• Journal of Genetic Medicine
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• v.8 no.1
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• pp.1-16
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• 2011

Owing to the risk of fetal loss associated with prenatal diagnostic procedures (amniocentesis, chorionic villus sampling), noninvasive prenatal diagnosis (NIPD) is ultimate goal of prenatal diagnosis. The discovery of circulating cell-free fetal DNA (cffDNA) in maternal plasma in 1997 has opened up new probabilities for NIPD by Dr. Lo et al. The last decade has seen great development in NIPD. Fetal sex and fetal RhD status determination by cffDNA analysis is already in clinical use in certain countries. For routine use, this test is limited by the amount of cell-free maternal DNA in blood sample, the lack of universal fetal markers, and appropriate reference materials. To improve the accuracy of detection of fetal specific sequences in maternal plasma, internal positive controls to confirm to presence of fetal DNA should be analyzed. We have developed strategies for noninvasive determination of fetal gender, and fetal RhD genotyping using cffDNA in maternal plasma, using real-time quantitative polymerase chain reaction (RT-PCR) including RASSF1A epigenetic fetal DNA marker (gender-independent) as internal positive controls, which is to be first successful study of this kind in Korea. In our study, accurate detection of fetal gender through gestational age, and fetal RhD genotyping in RhD-negative pregnant women was achieved. In this assay, we show that the assay is sensitive, easy, fast, and reliable. These developments improve the reliability of the applications of circulating fetal DNA when used in clinical practice to manage sex-linked disorders (e.g., hemophilia, Duchenne muscular dystrophy), congenital adrenal hyperplasia (CAH), RhD incompatibility, and the other noninvasive pregnant diagnostic tests on the coming soon. The study was the first successful case in Korea using cffDNA in maternal plasma, which has created a new avenue for clinical applications of NIPD.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.77-88
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• 2000

In this paper, the 3-mode variable gain high power amplifier for a transmitter of INMARSAT-B operating at L-band(1626.5-1646.5 MHz) was developed. This SSPA can amplify 42 dBm in high power mode, 38 dBm in medium power mode and 36 dBm in low power mode for INMARSAT-B. The allowable errol sets +1 dBm as the upper limit and -2 dBm as the lower limit, respectively. To simplify the fabrication process, the whole system is designed by two parts composed of a driving amplifier and a high power amplifier. The HP's MGA-64135 and Motorola's MRF-6401 were used for driving amplifier, and the ERICSSON's PTE-10114 and PTF-10021 for the high power amplifier. The SSPA was fabricated by the RP circuits, the temperature compensation circuits and 3-mode variable gain control circuits and 20 dB parallel coupled-line directional coupler in aluminum housing. In addition, the gain control method was proposed by digital attenuator for 3-mode amplifier. Then il has been experimentally verified that the gain is controlled for single tone signal as well as two tone signals. In this case, the SSPA detects the output power by 20 dB parallel coupled-line directional coupler and phase non-splitter amplifier. The realized SSPA has 41.6 dB, 37.6 dB and 33.2 dB for small signal gain within 20 MHz bandwidth, and the VSWR of input and output port is less than 1.3:1. The minimum value of the 1 dB compression point gets more than 12 dBm for 3-mode variable gain high power amplifier. A typical two tone intermodulation point has 36.5 dBc maximum which is single carrier backed off 3 dB from 1 dB compression point. The maximum output power of 43 dBm was achieved at the 1636.5 MHz. These results reveal a high power of 20 Watt, which was the design target.

• Progress in Medical Physics
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• v.14 no.4
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• pp.211-217
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• 2003

The Principal component analysis (PCA) is a well-known data analysis method that is useful in linear feature extraction and data compression. The PCA is a linear transformation that applies an orthogonal rotation to the original data, so as to maximize the retained variance. PCA is a classical technique for obtaining an optimal overall mapping of linearly dependent patterns of correlation between variables (e.g. neurons). PCA provides, in the mean-squared error sense, an optimal linear mapping of the signals which are spread across a group of variables. These signals are concentrated into the first few components, while the noise, i.e. variance which is uncorrelated across variables, is sequestered in the remaining components. PCA has been used extensively to resolve temporal patterns in neurophysiological recordings. Because the retinal signal is stochastic process, PCA can be used to identify the retinal spikes. With excised rabbit eye, retina was isolated. A piece of retina was attached with the ganglion cell side to the surface of the microelectrode array (MEA). The MEA consisted of glass plate with 60 substrate integrated and insulated golden connection lanes terminating in an 8${\times}$8 array (spacing 200 $\mu$m, electrode diameter 30 $\mu$m) in the center of the plate. The MEA 60 system was used for the recording of retinal ganglion cell activity. The action potentials of each channel were sorted by off­line analysis tool. Spikes were detected with a threshold criterion and sorted according to their principal component composition. The first (PC1) and second principal component values (PC2) were calculated using all the waveforms of the each channel and all n time points in the waveform, where several clusters could be separated clearly in two dimension. We verified that PCA-based waveform detection was effective as an initial approach for spike sorting method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.172-180
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• 2007

