• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.9
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• pp.14-23
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• 2010

This paper reports the fabrication of a DVB-T/H System in Package (SiP) that is able to receive and process the DVB-T/H signal. The DVB-T/H is the European telecommunication standard for Digital Video Broadcasting (DVB). An IC-embedded Printed Circuit Board (PCB) process, interpose a chip between PCB layers, has applied to the DVB-T/H SiP. The chip inserted in DVB-T/H SiP is the System on Chip (SoC) for mobile TV. It is comprised of a RF block for DVB-T/H RF signal and a digital block to convert received signal to digital signal for an application processor. To operate the DVB-T/H IC, a 3MHz DC-DC converter and LDO are on the DVB-T/H SiP. And a 38.4MHz crystal is used as a clock source. The fabricated DVB-T/H SiP form 4 layers which size is $8mm{\times}8mm$. The DVB-T/H IC is located between 2nd and 3rd layer. According to the result of simulation, the RF signal sensitivity is improved since the layout modification of the ground plane and via. And we confirmed the adjustment of LC value on power transmission is necessary to turn down the noise level in a SiP. Although the size of a DVB-T/H SiP is decreased over 70% than reference module, the power consumption and efficiency is on a par with reference module. The average power consumption is 297mW and the efficiency is 87%. But, the RF signal sensitivity is declined by average 3.8dB. This is caused by the decrease of the RF signal sensitivity which is 2.8dB, because of the noise from the DC-DC converter.

• Sleep Medicine and Psychophysiology
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• v.28 no.2
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• pp.78-85
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• 2021

Objectives: When elderly patients show depressive symptoms, discrimination between depressive disorder and prodromal phase of Alzheimer's disease is important. We tested whether a quantitative electroencephalogram (qEEG) marker was associated with cerebral amyloid-β (Aβ) deposition in older adults with depression. Methods: Non-demented older individuals (≥ 55years) diagnosed with depression were included in the analyses (n = 63; 76.2% female; mean age ± standard deviation 73.7 ± 6.87 years). The participants were divided into Aβ+ (n = 32) and Aβ- (n = 31) groups based on amyloid PET assessment. EEG was recorded during the 7min eye-closed (EC) phase and 3min eye-open (EO) phase, and all EEG data were analyzed using Fourier transform spectral analysis. We tested interaction effects among Aβ positivity, condition (EC vs. EO), laterality (left, midline, or right), and polarity (frontal, central, or posterior) for EEG alpha band power. Then, the EC-to-EO alpha reactivity index (ARI) was examined as a neurophysiological marker for predicting Aβ+ in depressed older adults. Results: The mean power spectral density of the alpha band in EO phase showed a significant difference between the Aβ+ and Aβ- groups (F = 6.258, p = 0.015). A significant 3-way interaction was observed among Aβ positivity, condition, and laterality on alpha-band power after adjusting for age, sex, educational years, global cognitive function, medication use, and white matter hyperintensities on MRI (F = 3.720, p = 0.030). However, post-hoc analyses showed no significant difference in ARI according to Aβ status in any regions of interest. Conclusion: Among older adults with depression, increased power in EO phase alpha band was associated with Aβ positivity. However, EC-to-EO ARI was not confirmed as a predictor for Aβ+ in depressed older individuals. Future studies with larger samples are needed to confirm our results.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.241-252
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• 2004

Purpose: Population based structural and functional maps of the brain provide effective tools for the analysis and interpretation of complex and individually variable brain data. Brain MRI and PET standard templates and statistical probabilistic maps based on image data of Korean normal volunteers have been developed and probabilistic maps based on cytoarchitectonic data have been introduced. A quantification method using these data was developed for the objective assessment of regional intensity in the brain images. Materials and Methods: Age, gender and ethnic specific anatomical and functional brain templates based on MR and PET images of Korean normal volunteers were developed. Korean structural probabilistic maps for 89 brain regions and cytoarchitectonic probabilistic maps for 13 Brodmann areas were transformed onto the standard templates. Brain FDG PET and SPGR MR images of normal volunteers were spatially normalized onto the template of each modality and gender. Regional uptake of radiotracers in PET and gray matter concentration in MR images were then quantified by averaging (or summing) regional intensities weighted using the probabilistic maps of brain regions. Regionally specific effects of aging on glucose metabolism in cingulate cortex were also examined. Results: Quantification program could generate quantification results for single spatially normalized images per 20 seconds. Glucose metabolism change in cingulate gyrus was regionally specific: ratios of glucose metabolism in the rostral anterior cingulate vs. posterior cingulate and the caudal anterior cingulate vs. posterior cingulate were significantly decreased as the age increased. 'Rostral anterior'/'posterior' was decreased by 3.1% per decade of age ($P<10^{-11}$, r=0.81) and 'caudal anterior'/'posterior' was decreased by 1.7% ($P<10^{-8}$, r=0.72). Conclusion: Ethnic specific standard templates and probabilistic maps and quantification program developed in this study will be useful for the analysis of brain image of Korean people since the difference in shape of the hemispheres and the sulcal pattern of brain relative to age, gender, races, and diseases cannot be fully overcome by the nonlinear spatial normalization techniques.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.