• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.201-210
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• 2005

This paper proposes a power efficient 2-dimensional DCT/IDCT architecture driven by input data to be processed. The architecture achieves low power by taking advantage of the typically large fraction of zero and small-valued input processing data in video and image data compression. In particular, it skips multiplication by zero and dynamically activates/deactivates required bit-slices of fine-grain bit partitioned adders within multipliers and accumulators using simple input ANDing and bit-slice MASKing. The processed results from 1-D DCT/IDCT do not have unnecessary sign extension bits (SEBs), which are used for further power reduction in matrix transposer. The results extracted by bit-level transition activity simulations indicate significant power reduction compared to conventional designs.

• Smart Media Journal
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• v.7 no.3
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• pp.64-71
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• 2018

Utilization of unmanned aerial vehicles (UAVs) are rapidly expanding to various fields ranging from defense, industry, entertainment and personal hobbies. Due to the increased activities of unmanned airplanes, many security problems have emerged, including flight path errors to undesired destinations, secondary threats due to exposed securities caused by the capture of unmanned airplanes in hostile countries. In this paper, we find security vulnerabilities in UAVs such as GPS spoofing, hacking captured video information, malfunction due to signal attenuation through jamming, and exposure of personal information due to image shooting. In order to solve this problem, the stability of the unstructured data is secured by setting the encryption of the video shooting information section using the virtual private network (VPN) to prevent the GPS spoofing attack. In addition, data integrity was ensured by applying personal information encryption and masking techniques to minimize the secondary damage caused by exposure of the UAV and to secure safety. It is expected that it will contribute to the safe use and stimulation of industry in the application field of UAV currently growing.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.234-239
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• 2021

Recently, as the installation of a large number of high-performance CCTVs for crime prevention and traffic control has increased rapidly, the problem of increasing system requirements for response to privacy infringement factors and analysis of high-definition image information transmitted from multiple cameras has been actively emerging. Accordingly, there is a need for a method for responding to privacy infringement and a method for efficiently processing surveillance images input from multiple cameras. In this paper, in order to reduce the processing cost of the input image and improve the processing speed, an integrated image is generated by grouping images input from a plurality of cameras. After analyzing the pre-generated integrated video, it detects a preset privacy event or an event that highlights interest. Depending on whether or not an event is detected, you will perform an editing operation corresponding to the event.