• Korean Journal of Optics and Photonics
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• v.8 no.2
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• pp.95-98
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• 1997

The Gaussian diffraction pattern initially assumed in the diffraction inverse problem is further sharply defined by multiplying $e^{-q{\omega}_0$\mid${\chi}$\mid$}$. The modified pupil function is obtained and the diffraction intensity distribution for the finite aperture ($-{\omega}_0~{\times}{\omega}_0$ is obtained, and then the OTF is derived analytically. It is found the OTF is equal to or less than the $(OTF)_{q=0}$, namely the modulation is not useful. It is shown that the narrowing down the initial Gaussian diffraction pattern does not give the anticipated improvement in OTF and the reason is clarified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.90-98
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• 1993

A Gaussian is formed by diffusing a spot excitation. In this paper, a diffusion neural network model is derived from the diffusion equation. And it is shown that a difference of two Gaussians(DOG) may have the same shape as a Laplacian of Gaussian(LOG), A neural network model executing a DOG convolution by diffusing an external excitation is proposed. By this model intensity changes of image may be detected. This model may be implemented economically because each neuron has only four fixed-valued synapes.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.845-850
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• 2003

The scattered light pattern from a machined surface generally contains much information concerning the surface roughness. The light pattern can be acquired by optical system and analyzed by statistical method. This kind of surface roughness measurement system can be easily adopted on the machine measurement. But the fully assembled system is too complex to implement on small systems using micro-controller. This study proposes the idea of reducing the number of optical components by removing screen and examines image processing of a light pattern to minimize the negative result of incomplete optical system. And the Gaussian blur filtering is concluded to be the best method of proposed measurement system. Furthermore light intensity variation of image pattern can be treated as a signal, therefore FIR filtering gives the similar result of Gaussian blur effect.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.33-42
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• Progress in Medical Physics
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• v.23 no.1
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• pp.62-69
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• 2012

The dose re-calculation process using Megavoltage cone-beam CT images is inevitable process to perform the Adaptive Radiation Therapy (ART). The purpose of this study is to improve dose re-calculation accuracy using MVCBCT images by applying intensity calibration method and three dimensional rigid body transform and filtering process. The three dimensional rigid body transform and Gaussian smoothing filtering process to MVCBCT Rando phantom images was applied to reduce image orientation error and the noise of the MVCBCT images. Then, to obtain the predefined modification level for intensity calibration, the cheese phantom images from kilo-voltage CT (kV CT), MVCBCT was acquired. From these cheese phantom images, the calibration table for MVCBCT images was defined from the relationship between Hounsfield Units (HUs) of kV CT and MVCBCT images at the same electron density plugs. The intensity of MVCBCT images from Rando phantom was calibrated using the predefined modification level as discussed above to have the intensity of the kV CT images to make the two images have the same intensity range as if they were obtained from the same modality. Finally, the dose calculation using kV CT, MVCBCT with/without intensity calibration was applied using radiation treatment planning system. As a result, the percentage difference of dose distributions between dose calculation based on kVCT and MVCBCT with intensity calibration was reduced comparing to the percentage difference of dose distribution between dose calculation based on kVCT and MVCBCT without intensity calibration. For head and neck, lung images, the percentage difference between kV CT and non-calibrated MVCBCT images was 1.08%, 2.44%, respectively. In summary, our method has quantitatively improved the accuracy of dose calculation and could be a useful solution to enhance the dose calculation accuracy using MVCBCT images.

• Korean Journal of Optics and Photonics
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• v.7 no.3
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• pp.196-199
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• 1996

The amplitude LSF(Line spread function, $C_1e^{{o^2}{x^2}}$ or amplitude impulse) of the Gaussian apodized annular pupil is found to be same to that of the full aperture LSF($C_0e^{{o^2}{x^2}}$). $C_0$ and $C_1$ depending on $\sigma$, ${\omega}_0=\frac{2{\pi}}{\lambda}\;\frac{a_0}{l}$ and ${\omega}_0'=\frac{2{\pi}}{\lambda}\;\frac{a_0'}{l}$ which are the geometric parameter and pupil coordinates of the annular pupil. The important inequality relation among ${\omega}_0,\;{\omega}_0'$, a (fraction of diffraction amplitude) and $\sigma$ is obtained. It is $\frac{{\omega}_0}{\sqrt{2}}<{\sigma}{\le}(\frac{1-a}{2a})^{1/2}\;{\omega}_0$, and in the case of $a=e^{-1},\;a_0'{\le}0.34a_0$. The case of λ=0.013${\mu}{\textrm}{m}$, l=20 cm, $a_0=5cm$ and $a_0=0.34a_0=1.7cm$ give a Gaussian apodized superresolution ${\Delta}=\frac{\sqrt{log2}}{\sigma}=0.008{\mu}m$ annular pupil with the intensity signal equal to $e_{-2}$ times the signal obtainable by using the full aperture system(a=1)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.325-330
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• 2012

In this paper, drain induced barrier lowering(DIBL) has been analyzed as one of short channel effects occurred in double gate(DG) MOSFET to be next-generation devices. Since Gaussian function been used as carrier distribution for solving Poisson's equation to obtain analytical solution of potential distribution, we expect our results using this model agree with experimental results. DIBL has been investigated according to projected range and standard projected deviation as variables of Gaussian function, and channel structure and channel doping intensity as device parameter. Since the validity of this analytical potential distribution model derived from Poisson's equation has already been proved in previous papers, DIBL has been analyzed using this model. Resultly, DIBL has been greatly changed for channel structure and doping concentration.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.69-73
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• 2022

In this paper, we propose a new method for measuring fine dust through a LoG(Laplacian of Gaussian)-based edge detection technique. CCTV-based images in a video are collected for fine dust measurement, and image ranges are designated through RoI(Region of Interest). After clustering by applying the GMM(Gaussian Mix Model) to the specified area, we detect edge through the LoG algorithm and measure the detected edge strength. The concentration of fine dust is determined based on the measured intensity data of the edge. In this paper, we propose algorithm as the effectiveness of experiment. As a result of collecting and applying CCTV image in the video installed around the laboratory of this school for a month from June to July, the measured result value was proved through this experiment to be sufficient to calculate the concentration and range of fine dust.