Professor Jaewoong Jung’s lab has been selected for 2023 National Research and Development TOP 100

Professor Jaewoong Jung’s lab has been selected for 2023 National Research and Development TOP 100

 

연구실 썸네일

[Professor Jaewoong Jeong, Ph.D candidate Choong-yeon Kim]

 

The wireless brain control system technology developed by Professor Jung Jae-woong’s laboratory at KAIST has been finally selected for the ‘2023 National Research and Development TOP 100’ by the Ministry of Science and ICT.

The National Research and Development TOP 100 is a policy that selects outstanding national research and development achievements across various ministries and agencies, marking its 18th year this year.

 

In the current year, a total of 854 candidate achievements recommended by each ministry underwent evaluation by a selection evaluation committee consisting of 100 experts from industry, academia, and research institutions. With this evaluation, the final 100 outstanding achievements are selected after public verification.

Particularly noteworthy is that Professor Jung Jae-woong’s research outcome was also chosen as one of the ‘Top 10 Achievements in Social Problem Solving’ through nationwide online voting. 

 
The selected research involves the ‘Development of a Wireless Brain Control System Based on the Internet of Things for Brain Research Automation and Remote Treatment of Brain Diseases.’
It was carried out with the support of the Mid-Career Researcher Support Project by the Ministry of Science and ICT.
 

As the world enters the era of aging, the number of patients suffering from brain diseases such as Parkinson’s is rapidly increasing.

However, the pace of progress in brain research to elucidate brain function and cure brain diseases has not kept up with the increasing rate of patients.

 

In response, Professor Jung Jae-woong’s research team combined wireless neural implant technology with IoT technology.

They successfully developed the world’s first IoT-based high-efficiency brain control system that can remotely select and control dozens to hundreds of brains with transplanted wireless neural implants simultaneously, anywhere in the world.

 

This technology not only supports fully automated, low-cost, high-efficiency brain research but also demonstrates the possibility of remote implementation for the efficient treatment of brain diseases such as Parkinson’s, dementia, and epilepsy.

Thus, it presents a new vision for brain research and the brain disease treatment.

 

Ph.D candidate Kim Chung-yeon, a KAIST undergraduate alumni, participated as co-first author in the introduced paper. It was published in the most prestigious international journal ‘Nature Biomedical Engineering’ on June 20, 2022 (Paper Title: Scalable and modular wireless-network infrastructure for large-scale behavioural neuroscience).

 

For more detailed information related to this news, you can check the link below.

https://m.dongascience.com/news.php?idx=62362

 

Ph.D. candidate Kent Edrian Lozada (Prof. Seung-Tak Ryu), received the Distinguished Design Award at 2023 IEEE A-SSCC

Ph.D. candidate Kent Edrian Lozada (Prof. Seung-Tak Ryu), received the Distinguished Design Award at 2023 IEEE A-SSCC

 

Inline image 2023 11 16 10.56.00.123

 

Ph.D. candidate Kent Edrian Lozada, advised by Professor Seung-Tak Ryu, received the Distinguished Design Award at the 2023 IEEE Asian Solid-State Circuits Conference (A-SSCC) Student Design Contest.
 
A-SSCC is an annual international conference organized by IEEE and this year’s conference took place in Hainan, China from November 5th to 8th.
Ph.D. candidate Kent Edrian Lozada and M.S. student Dong-Hun Lee have published a paper, titled “A 25kHz-BW 97.4dB-SNDR 100.2dB-DR 3rd-order SAR-Assisted CT DSM with 1-0 MASH and DNC”.
 
-Conference: 2023 IEEE Asian Solid-State Circuits Conference (A-SSCC)
-Date: November 5-8, 2023
-Award: Distinguished Design Award
-Authors: Kent Edrian Lozada*, Dong-Hun Lee* (*equal contribution), Ye-Dam Kim, Ho-Jin Kim, Youngjae Cho, Michael Choi, and Seung-Tak Ryu (Advisory Professor)
-Paper Title: A 25kHz-BW 97.4dB-SNDR 100.2dB-DR 3rd-order SAR-Assisted CT DSM with 1-0 MASH and DNC
 
 

M.S. Course Jiwon Choi (Prof. Hoi-Jun Yoo), won the Best Design Award at 2023 IEEE A-SSCC

M.S. Course Jiwon Choi (Prof. Hoi-Jun Yoo), won the Best Design Award at 2023 IEEE A-SSCC

 

Inline image 2023 11 15 14.17.37.356 1

<Certificate of Award & Award Ceremony>
 

M.S. student Jiwon Choi (Advised by Hoi-Jun Yoo) won the Best Design Award at the 2023 IEEE Asian Solid-State Circuits Conference (A-SSCC) Student Design Contest.

