• Journal of KIISE:Computing Practices and Letters
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• v.10 no.6
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• pp.435-447
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• 2004

For VOD systems, it is important to provide QoS to more users under the limited resources. To analyze QoS issues in real environment, we implement clustered VOD server and embedded client system based on the Linux open source platform. The parallel processing of MPEG data, load balancing for nodes and VCR like functions are implemented in the server side. To provide more user friendly interface, the general TV is used for a VOD client's terminal and the embedded board is used supporting for VCR functions. In this paper, we measure the performance of the implemented VOD system under the various user requirement features and evaluate the sources of performance limitations. From these analyses, we propose the dynamic admission control method based on the availability memory and network bandwidth. The proposed method enhances the utilization of the system resource for the more QoS media streams.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.173-180
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• 2017

In this paper, we propose a project-based learning using Arduino as an example of embedded system class in engineering students. By introducing these Project-Based Learning(PBL) into engineering education, students became able to actualize individual theories that they had learned through their major curriculum and they were given the experience to build up their field work ability by participating in the whole project development process. We conducted a questionnaire survey to investigate the education effect of PBL before and after class and the results were analyzed using SPSS statistical program. Since PBL is mainly operated by a team system, communication skills and teamwork within the organization can be improved through interactions among the members. All of the materials produced during the course of the project could be used to make portfolio of students, which could be of great help to data for employment activities after graduation.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.381-392
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• 2021

In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.459-465
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• 2022

This paper reports a method to use a wireless sensor network deployed in the field to real-time monitor soil moisture, warning when the moisture level reaches a specific value, and wirelessly controlling an additional device (LED or water supply system, etc.). In addition, we report all processes related to wireless irrigation system, including field deployment of sensors, real-time monitoring using a smartphone, data calibration, and control of additional devices deployed in the field by smartphone. A commercially available open-source Internet of Things (IoT) platform, NodeMCU, was used, which was combined with a 9V battery, LED and soil humidity sensor to be integrated into a portable prototype. The IoT-based soil humidity sensor prototype deployed in the field was installed next to a tree for on-site demonstration for the measurement of soil humidity in real-time for about 30 hours, and the measured data was successfully transmitted to a smartphone via Wifi. The measurement data were automatically transmitted via e-mail in the form of a text file, stored on the web, followed by analyses and calibrations. The user can check the humidity of the soil real-time through a personal smartphone. When the humidity of a soil reached a specific value, an additional device, an LED device, placed in the field was successfully controlled through the smartphone. This LED can be easily replaced by other electronic devices such as water supplies, which can also be controlled by smartphones. These results show that farmers can not only monitor the condition of the field real-time through a sensor monitoring system manufactured simply at a low cost but also control additional devices such as irrigation facilities from a distance, thereby reducing unnecessary energy consumption and helping improve agricultural productivity.

• Journal of Ocean Engineering and Technology
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• v.36 no.5
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• pp.313-325
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• 2022

Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10^-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.542-558
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• 2023

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure; however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram; however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration; the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.31-38
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• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.3
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• pp.240-247
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• 2001

Java is a promising runtime environment for embedded systems because it has many advantages such as platform independence, high security and support for multi-threading. One of the most famous Java run-time environments, Sun's ($PersonalJave^{TM}$) is based on Truffle architecture, which enables programmers to design various GUIs easily. For this reason, it has been ported to various embedded systems such as set-top boxes and personal digital assistants(PDA's). Basically, Truffle uses heavy-weight window managers such as Microsoft vVin32 API and X-Window. However, those window managers are not adequate for embedded systems because they require a large amount of memory and disk space. To come up with the requirements of embedded systems, we adopt Microwindows as the platform graphic system for Personal] ava A WT onto Embedded Linux. Although Microwindows is a light-weight window manager, it provides as powerful API as traditional window managers. Because Microwindows does not require any support from other graphics systems, it can be easily ported to various platforms. In addition, it is an open source code software. Therefore, we can easily modify and extend it as needed. In this paper, we implement Personal]ava A WT using Microwindows on embedded Linux and prove the efficiency of our approach.