School+of+Electrical+Engineering
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Prof. Junil Choi Receives the Neal Shepherd Memorial Award
Professor Junil Choi of the School of Electrical Engineering received the 2021 Neal Shepherd Memorial Award from the IEEE Vehicular Technology Society. The award recognizes the most outstanding paper relating to radio propagation published in major journals over the previous five years.
Professor Cho, the recipient of the 2015 IEEE Signal Processing Society’s and the 2019 IEEE Communications Society’s Best Paper Award, was selected as the awardee for his paper titled “The Impact of Beamwidth on Temporal Channel Variation in Vehicular Channels and Its Implications” in IEEE Transaction on Vehicular Technology in 2017.
In this paper, Professor Choi and his team derived the channel coherence time for a wireless channel as a function of the beamwidth, taking both Doppler effect and pointing error into consideration. The results showed that a nonzero optimal beamwidth exists that maximizes the channel coherence time. To reduce the impact of the overhead of doing realignment in every channel coherence time, the paper showed that the beams should be realigned every beam coherence time for the best performance.
Professor Choi said, “It is quite an honor to receive this prestigious award following Professor Joonhyun Kang who won the IEEE VTS’s Jack Neubauer Memorial Award this year. It shows that our university’s pursuit of excellence in advanced research is being well recognized.”
2021.07.26 View 6377 -
Professor Kang’s Team Receives the IEEE Jack Newbauer Memorial Award
Professor Joonhyuk Kang of the School of Electrical Engineering received the IEEE Vehicular Technology Society’s 2021 Jack Neubauer Memorial Award for his team’s paper published in IEEE Transactions on Vehicular Technology. The Jack Neubauer Memorial Award recognizes the best paper published in the IEEE Transactions on Vehicular Technology journal in the last five years.
The team of authors, Professor Kang, Professor Sung-Ah Chung at Kyungpook National University, and Professor Osvaldo Simeone of King's College London reported their research titled Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning in IEEE Transactions on Vehicular Technology, Vol. 67, No. 3, pp. 2049-2063, in March 2018.
Their paper shows how the trajectory of aircraft is optimized and resources are allocated when unmanned aerial vehicles perform edge computing to help mobile device calculations. This paper has currently recorded nearly 400 citations (based on Google Scholar). "We are very happy to see the results of proposing edge computing using unmanned aerial vehicles by applying optimization theory, and conducting research on trajectory and resource utilization of unmanned aerial vehicles that minimize power consumption," said Professor Kang.
2021.07.12 View 7485 -
Wearable Device to Monitor Sweat in Real Time
An on-skin platform for the wireless monitoring of flow rate, cumulative loss, and temperature of sweat in real time
An electronic patch can monitor your sweating and check your health status. Even more, the soft microfluidic device that adheres to the surface of the skin, captures, stores, and performs biomarker analysis of sweat as it is released through the eccrine glands.
This wearable and wireless electronic device developed by Professor Kyeongha Kwon and her collaborators is a digital and wireless platform that could help track the so-called ‘filling process’ of sweat without having to visually examine the device. The platform was integrated with microfluidic systems to analyze the sweat’s components.
To monitor the sweat release rate in real time, the researchers created a ‘thermal flow sensing module.’ They designed a sophisticated microfluidic channel to allow the collected sweat to flow through a narrow passage and a heat source was placed on the outer surface of the channel to induce a heat exchange between the sweat and the heated channel.
As a result, the researchers could develop a wireless electronic patch that can measure the temperature difference in a specific location upstream and downstream of the heat source with an electronic circuit and convert it into a digital signal to measure the sweat release rate in real time. The patch accurately measured the perspiration rate in the range of 0-5 microliters/minute (μl/min), which was considered physiologically significant. The sensor can measure the flow of sweat directly and then use the information it collected to quantify total sweat loss. Moreover, the device features advanced microfluidic systems and colorimetric chemical reagents to gather pH measurements and determine the concentration of chloride, creatinine, and glucose in a user's sweat.