The conference was held in Hainan, China from November 5th to 8th. A-SSCC is an international conference held annually by IEEE. M.S. student Jiwon Choi has published a paper titled “A Resource-Efficient Super-Resolution FPGA Processor with Heterogeneous CNN and SNN Core Architecture”.

 

Details are as follows. 

 

Conference: 2023 IEEE Asian Solid-State Circuits Conference (A-SSCC)

Date: November 5-8, 2023

Award: Best Design Award

Authors: Jiwon Choi, Sangyeob Kim, Wonhoon Park, Wooyoung Jo, and Hoi-Jun Yoo (Advisory Professor)

Paper Title: A Resource-Efficient Super-Resolution FPGA Processor with Heterogeneous CNN and SNN Core Architecture

 

An intravenous needle that irreversibly softens via body temperature on insertion

연구팀

Intravenous (IV) injection is a method commonly used in patient’s treatment worldwide as it induces rapid effects and allows treatment through continuous administration of medication by directly injecting drugs into the blood vessel.
 
However, medical IV needles, made of hard materials such as stainless steel or plastic which do not mechanically match the soft biological tissues of the body, can cause critical problems in healthcare settings, starting from minor tissue damages in the injection sites to serious inflammations.
 

The structure and dexterity of rigid medical IV devices also enable unethical reuse of needles for reduction of injection costs, leading to transmission of deadly blood-borne disease infections such as human immunodeficiency virus (HIV) and hepatitis B/C viruses.

 

Furthermore, unintended needlestick injuries are frequently occurring in medical settings worldwide, that are viable sources of such infections, with IV needles having the greatest susceptibility of being the medium of transmissible diseases.

 

For these reasons, the World Health Organization (WHO) in 2015 launched a policy on safe injection practices to encourage the development and use of “smart” syringes that have features to prevent re-use, after a tremendous increase in the number of deadly infectious disease worldwide due to medical-sharps related issues.

 

KAIST announced on the 13th that Professor Jae-Woong Jeong and his research team of its School of Electrical Engineering succeeded in developing the Phase-Convertible, Adapting and non-REusable (P-CARE) needle with variable stiffness that can improve patient health and ensure the safety of medical staff through convergent joint research with another team led by Professor Won-Il Jeong of the Graduate School of Medical Sciences.

 

The new technology is expected to allow patients to move without worrying about pain at the injection site as it reduces the risk of damage to the wall of the blood vessel as patients receive IV medication.

 

This is possible with the needle’s stiffness-tunable characteristics which will make it soft and flexible upon insertion into the body due to increased temperature, adapting to the movement of thin-walled vein.

 

It is also expected to prevent blood-borne disease infections caused by accidental needlestick injuries or unethical re-using of syringes as the deformed needle remains perpetually soft even after it is retracted from the injection site.

 

The results of this research, in which Karen-Christian Agno, a doctoral researcher of the School of Electrical Engineering at and Dr. Keungmo Yang of the Graduate School of Medical Sciences participated as co-first authors, was published in Nature Biomedical Engineering on October 30. (Paper title: A temperature-responsive intravenous needle that irreversibly softens on insertion)

 

images 000069 image1.jpg 7

< Figure 1. Disposable variable stiffness intravenous needle. (a) Conceptual illustration of the key features of the P-CARE needle whose mechanical properties can be changed by body temperature, (b) Photograph of commonly used IV access devices and the P-CARE needle, (c) Performance of common IV access devices and the P-CARE needle >

 

“We’ve developed this special needle using advanced materials and micro/nano engineering techniques, and it can solve many global problems related to conventional medical needles used in healthcare worldwide”, said Jae-Woong Jeong, Ph.D., an associate professor of Electrical Engineering at KAIST and a lead senior author of the study. 

 

The softening IV needle created by the research team is made up of liquid metal gallium that forms the hollow, mechanical needle frame encapsulated within an ultra-soft silicone material. In its solid state, gallium has sufficient hardness that enables puncturing of soft biological tissues.

