• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.786-791
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• 2009

Recently, digital camera module has been extensively utilized in mobile devices. The digital camera module should be smaller and lighter than digital still camera module to be used in mobile device. But, mobile camera can't get high quality image as good as the one of digital still camera due to the optical limitation of minimized module. Especially, AE system of mobile camera occurs excessive hunting and oscillation due to miniaturization of module. In this paper, improved AE algorithm which is applied fuzzy PI control is suggested to compensate this point.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.679-686
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• 2022

In this paper, we implemented an algae removal device that recognizes and removes algae existing in water using Raspberry Pi camera and DO (Dissolved Oxygen) sensor. The Raspberry Pi board recognizes the color of green algae by converting the RGB values obtained from the camera into HSV. Through this, the location of the algae is identified and when the amount of dissolved oxygen's decrease at the location is more than the reference value using the DO sensor, the algae removal device is driven to spray the algae removal solution. Raspberry Pi's camera uses OpenCV, and the motor movement is controlled according to the output value of the DO sensor and the result of the camera's green algae recognition. Algae recognition and spraying of algae removal solution were implemented through Arduino and Raspberry Pi, and the feasibility of the proposed portable algae removal device was verified through experiments.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.34-38
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• 2020

In this paper, we implement a Dashcam system capable of shooting 360 degrees using a Raspberry Pi, shock sensors, distance sensors, and rotating camera with a servo motor. If there is an object approaching the vehicle by the distance sensor, the camera rotates to take a video. In the event of an external shock, videos and images are stored in the server to analyze the cause of the vehicle's accident and prevent the user from forging or tampering with videos or images. We also implement functions that transmit the message with the location and the intensity of the impact when the accident occurs and send the vehicle information to an insurance authority with by linking the system with a smart device. It is advantage that the authority analyzes the transmitted message and provides the accident handling information giving the user's safety and convenience.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.393-399
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• 2018

As the existing face recognition CCTV and home IOT have each individual hardware component, they have a disadvantage that the measured results of their sensors and the CCTV can not be viewed on one screen at a time. In order to overcome the above disadvantages of existing CCTV and home IOT, this paper proposes an integrated platform which constitutes the CCTV and home IOT as one hardware component using Raspberry Pi and shows each result on one screen through Smartphone application. The proposed integrated platform CCTV and home IOT system is a system which can run the application as a Smartphone and check the sensor value measured by Raspberry Pi and the picture taken through the Pi camera. The implemented system measures temperature, humidity, gas, and dust, and implements face recognition technology on a screen shot through a Pi camera, allowing it to be seen at a glance with a Smartphone.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.538-539
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• 2018

This paper summarizes the implementation of lane recognition using OpenCV, one of the open source computer vision libraries. The Linux operating system Rasbian(r18.03.13) was installed on the ARM processor-based Raspberry Pi 3 board, and Raspberry Pi Camera was used for image processing. In order to realize the lane recognition, Canny Edge Detection and Hough Transform algorithm implemented in OpenCV library was used and RANSAC algorithm was used to prevent shaking of vanishing point and to detect only the desired straight line. In addtion, the DC motor and the Servo motor were controlled so that the vehicle would run according to the detected lane.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.79-87
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• 2014

In this paper, we implemented the HearCAM platform with Raspberry PI B+ model which is an open source platform. Raspberry PI B+ model consists of dual step-down (buck) power supply with polarity protection circuit and hot-swap protection, Broadcom SoC BCM2835 running at 700MHz, 512MB RAM solered on top of the Broadcom chip, and PI camera serial connector. In this paper, we used the Google speech recognition engine for recognizing the voice characteristics, and implemented the pattern matching with OpenCV software, and extended the functionality of speech ability with SVOX TTS(Text-to-speech) as the matching result talking to the microphone of users. And therefore we implemented the functions of the HearCAM for identifying the voice and pattern characteristics of target image scanning with PI camera with gathering the temperature sensor data under IoT environment. we implemented the speech recognition, pattern matching, and temperature sensor data logging with Wi-Fi wireless communication. And then we directly designed and made the shape of HearCAM with 3D printing technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.157-163
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• 2016

This paper presents Finger Keyboard which detects the user's key types on a keyboard drawn on the paper using a video camera. The Finger Keyboard software was written in standard C/C++ language and thus easy to port to other computing environments. We installed a popular USB-type web camera on a Windows PC and implemented the Finger Keyboard as a Windows application which detects key typing and then injects the key code into the message queue of the Windows operating system. Also we implemented the Finger Keyboard on the Raspberry Pi 2 embedded computer with a dedicated camera and connected it to the Android device as an external keyboard through the Bluetooth. The result of experiments showed that the average ratio of recognition success is around 80% at the typing speed of 120 characters per minute.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.2
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• pp.75-81
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• 2018

In this paper for developing and implementing the car driver's front lane attention enhancement developed system using the car camera. The developed system automatically alarm the car driver when front cars make the dangerous situation. We use Raspberry Pi camera module V2 as car camera module, Raspberry Pi 3 board as hardware main board of implementing embedded system and develop the application library module which can be operated on the Raspberry situation. The application library module widely consist of two part, front car recognition part and dangerous situation distinguish part. Our developed system satisfy the performance test of the target system at the software test certification laboratory of TTA(Telecommunication Technology Association). We test four items as attentive car recognition ability at day and night, system performance, response time. We get the performance of developed system based on the four goal. The car driver's front lane attention enhancement system in this paper will be widely used at the ADAS(Advanced Driving Assistance System) because of the better performance and function.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.41-43
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• 2021

Robot Operating System (ROS) has been a prominent and successful framework used in robotics business and academia.. However, the framework has long been focused and limited to navigation of robots and manipulation of objects in the environment. This focus leaves out other important field such as speech recognition, vision abilities, etc. Our goal is to take advantage of ROS capacity to integrate additional libraries of programming functions aimed at real-time computer vision with a depth-image camera. In this paper we will focus on the implementation of an upgraded vision with the help of a depth camera which provides a high quality data for a much enhanced and accurate understanding of the environment. The varied data from the cameras are then incorporated in ROS communication structure for any potential use. For this particular case, the system will use OpenCV libraries to manipulate the data from the camera and provide a face-detection capabilities to the robot, while navigating an indoor environment. The whole system has been implemented and tested on the latest technologies of Turtlebot3 and Raspberry Pi4.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.227-236
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• 2018

The focus control mechanism of the multi-purpose camera can be required for the better quality image acquisition. A good image acquisition through the hardware system including the optics and image sensor, has to be processed before the post correction for improvement of image quality. In the case of the high-resolution satellite camera, the focus control is not a necessity, unlike a normal camera due to a fixed optical system, but may be required due to various reasons. Although there is a basic focus control method using a motor for satellite electronic optical camera, a focus control method using thermal control can be a good alternative because of its various advantages in design, installation, operation, contamination, high reliability and etc. In this paper, we describe the design method and implementation results for the focus control mechanism using the temperature sensor and heater installed in the telescope structure. In the proposed focus control method, the measured temperature information is converted into temperature data by the Kalman filter and the converted temperature data are used in PI controller for the thermal focus control.