Professor Kwon said that these indicators could be used to diagnose various diseases related with sweating such as cystic fibrosis, diabetes, kidney dysfunction, and metabolic alkalosis. “As the sweat flowing in the microfluidic channel is completely separated from the electronic circuit, the new patch overcame the shortcomings of existing flow rate measuring devices, which were vulnerable to corrosion and aging,” she explained.
The patch can be easily attached to the skin with flexible circuit board printing technology and silicone sealing technology. It has an additional sensor that detects changes in skin temperature. Using a smartphone app, a user can check the data measured by the wearable patch in real time.
Professor Kwon added, “This patch can be widely used for personal hydration strategies, the detection of dehydration symptoms, and other health management purposes. It can also be used in a systematic drug delivery system, such as for measuring the blood flow rate in blood vessels near the skin’s surface or measuring a drug’s release rate in real time to calculate the exact dosage.”
-PublicationKyeongha Kwon, Jong Uk Kim, John A. Rogers, et al. “An on-skin platform for wireless monitoring of flow rate, cumulative loss and temperature of sweat in real time.” Nature Electronics (doi.org/10.1038/s41928-021-00556-2)
-ProfileProfessor Kyeongha KwonSchool of Electrical EngineeringKAIST
2021.06.25 View 9743 -
Acoustic Graphene Plasmons Study Paves Way for Optoelectronic Applications
- The first images of mid-infrared optical waves compressed 1,000 times captured using a highly sensitive scattering-type scanning near-field optical microscope. -
KAIST researchers and their collaborators at home and abroad have successfully demonstrated a new methodology for direct near-field optical imaging of acoustic graphene plasmon fields. This strategy will provide a breakthrough for the practical applications of acoustic graphene plasmon platforms in next-generation, high-performance, graphene-based optoelectronic devices with enhanced light-matter interactions and lower propagation loss.
It was recently demonstrated that ‘graphene plasmons’ – collective oscillations of free electrons in graphene coupled to electromagnetic waves of light – can be used to trap and compress optical waves inside a very thin dielectric layer separating graphene from a metallic sheet. In such a configuration, graphene’s conduction electrons are “reflected” in the metal, so when the light waves “push” the electrons in graphene, their image charges in metal also start to oscillate. This new type of collective electronic oscillation mode is called ‘acoustic graphene plasmon (AGP)’.
The existence of AGP could previously be observed only via indirect methods such as far-field infrared spectroscopy and photocurrent mapping. This indirect observation was the price that researchers had to pay for the strong compression of optical waves inside nanometer-thin structures. It was believed that the intensity of electromagnetic fields outside the device was insufficient for direct near-field optical imaging of AGP.
Challenged by these limitations, three research groups combined their efforts to bring together a unique experimental technique using advanced nanofabrication methods. Their findings were published in Nature Communications on February 19.
A KAIST research team led by Professor Min Seok Jang from the School of Electrical Engineering used a highly sensitive scattering-type scanning near-field optical microscope (s-SNOM) to directly measure the optical fields of the AGP waves propagating in a nanometer-thin waveguide, visualizing thousand-fold compression of mid-infrared light for the first time.
Professor Jang and a post-doc researcher in his group, Sergey G. Menabde, successfully obtained direct images of AGP waves by taking advantage of their rapidly decaying yet always present electric field above graphene. They showed that AGPs are detectable even when most of their energy is flowing inside the dielectric below the graphene.
This became possible due to the ultra-smooth surfaces inside the nano-waveguides where plasmonic waves can propagate at longer distances. The AGP mode probed by the researchers was up to 2.3 times more confined and exhibited a 1.4 times higher figure of merit in terms of the normalized propagation length compared to the graphene surface plasmon under similar conditions.
These ultra-smooth nanostructures of the waveguides used in the experiment were created using a template-stripping method by Professor Sang-Hyun Oh and a post-doc researcher, In-Ho Lee, from the Department of Electrical and Computer Engineering at the University of Minnesota.
