• Journal of Engineering Education Research
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• v.26 no.1
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• pp.3-11
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• 2023

The purpose of this study is to develop Raspberry Pi-based CanSat instruction materials for liberal arts classes to be used in university space education. The educational satellite simulation program is developed by applying the ADDIE program consisting of analysis, design, development, execution, and evaluation of 15 lessons per semester. The usefulness of the instruction materials is evaluated by a validity test of a total of 6 experts. The proposed materials are applied to 100 college students from various majors. To analyze the impact on creative problem-solving ability, a questionnaire is conducted before and after class, and as a result, it is confirmed that there is a significant improvement in all areas after class. The class satisfaction survey is conducted for a total of 10 questions, and the average score is 4.41 out of 5, which is high. In conclusion, the proposed instruction materials make it possible to achieve successful space education using Raspberry Pi and improve creative problem-solving ability in universities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.202-204
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• 2021

The Internet of Things (IoT) is a term first used by Kevin Ashton, director of MIT's Auto ID Center, in 1999, and refers to the connection of things to the Internet. Bluetooth, one of the wireless networks, is a technology developed as an industry standard for personal short-range wireless communication first developed by Ericsson in 1994. Since the Raspberry Pi has Bluetooth built-in, a wireless network is possible. This paper implements a module for the Internet of Things by implementing serial communication in the Bluetooth built-in Raspberry Pi 3 B+. There was a problem that Bluetooth communication was impossible between raspberries, so the discovery function was activated and serial communication was added to successfully link the communication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.637-639
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• 2021

As autonomous driving research is actively progressing, lane detection is an essential technology in ADAS (Advanced Driver Assistance System) to locate a vehicle and maintain a route. Lane detection is detected using an image processing algorithm such as Hough transform and RANSAC (Random Sample Consensus). This paper implements a linear shape detection algorithm using OpenCV on Raspberry Pi 3 B+. Thresholds were set through OpenCV Gaussian blur structure and Canny edge detection, and lane recognition was successful through linear detection algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.1-9
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• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1172-1179
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• 2015

A wireless license plate region extracting module is proposed for LPR system controlling multiple gates. This module is cheaply implemented using Raspberry Pi which is open source and high performance. First, as the upper 1/3 of the captured image is discarded as it has no useful information on license plate. Using the OpenCV libraries the edge image is got by Canny algorithm after applying Gaussian filtering to gray image, and the labeling is conducted for 4 consecutive numbers in license plate. These numbers are located using various decision equations, and expanding the numbers region the final license plate region can be extracted. The result image is transferred to Server using wifi direct. Using the proposed module it becomes easy to set up and maintain the LPR system. The experimental results showed that the successful extracting rate was 98.4% using 500 car images with 640 × 480 resolution.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.868-871
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• 2016

Recently, Unmanned vehicle and Wearable Technology using iot research is being carried out. The unmanned vehicle is the result of it technology. Robots, autonomous navigation vehicle and obstacle avoidance, data communications, power, and image processing, technology integration of a unmanned vehicle or an unmanned robot. The final goal of the unmanned vehicle manual not autonomous by destination safely and quickly reaching. This paper managed to cover One of the key skills of unmanned vehicle is to image processing. Currently battery technology of unmanned vehicle can drive for up to 1 hours. Therefore, we use the Raspberry Pi 3 to reduce power consumption to a minimum. Using the Raspberry Pi 3 and to develop an image recognition system. The goal is to propose a system that recognizes all the objects in the image received from the camera.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1193-1198
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• 2022

As unmanned processing systems emerged socially due to the rapid development of modern society, a face recognition attendance management system using Raspberry Pi 4 was studied and conceived to automatically analyze and process images and produce meaningful results using OpenCV. Based on Raspberry Pi 4, the software is designed with Python 3 and consists of technologies such as OpenCV, Haarcascade, Kakao API, and Google Drive, which are open sources, and can communicate with users in real time through Kakao API for face registration and face recognition.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.239-244
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• 2019

With the development of Cyber-Physical Systems(CPS), several technologies such as automation control, automotive and intelligent house systems have been developed. To enable communication among various components of such systems, several wired and wireless communication protocols are used. The Controller Area Network(CAN) is one of such wired communication protocols that is popularly used for communication in automobiles and other machinery in the industry. In this paper, we designed and implemented a response time analysis system for CAN communication. The reliable data transfer among various electronic components in a significant time is crucial for the smooth operation of an electric vehicle. Therefore, this system is designed to conveniently analyze the response time of various electronic components of a CAN enabled system. The priority for transmission of the messages in the CAN bus is determined by the message identifier. As the number of nodes increases the transmission of low priority messages is delayed due to the existence of higher priority messages on the bus. We used Raspberry Pi3 and PiCAN2 board to simulate the data transfer for studying the comparative delay in low priority nodes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.151-157
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• 2018

This paper presents an implementation of a smart plant pot which can supply light and water automatically according to the result of detection on current temperature, humidity and illumination, and deliver the images of the plant realtime by using a camera installed in the pot. We designed a container of the plant pot divided into five layers, printed each of them with a 3D printer, and then assembled them. Inside of the container, we installed sensors, a pump, and a camera. We developed an Android application so that the user can control the plant pot and monitor its state. In communication between the Android application and the Raspberry Pi, MQTT protocol was utilized.