed by the follow-up experiments using animal.
Then, why do the master clock neurons have such different molecular properties from the slave clock neurons? To answer this question, the research team again used the combination of mathematical model simulation and experiments. It was found that the faster rate of synthesis of PER in the master clock neurons allows them to generate synchronized rhythms with a high level of amplitude. Generation of such a strong rhythm with high amplitude is critical to delivering clear signals to slave clock neurons.
However, such strong rhythms would typically be unfavorable when it comes to adapting to environmental changes. These include natural causes such as different daylight hours across summer and winter seasons, up to more extreme artificial cases such as jet lag that occurs after international travel. Thanks to the distinct property of the master clock neurons, it is able to undergo phase dispersion when the standard light-dark cycle is disrupted, drastically reducing the level of PER. The master clock neurons can then easily adapt to the new diurnal cycle. Our master pacemaker’s plasticity explains how we can quickly adjust to the new time zones after international flights after just a brief period of jet lag.
It is hoped that the findings of this study can have future clinical implications when it comes to treating various disorders that affect our circadian rhythm. Professor Kim notes, “When the circadian clock loses its robustness and flexibility, the circadian rhythms sleep disorders can occur. As this study identifies the molecular mechanism that generates robustness and flexibility of the circadian clock, it can facilitate the identification of the cause of and treatment strategy for the circadian rhythm sleep disorders.” This work was supported by the Human Frontier Science Program.
-PublicationEui Min Jeong, Miri Kwon, Eunjoo Cho, Sang Hyuk Lee, Hyun Kim, Eun Young Kim, and Jae Kyoung Kim, “Systematic modeling-driven experiments identify distinct molecularclockworks underlying hierarchically organized pacemaker neurons,” February 22, 2022, Proceedings of the National Academy of Sciences of the United States of America
-ProfileProfessor Jae Kyoung KimDepartment of Mathematical SciencesKAIST
2022.02.23 View 10794 -
Research Finds Digital Music Streaming Consumption Dropped as a Result of Covid-19 and Lockdowns
Decline in human mobility has stunning consequences for content streaming
The Covid-19 pandemic and lockdowns significantly reduced the consumption of audio music streaming in many countries as people turned to video platforms. On average, audio music consumption decreased by 12.5% after the World Health Organization’s (WHO) pandemic declaration in March 2020.
Music streaming services were an unlikely area hit hard by the Covid-19 pandemic. New research in Marketing Science found that the drop in people’s mobility during the pandemic significantly reduced the consumption of audio music streaming. Instead, people turned more to video platforms.
“On average, audio music consumption decreased by more than 12% after the World Health Organization’s (WHO) pandemic declaration on March 11, 2020. As a result, during the pandemic, Spotify lost 838 million dollars of revenue in the first three quarters of 2020,” said Jaeung Sim, a PhD candidate in management engineering at KAIST and one of the authors of the research study on this phenomenon. “Our results showed that human mobility plays a much larger role in the audio consumption of music than previously thought.”
The study, “Frontiers: Virus Shook the Streaming Star: Estimating the Covid-19 Impact on Music Consumption,” conducted by Sim and Professor Daegon Cho of KAIST, Youngdeok Hwang of City University of New York, and Rahul Telang of Carnegie Mellon University, looked at online music streaming data for top songs for two years in 60 countries, as well as Covid-19 cases, lockdown statistics, and daily mobility data, to determine the nature of the changes.
The study showed how the pandemic adversely impacted music streaming services despite the common expectation that the pandemic would universally benefit online medias platforms. This implies that the substantially changing media consumption environment can place streaming music in fiercer competition with other media forms that offer more dynamic and vivid experiences to consumers.
The researchers found that music consumption through video platforms was positively associated with the severity of Covid-19, lockdown policies, and time spent at home.
-PublicationJaeung Sim, Daegon Cho, Youngdeok Hwang, and Rahul Telang,“Frontiers: Virus Shook the Streaming Star: Estimating the Covid-19 Impact on Music Consumption,” November 30 in Marketing Science online (doi.org/10.1287/mksc.2021.1321)
-Profile Professor Daegon ChoGraduate School of Information and Media ManagementCollege of BusinessKAIST
2022.02.15 View 10508 -
Label-Free Multiplexed Microtomography of Endogenous Subcellular Dynamics Using Deep Learning
AI-based holographic microscopy allows molecular imaging without introducing exogenous labeling agents
A research team upgraded the 3D microtomography observing dynamics of label-free live cells in multiplexed fluorescence imaging. The AI-powered 3D holotomographic microscopy extracts various molecular information from live unlabeled biological cells in real time without exogenous labeling or staining agents.
