• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2006-2013
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• 2015

Deaf are it is difficult to communicate to represent the voice heard, so theay use mostly using the speech, sign language, writing, etc. to communicate. It is the best way to use sign language, in order to communicate deaf and normal people each other. But they must understand to use sign language. In this paper, we designed and implementated finger language translation system to support communicate between deaf and normal people. We used leap motion as input device that can track finger and hand gesture. We used raspberry pi that is low power sing board computer to process input data and translate finger language. We implemented application used Node.js and MongoDB. The client application complied with HTML5 so that can be support any smart device with web browser.

• Smart Media Journal
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• v.7 no.2
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• pp.40-46
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• 2018

The conventional CCTV surveillance system for preventing accidents and incidents misses 95% of the data after 22 minutes where one person monitors multiple CCTV. To address this issue, researchers have studied the computer-based intelligent video surveillance system for notifying people of the abnormal situation. However, because the system is involved in the problems of power consumption and costs, the intelligent video surveillance system based on embedded modules has been studied. This paper implements the intelligent video surveillance system based on embedded modules for detecting intruders, detecting fires and detecting loitering, falling. Moreover, the algorithm and the embedded module optimization method are applied to implement real-time processing. The intelligent video surveillance system based on embedded modules is implemented in Raspberry Pi. The algorithm processing time is 0.95 seconds on Raspberry Pi before optimization, and 0.47 seconds on Raspberry Pi after optimization, reduced processing time by 50.52%. Therefore, this suggests real processing possibility of the intelligent video surveillance system based on the embedded modules is possible.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.97-110
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• 2019

Recently, the 4th Industrial Revolution has been emerged and various products are developed and commercialized in preparation of the communication failure. Many solutions are underway in Back-Up Network for IDC Servers, but not in the personal or sensor for low-power system use. Therefore we used the OpenWRT Firmware in Raspberry Pi which can be easily obtained in online market, and it created a low-power load balancer. Therefore, we developed the device that uses LTE Antenna based on USB Interface for communication fault notification and important data. The equipment used in this paper is easy to buy in online shop for anyone. Also, it can be applied in other vendors' boards by using USB. We hope that this paper will contribute to the stability of individual sensor networks.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.426-439
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• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.547-554
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• 2022

Recently, the field of telemedicine is growing rapidly due to the COVID-19 pandemic. However, the cost of telemedicine services is relatively high, since cloud computing, video conferencing, and cyber security should be considered. Therefore, in this paper, we design and implement a cost-effective P2P-based telemedicine system. It is implemented using the widely used the open source computing platform, Raspberry Pi, and P2P network that frees users from security problems such as the privacy leakage by the central server and DDoS attacks resulting from the server/client architecture and enables trustworthy identifying connection system using SSL protocol. Also it enables users to check the other party's status including body temperature in real time by installing a thermal imaging camera using Raspberry Pi. This allows several medical diagnoses that requires visual aids. The proposed telemedicine system will popularize telemedicine service and meet the ever-increasing demand for telemedicine.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.125-132
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• 2022

In this paper, we presented a cart which can automatically transport loads to the distribution center of the appointed indoor place, based on Raspberry pi 4. It can recognize the obstacles by using the ultrasonic sensors so that it prevents the collision and takes a detour. Further, we entered the direction control code in the RFID. It has installed at important points such as the intersections of the destinations, so that if the RFID reader of the cart senses the RFID, the cart would stop or change the direction. After the transportation, if the load cell(weight sensor) recognizes that the baggage is unloaded, the cart returns to the initial point and would be retrieved. Therefore, we embodied the transportation cart which reduces the use of manpower and solves the problems conveniently across the transportation strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1935-1941
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• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.508-510
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• 2021

In this study, the difference in resilience was analyzed based on the results of a study of classes that practiced using Raspberry Pi, which had to cross school due to temporarily conducting online classes due to the COVID-19 pandemic. As a result of the study, in online classes, 14 people had resilience less than 150, 32 people who had 150 or more and less than 180, and 9 people who had 180 or more. On the other hand, in the school attendance class, there were 7 people with resilience less than 150, 29 people with resilience less than 150 and less than 180, and 20 people with more than 180. Therefore, in subjects where programming using Raspberry Pi and circuit manufacturing are taught at the same time, the laboratory and practice environment should be able to proceed properly as much as possible. should proceed mainly.