• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.6
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• pp.69-78
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• 2017

Shannon of the 5G smartphone and Fourier of the signal processing meet in the sampling theorem (2 times the highest frequency 1). In this paper, the initial Shannon Theorem finds the Shannon capacity at the point-to-point, but the 5G shows on the Relay channel that the technology has evolved into Multi Point MIMO. Fourier transforms are signal processing with fixed parameters. We analyzed the performance by proposing a 2N-1 multivariate Fourier-Jacket transform in the multimedia age. In this study, the authors tackle this signal processing complexity issue by proposing a Jacket-based fast method for reducing the precoding/decoding complexity in terms of time computation. Jacket transforms have shown to find applications in signal processing and coding theory. Jacket transforms are defined to be $n{\times}n$ matrices $A=(a_{jk})$ over a field F with the property $AA^{\dot{+}}=nl_n$, where $A^{\dot{+}}$ is the transpose matrix of the element-wise inverse of A, that is, $A^{\dot{+}}=(a^{-1}_{kj})$, which generalise Hadamard transforms and centre weighted Hadamard transforms. In particular, exploiting the Jacket transform properties, the authors propose a new eigenvalue decomposition (EVD) method with application in precoding and decoding of distributive multi-input multi-output channels in relay-based DF cooperative wireless networks in which the transmission is based on using single-symbol decodable space-time block codes. The authors show that the proposed Jacket-based method of EVD has significant reduction in its computational time as compared to the conventional-based EVD method. Performance in terms of computational time reduction is evaluated quantitatively through mathematical analysis and numerical results.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.143-150
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• 2011

The adoption of VoIP is continuously increasing in public institutions, private enterprises and households due to cheaper cost and various supplementary services. Also, it is expected to spread widely the use of VoIP in mobile environment through the increasing use of smartphone. With the growing concern over the incidents of VoIP service while the VoIP service has become increasingly. Especially eavesdropping, it is possible to invade user privacy and drain the secret of company. So, it is important to adopt the protocols for VoIP secure communication. VoIP security protocols are already adopted in public institutions, but it is not adopted in private enterprises and households. In addition, it is necessary to verify whether the VoIP security protocol could be adopted or not in mobile VoIP due to its limited computing power. This paper compared the VoIP security protocol under fixed network and mobile network through performance evaluation. Finally, we found that it is possible to adopt the VoIP security protocols in mobile network.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.343-352
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• 2021

A convergence study was conducted to develop a smartphone application for self-practice of core nursing skills and evaluate its usefulness for undergraduate nursing students. Mobile Application Rating Scale and seven essay questionnaire were used to for usability evaluation among 22 undergraduate nursing students. The score of the information domain was the highest with 4.19(SD 0.79). The subjective quality domain showed the lowest score of 3.08(SD 0.87). Participants' performance confidence score was 8.23(SD 1.60), and learning satisfaction score was 7.89(SD 0.87). Participants reported that the convenience and repetitive self-learning were the strengths of the app. In addition, design and technical supplementation, and lecturer-feedback would improve effectiveness of the current educational app. Findings of this convergent study would be helpful to promote the application of mobile apps for effective self-learning of core nursing skills in undergraduate nursing education. Future resesarch is needed to examine effectiveness study of mobile app on the performance of core nursing skills.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.595-602
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• 2013

WiFi fingerprinting is well known as an effective localization technique used for indoor environments. However, this technique requires a large amount of pre-built fingerprint maps over the entire space. Moreover, due to environmental changes, these maps have to be newly built or updated periodically by experts. As a way to avoid this problem, crowd-sourced fingerprint mapping attracts many interests from researchers. This approach supports many volunteer users to share their WiFi fingerprints collected at a specific environment. Therefore, crowd-sourced fingerprinting can automatically update fingerprint maps up-to-date. In most previous systems, however, individual users were asked to enter their positions manually to build their local fingerprint maps. Moreover, the systems do not have any principled mechanism to keep fingerprint maps clean by detecting and filtering out erroneous fingerprints collected from multiple users. In this paper, we present the design of a crowd-sourced fingerprint mapping and localization(CMAL) system. The proposed system can not only automatically build and/or update WiFi fingerprint maps from fingerprint collections provided by multiple smartphone users, but also simultaneously track their positions using the up-to-date maps. The CMAL system consists of multiple clients to work on individual smartphones to collect fingerprints and a central server to maintain a database of fingerprint maps. Each client contains a particle filter-based WiFi SLAM engine, tracking the smartphone user's position and building each local fingerprint map. The server of our system adopts a Gaussian interpolation-based error filtering algorithm to maintain the integrity of fingerprint maps. Through various experiments, we show the high performance of our system.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.85-92
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• 2016