Professor YongKeum Park’s team and the startup Tomocube encoded 3D refractive index tomograms using the refractive index as a means of measurement. Then they decoded the information with a deep learning-based model that infers multiple 3D fluorescence tomograms from the refractive index measurements of the corresponding subcellular targets, thereby achieving multiplexed micro tomography. This study was reported in Nature Cell Biology online on December 7, 2021.
Fluorescence microscopy is the most widely used optical microscopy technique due to its high biochemical specificity. However, it needs to genetically manipulate or to stain cells with fluorescent labels in order to express fluorescent proteins. These labeling processes inevitably affect the intrinsic physiology of cells. It also has challenges in long-term measuring due to photobleaching and phototoxicity. The overlapped spectra of multiplexed fluorescence signals also hinder the viewing of various structures at the same time. More critically, it took several hours to observe the cells after preparing them.
3D holographic microscopy, also known as holotomography, is providing new ways to quantitatively image live cells without pretreatments such as staining. Holotomography can accurately and quickly measure the morphological and structural information of cells, but only provides limited biochemical and molecular information.
The 'AI microscope' created in this process takes advantage of the features of both holographic microscopy and fluorescence microscopy. That is, a specific image from a fluorescence microscope can be obtained without a fluorescent label. Therefore, the microscope can observe many types of cellular structures in their natural state in 3D and at the same time as fast as one millisecond, and long-term measurements over several days are also possible.
The Tomocube-KAIST team showed that fluorescence images can be directly and precisely predicted from holotomographic images in various cells and conditions. Using the quantitative relationship between the spatial distribution of the refractive index found by AI and the major structures in cells, it was possible to decipher the spatial distribution of the refractive index. And surprisingly, it confirmed that this relationship is constant regardless of cell type.
Professor Park said, “We were able to develop a new concept microscope that combines the advantages of several microscopes with the multidisciplinary research of AI, optics, and biology. It will be immediately applicable for new types of cells not included in the existing data and is expected to be widely applicable for various biological and medical research.”
When comparing the molecular image information extracted by AI with the molecular image information physically obtained by fluorescence staining in 3D space, it showed a 97% or more conformity, which is a level that is difficult to distinguish with the naked eye.
“Compared to the sub-60% accuracy of the fluorescence information extracted from the model developed by the Google AI team, it showed significantly higher performance,” Professor Park added.
This work was supported by the KAIST Up program, the BK21+ program, Tomocube, the National Research Foundation of Korea, and the Ministry of Science and ICT, and the Ministry of Health & Welfare.
-Publication
Hyun-seok Min, Won-Do Heo, YongKeun Park, et al. “Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning,” Nature Cell Biology (doi.org/10.1038/s41556-021-00802-x) published online December 07 2021.
-Profile
Professor YongKeun Park
Biomedical Optics Laboratory
Department of Physics
KAIST
2022.02.09 View 11936 -
AI Light-Field Camera Reads 3D Facial Expressions
Machine-learned, light-field camera reads facial expressions from high-contrast illumination invariant 3D facial images
A joint research team led by Professors Ki-Hun Jeong and Doheon Lee from the KAIST Department of Bio and Brain Engineering reported the development of a technique for facial expression detection by merging near-infrared light-field camera techniques with artificial intelligence (AI) technology.
Unlike a conventional camera, the light-field camera contains micro-lens arrays in front of the image sensor, which makes the camera small enough to fit into a smart phone, while allowing it to acquire the spatial and directional information of the light with a single shot. The technique has received attention as it can reconstruct images in a variety of ways including multi-views, refocusing, and 3D image acquisition, giving rise to many potential applications.
However, the optical crosstalk between shadows caused by external light sources in the environment and the micro-lens has limited existing light-field cameras from being able to provide accurate image contrast and 3D reconstruction.
The joint research team applied a vertical-cavity surface-emitting laser (VCSEL) in the near-IR range to stabilize the accuracy of 3D image reconstruction that previously depended on environmental light. When an external light source is shone on a face at 0-, 30-, and 60-degree angles, the light field camera reduces 54% of image reconstruction errors. Additionally, by inserting a light-absorbing layer for visible and near-IR wavelengths between the micro-lens arrays, the team could minimize optical crosstalk while increasing the image contrast by 2.1 times.
