• Journal of Radiopharmaceuticals and Molecular Probes
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• v.2 no.2
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• pp.118-122
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• 2016

Integrin ${\alpha}_v{\beta}_3$ plays an important role in the tumor metastases and angiogenesis. Arginine-glycine-aspartate (RGD) peptide motif binds to the integrin ${\alpha}_v{\beta}_3$. General $^{68}Ga$-labeled cyclic RGD peptides was rapidly eliminated from the circulatory system by renal excretion because of its hydrophilic property. The purpose of this study was to develop a novel $^{68}Ga$-labeled cyclic RGD peptides, which could acquire enhanced PET tumor images with improved pharmacokinetics by adopting biphenyl group between chelator and RGD peptides. $^{68}Ga$-DOTA-2P-c(RGDyK) was demonstrated a 12% higher lipophilicity level than $^{68}Ga$-DOTA-c(RGDyK) as a reference compound. In the animal PET, $^{68}Ga$-DOTA-2P-c(RGDyK) represented relatively lower blood-clearance, and an increased signal to noise ratio compared to $^{68}Ga$-DOTA-c(RGDyK). From these perspective, $^{68}Ga$-DOTA-2P-c(RGDyK) could be a good candidate for in integrin ${\alpha}_v{\beta}_3$-expressed tumor imaging.

• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.1-9
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• 2019

In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall. Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images. The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery. In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pre-trained models with those new dataset. The performance of fine-tuned models is improved by comparing the results from pre-trained models. The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the fine-tuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.69-75
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• 2018

Recently, Non-orthogonal multiple access (NOMA) has been focused for the next generation multiple access, which has more spectral efficiency under the limited spectrum bandwidth. Moreover, the spectrum efficiency can be improved by cognitive radio in which the unlicensed secondary users can access the spectrum that is used by the licensed primary user under the limited interference. Hence, we consider the combination of NOMA and cognitive radio, and derive the performance of the cognitive cooperative NOMA system. For the cooperation, a relay is selected among near users, and the selection combining is assumed at a far user. The outage probability of the selected relay and the far user is derived in closed-form, respectively. The provided numerical results are matched well with the Monte Carlo simulation. Numerical results showed that the performance of the relay is affected from the power allocation coefficient, the minimum outage probability is observed at 0.86 of the power allocation coefficient for far user under the given conditions. More than 15 dB of signal-to-noise ratio is required to meet the outage probability of $1{\times}10^{-13}$ for the far user with the frequency acquisition probability of 0.5 compared to that of 1. It shows that the performance of the far user is very sensitive to the acquisition probability of the cognitive relay.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2027-2035
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• 2001

In this paper, we present an audio stream delivery using the AMR (Adaptive Multi-Rate) coder that was adopted by ETSI and 3GPP as a standard vocoder for next generation IMT-2000 service in which includes combined sender (FEC) and receiver reconstruction technique in the Internet. By use of the media-specific FEC scheme, the possibility to recover lost packets can be much increased due to the addition of repair data to a main data stream, by which the contents of lost packets can be recovered. The AMR codec is based on the code-excited linear predictive (CELP) coding model. So we use a frame erasure concealment for CELP-based coders. The proposed scheme is evaluated with ITU-T G.729 (CS-ACELP) coder and AMR - 12.2 kbit/s through the SNR (Signal to Noise Ratio) and the MOS (Mean Opinion Score) test. The proposed scheme provides 1.1 higher in Mean Opinion Score value and 5.61 dB higher than AMR - 12.2 kbit/s in terms of SNR in 10% packet loss, and maintains the communicab1e quality speech at frame erasure rates lop to 20%.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.1
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• pp.59-65
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• 2008

In this paper, a new adaptive resource allocation scheme is proposed in orthogonal frequency-division multiple access(OFDMA) systems with rate proportionality constraints. The problem of maximizing the overall system capacity with constraints on bit error rate, total transmission power and rate-proportionality for user requiring different classes of service is formulated. Since the optimal solution to the constrained fairness problem is extremely complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. Firstly, the number of subchannels to be assigned to each user is determined based on the users' average signal-to-noise ratio and rate-proportion. Subchannels are subsequently distributed according to the modified max-min criterion. Lastly, based on the subchannel allocation, the optimal power allocation by solving the Language dual problem is proposed. Additionally, in order to reduce the computational complexity, iterative rate proportionality tracking algorithm is proposed for maximizing the capacity together with maintaining the rate proportionality constraint.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.387-413
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• 1998