Professor Young Hee Lee and his researchers at the Center for Integrated Nanostructure Physics (CINAP) of the Institute of Basic Science (IBS) at Sungkyunkwan University synthesized the graphene with a monocrystalline structure, and this high-quality, large-area graphene enabled low-loss plasmonic propagation.
The chemical and physical properties of many important organic molecules can be detected and evaluated by their absorption signatures in the mid-infrared spectrum. However, conventional detection methods require a large number of molecules for successful detection, whereas the ultra-compressed AGP fields can provide strong light-matter interactions at the microscopic level, thus significantly improving the detection sensitivity down to a single molecule.
Furthermore, the study conducted by Professor Jang and the team demonstrated that the mid-infrared AGPs are inherently less sensitive to losses in graphene due to their fields being mostly confined within the dielectric. The research team’s reported results suggest that AGPs could become a promising platform for electrically tunable graphene-based optoelectronic devices that typically suffer from higher absorption rates in graphene such as metasurfaces, optical switches, photovoltaics, and other optoelectronic applications operating at infrared frequencies.
Professor Jang said, “Our research revealed that the ultra-compressed electromagnetic fields of acoustic graphene plasmons can be directly accessed through near-field optical microscopy methods. I hope this realization will motivate other researchers to apply AGPs to various problems where strong light-matter interactions and lower propagation loss are needed.”
This research was primarily funded by the Samsung Research Funding & Incubation Center of Samsung Electronics. The National Research Foundation of Korea (NRF), the U.S. National Science Foundation (NSF), Samsung Global Research Outreach (GRO) Program, and Institute for Basic Science of Korea (IBS) also supported the work.
Publication:
Menabde, S. G., et al. (2021) Real-space imaging of acoustic plasmons in large-area graphene grown by chemical vapor deposition. Nature Communications 12, Article No. 938. Available online at https://doi.org/10.1038/s41467-021-21193-5
Profile:
Min Seok Jang, MS, PhD
Associate Professorjang.minseok@kaist.ac.krhttp://jlab.kaist.ac.kr/
Min Seok Jang Research GroupSchool of Electrical Engineering
http://kaist.ac.kr/en/Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea
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2021.03.16 View 14088 -
ACS Nano Special Edition Highlights Innovations at KAIST
- The collective intelligence and technological innovation of KAIST was highlighted with case studies including the Post-COVID-19 New Deal R&D Initiative Project. -
KAIST’s innovative academic achievements and R&D efforts for addressing the world’s greatest challenges such as the COVID-19 pandemic were featured in ACS Nano as part of its special virtual issue commemorating the 50th anniversary of KAIST. The issue consisted of 14 review articles contributed by KAIST faculty from five departments, including two from Professor Il-Doo Kim from the Department of Materials Science and Engineering, who serves as an associate editor of the ACS Nano.
ACS Nano, the leading international journal in nanoscience and nanotechnology, published a special virtual issue last month, titled ‘Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues.’
This special virtual issue introduced KAIST’s vision of becoming a ‘global value-creative leading university’ and its progress toward this vision over the last 50 years. The issue explained how KAIST has served as the main hub for advanced scientific research and technological innovation in South Korea since its establishment in 1971, and how its faculty and over 69,000 graduates played a key role in propelling the nation’s rapid industrialization and economic development.
The issue also emphasized the need for KAIST to enhance global cooperation and the exchange of ideas in the years to come, especially during the post-COVID era intertwined with the Fourth Industrial Revolution (4IR). In this regard, the issue cited the first ‘KAIST Emerging Materials e-Symposium (EMS)’, which was held online for five days in September of last year with a global audience of over 10,000 participating live via Zoom and YouTube, as a successful example of what academic collaboration could look like in the post-COVID and 4IR eras.
In addition, the “Science & Technology New Deal Project for COVID-19 Response,” a project conducted by KAIST with support from the Ministry of Science and ICT (MSIT) of South Korea, was also introduced as another excellent case of KAIST’s collective intelligence and technological innovation. The issue highlighted some key achievements from this project for overcoming the pandemic-driven crisis, such as: reusable anti-virus filters, negative-pressure ambulances for integrated patient transport and hospitalization, and movable and expandable negative-pressure ward modules.