However, gallium melts when it is exposed to body temperature upon insertion, and changes it into a soft state like the surrounding tissue, enabling stable delivery of the drug without damaging blood vessels. Once used, a needle remains soft even at room temperature due to the supercooling phenomenon of gallium, fundamentally preventing needlestick accidents and reuse problems.

 

Biocompatibility of the softening IV needle was validated through in vivo studies in mice. The studies showed that implanted needles caused significantly less inflammation relative to the standard IV access devices of similar size made of metal needles or plastic catheters. The study also confirmed the new needle was able to deliver medications as reliably as commercial injection needles.

 

images 000069 image2.jpg 5

< Photo 1. Photo of the P-CARE needle that softens with body temperature. >

 

Researchers also showed possibility of integrating a customized ultra-thin temperature sensor with the softening IV needle to measure the on-site temperature which can further enhance patient’s well-being.

The single assembly of sensor-needle device can be used to monitor the core body temperature, or even detect if there is a fluid leakage on-site during indwelling use, eliminating the need for additional medical tools or procedures to provide the patients with better health care services.

 

The researchers believe that this transformative IV needle can open new opportunities for wide range of applications particularly in clinical setups, in terms of redesigning other medical needles and sharp medical tools to reduce muscle tissue injury during indwelling use.

The softening IV needle may become even more valuable in the present times as there is an estimated 16 billion medical injections administered annually in a global scale, yet not all needles are disposed of properly, based on a 2018 WHO report.

 

 

images 000069 image3.jpg

< Figure 2. Biocompatibility test for P-CARE needle: Images of H&E stained histology (the area inside the dashed box on the left is provided in an expanded view in the right), TUNEL staining (green), DAPI staining of nuclei (blue) and co-staining (TUNEL and DAPI) of muscle tissue from different organs. >

 

images 000069 image4.jpg

< Figure 3. Conceptual images of potential utilization for temperature monitoring function of P-CARE needle integrated with a temperature sensor. >

 

(a) Schematic diagram of injecting a drug through intravenous injection into the abdomen of a laboratory mouse (b) Change of body temperature upon injection of drug (c) Conceptual illustration of normal intravenous drug injection (top) and fluid leakage (bottom) (d) Comparison of body temperature during normal drug injection and fluid leakage: when the fluid leak occur due to incorrect insertion, a sudden drop of temperature is detected.

 

This work was supported by grants from the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

* Link : 한 번만 찔러도 흐물흐물…”재사용 금지” 주삿바늘 등장 / SBS 8뉴스 – YouTube

2023 IEEE Electron Device Society(EDS) PhD Fellowship Recipients

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<Jeong Jae Yong, phD student>
 
 
Jeong Jae Yong, a third year PhD student advised by Professor Sanghyeon Kim, has been selected for the 2023 IEEE Electron Device Society (EDS) PhD Student Fellowship organized by IEEE. This Fellowship selects three students annually from among the global IEEE EDS student members who demonstrate excellent research capabilities in the field of Electron Devices (Through demonstration of his/her significant ability to perform independent research in the fields of electron devices and a proven history of academic excellence). 
The Fellowship is awarded to one student each from three continents (America, Europe/Middle East/Africa, Asia & Pacific).
 
Jeong Jae Yong has achieved remarkable results as a graduate student, having presented as the first author in both the IEEE International Electron Devices Meeting (IEDM) and the IEEE Symposium on VLSI Technology & Circuits (VLSIT), with four papers at IEDM and three at VLSIT (including a planned presentation at 2023 IEDM). Notably, his paper presented at the 2023 VLSIT was selected as a highlight paper, and his upcoming paper at the 2023 IEDM has been chosen as a top-ranked student paper.
 
Jeong Jae Yong is the second recipient of the IEEE EDS PhD Student Fellowship from a university in Korea since its establishment in 2001 (the first recipient: Hak-Yeol Bae from KAIST EE, PhD program in 2016, currently an assistant professor at Jeonbuk National University). The award ceremony is scheduled to be held this December in San Francisco, which is also the venue for the IEDM event.
 