Mobile cloud computing (MCC) is not just extensions of cloud concepts into mobile environments, but the service technologies that all mobile devices including smartphone can use the desired services by using cloud technology without the constraints of time and space. Currently, a lot of works on mobile cloud computing have been actively researching, whereas user interfaces are not so much researched. The main features and contributions of this paper are as follows. First, develop UI considering UX that is different from conventional interfaces supported by SPICE. Second, combine two button interface into one button interface when keyboard is used in mobile cloud computing clients. Third, develop a mouse interface suitable for mobile cloud computing clients. Fourth, in mobile cloud computing client, solve a problem that the selection of button/files/folder has at the corner. Finally, in mobile cloud computing clients we change mouse scroll mapping functions from volume button to scroll interface in touch-screen. The results of performance evaluation shows that users can input easily with the increased and fixed mouse interface. Since shortcut keys instead of the complex button keys of keyboard are provided, the input with 3-6 steps is reduced into 1 step, which can simply support complex keys and mouse input for users.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.34-39
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• 2018

Along with the development of ICT technology, wearable devices of various sizes and shapes have been developed. In addition, performance and specifications are rebuilt with IOT fusion products so that they can connect with the current smartphone. This is one of the general-purpose technologies of the 4th industrial revolution, which is spot-lighted with technology that changes the quality and environment of our lives. Along with this, as new technology products combining health care technology increases, various functions are provided to users who need it. Wearable technology is ongoing trend of technology development. It also sells products developed as products in the form of smart watches. At present, various related products are made in various ways, and it is recommended to use the Arduino processor in accordance with the application. In this study, we developed wearable physical activity monitoring system using open source hardware based TinyDuino. TinyDuino is an ultra-compact Arduino compatible board made on the basis of Atmega process Board, and it can be programmed in open source integrated development environment(named Sketch). The physical activity monitoring system of the welfare body can be said to be a great advantage, as a smart u-Healthcare system that can perform daily health management.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.183-195
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• 2013

Real-time high-precision image georeferencing is important for the realization of image based precise navigation or sophisticated augmented reality. In general, high-precision image georeferencing can be achieved using the conventional simultaneous bundle adjustment algorithm, which can be performed only as post-processing due to its processing time. The recently proposed sequential bundle adjustment algorithm can rapidly produce the results of the similar accuracy and thus opens a possibility of real-time processing. However, since the processing time still increases linearly according to the number of images, if the number of images are too large, its real-time processing is not guaranteed. Based on this algorithm, we propose a modified fast algorithm, the processing time of which is maintained within a limit regardless of the number of images. Since the proposed algorithm considers only the existing images of high correlation with the newly acquired image, it can not only maintain the processing time but also produce accurate results. We applied the proposed algorithm to the images acquired with 1Hz. It is found that the processing time is about 0.02 seconds at the acquisition time of each image in average and the accuracy is about ${\pm}5$ cm on the ground point coordinates in comparison with the results of the conventional simultaneous bundle adjustment algorithm. If this algorithm is converged with a fast image matching algorithm of high reliability, it enables high precision real-time georeferencing of the moving images acquired from a smartphone or UAV by complementing the performance of position and attitude sensors mounted together.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.8-14
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• 2020

Recently, there was an increasing demand for an integrated access control system which is capable of user recognition, door control, and facility operations control for smart buildings automation. The market available door lock access control solutions need to be improved from the current level security of door locks operations where security is compromised when a password or digital keys are exposed to the strangers. At present, the access control system solution providers focusing on developing an automatic access control system using (RF) based technologies like bluetooth, WiFi, etc. All the existing automatic door access control technologies required an additional hardware interface and always vulnerable security threads. This paper proposes the user identification and authentication solution for automatic door lock control operations using camera based visible light communication (VLC) technology. This proposed approach use the cameras installed in building facility, user smart devices and IoT open source controller based LED light sensors installed in buildings infrastructure. The building facility installed IoT LED light sensors transmit the authorized user and facility information color grid code and the smart device camera decode the user informations and verify with stored user information then indicate the authentication status to the user and send authentication acknowledgement to facility door lock integrated camera to control the door lock operations. The camera based VLC receiver uses the artificial intelligence (AI) methods to decode VLC data to improve the VLC performance. This paper implements the testbed model using IoT open-source based LED light sensor with CCTV camera and user smartphone devices. The experiment results are verified with custom made convolutional neural network (CNN) based AI techniques for VLC deciding method on smart devices and PC based CCTV monitoring solutions. The archived experiment results confirm that proposed door access control solution is effective and robust for automatic door access control.