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Paper of Ph.D. candidate Seong Tae Kim, Hakmin Lee, Hak Gu Kim (Advisor: Prof. Yong Man Ro) was selected as Robert F. Wagner All-Conference Best Student Paper Award Finalist (Field of Computer-Aided Diagnosis Conference).

The SPIE Medical Imaging Conference is one of the world’s largest conferences on medical imaging. The selected paper is “ICADx: Interpretable computer aided diagnosis of breast masses” as a result of “Interpretable Deep Learning” study.

 

Conference: SPIE Medical Imaging (http://spie.org/conferences-and-exhibitions/medical-imaging&nbsp😉

Date: 2018.02.10 – 2018.02.15

Award: Robert F. Wagner All-Conference Best Student Paper Award Finalist (First place in Computer-Aided Diagnosis Conference)

Place: Marriott Marquis Houston, Houston, Texas, United States

Title of the paper: ICADx: interpretable computer aided diagnosis of breast masses

Authors: Seong Tae Kim, Hakmin Lee, Hak Gu Kim, and Yong Man Ro

Master candidate Sung-Jun Yoon and Ph.D. candidate Yongwoo Kim (Advisor: Prof. Munchurl Kim) received Gold Award (Selected 2 first prize out of 280 published papers) at the “Workshop on Image Processing and Image Understanding (IPIU 2018)” conference held once a year as an academic event in the field of computer vision and image processing.

-Name of the Academic Event: Workshop on Image Processing and Image Understanding (IPIU 2018)

-Event Period: February 7th (Wed), 2018 – February 9th (Fri), 2018

-Place of Event: Maison Glad Jeju Hotel

-Title of Paper: A study on frame rate improvement using hierarchical convolution neural network

-Authors: Sung-Jun Yoon, Yongwoo Kim, Munchurl Kim

Introduction:

In this paper, we propose a frame rate enhancement method using hierarchical convolution neural network. The convolutional neural network is constructed hierarchically, and the convolution operation is performed at the optimal position adaptively, so that the performance is robust against the interpolation of the fast moving object.

As a result, compared with the existing ICCV 2017 “Video Frame Interpolation via Adaptive Separable Convolution” algorithm, the improvement was 0.41dB in terms of peak signal-to-noise ratio (PSNR)

Prof. Sung-yool Choi and Prof. Sang-Hee Park (Dept. of Materials Science and Engineering)’s co-work was published as a front cover paper on January 10 in Advanced Functional Materials.

The subject of this paper is ‘Memristive Logic-in-Memory Integrated Circuits for Energy-Efficient Flexible Electronics’. The Main authors are Ph.D candidate Jang Byeong-cheol (co-author), Ph.D candidate Nam Yoon-yong (co-author), Prof. Sang Hee Park (Coressponding author), and Prof. Sung-yool Choi (Coreesponding author).

 

Journal : Advanced Functional Materials

Title : Memristive Logic-in-Memory Integrated Circuits for Energy-Efficient Flexible Electronics

Authors : Ph.D candidate Jang Byeong-cheol ,  Ph.D candidate Nam Yoon-yong (Dept. of Materials science & Engineering)

Link : http://onlinelibrary.wiley.com/doi/10.1002/adfm.201704725/full

 

 

Professor Jae-Woong Jeong and the Washington University School of Medicine in St. Louis. Northwestern University’s co-work was published in the Front Cover in Small issue of January 25.

The subject of this paper is ‘Ultra small wireless neural implant that can deliver drug and light stimulation to the brain without battery’. The authors are Raza Qazi, visiting researcher of KAIST, and Jae-Woong Jeong (Corresponding author).

 

• Paper: Small

• Title: Miniaturized, Battery-Free Optofluidic Systems with Potential for Wireless Pharmacology and Optogenetics

• Link: http://onlinelibrary.wiley.com/doi/10.1002/smll.201702479/full

Professor Munchurl Kim of our department developed a technology that can convert full HD video into super-high definition 4K UHD video using deep learning technology.

This technology has been implemented in hardware by Deep Convolution Neural Network (DNCC), which is a key techonology in artificial intelligence. It is expected to contribute premium UHD TV, 360 VR, and 4K IPTV in the future by developing algorithms and hardware that can create ultra high resolution 4K UHD screen in 60 frames per second in real time.

This study was lead by Yongwoo Kim and Jae-Seok Choi Ph.D. candidates and they are preparing for patent application.

In recent years, efforts have been made to apply Deep Convolutional Neural Network (DNCC) on image quality improvement research. However, Deep Convolutional Neural Network (DNCC) technology has a high computational complexity, and there is a limit to real-time conversion to ultrahigh-resolution images through small hardware because it needs large memory.

In the conventional frame-by-frame image processing method, it is necessary to use external memory such as DRAM, which causes memory bottleneck and power comsumption due to excessive external memory access when processing image data.

Professor Kim’s team developed an efficient Deep Convolution Neural Network structure that can process data in units of lines instead of frames to implement 4K UHD super resolution at 60 frames per second on small hardware without using external memory.

His team maintained a similar picture quality with only 65% of the filter parameters compared to the fast algorithm based on deep convolution neural networks and software.

This is the first example to implement 60 frames per second 4K UHD super resolution by using hardware.

Professor Kim said, “This research is a very important example of the Deep Convolutional Neural Network that is practically applicable to ultra-high-quality image processing in small hardware. Currently, it can be applied to premium UHD TV and UHD broadcast contents, 360 VR contents, 4K IPTV services.”  

This research was carrier out with the support from the Institute for Information & communications Technology Promotion

 

A study on the physical unclonable function device based on nano-electro mechanical switch, developed by Hwang Gyu-man (Master’s course) from Prof. Choi Yang-kyu’s lab, was published in ACS Nano and at the same has been introduced in Science Daily, phys.org, EurekAlert.

PUF (Physical Unclonable Function), a hardware (HW) -based security technology that solves the structural problems of software-based security solutions and is easily applicable to IoT devices, is attracting attention. However, the existing PUF has a disadvantage that its performance deteriorates due to its surrounding environment. To improve this, PUF which can operate in extreme environment while guaranteeing basic operation characteristics (randomness, uniqueness and repeatability) by using nano-electro mechanical switch was developed. It is expected to be used as a security module for the military or space industry, which is subject to extreme environmental conditions.

 

Journal : ACS Nano

Paper : Nano-electromechanical Switch Based on a Physical Unclonable Function for Highly Robust and Stable Performance in Harsh Environments

Author : Hwang Gyu-man (Master’s course) (Prof. Choi Yang-kyu’s lab)

Link : http://pubs.acs.org/doi/abs/10.1021/acsnano.7b06658