• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.397-404
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• 1999

For two-dimensional causal spatial autoregressive processes, we propose and illustrate a method for determining asymptotic simultaneous confidence regions using Yule-Walker, unbiased Yule-Walker and least squres estimators. The spectral density for first-order spatial autoregressive model are looked at in more detail. Finite sample properties based on simulation study we also presented.

• 한국통신학회논문지
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• 제21권10호
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• pp.2653-2659
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• 1996

In this paper, DC-free and minimum bandwidth binary line codes with look-up table are proposed and their performances are analyzed. As results of performance analysis, the proposed codes are shown to have spectral nulls at DC and Nyquist frequency. Among the proposed codes, binary line codes of which both codeword digital sum and alternating digital sum are zero have lower code rate but better spectral characteristics. Furthermore, binary line codes which consist of all codewords including those with nonzero digital sum and alternating digital sum have worese spectral characteristics but higher code rate.

• Journal of the Optical Society of Korea
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• 제7권2호
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• pp.89-96
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• 2003

A new method to control the free spectral range of a long period fiber grating is proposed and theoretically analyzed. As the refractive index decreases radially outward in the silica cladding due to graded doping of fluorine, waveguide dispersion in the cladding modes was modified to result in the effective indices change and subsequently the phase matching conditions for coupling with the core mode in a long period fiber grating. Enlargement of the free spectral range in a long period fiber grating was theoretically confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.2047-2066
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• 2024

In order to improve the coverage and achieve massive spectrum access, non-orthogonal multiple access (NOMA) technology is applied in millimeter wave massive multiple-input multiple-output (mMIMO) communication network. However, the power assumption of active sensors greatly limits its wide applications. Recently, Intelligent Reconfigurable Surface (IRS) technology has received wide attention due to its ability to reduce power consumption and achieve passive transmission. In this paper, spectral efficiency maximum problem in the millimeter wave mMIMO-NOMA system with IRS is considered. The sparse RF chain antenna structure is designed at the base station based on continuous phase modulation. Furthermore, a joint optimization problem for power allocation, power splitting, analog precoding and IRS reconfigurable matrices are constructed, which aim to achieve the maximum spectral efficiency of the system under the constraints of user's quality of service, minimum energy harvesting and total transmit power. A three-stage iterative algorithm is proposed to solve the above mentioned non-convex optimization problems. We obtain the local optimal solution by fixing some optimization parameters firstly, then introduce the relaxation variables to realize the global optimal solution. Simulation results show that the spectral efficiency of the proposed scheme is superior compared to the conventional system with phase shifter modulation. It is also demonstrated that IRS can effectively assist mmWave communication and improve the system spectral efficiency.

• 한국지구과학회지
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• 제38권7호
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• pp.535-545
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• 2017

한반도 주변 해상사고가 증가함에 따라 원격탐사 자료를 활용한 선박탐지 연구의 중요성이 점점 더 강조되고 있다. 이 연구는 고해상도 광학영상에 의존하는 기존 선박탐지 분야에 수백 개 채널의 분광정보를 포함하는 초분광영상을 활용하여 새로운 선박탐지 알고리즘 제시하였다. 두 차례의 현장관측을 통해 측정한 선박 선체의 반사 스펙트럼과 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) 초분광센서 영상의 선박 및 해수 반사 스펙트럼 간의 분광정합 기법을 적용하였다. 총 다섯 개의 탐지 알고리즘 spectral distance similarity (SDS), spectral correlation similarity(SCS), spectral similarity value (SSV), spectral angle mapper (SAM), spectral information divergence (SID)를 사용하였다. SDS는 선박 일부가 해수로 탐지되는 오차를 나타내었고, SAM은 선박과 해수 사이에 약 1.8배의 차이를 나타내어 명확한 분류 결과를 보여주었다. 이와 더불어 본 연구에서는 각 기법의 최적 임계값을 제시하여 초분광 영상에 포함되어 있는 선박을 분류하였으며 그 결과 SAM, SID가 다른 탐지 알고리즘에 비해 우수한 선박탐지 능력을 보여주었다.

• 대한수학회보
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• 제40권2호
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• pp.281-288
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• 2003

We compute the mod p homology of the double loop space of 50(2n)/U(n) by the Serre spectral sequence of Hopf algebras. We also obtain the torsion information of the integral homology.

• 한국측량학회지
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• 제33권3호
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• 항공우주기술
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• 제8권2호
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• pp.26-32
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• 2009

위성영상의 융합에 있어, 가장 중요한 점은 전정영상의 공간적 세밀함과 다중분광영상의 분광정보 모두를 보존하는 것이다. 다양한 영상융합 기법 중에서, IHS 변환을 이용한 융합기법은 폭넓게 사용되고 있으며, 계산과정이 매우 단순하다는 장점을 갖고 있다. 본 연구에서는, fast IHS 변환과 trade-off 파라미터 $\alpha^i$를 이용한 융합기법을 제안한다. 제안한 융합 기법은 분광 ERGAS와 공간 ERGAS의 평가를 통하여, 융합영상에서 분광정보와 공간적 세밀함 사이의 trade-off 최적화를 가능하게 한다. IKONOS 영상의 실험결과, 제안한 기법은 기존의 fast IHS 변환을 이용한 융합기법에 비해 공간적 세밀함과 분광정보의 보존측면에서 더 효과적임을 확인할 수 있었다.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.208-215
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• 2023

Deep learning techniques provide powerful solutions to several pattern-recognition problems, including Raman spectral classification. However, these networks require large amounts of labeled data to perform well. Labeled data, which are typically obtained in a laboratory, can potentially be alleviated by data augmentation. This study investigated various data augmentation techniques and applied multiple deep learning methods to Raman spectral classification. Raman spectra yield fingerprint-like information about chemical compositions, but are prone to noise when the particles of the material are small. Five augmentation models were investigated to build robust deep learning classifiers: weighted sums of spectral signals, imitated chemical backgrounds, extended multiplicative signal augmentation, and generated Gaussian and Poisson-distributed noise. We compared the performance of nine state-of-the-art convolutional neural networks with all the augmentation techniques. The LeNet5 models with background noise augmentation yielded the highest accuracy when tested on real-world Raman spectral classification at 88.33% accuracy. A class activation map of the model was generated to provide a qualitative observation of the results.