“We hold our expectations high for the outstanding achievements and progress KAIST will have made by its centennial,” said Professor Kim on the background of curating the 14 review articles contributed by KAIST faculty from the fields of Materials Science and Engineering (MSE), Chemical and Biomolecular Engineering (CBE), Nuclear and Quantum Engineering (NQE), Electrical Engineering (EE), and Chemistry (Chem).
Review articles discussing emerging materials and their properties covered photonic carbon dots (Professor Chan Beum Park, MSE), single-atom and ensemble catalysts (Professor Hyunjoo Lee, CBE), and metal/metal oxide electrocatalysts (Professor Sung-Yoon Chung, MSE).
Review articles discussing materials processing covered 2D layered materials synthesis based on interlayer engineering (Professor Kibum Kang, MSE), eco-friendly methods for solar cell production (Professor Bumjoon J. Kim, CBE), an ex-solution process for the synthesis of highly stable catalysts (Professor WooChul Jung, MSE), and 3D light-patterning synthesis of ordered nanostructures (Professor Seokwoo Jeon, MSE, and Professor Dongchan Jang, NQE).
Review articles discussing advanced analysis techniques covered operando materials analyses (Professor Jeong Yeong Park, Chem), graphene liquid cell transmission electron microscopy (Professor Jong Min Yuk, MSE), and multiscale modeling and visualization of materials systems (Professor Seungbum Hong, MSE).
Review articles discussing practical state-of-the-art devices covered chemiresistive hydrogen sensors (Professor Il-Doo Kim, MSE), patient-friendly diagnostics and implantable treatment devices (Professor Steve Park, MSE), triboelectric nanogenerators (Professor Yang-Kyu Choi, EE), and next-generation lithium-air batteries (Professor Hye Ryung Byon, Chem, and Professor Il-Doo Kim, MSE).
In addition to Professor Il-Doo Kim, post-doctoral researcher Dr. Jaewan Ahn from the KAIST Applied Science Research Institute, Dean of the College of Engineering at KAIST Professor Choongsik Bae, and ACS Nano Editor-in-Chief Professor Paul S. Weiss from the University of California, Los Angeles also contributed to the publication of this ACS Nano special virtual issue.
The issue can be viewed and downloaded from the ACS Nano website at https://doi.org/10.1021/acsnano.1c01101.
Image credit: KAIST
Image usage restrictions: News organizations may use or redistribute this image,with proper attribution, as part of news coverage of this paper only.
Publication:
Ahn, J., et al. (2021) Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues. ACS Nano 15(3): 1895-1907. Available online at https://doi.org/10.1021/acsnano.1c01101
Profile:
Il-Doo Kim, Ph.D
Chair Professor
idkim@kaist.ac.kr
http://advnano.kaist.ac.kr
Advanced Nanomaterials and Energy Lab.
Department of Materials Science and Engineering
Membrane Innovation Center for Anti-Virus and Air-Quality Control
https://kaist.ac.kr/
Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
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2021.03.05 View 29213 -
Wirelessly Rechargeable Soft Brain Implant Controls Brain Cells
Researchers have invented a smartphone-controlled soft brain implant that can be recharged wirelessly from outside the body. It enables long-term neural circuit manipulation without the need for periodic disruptive surgeries to replace the battery of the implant. Scientists believe this technology can help uncover and treat psychiatric disorders and neurodegenerative diseases such as addiction, depression, and Parkinson’s.
A group of KAIST researchers and collaborators have engineered a tiny brain implant that can be wirelessly recharged from outside the body to control brain circuits for long periods of time without battery replacement. The device is constructed of ultra-soft and bio-compliant polymers to help provide long-term compatibility with tissue. Geared with micrometer-sized LEDs (equivalent to the size of a grain of salt) mounted on ultrathin probes (the thickness of a human hair), it can wirelessly manipulate target neurons in the deep brain using light.
