• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.3
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• pp.113-121
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• 2019

Algorithms for geographical routing and location update attract the researchers' interests in wireless ad hoc networks. Even though many various schemes have been proposed, most of them cause scalability problem in small groups of nodes. To defeat this problem, flooding algorithm is widely utilized due to low complexity. However, there is no previous research work to evaluate flooding algorithm through implementation instead of simulation. In this paper, we present implementation of flooding algorithm on Raspberry Pi and performance evaluation results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.84-87
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• 2018

Handwritten letter recognition is important for numerous real-world applications and many topics like human-machine interaction, education, entertainment, and more. This paper describes the implementation of a real-time handwritten letters recognition system on a common embedded computer. Recognition is performed using a customized convolutional neural network, which was designed to work with low computational resources such as the Raspberry Pi platform. The experimental results show that the proposed real-time system achieves an outstanding performance in the accuracy rate and the response time for recognition of twenty-six handwritten letters.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.130-134
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• 2020

This paper proposes a new method for the digit recognition on a 7-segment display. The proposed method uses morphological processing that dilates segments of digits and connects them into strokes. The digits are extracted by connected component analysis and finally, template matching method recognizes the extracted digits. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. Experiments were conducted by using various 7-segment LED displays and 7-segment mono LCD displays. The results show that the proposed method is successful for the digit recognition on the 7-segment displays.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1742-1745
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• 2019

Hidden flame detection after allegedly extinguishing a fire cannot be emphasized enough. There are a few commercial hidden fire detection equipments which are imported, but the cost is relatively high. In this contribution, we propose a development of a low-cost, high-performance hidden flame detector using open-source hardware/software. We use Raspberry-pi based hardware board equipped with a TFT touch-screen LCD, a 3G modem, and an attachable battery device altogether integrated in a plastic case fabricated with a 3D printer. The proposed hidden flame detector shows the same performance of a commercial product FLIR E5 while consuming less than a half of the cost.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Journal of Korea Game Society
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• v.21 no.1
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• pp.91-98
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• 2021

In this paper, we propose the system that simultaneously controls objects in virtual reality and objects in real environments using brain waves. We propose a system that measures brain waves to grasp the user's concentration and quantifies them to raise or lower virtual and real objects. We implemented a web-based virtual reality system and an embedded system based on a raspberry pi for test of design. It was confirmed that the control of virtual and real objects is possible using BCI. The result was that it was possible to develop various contents using this.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.22-26
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• 2022

This paper proposes a new method for the recognition of road surface marks and numbers. The proposed method designates a region of interest on the road surface without first detecting a lane. The road surface markings are extracted by location and size using a connection component analysis. Distortion due to the perspective effect is minimized by normalizing the size of the road markings. The road surface marking of the connected component is recognized by matching it with the stored road marking templates. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the recognition of road surface marks and numbers.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.85-97
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• 2020

As a solution to the rapid urbanization problem, interest in and use of personal mobility devices such as bicycles and electric kickboards are increasing, and accidents are also rising every year. In this study, a smart vest to prevent traffic accidents and promote safe driving of personal mobility users was designed and implemented. Unlike the existing products on the market that indicate directions using buttons, the smart vest designed and implemented in this study indicates the moving direction by shaking a bracelet and displays the direction by LEDs on the vest. Therefore, this product was designed and implemented to be safer and differentiated from other existing products.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.218-221
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• 2022

본 논문은 라즈베리 파이(Raspberry Pi) 아두이노(Arduino)을 이용하여 무단 침입자를 스마트폰을 통해 실시간으로 스트리밍 되는 영상을 통해 확인하고, 즉각적으로 신고할 수 있는 홈 케어 방범 IoT 시스템 구축에 대한 내용을 다룬다. 이는 1인 가구 및 비어있는 원룸 등의 무단 침입을 방지하고 범죄를 예방하는데 큰 도움이 될 것으로 기대한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.260-263
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• 2021

코로나19로 인해 출입 관련 시스템도 비대면으로 변화하고 있다. 변화에 맞추어 비대면으로 출입자를 관리할 수 있는 프로그램을 개발하여 접촉 위험을 줄이고 출입자 모니터링에 실용성을 제공하고자 한다. 본 연구에서는 Raspberry Pi 카메라에 Alchera Face Authentication API를 적용하여 얼굴인식을 실시하며 정보를 AWS 클라우드에서 저장·관리 하는 시스템을 개발하였다. 챗봇 서비스를 통해 출입자를 확인할 수 있으며 메신저에서 쉽게 클라우드에 접근하여 정보를 확인할 수 있게 하였다. 이를 통해, 특정 장소를 비대면으로 관리하며 간편하게 출입자를 모니터링할 수 있을 것으로 기대한다.