• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.500-505
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• 2013

In this paper, we propose a novel framework to evaluate the depth resolution of axially distributed stereo sensing (ADSS) under fixed resource constraints. The proposed framework can evaluate the performance of ADSS systems based on various sensing parameters such as the number of cameras, the number of total pixels, pixel size and so on. The Monte Carlo simulations for the proposed framework are performed and the evaluation results are presented.

• Journal of applied mathematics & informatics
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• v.41 no.3
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• pp.603-613
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• 2023

In this paper, the concept of geodesic centered tractography is explored for diffusion tensor imaging (DTI). In DTI, where geodesics has been tracked and the inverse of the fourth-order diffusion tensor is inured to determine the diversity. Specifically, we investigated geodesic tractography technique for High Angular Resolution Diffusion Imaging (HARDI). Riemannian geometry can be extended to a direction-dependent metric using Finsler geometry. Euler Lagrange geodesic calculations have been derived by Finsler geometry, which is expressed as HARDI in sixth order tensor.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1392-1400
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• 2011

In most digital imaging applications, high-resolution images or videos are usually desired for later image processing and analysis. The image signal obtained from general imaging system occurs image degradation during the process of image acquirement caused by the optics, physical constraints and the atmosphere effects. Super-resolution reconstruction, one of the solution to address this problem, is image reconstruction technique that produces a high-resolution image from several low-resolution frames in video sequences. In this paper, we propose an improved super-resolution method using Projection onto Convex Sets (POCS) method based on Shift & Add (S&A). The image using conventional algorithms is sensitive to noise. To solve this problem, we propose a fusion algorithm of S&A and POCS. Also we solve the problem using BLPF (Butterworth Low-pass Filter) in frequency domain as optical blur. Our method is robust to noise and has sharpness enhancement ability. Experimental results show that the proposed super-resolution method has better resolution enhancement performance than other super-resolution methods.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6619-6623
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• 2013

Background:The aim of the research was to explore a cost effective, fast, easy to perform, and sensitive method for epidermal growth factor receptor (EGFR) mutation testing. Methods: High resolution melting analysis (HRM) was introduced to evaluate the efficacy of the analysis for dectecting EGFR mutations in exons 18 to 21 using formalin-fixed paraffin-embedded (FFPE) tissues and plasma free DNA from 120 patients. Results: The total EGFR mutation rate was 37.5% (45/120) detected by direct sequencing. There were 48 mutations in 120 FFPE tissues assessed by HRM. For plasma free DNA, the EGFR mutation rate was 25.8% (31/120). The sensitivity of HRM assays in FFPE samples was 100% by HRM. There was a low false-positive mutation rate but a high false-negative rate in plasma free DNA detected by HRM. Conclusions: Our results show that HRM analysis has the advantage of small tumor sample need. HRM applied with plasma free DNA showed a high false-negative rate but a low false-positive rate. Further research into appropriate methods and analysis needs to be performed before HRM for plasma free DNA could be accepted as an option in diagnostic or screening settings.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.395-400
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• 1994

High resolution spectral analysis is essential for ECG anaysis. The fast Fourier transform has been widely used for frequency analysis of ECG signals but this procedure provides poor resolution when the data record is short and shows Gibb's phenomena. The ARMA FTF (Fast Transversal Filter) algorithm is used for high resolution spectral analysis. The reason of unsalability of this algorithm is investigated and the method for improving the numerical stability is proposed. The proposed algorithm is applied to spectral analysis of the ECG. Since this result has less variations than the FFT based results, it can be used for the computerized diagonosis of the ECG.

• BMB Reports
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• v.55 no.11
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• pp.553-558
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• 2022

Hepatocellular carcinoma (HCC) is dangerous cancer that often evades early detection because it is asymptomatic and an effective detection method is lacking. For people with chronic liver inflammation who are at high risk of developing HCC, a sensitive detection method for HCC is needed. In a meta-analysis of The Cancer Genome Atlas pan-cancer methylation database, we identified a CpG island in the USP44 promoter that is methylated specifically in HCC. We developed methylation-sensitive high-resolution melting (MS-HRM) analysis to measure the methylation levels of the USP promoter in cell-free DNA isolated from patients. Our MS-HRM assay correctly identified 40% of patients with early-stage HCC, whereas the α-fetoprotein test, which is currently used to detect HCC, correctly identified only 25% of early-stage HCC patients. These results demonstrate that USP44 MS-HRM analysis is suitable for HCC surveillance.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.49-60
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• 2012

Synthetic aperture radar (SAR) has been employed in various fields due to its capability to generate high resolution images regardless of weather and visibility. This paper presents a performance analysis on the image formation of high resolution SAR according to various slant range distance and synthetic aperture lengths using a range migration algorithm simulator. Although the visual performance on the SAR image is more accurate, a numeric analysis resulted in a comparable measurement. More specifically, raw data were generated for an ideal point target upon imaging geometries and design parameters such as slant range distance and synthetic aperture lengths. Finally, spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio are drawn to provide SAR capabilities in the initial concept design, final in-flight calibration and validation stages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.164-173
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• 2013

In this paper, we propose multi-frame based super resolution algorithm by using motion vector normalization and edge pattern analysis. Existing algorithms have constraints of sub-pixel motion and global translation between frames. Thus, applying of algorithms is limited. And single-frame based super resolution algorithm by using discrete wavelet transform which robust to these problems is proposed but it has another problem that quantity of information for interpolation is limited. To solve these problems, we propose motion vector normalization and edge pattern analysis for 2*2 block motion estimation. The experimental results show that the proposed algorithm has better performance than other conventional algorithms.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.