 
Related Link: https://eds.ieee.org/education/student-fellowships/phd-student-fellowship

Professor Seungwon Shin’s NSS Lab Wins a Total of 7 Awards in the 2023 Cybersecurity Paper Competition Sponsored by the National Intelligence Service

Professor Seungwon Shin’s NSS Lab Wins a Total of 7 Awards in the 2023 Cybersecurity Paper Competition Sponsored by the National Intelligence Service

 

<From the upper left, Myeongseong Yoo, Jaehan Kim, Suhyun Kim, Minkyu Song, Hanna Kim, Jaehan Kim>
 

The research team (NSS lab) led by Professor Seungwon Shin from KAIST’s Department of Electrical and Electronic Engineering has received a total of 7 awards in the 2023 Cybersecurity Paper Competition, including the top prize in the technology.

Starting in 2017, the competition is organized by National Intelligence Service to foster cybersecurity expertise and enhance research capabilities in cybersecurity.

Exceptional papers presented in previous editions of this competition have made significant contributions to national cybersecurity technology development, policy formulation, and strategy.

 

Professor Seungwon Shin’s research team, including Ph.D. candidate Myeongseong Yoo from KAIST and Professor Jaehyun Nam from Dankook University, received the best paper award in technology section along with cash reward of 4 million KRW for their paper titled “HELIOS: Hardware-assisted High-performance Security Extension for Cloud,” which focuses on a hardware-based network security system for a secure cloud environment.

 

Furthermore, a team consisting of Ph.D. candidates Jaehan Kim, Minkyu Song, and Youngjin Jin from Professor Shin’s lab received the outstanding paper award in the technology section for their work on “Graph-based Deep Learning Framework for Credential Stuffing Risk Prediction,” a framework that uses graph neural networks to proactively predict the risk of credential stuffing attacks between websites by modeling the reused password relationship.

 

In addition, students Suhyun Kim, Hanna Kim, Seungho Na, and Somin Cho from Professor Shin’s research lab received one award and four honorable mentions in the technology section.

 

Myeongseong Yoo, a top prized student, mentioned that “The system we developed utilizes smartNIC to accelerate communication between virtual machines and containers. It not only provides better security than traditional software-based security systems but also is expected to be highly useful in creating a safe cloud environment.”

 

The complete list of awards received by Professor Seungwon Shin’s research team in the 2023 Cybersecurity Paper Competition is as follows:

  *Best Paper Award in Technology: Myeongseong Yoo (Ph.D. candidate) and Professor Jaehyun Nam (Dankook University)

  *Outstanding Paper Award in Technology Category: Jaehan Kim, Minkyu Song, and Youngjin Jin (Ph.D. candidates)

  *Paper Award in Technology Category: Suhyun Kim and Seungho Na (Ph.D. candidates)

  *Honorable Mentions in Technology Category (4 awards):

    -Hanna Kim (Ph.D. candidate) and Geon Choi (Student, Indiana University Bloomington)

    -Jaehan Kim and Minkyu Song (Ph.D. candidates)

    -Seungho Na and Somin Cho (Ph.D. candidates)

    -Minkyu Song and Jaehan Kim (Ph.D. candidates)

CAMEL research team has been successively selected for 2023 Samsung Future Technology Development Program

CAMEL research team has been successively selected for 2023 Samsung Future Technology Development Program

 

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The CAMEL research team from our department, led by Professor Myoungsoo Jung, has been chosen to participate in the Samsung Future Technology Development Program.

 

This recognition comes with support for their study titled, “Software-Hardware Co-Design for Dynamic Acceleration in Sparsity-aware Hyperscale AI.”

 

Large AI models, such as mixture of experts (MoE), autoencoders, and multimodal learning, grouped under the umbrella of Hyperscale AI, have gained traction due to the success of expansive model-driven applications, including ChatGPT.

Based on the insight that computational traits of these models often shift during training, the research team has suggested acceleration strategies.

These encompass software technologies, unique algorithms, and hardware accelerator layouts.

A key discovery by the team was the inability of existing training systems to account for variations in data sparsity and computational dynamics between model layers. This oversight obstructs adaptive acceleration. 

 

To address this, the CAMEL team introduced a dynamic acceleration method that can detect shifts in computational traits and adapt computation techniques in real-time.

The findings from this research could benefit not only Hyperscale AI but also the larger domain of deep learning and the burgeoning services sector.

The team’s goals include producing tangible hardware models and offering open-source software platforms.

 

Samsung Electronics, since 2013, has initiated the ‘Future Technology Development Program’, investing KRW 1.5 trillion to stimulate technological innovation pivotal for future societal progress.

For a decade, they have backed initiatives in foundational science, innovative materials, and ICT, particularly favoring ventures that are high-risk but offer significant returns.

