• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.4
• /
• pp.79-87
• /
• 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
• /
• v.17 no.5
• /
• pp.181-186
• /
• 2017

IEC 61850 is a standard for power utility automation. Using IEC 61850 that uses ethernet may consume more costs for the automation than its value in small distribution substations. Thus, less expense and installation cost are required for the automation of small distribution substations. This study used inexpensive and easy-to-install IEEE 802.15.4 and implemented a bridge between IEC 61850 and IEEE 802.15.4, using Raspberry Pi to connect the existing IEC 61850. Using IEEE 1588, IEC 61850 traffic performances were evaluated, such as SV, GOOSE and MMS. Analyzing IEC 61850 requirements and performance evaluation results, the scope of application of IEEE 802.15.4 was decided.

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

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.458-463
• /
• 2023

This study presents a paper on the implementation of a Raspberry Pi-based educational smart farm system. It confirms that in a real smart farm environment, the control of temperature, humidity, soil moisture, and light intensity can be smoothly managed. It also includes remote monitoring and control of sensor information through a web service. Additionally, information about intruders collected by the Pi camera is transmitted to the administrator. Although the cost of existing smart farms varies depending on the location, material, and type of installation, it costs 400 million won for polytunnel and 1.5 billion won for glass greenhouses when constructing 0.5ha (1,500 pyeong) on average. Nevertheless, among the problems of smart farms, there are lax locks, malfunctions to automation, and errors in smart farm sensors (power problems, etc.). We believe that this study can protect crops at low cost if it is complementarily used to improve the security and reliability of expensive smart farms. The cost of using this study is about 100,000 won, so it can be used inexpensively even when applied to the area. In addition, in the case of plant cultivators, cultivators with remote control functions are sold for more than 1 million won, so they can be used as low-cost plant cultivators.

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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.4
• /
• pp.433-440
• /
• 2016

In order to construct a system for big data processing, one needs to configure the node by using network equipments to connect multiple computers or establish cloud environments through virtual hosts on a single computer. However, there are many restrictions on constructing the big data analysis system including complex system configuration and cost. These constraints are becoming a major obstacle to professional manpower training for big data areas which is emerging as one of the most important national competitiveness. As a result, for professional manpower training of big data areas, this paper proposes a Raspberry Pi Board based educational big data processing system which is capable of practical training at an affordable price.

• Journal of Digital Convergence
• /
• v.14 no.4
• /
• pp.251-258
• /
• 2016

Big data processing is a broad term for processing data sets so large or complex that traditional data processing applications are inadequate. Widespread use of smart devices results in a huge impact on the way we process data. Many organizations are contemplating how to incorporate or integrate those devices into their enterprise data systems. We have proposed a way to process big data by way of integrating Raspberry Pi into a Hadoop cluster as a computational grid. We have then shown the efficiency through several experiments and the ease of scaling of the proposed system.

• Journal of Information Processing Systems
• /
• v.14 no.4
• /
• pp.989-1009
• /
• 2018

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed 'big data'. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.

• Journal of Multimedia Information System
• /
• v.4 no.4
• /
• pp.239-242
• /
• 2017

This paper proposes a low-cost method for counting people based on blob detection and blob tracking. Here background subtraction is used to detected blob and then the blob is classified with its width and height to specify that the blob is a person. In this system we first define the area of entry and exit point in the video frame. The counting of people starts when midpoint of the people blob crosses the defined point. Finally, total number of people entry and exit from the place is displayed. Experiment result of this proposed system has high accuracy in real-time performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2021.06a
• /
• pp.283-285
• /
• 2021

본 논문에서는 수중에서 선박 하부에 붙은 오손생물(fouling organism)을 탐지하고 격자지도(grid map)로 나타내는 시스템을 제안한다. 제안하는 시스템은 소나(sound navigation and ranging: SONAR) 센서와 오손생물사이의 시간 데이터를 수집한 후, 라즈베리 파이 4(raspberry pi 4)에서 수집된 데이터를 이용해 격자지도에 맵핑(mapping)함으로써, 선저하부의 상태를 파악하는데 도움을 줄 수 있다. 본 논문에서는 제안된 지도 시스템을 이용하여 선박 하부에 붙은 오손생물의 분포를 확인할 수 있다.