This study, led by Professor Jae-Woong Jeong, is a step forward from the wireless head-mounted implant neural device he developed in 2019. That previous version could indefinitely deliver multiple drugs and light stimulation treatment wirelessly by using a smartphone. For more, Manipulating Brain Cells by Smartphone.
For the new upgraded version, the research team came up with a fully implantable, soft optoelectronic system that can be remotely and selectively controlled by a smartphone. This research was published on January 22, 2021 in Nature Communications.
The new wireless charging technology addresses the limitations of current brain implants. Wireless implantable device technologies have recently become popular as alternatives to conventional tethered implants, because they help minimize stress and inflammation in freely-moving animals during brain studies, which in turn enhance the lifetime of the devices. However, such devices require either intermittent surgeries to replace discharged batteries, or special and bulky wireless power setups, which limit experimental options as well as the scalability of animal experiments.
“This powerful device eliminates the need for additional painful surgeries to replace an exhausted battery in the implant, allowing seamless chronic neuromodulation,” said Professor Jeong. “We believe that the same basic technology can be applied to various types of implants, including deep brain stimulators, and cardiac and gastric pacemakers, to reduce the burden on patients for long-term use within the body.”
To enable wireless battery charging and controls, researchers developed a tiny circuit that integrates a wireless energy harvester with a coil antenna and a Bluetooth low-energy chip. An alternating magnetic field can harmlessly penetrate through tissue, and generate electricity inside the device to charge the battery. Then the battery-powered Bluetooth implant delivers programmable patterns of light to brain cells using an “easy-to-use” smartphone app for real-time brain control.
“This device can be operated anywhere and anytime to manipulate neural circuits, which makes it a highly versatile tool for investigating brain functions,” said lead author Choong Yeon Kim, a researcher at KAIST.
Neuroscientists successfully tested these implants in rats and demonstrated their ability to suppress cocaine-induced behaviour after the rats were injected with cocaine. This was achieved by precise light stimulation of relevant target neurons in their brains using the smartphone-controlled LEDs. Furthermore, the battery in the implants could be repeatedly recharged while the rats were behaving freely, thus minimizing any physical interruption to the experiments.
“Wireless battery re-charging makes experimental procedures much less complicated,” said the co-lead author Min Jeong Ku, a researcher at Yonsei University’s College of Medicine.
“The fact that we can control a specific behaviour of animals, by delivering light stimulation into the brain just with a simple manipulation of smartphone app, watching freely moving animals nearby, is very interesting and stimulates a lot of imagination,” said Jeong-Hoon Kim, a professor of physiology at Yonsei University’s College of Medicine. “This technology will facilitate various avenues of brain research.”
The researchers believe this brain implant technology may lead to new opportunities for brain research and therapeutic intervention to treat diseases in the brain and other organs.
This work was supported by grants from the National Research Foundation of Korea and the KAIST Global Singularity Research Program.
-Profile
Professor Jae-Woong Jeong
https://www.jeongresearch.org/
School of Electrical Engineering
KAIST
2021.01.26 View 25236 -
Team USRG’s Winning Streak Continues at the AI Grand Challenge
Team USRG (Unmanned Systems Research Group) led by Professor Hyunchul Shim from the School of Electrical Engineering has won the AI Grand Challenge 2020 held on Nov. 23 at Kintex in Ilsan, Kyonggi-do for the second consecutive year. The team received 7.7 million KRW in research funding from the Ministry of Science and ICT, the organizer of the challenge.
The team took a little over two minutes to complete the rescue operation mission of the challenge. The mission included swerving around seven obstacles, airdropping an aid package, and safely landing after identifying the landing spot. Their drone is the only one that successfully passed through a 10-meter tunnel out of five pre-qualified teams: three from universities and two from companies.
The AI Grand Challenge, which began in 2017, was designed to promote AI technology and its applications for addressing high-risk technical challenges, especially for conducting complex disaster relief operations.
