• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.97-99
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• 2022

Recently, the amount of data is increasing with the development of industries and technologies. Research on the processing and transmission of large amounts of data is attracting attention. Therefore, in this paper, compressed sensing was used to reduce the amount of data and its effect on Mask R-CNN algorithm was analyzed. We confirmed that as the compressed sensing rate increases, the amount of data in the image and the resolution decreases. However, it was confirmed that there was no significant degradation in the performance of object detection.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.938-942
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• 2009

In this paper, we propose a novel filtering method for processing continuous skyline queries on wireless sensor network environments. The existing filtering methods use the filter based on router paths. However, because these filters are applied not to a whole area but to a partial area, these methods send almost data of sensor nodes to transmit to the base station and have no sufficient effect in terms of energy efficiency. Therefore, we propose an efficient method to dramatically reduce the transmission data of sensors through applying a low-cost and effective filter to all sensor nodes. The proposed effective filter is generated by using characteristics such as the data locality and the clustering of sensors. An extensive performance study verifies the merits of our new method.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.37-43
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• 2022

Drones, one of the 4th industrial technologies that are expanding from military use to industrial use, are being actively used in the search missions of the National Police Agency and finding missing persons, thereby reducing interest in a wide area and the input of large-scale search personnel. However, legal review of police drone operation is continuously required, and the importance of advanced system for related operations and analysis of captured images in connection with search techniques is increasing at the same time. In this study, in order to facilitate recording, preservation, and monitoring in the concept of precise search and monitoring, it is possible to achieve high efficiency and secure golden time when precise search is performed by constructing spatial information based on photo rather than image data-based search. Therefore, we intend to propose a spatial information construction technique that reduces the resulting data volume by adjusting the unnecessary spatial information completion rate according to the size of the subject. Through this, the scope of use of drone search missions for large-scale areas is advanced and it is intended to be used as basic data for building a drone operation manual for police searches.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.6
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• pp.326-336
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• 2006

Due to the evolution of wired and wireless communication technologies and the Internet, multimedia services such as Internet broadcast and VOD have been prevalent recently. RTP is designed to be suitable for transmission of real-time multimedia data on the Internet by IETF While a variety of applications have utilized different RTPs implemented as a library, embeddedRTP is RTP-based kernel-level protocol that resolved performance issues of this kind of RTPs. This paper proposes the ExtendedERTP protocol based on existing embeddedRTP. This new protocol resolves a couple of issues such as packet processing overhead and buffer requirement and combines its APIs with BSD socket APIs which have been widely utilized in network applications. This paper demonstrates that this integration makes it possible to transmit real-time multimedia data through the accustomed interface of BSD socket APIs with nominal extra overhead. This paper also proposes a scheme for improving packet processing time by 15$\sim$20% and another scheme for reducing memory requirement for packet processing to about 3.5%, comparing with those of embeddedRTP.

• Journal of Korea Game Society
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• v.11 no.1
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• pp.147-157
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• 2011

Webcam doesn't perform memory-alignment in order to reduce the transmission time of image data. Memory-unaligned image data is unsuitable for the processing on GPU. Accordingly, we convert it to available color format for optimization in high speed image processing. In this paper, we propose a technique that accelerates webcam's color format conversion by using NVDIA CUDA. We propose an optimization which is about memory accesses and thread composition, also evaluate memory and computing performance for verifying a hypothesis which is the performance of the proposed architecture and optimizing degree on low-performance GPU. Following the optimization technique, we show performance improvements over maximum 68 percent.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.9
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• pp.381-392
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• 2013

During the last decade, the multitude of advances attained in terminal computers, along with the introduction of mobile hand-held devices, and the deployment of high speed networks have led to a recent surge of interest in Quality of Service (QoS) for video applications. The main difficulty is that mobile devices experience disparate channel conditions, which results in different rates and patterns of packet loss. One way of making more efficient use of network resources in video services over wireless channels with heterogeneous characteristics to heterogeneous types of mobile device is to use a scalable video coding (SVC). An SVC divides a video stream into a base layer and a single or multiple enhancement layers. We have to ensure that the base layer of the video stream is successfully received and decoded by the subscribers, because it provides the basis for the subsequent decoding of the enhancement layer(s). At the same time, a system should be designed so that the enhancement layer(s) can be successfully decoded by as many users as possible, so that the average QoS is as high as possible. To accommodate these characteristics, we propose an efficient transmission scheme which incorporates SVC-aware dual-channel repetition to improve the perceived quality of services. We repeat the base-layer data over two channels, with different characteristics, to exploit transmission diversity. On the other hand, those channels are utilized to increase the data rate of enhancement layer data. This arrangement reduces service disruption under poor channel conditions by protecting the data that is more important to video decoding. Simulations show that our scheme safeguards the important packets and improves perceived video quality at a mobile device.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.1
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• pp.10-23
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• 1994

To transmit information efficiently and securely in On-line transmission, data compression, secrecy and authentication are required. In this paper, we propose LZWH4 which creates two compression strings with applying Hnageul to LZW. design HDES1 by extending S-box (S1-S16) which satsfies SAC and correlation coefficient as a partial countermeasure of Differential Cryptanalysis and implement LZWHDES1 which concatenates efficiently these for digital signature in On-line transmission. Also HDES1 is more in U.D.(Unicity Distance) than DES and HDES. We show that the proposed LZWHDES1 reduces processing times than LZWHDES which LZW is directly concatnated to DES and LZWHDES which LZWH1 is directly concatenated to HDES. LZWHDES1 can be used to digital signature system as conventional key cryptosystem.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.23-29
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• 2020

As IoT devices increase exponentially, minimizing MIMO interference and increasing transmission capacity for sending and receiving IoT information through multiple antennas remain the biggest issues. In this paper, secret key-level distribution mechanism using deep learning is proposed to minimize MIMO-based IoT communication noise. The proposed mechanism minimizes resource loss during transmission and reception process by dispersing IoT information sent and received through multiple antennas in batches using deep learning. In addition, the proposed mechanism applied a multidimensional key distribution processing process to maximize capacity through multiple antenna multiple stream transmission at base stations without direct interference between the APs. In addition, the proposed mechanism synchronizes IoT information by deep learning the frequency of use of secret keys according to the number of IoT information by applying the method of distributing secret keys in dimension according to the number of frequency channels of IoT information in order to make the most of the multiple antenna technology.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.367-372
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• 2019

With the development of various IT technologies, data generated in the ship can be controlled through CAN communication, autonomous operation, and information provision in various situation. In addition, electronic navigation vessels with various functions have emerged, and navigation and communication equipment used in these vessels are mainly following the NMEA standard. Currently, NMEA-0183 is still mainly used, but more efficient multimedia transmission processing method is needed for multimedia transmission and USN equipment compatible using NMEA-2000 standard. Furthermore, Ethernet-based ship control is required. However, this paper proposes a multimedia transmission scheme to be smoothly linked with existing ship devices by utilizing CAN communication that can be easily used in the ship.