Image resampling is of particular interest in digital radiology. When resampling an image to a new set of coordinate, there appears blocking artifacts and image changes. To enhance image quality, interpolation algorithms have been used. Resampling is used to increase the number of points in an image to improve its appearance for display. The process of interpolation is fitting a continuous function to the discrete points in the digital image. The purpose of this study was to determine the effects of the seven interpolation functions when image resampling in digital periapical images. The images were obtained by Digora, CDR and scanning of Ektaspeed plus periapical radiograms on the dry skull and human subject. The subjects were exposed to intraoral X-ray machine at 60kVp and 70 kVp with exposure time varying between 0.01 and 0.50 second. To determine which interpolation method would provide the better image, seven functions were compared; (1) nearest neighbor (2) linear (3) non-linear (4) facet model (5) cubic convolution (6) cubic spline (7) gray segment expansion. And resampled images were compared in terms of SNR(Signal to Noise Ratio) and MTF(Modulation Transfer Function) coefficient value. The obtained results were as follows ; 1. The highest SNR value(75.96dB) was obtained with cubic convolution method and the lowest SNR value(72.44dB) was obtained with facet model method among seven interpolation methods. 2. There were significant differences of SNR values among CDR, Digora and film scan(P<0.05). 3. There were significant differences of SNR values between 60kVp and 70kVp in seven interpolation methods. There were significant differences of SNR values between facet model method and those of the other methods at 60kVp(P<0.05), but there were not significant differences of SNR values among seven interpolation methods at 70kVp(P>0.05). 4. There were significant differences of MTF coefficient values between linear interpolation method and the other six interpolation methods (P< 0.05). 5. The speed of computation time was the fastest with nearest -neighbor method and the slowest with non-linear method. 6. The better image was obtained with cubic convolution, cubic spline and gray segment method in ROC analysis. 7. The better sharpness of edge was obtained with gray segment expansion method among seven interpolation methods.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.818-826
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• 2015

Recently, objective image quality assessment (IQA) methods that elaborately reflect the visual quality perception characteristics of human visual system (HVS) have actively been studied. Among those characteristics of HVS, luminance adaptation (LA) effect, indicating that HVS has different sensitivities depending on background luminance values to distortions, has widely been reflected into many existing IQA methods via Weber's law model. In this paper, we firstly reveal that the LA effect based on Weber's law model has inaccurately been reflected into the conventional IQA methods. To solve this problem, we firstly derive a new LA effect-based Local weight Function (LALF) that can elaborately reflect LA effect into IQA methods. We validate the effectiveness of our proposed LALF by applying LALF into SSIM (Structural SIMilarity) and PSNR methods. Experimental results show that the SSIM based on LALF yields remarkable performance improvement of 5% points compared to the original SSIM in terms of Spear rank order correlation coefficient between estimated visual quality values and measured subjective visual quality scores. Moreover, the PSNR (Peak to Signal Noise Ratio) based on LALF yields performance improvement of 2.5% points compared to the original PSNR.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.8
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• pp.1075-1081
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• 2010

Recently, the results of many studies have clarified the successful performance of magnetostrictive transducers in which a ferromagnetic patch is used for the transduction of guided shear waves; this is because a thin ferromagnetic patch with strong magnetostriction is very useful for generating and detecting shear wave. This investigation deals with bulk shear wave transduction by means of magnetostriction; on the other hand, the existing studies have been focused on guided shear waves. A modular transducer was developed; this transducer comprised a coil, magnets, and a thin ferromagnetic patch that was made of Fe-Co alloy. Some experiments were conducted to verify the performance of the developed transducer. Radiation directivity pattern of the developed transducer was obtained, and a test to detect the damage on a side drill hole of a steel block specimen was carried out. From the results of these tests, the good performance of the transducer for nondestructive testing was verified on the basis of the signal-to-noise ratio and narrow beam directivity.

• Journal of the Korean Society of Radiology
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• v.10 no.5
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• pp.367-373
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• 2016

This study conducted an analysis to compare the differences in the properties of the magnetic field and the generation of artifacts because of the difference in the magnetic field between 1.5 T equipment and 3.0 T equipment, centering around four types of pulse sequences, mainly applied to the abdominal Magnetic Resonance Imaging (MRI). With data on 500 persons transmitted to the PACS, this study analyzed the SNR value, quantitatively and carried out a qualitative evaluation, dividing MSA, CSA, and DA into three steps. As a result of the quantitative evaluation, the SNR value was significantly higher in the 1.5 T equipment; however, there was a factor deteriorating the image quality, too, as artifacts were generated in the images. The 1.5 T equipment generated fewer artifacts than the 3.0 T equipment did, so it could compensate the image quality for 3.0 T. In conclusion, based on these findings, this study could understand the differences in the properties of the magnetic field and the generation of artifacts occurring because of the difference in the magnetic field and could provide a measure for them. This study would be guidelines for MRI users who directly examine the patients in abdominal MRI using the two types of equipment in the clinical setting in the future.

• Journal of the Korean Society of Radiology
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• v.14 no.3
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• pp.211-220
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• 2020

High-quality computed tomography (CT) images enable precise lesion detection and accurate diagnosis. A lot of studies have been performed to improve CT image quality while reducing radiation dose. Recently, deep learning-based techniques for improving CT image quality have been developed and show superior performance compared to conventional techniques. In this study, a super-resolution convolutional neural network (SRCNN) model was used to improve the spatial resolution of CT images, and image quality according to the hyperparameters, which determine the performance of the SRCNN model, was evaluated in order to verify the effect of hyperparameters on the SRCNN model. Profile, structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and full-width at half-maximum (FWHM) were measured to evaluate the performance of the SRCNN model. The results showed that the performance of the SRCNN model was improved with an increase of the numbers of epochs and training sets, and the learning rate needed to be optimized for obtaining acceptable image quality. Therefore, the SRCNN model with optimal hyperparameters is able to improve CT image quality.