For autonomous flying drones, swerving to avoid objects has always been an essential skill and a big challenge. For their flawless performance in the rescue operation, the team loaded an AI algorithm and upgraded their drone by improving the LiDAR-based localization system and a stronger propulsion system to carry more sensors. The drone weighs 2.4 kg and carries a small yet powerful computer with a GPU.
This AI-powered drone can complete rescue missions more efficiently in complicated and disastrous environments by precisely comprehending where the drone should go without needing GPS. The team also designed an all-in-one prop guard and installed a gripper onto the bottom of the drone to hold the aid package securely.
“We tried hard to improve our localization system better to resolve issues we had in the previous event,” said Professor Shim. Two PhD candidates, Han-Sob Lee and Bo-Sung Kim played a critical role in developing this drone.
After their two-year winning streak, their prize money now totals 2.4 billion KRW, equivalent to the winning prize of the DARPA Challenge. As the winning team, they will collaborate with other champions at the AI track challenge to develop rescue mission technology for a more complex environment.
“The importance of AI technology is continuing to grow and the government is providing large amounts of funding for research in this field. We would like to develop very competitive technology that will work in the real world,” Professor Shim added.
His group is investigating a wide array of AI technologies applicable to unmanned vehicles including indoor flying drones, self-driving cars, delivery robots, and a tram that circles the campus.
2020.12.01 View 8490 -
Advanced NVMe Controller Technology for Next Generation Memory Devices
KAIST researchers advanced non-volatile memory express (NVMe) controller technology for next generation information storage devices, and made this new technology named ‘OpenExpress’ freely available to all universities and research institutes around the world to help reduce the research cost in related fields.
NVMe is a communication protocol made for high-performance storage devices based on a peripheral component interconnect-express (PCI-E) interface. NVMe has been developed to take the place of the Serial AT Attachment (SATA) protocol, which was developed to process data on hard disk drives (HDDs) and did not perform well in solid state drives (SSDs).
Unlike HDDs that use magnetic spinning disks, SSDs use semiconductor memory, allowing the rapid reading and writing of data. SSDs also generate less heat and noise, and are much more compact and lightweight.
Since data processing in SSDs using NVMe is up to six times faster than when SATA is used, NVMe has become the standard protocol for ultra-high speed and volume data processing, and is currently used in many flash-based information storage devices.
Studies on NVMe continue at both the academic and industrial levels, however, its poor accessibility is a drawback. Major information and communications technology (ICT) companies around the world expend astronomical costs to procure intellectual property (IP) related to hardware NVMe controllers, necessary for the use of NVMe. However, such IP is not publicly disclosed, making it difficult to be used by universities and research institutes for research purposes.
Although a small number of U.S. Silicon Valley startups provide parts of their independently developed IP for research, the cost of usage is around 34,000 USD per month. The costs skyrocket even further because each copy of single-use source code purchased for IP modification costs approximately 84,000 USD.
In order to address these issues, a group of researchers led by Professor Myoungsoo Jung from the School of Electrical Engineering at KAIST developed a next generation NVMe controller technology that achieved parallel data input/output processing for SSDs in a fully hardware automated form.
The researchers presented their work at the 2020 USENIX Annual Technical Conference (USENIX ATC ’20) in July, and released it as an open research framework named ‘OpenExpress.’
This NVMe controller technology developed by Professor Jung’s team comprises a wide range of basic hardware IP and key NVMe IP cores. To examine its actual performance, the team made an NVMe hardware controller prototype using OpenExpress, and designed all logics provided by OpenExpress to operate at high frequency.
The field-programmable gate array (FPGA) memory card prototype developed using OpenExpress demonstrated increased input/output data processing capacity per second, supporting up to 7 gigabit per second (GB/s) bandwidth. This makes it suitable for ultra-high speed and volume next generation memory device research.
In a test comparing various storage server loads on devices, the team’s FPGA also showed 76% higher bandwidth and 68% lower input/output delay compared to Intel’s new high performance SSD (Optane SSD), which is sufficient for many researchers studying systems employing future memory devices. Depending on user needs, silicon devices can be synthesized as well, which is expected to further enhance performance.
