• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.271-274
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• 2014

This paper proposes high-performance SAO(Sample Adaptive Offset) in HEVC(High Efficiency Video Coding) encoder for Ultra HD video processing in real time. SAO is a newly adopted technique belonging to the in-loop filter in HEVC. The proposed SAO encoder hardware architecture uses three-layered buffers to minimize memory access time and to simplify pixel processing and also uses only adder, subtractor, shift register and feed-back comparator to reduce area. Furthermore, the proposed architecture consists of pipelined pixel classification and applying SAO parameters, and also classifies four consecutive pixels into EO and BO concurrently. These result in the reduction of processing time and computation. The proposed SAO encoder architecture is designed by Verilog HDL, and implemented by 180k logic gates in TSMC $0.18{\mu}m$ process. At 110MHz, the proposed SAO encoder can support 4K Ultra HD video encoding at 30fps in real time.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.94-100
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• 2008

Recently, global warming for climate system is a crucial issue over the world and it brings about severe climate change, abnormal temperature, a downpour, a drought, and so on. Especially, a drought over the earth surface accelerates desertification which has been advanced over the several years mainly originated from a climatic change. The objective of this study is to detect variation of vegetation water condition around China and Mongolia desert by using satellite data having advantage in observing surface biological system. In this study, we use SPOT/VEGETATION satellite image to calculate NDWI (Normalized Difference Water Index) around study area desert for monitoring of status of vegetation characteristics. The vegetation water status index from remotely sensing data is related to desertification since dry vegetation is apt to desertify. We can infer vegetation water status using NDWI acquired by NIR (Near infrared) and SWIR (Short wave infrared) bands from SPOT/VGT. The consequence is that NDWI decreased around desert from 1999 to 2006. The areas that NDWI was decreased are located in the northeast of Mongolian Gobi desert and the southeast of China Taklamakan desert.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.18-26
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• 2015

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.311-321
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• 2009

This paper proposes an efficient frequency offset estimation algorithm for MB-OFDM based UWB systems. The time-frequency interleaving in MB-OFDM extends the time-interval between two transmitted OFDM symbols in the same sub-band. The extended time-interval causes not only the degradation of the system performance by reducing frequency offset estimation range, but also the increase of the hardware complexity by requiring the larger number of storing samples. The proposed estimation algorithm expands the estimation range by applying the proposed sign detection scheme. Simulation results show that the estimation range is increased above 30 ppm compared with a conventional auto-correlation based scheme. The estimation is performed on only one sub-band, and the frequency offsets of the others are calculated by relation to center frequency. This way reduced the number of the storing samples by about l/3. The frequency offset estimator with the proposed algorithm was designed into the architecture which minimizes hardware overhead by time-sharing operators and memory units, and which was synthesized to gate-level circuits using $0.13{\mu}m$ CMOS technology, and the total gates were about 47K.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.187-194
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• 2017

The present study introduces a method to parallelize k-means clustering algorithm for fast unsupervised classification of large satellite imagery. Known as a representative algorithm for unsupervised classification, k-means clustering is usually applied to a preprocessing step before supervised classification, but can show the evident advantages of parallel processing due to its high computational intensity and less human intervention. Parallel processing codes are developed by using multi-threading based on OpenMP. In experiments, a PC of 8 multi-core integrated CPU is involved. A 7 band and 30m resolution image from LANDSAT 8 OLI and a 8 band and 10m resolution image from Sentinel-2A are tested. Parallel processing has shown 6 time faster speed than sequential processing when using 10 classes. To check the consistency of parallel and sequential processing, centers, numbers of classified pixels of classes, classified images are mutually compared, resulting in the same results. The present study is meaningful because it has proved that performance of large satellite processing can be significantly improved by using parallel processing. And it is also revealed that it easy to implement parallel processing by using multi-threading based on OpenMP but it should be carefully designed to control the occurrence of false sharing.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.158-158
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• 2017

비닐포장 하부에 위치한 콩의 생장 초기에 발생한 초엽을 인식하기 위한 연구를 수행중이다. 선행 연구에서 비닐포장에 접촉한 콩 초엽으로 인해 비닐포장 상부 표면의 열 반응 분포에 변화가 있음을 발견하였다. 현장에서 주행 중에 콩 초엽의 위치를 실시간으로 인식하고 연동된 선형 또는 회전형 엑츄에이터를 제어하여 정확한 위치에 천공을 수행하기 위해서는 계측 시스템과 제어 시스템간의 시간적 차이를 최소할 수 있는 실시간 신호 처리 기술이 필수적이다. 선행 연구에서 사용한 다중 IR 센서의 분해능은 $16{\times}4pixel$이며 주파수는 3 Hz로, 폭이 30cm 내외인 비닐포장 상부의 정밀 분석에 한계가 있음을 발견하였다. 이를 해결하기 위하여 분해능과 계측 주기를 개선할 수 있는 초소형 ($1cm{\times}1cm{\times}1cm$) 열화상 센서를 이용하였다. LWIR(Longwave infrared)영역에 해당하는 $8{\mu}m{\sim}14{\mu}m$의 영역에서 $0.05^{\circ}C$의 분해능을 보이는 $ Lepton^{TM}$ (500-0690-00, FLIR, Goleta, CA)모델을 사용하였다. 프레임당 $80{\times}60$ 픽셀의 정보가 2 Byte의 단위로 계측이 되며 9 Hz의 주파수로 대상면의 열 분포를 측정할 수 있다. 이론적으로 초당 정보 전송량은 86,400 Byte ($80{\times}60{\times}2{\times}9$)이며, 1 m를 진행하는 주행형 천공기에 적용할 경우 1 프레임당 10cm 정도의 면적을 측정하므로, 최대 위치 판정 분해능은 약 10 cm / 60 pixel = 0.17 cm/pixel로 상대적으로 정밀한 위치 판별이 가능하다. $80{\times}60{\times}2Byet$의 정보를 0.1초 이내에 분석해야 하는 기술적 과제를 해결하기 위하여 천공 작업기에 적합한 상용 SBC(Single board computer)의 클럭 속도(1 Ghz)로 처리 가능한 공간 분포 분석 알고리즘을 개발하였다. 전체 이미지 도메인을 한 번에 분석하는데 소요되는 시간을 최소화하기 위하여 공간정보 행렬을 균등히 배분하고 별도의 프로세서에서 Feature를 분석한 후 개별 프로세서의 결과를 경합식으로 판정하는 기술을 연구하였다. 오픈 소스인 MPICH(www.mpich.org) 라이브러리를 이용하여 개발한 신호 분석 프로그램을 클러스터링으로 연동된 개별 코어에 설치/수행 하였다. 2D 행렬인 열분포 정보를 공간적으로 균등 분배하여 개별 코어에서 행렬의 Spatial domain analysis를 수행하였다. $20{\times}20$의 클러스터링 단위를 이용할 경우 총 12개의 코어가 필요하였으며, 초당 10회의 연산이 가능함을 확인하였다. 병렬 클러스터링 기술을 이용하여 1m/s 내외의 주행 속도에 대응이 가능한 비닐포장 상부 열 분포 분석 시스템을 구현하였다.