• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.105-111
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• 2017

In this paper, the analysis results are shown about several weights of Weighted K-Nearest Neighbor method, Recently, it is employed for the indoor positioning technologies using WiFi fingerprint which has been actively studied. In spite of the simplest feature, the W-KNN method shows comparable performance to another methods using WiFi fingerprint technology. So W-KNN method has employed in the existing indoor positioning system. It shows positioning error performance according to data preprocessing and weight factor, and the analysis on the weight is very important. In this paper, based on the real measured WiFi fingerprint data, the estimation error is analyzed and the performances are compared, for the case of data processing methods, of the weight of average, variance, and distance, and of the averaging several position of number K. These results could be practically useful to construct the real indoor positioning system.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.13-24
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• 2013

IEEE 802.15.4 network and WiFi network are installed to overlap each other and configured to use adjacent frequency bands in which case the communication service required by applications can not be guaranteed because of randomly increased frame transmission delay and frequent frame transmission failures at nodes in IEEE 802.15.4 network. In this paper, transmission delay model at IEEE 802.15.4 nodes and an experimental system to evaluate the interference from WiFi traffic are described, then elements for the evaluation of interference are measured with the analysis of their characteristics. A sequential method of using medium access layer and physical layer elements of IEEE 802.15.4 protocols is proposed to decide interference from WiFi network. With the proposed method, if an evaluation function having frame transmission failures and transmission delay as variables returns a value greater than a threshold, intensive measurements of wireless channel power are carried out subsequently and the final decision of interference is made by the calculated average channel power. Experimental results of the method show that the decision time is reduced with increased frequency of decision in comparison to an other similar method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.879-888
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• 2017

Recently, the Smart household appliances to provide Smart services have been actively spreaded by providing IoT(Internet of Things). Also, the appliances which Wi-Fi and Bluetooth can directly control are preferred. But until now, the most household appliances are based on IR(InfraRed) and the each procotols of them are different. In this paper, to solve these problems, we suggested the system to control both smart appliances and general appliances by using Smart phone. The system is "Wi-Fi to IR" gateway which receives the Wi-Fi signal to convert to IR signal and transmit it. Also, we made the remote control function in specific brands' appliances possible to most brands without limitation and tried to control not only one remote controller for one TV brand but also for all brand without limitation.

• Journal of IKEEE
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• v.24 no.1
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• pp.46-51
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• 2020

With Wi-Fi and Bluetooth connectivity becoming more common in today's handheld mobile devices, single-chip multi-radio combo-modules, which integrate two or more heterogeneous wireless radios on a single chip, are becoming more and more popular. The key requirement for combo solutions is that the quality of the user experience (QoE) must not be compromised by degrading connectivity performance. Therefore, characterizing and understanding the behaviour of combo-module is of vital importance to ensure this requirement in various environments. In this paper, we investigate the impact of the use of combo-modules on the performance of mobile video streaming over a Wi-Fi network. Our study reveals that the use of combo-modules incurs considerable side effects on QoE for mobile video streaming applications when the Wi-Fi and Bluetooth operate at the same time in the 2.4GHz ISM band. We reveal that rate-based adaptive algorithms, including the most popular adaptive bitrate streaming MPEG-DASH, is more severely affected by this phenomenon than buffer-based adaptive algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.759-762
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• 2015

As a result of the popularization of smart devices, smart phones or the tablets have become necessities in modern society. In this current of the times, the bring-your-own-device environment, in which personally owned smart devices are utilized in life, is attracting attention. In this context, this paper designed a Wi-Fi remote ordering system focused on the BYOD environment. If the Wi-Fi remote ordering system based on the BYOD environment is popularized, positive effects can be expected such as the enhancement of efficiency and the increase of customer benefits. As a typical example, real-time customer requests via smart devices enable the provision of fast services. This paper designed a mobile app for customers' remote ordering and web pages on which member stores can inquire details of orders, manage a menu, and register statistical data on sales. If the Wi-Fi remote ordering system designed by this paper is widely distributed internationally, it is expected that the system will be used usefully by many people.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1879-1888
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• 2015