The NVMe controller technology of Professor Jung’s team can be freely used and modified under the OpenExpress open-source end-user agreement for non-commercial use by all universities and research institutes. This makes it extremely useful for research on next-generation memory compatible NVMe controllers and software stacks.
“With the product of this study being disclosed to the world, universities and research institutes can now use controllers that used to be exclusive for only the world’s biggest companies, at no cost,ˮ said Professor Jung. He went on to stress, “This is a meaningful first step in research of information storage device systems such as high-speed and volume next generation memory.”
This work was supported by a grant from MemRay, a company specializing in next generation memory development and distribution.
More details about the study can be found at http://camelab.org.
Image credit: Professor Myoungsoo Jung, KAIST
Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only.
-Publication:
Myoungsoo Jung. (2020). OpenExpress: Fully Hardware Automated Open Research Framework for Future Fast NVMe Devices. Presented in the Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), Available online at https://www.usenix.org/system/files/atc20-jung.pdf
-Profile: Myoungsoo Jung
Associate Professor
m.jung@kaist.ac.kr
http://camelab.org
Computer Architecture and Memory Systems Laboratory
School of Electrical Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, Republic of Korea
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2020.09.04 View 10810 -
Professor Jaehyouk Choi, IT Young Engineer of the Year
Professor Jaehyouk Choi from the KAIST School of Electrical Engineering won the ‘IT Young Engineer Award’ for 2020. The award was co-presented by the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Electronics Engineers of Korea (IEIE), and sponsored by the Haedong Science and Culture Foundation.
The ‘IT Young Engineer Award’ selects only one mid-career scientist or engineer 40 years old or younger every year, who has made a great contribution to academic or technological advancements in the field of IT.
Professor Choi’s research topics include high-performance semiconductor circuit design for ultrahigh-speed communication systems including 5G communication. In particular, he is widely known for his field of the ‘ultra-low-noise, high-frequency signal generation circuit,’ key technology for next-generation wired and wireless communications, as well as for memory systems. He has published 64 papers in SCI journals and at international conferences, and applied for and registered 25 domestic and international patents.
Professor Choi is also an active member of the Technical Program Committee of international symposiums in the field of semiconductor circuits including the International Solid-State Circuits Conference (ISSCC) and the European Solid-State Circuit Conference (ESSCIRC). Beginning this year, he also serves as a distinguished lecturer at the IEEE Solid-State Circuit Society (SSCS).
(END)
2020.08.20 View 10865 -
Quantum Classifiers with Tailored Quantum Kernel
Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.
The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.
Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.
More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods.
The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data.
The KAIST team, in collaboration with researchers from the University of KwaZulu-Natal (UKZN) in South Africa and Data Cybernetics in Germany, has further advanced the rapidly evolving field of quantum machine learning by introducing quantum classifiers with tailored quantum kernels.This study was reported at npj Quantum Information in May.
The input data is either represented by classical data via a quantum feature map or intrinsic quantum data, and the classification is based on the kernel function that measures the closeness of the test data to training data.
Dr. Daniel Park at KAIST, one of the lead authors of this research, said that the quantum kernel can be tailored systematically to an arbitrary power sum, which makes it an excellent candidate for real-world applications.
Professor Rhee said that quantum forking, a technique that was invented by the team previously, makes it possible to start the protocol from scratch, even when all the labeled training data and the test data are independently encoded in separate qubits.
Professor Francesco Petruccione from UKZN explained, “The state fidelity of two quantum states includes the imaginary parts of the probability amplitudes, which enables use of the full quantum feature space.”
To demonstrate the usefulness of the classification protocol, Carsten Blank from Data Cybernetics implemented the classifier and compared classical simulations using the five-qubit IBM quantum computer that is freely available to public users via cloud service. “This is a promising sign that the field is progressing,” Blank noted.