The CAMEL team has been collaborating with Samsung since 2021 on a project focusing on accelerating Graph Neural Networks (GNNs). We extend our hearty congratulations to them as they embark on this fresh exploration into the realm of Hyperscale AI.

 

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Professor Song Min Kim’s Team (Team SMILE) won the IITP Directors’s Award at the ICT Challenge 2023

Professor Song Min Kim’s Team (Team SMILE) won the IITP Directors’s Award at the ICT Challenge 2023

 

수상

             <Professor Song Min Kim>                                            <Award Ceremony>
 
 
 
Professor Song Min Kim’s team(Team SMILE) won the IITP (Information and Communication Technology Planning and Evaluation Institute) Director’s Award at the ICT Challenge 2023.
 

The ICT Challenge 2023, hosted by the Ministry of Science and ICT, organized by the Information and Communication Technology Planning and Evaluation Institute (IITP) and the University Information and Communication Research Center Council (ITRC), and sponsored by SK Telecom, is a competition with the theme “New Door to the Future”.

 

It aims to concretize and practicalize creative ideas that will lead future innovative technologies in an era of great transition where cutting-edge technologies are advancing and evolving.

 

“Team Smile”, consisting of PhD candidates Kangmin Bae, Hankyeol Moon, and Haksun Son from the Department of Electrical and Electronic Engineering at KAIST, and Lee Geon-woong, an undergraduate from the Department of Electrical and Electronic Engineering at Korea University, successfully implemented a location-aware tag system for real-time large-scale inventory management using millimeter wave backscatter.

 

They won the IITP (Information and Communication Technology Planning and Evaluation Institute) Director’s Award, ranking 7th out of a total of 83 teams participating in the ICT Challenge 2023.

 

Team leader Researcher Kangmin Bae expressed, “The system can recognize tags that operate without power with millimeter-level accuracy from a distance of more than 100 meters.

Given its high practicality and business value, we have high expectations for its future potential.”

 
 

 

Professor Youngsoo Shin receives the October Scienc and Technology Award, Optimizing Semiconductor Process with AI

[Professor Youngsoo Shin receives the October Scienc and Technology Award, Optimizing Semiconductor Process with AI]

 

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<Professor Youngsoo Shin>

 

The Ministry of Science and ICT and the Korea Research Foundation announced on the 4th that Professor Shin Youngsoo from the School of Electrical Engineering at KAIST was selected as the winner of the ‘Science and Technology Award’ for October. 

Professor Shin was recognized for his contribution in developing a semiconductor lithography optimization technology that is 10 times faster and has a higher resolution than existing methods using machine learning.

 

Lithography is a process in which light is shone on a mask engraved with patterns to create devices on a wafer.

It is a critical process that determines the yield of semiconductors. In order to create polygons on the wafer, complex patterns must be drawn on the mask.

This process,  known as OPC (Optical Proximity Correction), involves repeatedly adjusting the mask shape and simulating the image on the wafer, taking a significant amount of time.

 

Professor Shin trained artificial intelligence (AI) on sets of mask shapes and the resulting wafer images to develop a faster and higher-resolution OPC optimization technique.

 Additionally, Professor Shin developed a method to create a layout pattern (semiconductor blueprint) similar in structure to existing patterns but not previously existing, using generative AI.

 

It was also confirmed that applying the newly created layout patterns and the existing sample patterns to the optimization improves the accuracy of the machine learning model.

The research results were published in the 2021 international academic journal IEEE TSM, and it also received the journal’s ‘Best Paper Award,’ which is selected once a year.

 

Professor Shin commented, “This study is unique in that it applies machine learning and artificial intelligence differently from existing semiconductor lithography research,” and added, “I hope it can contribute to resolving the issues of licensing costs and stagnation in technological development caused by the monopoly of a small number of companies worldwide.”

 

 

*Reference : 10월 과기인상에 신영수 교수…AI로 반도체 공정 최적화 (naver.com)

Professor Yoon Young-Gyu’s research team develops AI imageing analysis technology ”SUPPORT” which enables high-precision measurement of biological fluorescence signals

Professor Yoon Young-Gyu’s research team develops AI imageing analysis technology ”SUPPORT” which enables high-precision measurement of biological fluorescence signals

 

연구팀

< (From the left) Professor Young-Gyu Yoon from the School of Electrical Engineering, Ph.D. student Minho Eom, and Ph.D. student Seungjae Han.>

 
KAIST (President Kwang-Hyung Lee) announced on the 19th that a research team led by Professor Young-Gyu Yoon from the School of Electrical Engineering has developed an AI imaging analysis technology that can measure biological fluorescence signals with over 10 times the precision of existing technologies.
 