This paper evaluates the adjacent channel coexistence issues between LAA(License Assisted Access) system and other system (e.g. Wi-Fi) system in 5 GHz unlicensed spectrum. LAA is a technology to achieve enhanced data rate by aggregating licensed and unlicensed spectrum using CA(Carrier Aggregation). The coexistence study is essential before deploying LTE in unlicensed spectrum to verify the impact of LTE on the existing system such as Wi-Fi including system in throughput and regulatory aspects. This paper evaluates and analyzes the RF requirements of LTE system using interference analysis of coexistence study when operating at adjacent frequency channel of the Wi-Fi system in order to minimize the impact of LTE system into Wi-Fi system.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.113-120
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• 2017

In recent years, indoor localization has been researched for the improvement of its localization accuracy capability in Wi-Fi environment. The fingerprint and RF propagation models has been the main approach in determining indoor positioning. With the use of fingerprint, a low-cost, versatile localization system can be achieved without the use of external hardware. However, only a few research have been made on virtual access points (VAPs) among indoor localization models. In this paper, the idea of indoor localization system using fingerprint with the addition of VAP in Wi-Fi environment is discussed. The idea is to virtually add APs in the existing indoor Wi-Fi system, this would mean additional virtually APs in the network. The experiments of the proposed algorithm shows the positive results when 2VAPs are used compared with only APs. A combination of 3APs and 2VAPs in the 3rd case had the lowest average error of 3.99 among its 4 scenarios.

• Journal of Digital Contents Society
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• v.15 no.1
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• pp.129-136
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• 2014

Wi-Fi fingerprinting system is a very popular positioning method used in indoor spaces. The system depends on Wi-Fi Received Signal Strength (RSS) from Access Points (APs). However, the Wi-Fi RSS is changeable by multipath fading effect and interference due to walls, obstacles and people. Therefore, the Wi-Fi fingerprinting system produces low position accuracy. Also, Wi-Fi signals pass through walls. For this reason, the existing system cannot distinguish users' floor. To solve these problems, this paper proposes a LED fingerprinting system for accurate indoor positioning. The proposed system uses a received optical power from LEDs and LED-Identification (LED-ID) instead of the Wi-Fi RSS. In training phase, we record LED fingerprints in database at each place. In serving phase, we adopt a K-Nearest Neighbor (K-NN) algorithm for comparing existing data and new received data of users. We show that our technique performs in terms of CDF by computer simulation results. From simulation results, the proposed system shows that a positioning accuracy is improved by 8.6 % on average.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.27-32
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• 2012

In this paper, the wastewater treatment plant sludge proposed TN/TP sensor data collected an efficient monitoring system in order to implement status monitoring to build WiFi networks. Also we sludge concentration and TN/TP sensor data were collected from wastewater treatment plant. It is able to be monitored sensor data through smart devices(Smart phones, smart pad, tablet PC, etc.) and pc. In addition, when certain events occur immediately be able to cope by adding features to enable efficient and rapid processing, real-time status can be checked by ensuring improved user access and convenience. We has built a WiFi network for to transfer data efficiently. It proved its effectiveness by analysis of sensor communication network. Therefore, we have verified the usefulness of the proposed technology.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.299-304
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• 2021

Existing human activity recognition systems detect activities through devices such as wearable sensors and cameras. However, these methods require additional devices and costs, especially for cameras, which cause privacy issue. Using WiFi signals that are already installed can solve this problem. In this paper, we propose a CNN-based human activity recognition system using channel state information of WiFi signals, and present results of designing and implementing accelerated hardware structures. The system defined four possible behaviors during studying in indoor environments, and classified the channel state information of WiFi using convolutional neural network (CNN), showing and average accuracy of 91.86%. In addition, for acceleration, we present the results of an accelerated hardware structure design for fully connected layer with the highest computation volume on CNN classifiers. As a result of performance evaluation on FPGA device, it showed 4.28 times faster calculation time than software-based system.