Link to download the full-text paper:
https://www.nature.com/articles/s41534-020-0272-6
-Profile
Professor June-Koo Kevin Rhee
rhee.jk@kaist.ac.kr
Professor, School of Electrical Engineering
Director, ITRC of Quantum Computing for AIKAIST
Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
2020.07.07 View 11743 -
‘Mole-bot’ Optimized for Underground and Space Exploration
Biomimetic drilling robot provides new insights into the development of efficient drilling technologies
Mole-bot, a drilling biomimetic robot designed by KAIST, boasts a stout scapula, a waist inclinable on all sides, and powerful forelimbs. Most of all, the powerful torque from the expandable drilling bit mimicking the chiseling ability of a mole’s front teeth highlights the best feature of the drilling robot.
The Mole-bot is expected to be used for space exploration and mining for underground resources such as coalbed methane and Rare Earth Elements (REE), which require highly advanced drilling technologies in complex environments.
The research team, led by Professor Hyun Myung from the School of Electrical Engineering, found inspiration for their drilling bot from two striking features of the African mole-rat and European mole.
“The crushing power of the African mole-rat’s teeth is so powerful that they can dig a hole with 48 times more power than their body weight. We used this characteristic for building the main excavation tool. And its expandable drill is designed not to collide with its forelimbs,” said Professor Myung.
The 25-cm wide and 84-cm long Mole-bot can excavate three times faster with six times higher directional accuracy than conventional models. The Mole-bot weighs 26 kg.
After digging, the robot removes the excavated soil and debris using its forelimbs. This embedded muscle feature, inspired by the European mole’s scapula, converts linear motion into a powerful rotational force. For directional drilling, the robot’s elongated waist changes its direction 360° like living mammals.
For exploring underground environments, the research team developed and applied new sensor systems and algorithms to identify the robot’s position and orientation using graph-based 3D Simultaneous Localization and Mapping (SLAM) technology that matches the Earth’s magnetic field sequence, which enables 3D autonomous navigation underground.
According to Market & Market’s survey, the directional drilling market in 2016 is estimated to be 83.3 billion USD and is expected to grow to 103 billion USD in 2021. The growth of the drilling market, starting with the Shale Revolution, is likely to expand into the future development of space and polar resources. As initiated by Space X recently, more attention for planetary exploration will be on the rise and its related technology and equipment market will also increase.
The Mole-bot is a huge step forward for efficient underground drilling and exploration technologies. Unlike conventional drilling processes that use environmentally unfriendly mud compounds for cleaning debris, Mole-bot can mitigate environmental destruction. The researchers said their system saves on cost and labor and does not require additional pipelines or other ancillary equipment.
“We look forward to a more efficient resource exploration with this type of drilling robot. We also hope Mole-bot will have a very positive impact on the robotics market in terms of its extensive application spectra and economic feasibility,” said Professor Myung.
This research, made in collaboration with Professor Jung-Wuk Hong and Professor Tae-Hyuk Kwon’s team in the Department of Civil and Environmental Engineering for robot structure analysis and geotechnical experiments, was supported by the Ministry of Trade, Industry and Energy’s Industrial Technology Innovation Project.
Profile
Professor Hyun Myung
Urban Robotics Lab
http://urobot.kaist.ac.kr/
School of Electrical Engineering
KAIST
2020.06.05 View 10559 -
Professor Dongsu Han Named Program Chair for ACM CoNEXT 2020
Professor Dongsu Han from the School of Electrical Engineering has been appointed as the program chair for the 16th Association for Computing Machinery’s International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2020). Professor Han is the first program chair to be appointed from an Asian institution.
ACM CoNEXT is hosted by ACM SIGCOMM, ACM's Special Interest Group on Data Communications, which specializes in the field of communication and computer networks.
Professor Han will serve as program co-chair along with Professor Anja Feldmann from the Max Planck Institute for Informatics. Together, they have appointed 40 world-leading researchers as program committee members for this conference, including Professor Song Min Kim from KAIST School of Electrical Engineering.
Paper submissions for the conference can be made by the end of June, and the event itself is to take place from the 1st to 4th of December.
Conference Website: https://conferences2.sigcomm.org/co-next/2020/#!/home
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2020.06.02 View 10009