With the recent advancement of genetic engineering technology, it has become possible to convert various biological signals, such as specific ion concentrations or voltages within living biological tissues, into fluorescence signals. Technologies that utilize fluorescence microscopy to capture time-lapse images of biological tissues and rapidly measure these signals have been developed and are in use.
 
However, because the fluorescence signals emitted from biological tissues are weak, measuring rapidly changing signals results in a very low signal-to-noise ratio, making precise measurements difficult. In particular, the accuracy of measurements becomes extremely low when measuring signals that change on a millisecond scale, such as the action potentials of neurons.
 
In response to this technical challenge, Professor Yoon’s research team developed an AI image analysis technology that enables measurements with over 10 times the precision of existing technologies.
 
This technology can autonomously learn the statistical distribution of data from fluorescence microscope images with a low signal-to-noise ratio and improve the signal-to-noise ratio of the images by more than tenfold even without the use of training data.
 
Utilizing this method, the measurement precision of various biological signals can be significantly enhanced. It is anticipated that this technology will be broadly applicable in the overall field of biological sciences and in the development of treatments for brain disorders.
 
Professor Yoon stated, “We named this technology SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa) in the hope that it will support various neuroscience and biological science research.”
He added, “This is a technology that researchers using various fluorescence imaging devices can easily utilize without the need for separate training data. It has the potential to be broadly applied in uncovering new biological phenomena.”
 
Co-first author Minho Eom stated, “Through SUPPORT, we succeeded in precisely measuring rapid changes in biological signals that were difficult to observe. In particular, it’s now possible to optically measure the action potentials of neurons that change on a millisecond scale, which will be very useful for neuroscience research.” Co-first author Seungjae Han added, “While SUPPORT was developed for precise measurements of biological signals within fluorescence microscopy images, it can also be widely used to enhance the quality of general time-lapse images.”
 
This technology was developed under the supervision of Professor Young-Gyu Yoon’s team from the School of Electrical Engineering at KAIST, in multidisciplinary and multinational collaboration with researchers from the Department of Materials Science Engineering at KAIST (Professor Jae-Byum Chang), the Graduate School of Medical Science and Engineering at KAIST (Professor Pilhan Kim), Chungnam National University, Seoul National University, Harvard University, Boston University, the Allen Institute, and Westlake University.
 
This research was conducted with the support of the National Research Foundation of Korea and was published online in the international journal “Nature Methods” on September 19th. It was also selected as the cover article for the October issue.
 
1. Fluorescence signal: The brightness of light (fluorescence) changes in proportion to specific biological signal variations.
2. Timelapse: A video that continuously captures the subject at regular intervals.
 
 
AI영상분석기술 1

Figure 1. Concept of SUPPORT technology:

(a) For each pixel in the image, the artificial neural network removes noise without separate training data by utilizing the surrounding pixel information within the current frame and information from adjacent frames.

(b) Impulse response of the designed artificial neural network.

 

2

Figure 2. Ultra-precise neural cell voltage measurement using SUPPORT:

(Top) In the original fluorescence image, it’s impossible to observe the action potentials of neurons due to the low signal-to-noise ratio.

(Bottom) By enhancing the signal-to-noise ratio using SUPPORT, it is possible to precisely observe the action potentials of each neural cell.

 

3

Figure 3. Improvement of in vivo ear tissue fluorescence images of mice using SUPPORT:

(Left) In the original fluorescence image, it’s impossible to observe the detailed structure of the tissue due to the low signal-to-noise ratio.

(Right) By enhancing the signal-to-noise ratio using SUPPORT, it is possible to observe the detailed structure and rapidly moving red blood cells.

 

 

 
5 1

Figure 4. Improvement of in vivo muscle tissue fluorescence images of mice using SUPPORT:

(Left) In the original fluorescence image, it’s impossible to observe the detailed structure of the tissue due to the low signal-to-noise ratio.

(Right) By enhancing the signal-to-noise ratio using SUPPORT, it is possible to observe the detailed structure of muscle fibers and rapidly moving red blood cells.