Through this technique, the team could overcome the limitations of existing light-field cameras and was able to develop their NIR-based light-field camera (NIR-LFC), optimized for the 3D image reconstruction of facial expressions. Using the NIR-LFC, the team acquired high-quality 3D reconstruction images of facial expressions expressing various emotions regardless of the lighting conditions of the surrounding environment.
The facial expressions in the acquired 3D images were distinguished through machine learning with an average of 85% accuracy – a statistically significant figure compared to when 2D images were used. Furthermore, by calculating the interdependency of distance information that varies with facial expression in 3D images, the team could identify the information a light-field camera utilizes to distinguish human expressions.
Professor Ki-Hun Jeong said, “The sub-miniature light-field camera developed by the research team has the potential to become the new platform to quantitatively analyze the facial expressions and emotions of humans.” To highlight the significance of this research, he added, “It could be applied in various fields including mobile healthcare, field diagnosis, social cognition, and human-machine interactions.”
This research was published in Advanced Intelligent Systems online on December 16, under the title, “Machine-Learned Light-field Camera that Reads Facial Expression from High-Contrast and Illumination Invariant 3D Facial Images.” This research was funded by the Ministry of Science and ICT and the Ministry of Trade, Industry and Energy.
-Publication“Machine-learned light-field camera that reads fascial expression from high-contrast and illumination invariant 3D facial images,” Sang-In Bae, Sangyeon Lee, Jae-Myeong Kwon, Hyun-Kyung Kim. Kyung-Won Jang, Doheon Lee, Ki-Hun Jeong, Advanced Intelligent Systems, December 16, 2021 (doi.org/10.1002/aisy.202100182)
ProfileProfessor Ki-Hun JeongBiophotonic LaboratoryDepartment of Bio and Brain EngineeringKAIST
Professor Doheon LeeDepartment of Bio and Brain EngineeringKAIST
2022.01.21 View 13306 -
Seven Faculty Members Elected to Join the National Academy of Engineering of Korea
< Clockwise from top left: Professor Doo-Hwan Bae, Professor Seung Seob Lee, Professor Kyung Cheol Choi, Professor JaeYong Choung >
Seven KAIST faculty members have been elected as National Academy of Engineering of Korea (NAEK) members and associate members. NAEK, the most prestigious engineering society in Korea, elects new members with a minimum of 15 years of experience in engineering in academia and business every year.
In 2022, 24 members were newly elected from academia, including four KAIST faculty members: Professor Doo-Hwan Bae from the SW Education Center, KAIST Provost and Executive Vice President Seung Seob Lee, Professor JaeYong Choung from the School of Business and Technology Management, and Professor Kyung Cheol Choi of the School of Electrical Engineering. In the business sector, 21 members were elected as members in business, including Vice Chairman Jong-hee Han of Samsung Electronics, CEO Hyeon-Mo Ku of KT, President Sang-Ryul Lee of the Korea Aerospace Research Institute, President Kyo Won Jin of SK Hynix, CEO Eunkang Song of Capstone Partners, and Executive Vice President Se-hoon Kim of Hyundai Motor Company.
Among the newly elected 40 associate members from academia, three KAIST professors were listed: Professor Sukyoung Ryu from the School of Computing, Professor Joongmyeon Bae from the Department of Mechanical Engineering, and Professor EunAe Cho from the Department of Materials Science and Engineering. Another 44 members were elected as associate members in business, including Vice Chairman Hag-Dong Kim of POSCO, President Seong-Hyeon Cho of Mando Corp, President Siyoung Choi of Samsung Electronics, President Joo Sun Choi of Samsung Display, and Chairman Byung-Gyu Chang of Krafton.
NAEK evaluates candidates not only on their academic achievements, but on various other criteria including technological achievements, patents, the nurturing of talents, and contributions to the advancements of the industry. Candidates are then elected through written ballots by the members of NAEK. There are now 294 members and 360 associate members of NAEK.
2022.01.14 View 6850 -
Perigee-KAIST Rocket Research Center Launches Scientific Rocket
Undergraduate startup Perigree Aerospace develops suborbital rocket called Blue Whale 0.1
On December 29, Perigee Aerospace, an undergraduate startup, launched a test rocket with a length of 3.2 m, a diameter of 19 cm, and a weight of 51 kg, using ethanol and liquid oxygen as fuel. The launch took place off Jeju Island. It was aimed at building experience and checking the combustion of a liquid propulsion engine and the performance of pre-set flight and trajectory, communication, and navigation devices. It was also one of the projects marking the 50th anniversary of KAIST in 2021.
