• Journal of rehabilitation welfare engineering & assistive technology
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• v.1 no.1
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• pp.1-6
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• 2007

Applying the gyroscopic technology, HeadZmouse has been developed to simulate left and right mouse click, double click, drag and drop, and even a wheel function for navigating web. This device was designed to work on both PC and Macintosh environments using a USB cable. The first time you use this device, you'll find out how much freedom it offers to someone who can't use his or her hands freely. Rather than being tied to your computer, simple manipulation such as blowing an air (breathing) into a sonic sensor can simulate all the functions which standard mouse has, even including a wheel function. Also, a macro-interface device has been developed. By storing repetitive tasks into a memory, you can carry out repetitive tasks just by clicking a button once.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.39-42
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• 2017

Communication between human and machine is having been researched from last few decades and still it's a challenging task because human behavior is unpredictable. When it comes on handwritten digits almost each human has their own writing style. Handwritten digit recognition plays an important role, especially in the courtesy amounts on bank checks, postal code on mail address etc. In our study, we proposed an efficient feature extraction system for recognizing single digit number drawn by mouse or by a finger on a screen. Our proposed method combines basic image processing and reading the strokes of a line drawn. It is very simple and easy to implement in various platform as compare to the system which required high system configuration. This system has been designed, implemented, and tested successfully.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1821-1823
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• 2006

This paper presents a development of interface device for electro-oculogram(EOG) signal and it's application to the wireless mouse of wearable PC. The interface device is composed of five bio-electrodes for detecting oculomotor motion, several band-pass filters, instrumentation amplifier and a microprocessor. we have first analyzed impedance characteristics between skin and a bio-electrode. since the impedance highly depends on human face, it's magnitude differs from person. this interface device was applied to develop a wireless mouse for wearable PC, as a Bio Machine Interface(BMI). Where in the prompt on PC monitor is controlled by only EOG signals. this system was implemented in a Head Mount Display(HMD) unit. experimental results show the accuracy of above 90%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.177-178
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• 2018

키보드 정보가 노출되는 취약점이 발견되면서 키보드를 통하여 아이디 및 비밀번호를 입력하는 인증의 보안성 결여 문제가 대두되었다. 이를 대응하기 위하여 마우스를 통하여 비밀번호를 입력하는 이미지 기반 인증이 등장하였으며, 이 인증방식은 인터넷 뱅킹 및 결제 서비스와 같이 중요도가 높은 서비스에 도입되어 사용자가 입력하는 비밀번호를 안전하게 보호한다. 하지만 키보드와 동일하게 사용자가 입력하는 마우스 데이터가 노출되는 취약점이 발견되고 있으며, 본 논문에서는 WM_INPUT 메시지를 활용하여 노출되는 마우스 데이터를 보호하는 방안을 제시한다. 제시하는 방안은 WM_INPUT 메시지를 활용하는 공격을 효과적으로 방지하며, 이를 통하여 이미지 기반 인증방식의 안전성을 강화할 수 있을 것으로 사료된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.447-450
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• 2014

In this paper efficient design of wireless tablet system using GalaxyNote device for PC was proposed. The designed portable tablet consists of GalaxyNote device, Stylus Pen, and Bluetooth-to-serial converter. To transmit coordinate information of Stylus Pen on GalaxyNote device to PC, wireless portable tablet uses bluetooth wireless communications. After the custom mouse filter driver divides received coordinate into x-coordinate and y-coordinate, it controls position of mouse pointer using the converted coordinates while Windows application programs are running.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.275-276
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• 2023

본 논문은 Windows 플랫폼에서 마우스 움직임 데이터를 수집하고 이를 웹사이트와 통합하여 시각화하는 서비스의 개발을 제시한다. 이 서비스는 마우스 움직임 데이터의 수치화와 표현을 가능하게 하여 사용자 행동에 대한 유용한 통찰력을 제공한다. 윈도우를 기반으로 마우스 움직임 데이터를 수집하여 데이터베이스에 데이터를 저장한다. 데이터베이스에 저장된 데이터는 실시간으로 연동하여 시각화하여 웹사이트를 통하여 보여준다. 본 연구를 통해 무의미하게 여겨질 수 있는 데이터를 사용자에게 의미있게 전환하여 보여주기 위한 시도를 하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1087-1104
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• 2015

An eye location based head posture recognition method is proposed in this paper. First, face is detected using skin color method, and eyebrow and eye areas are located based on gray gradient in face. Next, pupil circles are determined using edge detection circle method. Finally, head postures are recognized based on eye location information. The proposed method has high recognition precision and is robust for facial expressions and different head postures, and can be used in mouse operation. The experimental results reveal the validity of proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.209-212
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• 2006

This paper presents design and implementation methods of the eye-controlled mouse using the real-time tracking of the three dimensional gazing point. The proposed method is based on three dimensional data processing of eye images in the 3D world coordinates. The system hardware consists of two conventional CCD cameras for acquisition of stereoscopic image and computer for processing. And in this paper, the advantages of the proposed algorithm and test results are described.

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.11-11
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• 2003