• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.209-212
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• 2013

본 논문에서 적응적 탐색 영역(Adaptive Search Range)을 이용하여 개선한 제한된 1비트 변환 알고리즘을 제안하였다. 이 변환은 전역 검색 알고리즘 (Full Search Algorithm)을 사용한다. 그러나 이것은 매우 많은 연산량과 복잡도를 가진다. 제안된 알고리즘에서는 각 블록의 탐색범위를 결정하기 위한 움직임 벡터 (Motion Vector)와 함께 제한된 1비트 변환 알고리즘의 제한된 마스크 (Constrained Mask)를 사용한다. 실험결과를 통해 제안된 알고리즘은 움직임 예측의 정확도에 대한 성능을 비슷하게 유지하면서 평균적으로 Search Point의 수를 84% 줄일 수 있음을 보여준다.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.75.1-75.1
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• 2016

KAP82 (KHU Auto-guiding software Package for McDonald 82 inch telescope)는 천체망원경의 정밀한 추적을 가능하게 하는 가이드 프로그램으로, 미국 텍사스주에 위치한 McDonald 천문대의 82 inch 망원경에 장착된 광학 및 적외선 영역의 관측기기 SQUEAN (SED camera for Quasars in EArly uNiverse)과 함께 개발되었다. KAP82는 지난 한 해 동안 두 차례의 개정을 통해 프로그램 작동의 안정성을 확보하고, 동시에 가이드의 성능에도 많은 개선이 이루어졌다. 대상 별 중심을 찾는 알고리즘에 따라 KAP82 1.0에서는 가중 평균(weighted mean)을, KAP82 2.0에서는 산술평균을 활용해 자체 개발한 J-J 함수를 사용해 가이드를 구현한 것이 특징이다. 이번에 개발한 KAP82 3.0은 가이드 알고리즘으로 가우스 함수를 채택하고, 제조사가 다른 다수의 상용 CCD카메라 및 망원경과 연결이 가능한 ASCOM Platform에서 작동하므로, 다른 시스템에도 쉽게 적용할 수 있는 장점이 있다. 본 포스터에서는 KAP82 3.0을 소개하면서 기존 KAP82버전들과 KAP82 3.0의 차이로 알아본 서로 다른 알고리즘에 따른 가이드의 정확성을 비교 분석한 결과를 제시한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.65-77
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• 2009

This paper proposes fast image processing algorithms using SSE (Streaming SIMD Extensions) instructions. The CPU's supporting SSE instructions have 128bit XMM registers; data included in these registers are processed at the same time with the SIMD (Single Instruction Multiple Data) mode. This paper develops new SIMD image processing algorithms for Mean filter, Sobel horizontal edge detector, and Morphological erosion operation which are most widely used in automated optical inspection systems and compares their processing times. In order to objectively evaluate the processing time, the developed algorithms are compared with OpenCV 1.0 operated in SISD (Single Instruction Single Data) mode, Intel's IPP 5.2 and MIL 8.0 which are fast image processing libraries supporting SIMD mode. The experimental result shows that the proposed algorithms on average are 8 times faster than the SISD mode image processing library and 1.4 times faster than the SIMD fast image processing libraries. The proposed algorithms demonstrate their applicability to practical image processing systems at high speed without commercial image processing libraries or additional hardwares.

• Journal of KIISE
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• v.44 no.5
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• pp.537-544
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• 2017

As a special form of the signed-digit representation, the NAF(non-adjacent form) minimizes the hamming weight by reducing the average density of the non-zero bits from the binary representation of the positive integer k. Due to this advantage, the NAF is used in various fields; in particular, it is actively used in cryptology. The existing NAF-conversion algorithm, however, is problematic because the conversion speed decreases when the LSB(least significant bit) frequently becomes "1" during the binary positive integer conversion process. This paper suggests a method for the improvement of the NAF-conversion speed for which the problems that occur in the existing NAF-conversion process are solved. To verify the performance improvement of the algorithm, the CPU cycle for the various inputs were measured on the ATmega128, a low-performance 8-bit microprocessor. The results of this study show that, compared with the existing algorithm, the suggested algorithm not only improved the processing speed of the major patterns by 20% or more on average, but it also reduced the NAF-conversion time by 13% or more.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1349-1354
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• 2017

In this paper, we propose a fast hand-gesture recognition algorithm for embedded system. Existing hand-gesture recognition algorithm has a difficulty to use in a low performance system such as embedded systems and mobile devices because of high computational complexity of contour tracing method that extracts all points of hand contour. Instead of using algorithms based on contour tracing, the proposed algorithm uses concentric-circle tracing method to estimate the abstracted contour of fingers, then classify hand-gestures by extracting features. The proposed algorithm has an average recognition rate of 95% and an average execution time of 1.29ms, which shows a maximum performance improvement of 44% compared with algorithm using the existing contour tracing method. It is confirmed that the algorithm can be used in a low performance system such as embedded systems and mobile devices.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.127-129
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• 2004

모바일 컴퓨팅 장비에서 전송 데이터를 압축해 송수신하는 데이터의 양을 줄임으로써 궁극적으로는 통신에 사용되는 전력 소모를 줄일 수 있다. 본 논문에서는 이 기법을 활용하여 한글 데이터를 에너지 효율적으로 전송하는 기법을 제안한다. 제안하는 알고리즘은 한글의 표기 단위인 2 바이트 단위로 데이터를 압축하며 한글의 표기상의 특성을 활용하는 장점이 있다. 실험 결과 제안하는 알고리즘은 다양한 한글 데이터에 대해서 평균적으로 압축 효율을 약 5% 가량 증가시킨다. 이와 함께 제안하는 알고리즘은 실행 시에 사용하는 에너지가 비교적 적어 기존 알고리즘에 비해 한글을 보다 에너지 효율적인 방식으로 압축해 전송함으로써 모바일 장비의 소모 전력 측면의 효율을 증가시킬 수 있다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.10
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• pp.1212-1215
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• 2012

This study describes the accuracy improvement of the estimated angle using phase averaging in phase-comparison monopulse algorithm. In addition, to compensate the time-delay due to the phase averaging, we propose the time-delay compensation algorithm which uses the derivative of the estimated angle. These derivative is calculated by the curve fitting method. Using the real radar interferometer, we have verified that the phase averaging and time-delay compensation algorithms are effective in real-time signal processing application.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.81-89
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• 1999

Image obtained by incomplete communication always include noise, blur and distortion, etc. In this paper, we propose and apply the new spatial filter algorithm, called an optimal adaptive generalized stack filter(AGSF), which optimizes adaptive generalized stack filter(AGSF) using neural network weight learning algorithm of back-propagation learning algorithm for improving noise removal and edge preservation rate. AGSF divides into two parts: generalized stack filter(GSF) and adaptive multistage median filter(AMMF), GSF improves the ability of stack filter algorithm and AMMF proposes the improved algorithm for reserving the sharp edge. Applied to neural network theory, the proposed algorithm improves the performance of the AGSF using two weight learning algorithms, such as the least mean absolute(LAM) and least mean square (LMS) algorithms. Simulation results of the proposed filter algorithm are presented and discussed.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.