However, after flying for several seconds, the rocket lost its track due to a gust of wind that activated the rocket’s automatic flight suspension system. "At the moment the rocket took off, there was a much stronger gust than expected," Dong-Yoon Shin, CEO of Perigee said. "The wind sent it flying off course and the automatic flight suspension system stopped its engine." However, Shin was not disappointed, saying the launch, which was conducted in collaboration with Perigee-KAIST Rocket Research Center provided a good experience.
"Some people say that Blue Whale 0.1 is like a toy because of its small size. Of course, it's much smaller than the rockets I’ve dreamed of, but like other rockets, it has all the technology needed for launch," said Shin, who established his company in 2018 as a KAIST aerospace engineering student to develop small liquid-propellant orbital rockets. Perigee Aerospace aims to develop the world’s lightest launch vehicle using high-powered engines, with a goal of leading the global market for small launch vehicles in the new space generation.
Perigee-KAIST Rocket Research Center was founded in 2019 for the research and development of rocket propellants and has been testing the combustion of rocket engines of various sizes in their liquid propellant rocket combustion lab located on the KAIST Munji Campus.
The research center initiated the 50th anniversary rocket launch project in late April of last year, finished the examination of their preliminary design in late May, and secured a tentative launching site through the KAIST-Jejudo agreement in early July.
The ethanol engine combustion was tested in late July, and an examination meeting regarding the detailed design that took place in late August was followed by two months of static firing tests of the assembled rocket in October and November.
This was a very meaningful trial in which a domestic private enterprise founded by a college student collaborated with a university to successfully develop and launch a technically challenging liquid propellant rocket.
Shin's near-term goal is to launch a two-stage orbital rocket that uses liquid methane as fuel and weighs 1.8 tons. To secure competitiveness in the small projectile market, KAIST and Perigee Aerospace have set up a joint research center to test various rocket engine sizes and develop the world's lightest projectile using a high-performance engine.
Professor Jae-Hung Han, head of the Department of Aerospace Engineering, said, “The scientific rocket system secured through the launch of the celebratory rocket will be utilized for design and system-oriented education, and for carrying out various scientific missions.” He added, “It is very rare both domestically and globally that a scientific rocket designed by the initiatives of a department should be incorporated as part of a regular aerospace system design curriculum. This will be an exemplary case we can boast about to the rest of the world.”
Perigee Aerospace will improve the technology they have developed through the course of this project to develop subminiature vehicles they may use to launch small satellites into the low Earth orbit.
Shin said, “I am happy just with the fact that we have participated in a rocket project to celebrate the 50th anniversary of KAIST, and I would like to thank the engineers at my company and members of the KAIST Department of Aerospace Engineering.” He added, “I’m looking forward to the day that we develop a space launch vehicle that can deliver satellites even higher.”
2022.01.14 View 9630 -
AI Weather Forecasting Research Center Opens
The Kim Jaechul Graduate School of AI in collaboration with the National Institute of Meteorological Sciences (NIMS) under the National Meteorological Administration launched the AI Weather Forecasting Research Center last month.
The KAIST AI Weather Forecasting Research Center headed by Professor Seyoung Yoon was established with funding from from the AlphaWeather Development Research Project of the National Institute of Meteorological Sciences. KAIST was finally selected asas the project facilitator.
AlphaWeather is an AI system that utilizes and analyzes approximately approximately 150,000 ,000 pieces of weather information per hour to help weather forecasters produce accurate weather forecasts. The research center is composed of three research teams with the following goals: (a) developdevelop AI technology for precipitation nowcasting, (b) developdevelop AI technology for accelerating physical process-based numerical models, and (c) develop dAI technology for supporting weather forecasters. The teams consist of 15 staff member members from NIMS and 61 researchers from the Kim Jaechul Graduate School of AI at KAIST.
The research center is developing an AI algorithm for precipitation nowcasting (with up to six hours of lead time), which uses satellite images, radar reflectivity, and data collected from weather stations. It is also developing an AI algorithm for correcting biases in the prediction results from multiple numerical models. Finally, it is Finally, it is developing AI technology that supports weather forecasters by standardizing and automating repetitive manual processes. After verification, the the results obtained will be used by by the Korean National Weather Service as a next-generation forecasting/special-reporting system intelligence engine from 2026.
2022.01.10 View 6860