• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.685-690
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• 2013

This paper has proposed a multi-channel and wide gain-range baseband circuit blocks for the IEEE 802.15.4g MR-OFDM SUN systems. The proposed baseband circuit blocks consist of two negative-feedback VGAs, an active-RC 5th-order chebyshev low-pass-filter, and a DC-offset cancellation circuit. The proposed baseband circuit blocks provide 1 dB cut-off frequencies of 100 kHz, 200 kHz, 400 kHz, and 600 kHz respectively, and achieve a wide gain-range of +7 dB~+84 dB with 1 dB step. In addition, a DC-offset cancellation circuit has been adopted to mitigate DC-offset problems in direct-conversion receiver. Simulation results show a maximum input differential voltage of $1.5V_{pp}$ and noise figure of 42 dB and 37.6 dB at 5 kHz and 500 kHz, respectively. The proposed I-and Q-path baseband circuits have been implemented in $0.18-{\mu}m$ CMOS technology and consume 17 mW from a 1.8 V supply voltage.

• Korean Journal of Radiology
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• v.23 no.3
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

• Economic and Environmental Geology
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• v.44 no.6
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• pp.493-502
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• 2011

We study the deep structure of Korean Peninsula by estimating Moho depth and crustal thickness from using land and oceanic topography and free-air gravity anomaly data. Based on Airy-Heiskanen isostatic hypothesis, the correlated components between the terrain gravity effects and free-air gravity anomalies by wavenumber correlation analysis(WCA) are extracted to estimate the gravity effects that will be resulted from isostatic compensation for the area. With the resulting compensated gravity estimates, Moho depth that is a subsurface between the crust and mantle is estimated by the inversion in an iterative method with the constraints of 20 seismic depth estimates by the receiver function analysis, to minimize the uncertainty of non-uniqueness. Consequently, the average of the resulting crustal thickness estimate of Korean Peninsula is 32.15 km and the standard deviation is 3.12 km. Moho depth of South Korea estimated from this study is compared with the ones from the previous studies, showing they are approximately consistent. And the aspects of Moho undulation from the respective study are in common deep along Taebaek Mountains and Sobaek Mountains and low depth in Gyeongsang Basin relatively. Also, it is discussed that the terrain decorrelated free-air gravity anomalies inferring from the intracrustal characteristics of the crust are compared to the heat flow distributions of South Korea. The low-frequency components of terrain decorrelated Free-air gravity anomalies are highly correlated with the heat flow data, especially in the area of Gyeongsang basin where high heat flow causes to decrease the density of the rocks in the lower crust resulting in lowering the Moho depth by compensation. This result confirms that the high heat sources in this area coming from the upper mantle by Kim et al. (2008).