Among the nuclear medicine imaging methods available today, $H_2^{15}O-PET$ is most widely used by cognitive neuroscientists to examine regional brain function via the measurement of regional cerebral blood flow (rCBF). The short half-life of the radioactively labeled probe, $^{15}O$, often allows repeated measures from the same subjects in many different task conditions. $H_2^{15}O-$ PET, however, has technical limitations relative to other methods of functional neuroimaging, e.g., fMRI, including relatively poor time and spatial resolutions, and, frequently, insufficient statistical power for analysis of individual subjects. However, recent technical developments, such as the 3-D acquisition method provide relatively good image quality with a smaller radioactive dosage, which in turn results in more PET scans from each individual, thus providing sufficient statistical power for the analysis of individual subject's data. Furthermore, the noise free scanner environment $H_2^{15}O$ PET, along with discrete acquisition of data for each task condition, are important advantages of PET over other functional imaging methods regarding studying state-dependent changes in brain activity. This review presents both the limitations and advantages of $^{15}O-PET$, and outlines the design of efficient PET protocols, using examples of recent PET studies both in the normal healthy population, and in the clinical population.

• Korean Journal of Soil Science and Fertilizer
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• v.41 no.2
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• pp.126-136
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• 2008

Tobacco plants grown in pots by sand culture for 70 days after transplanting were used to evaluate the sensing distance and measurement efficiency of ground-based remote sensors. The leaf distribution of tobacco plant and sensing distance from the sensors to the target leaves were controlled by two removal methods of leaves, top-down and bottom-up removal. In the case of top-down removal, the canopy reflectance was measured by the sensor located at a fixed position having an optimum distance from the detector to the uppermost leaf of tobacco every time that the higher leaves were one at a time. The measurement of bottom-up removal, a the other hand, was conducted in the same manner as that of the top-down removal except that the lower leaves were removed one by one. Canopy reflectance measurements were made with hand held spectral sensors including the active sensors such as $GreenSeeker^{TM}$ red and green, $Crop\;Circle\;ACS-210^{TM}$ red and amber, the passive sensors of $Crop\:Circle^{TM}$, and spectroradiometer $SD2000^{TM}$. The reflectance indices by all sensors were generally affected by the upper canopy condition rather than lower canopy condition of tobacco regardless of sensor type, passive or active. The reflectance measurement by $GreenSeeker^{TM}$ was affected sensitively at measurement distance longer than 120 cm, the upper limit of effective sensing distance, beyond which measurement errors are appreciable. In case of the passive sensors that has no upper limit of effective distance and $Crop\;Circle^{TM}(ACS210)$ that has the upper limit of effective sensing distance specified with 213 cm, longer than that of estimated distance, the measurement efficiency affected by the sensing distance showed no difference. This result suggests that it is necessary to use the sensor specified optimum distance. The result revealed that active sensors are more superior than their passive counterparts in establishing between the relative ratio of reflectance index and the dry weight of tobacco treated by top-down removal, and in the evaluation of biomass. $The\;Crop\;Circle\;ACS-210^{TM}$ red was proved to have the highest efficiency of measurement, followed by $Crop\;Circle^{TM}(ACS210)$ amber and $GreenSeeker^{TM}$ red, $Crop\;Circle^{TM}$ passive, $GreenSeeker^{TM}$ green, and spectroradiometer, in descending order.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.

• Journal of radiological science and technology
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• v.33 no.1
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• pp.37-44
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• 2010

We sought to confirm an independent factor about in-stent restenosis (ISR) in the patients who underwent drug-eluting stent (DES) and know a possibility as a predictor of measured coronary artery calcium score by MDCT. A total of 178 patients (159 men, $61.7{\pm}10.0$ years of age) with 190 coronary artery lesions were included in this study out of 1,131 patients who underwent percutaneous coronary intervention (PCI) with DES implantation for significant stenosis on MDCT at Chonnam National University Hospital between May 2006 and May 2009. All lesions were divided into two groups with the presence of ISR : group I (re ISR, N = 57) and group II (no ISR, N = 133). Compared to group II, group I was more likely to be older ($65.8{\pm}9.0$ vs. $60.2{\pm}9.9$ years, p = 0.0001), diabetic (21.8% vs. 52.6%, p = 0.0001), have old myocardial infarction (8.8% vs. 2.3%, p = 0.040), left main stem disease (5.3% vs. 0.8%, p = 0.047), and smaller stent size ($3.1{\pm}0.3\;mm$ vs. $3.3{\pm}0.4\;mm$, p = 0.004). Group II was more likely to be smokers (19.3% vs. 42.1%, p = 0.003), have dyslipidemia (8.8% vs. 23.3%, p = 0.019). Left ventricular ejection fraction, lesion complexity, and stent length were not different between the two groups. Total CAC score was $389.3{\pm}458.3$ in group I and $371.2{\pm}500.8$ in group II (p = 0.185). No statistical difference was observed between the groups in CAC score in the culprit vessel, left main stem, left anterior descending artery, left circumflex artery, and right coronary artery. On multivariate logistic regression analysis, left main stem disease (OR = 168.0, 95% CI = 7.83-3,604.3, p = 0.001), male sex (OR = 36.5, 95% CI = 5.89-2,226.9, p = 0.0001), and the presence of diabetes (OR = 2.62, 95% CI = 1.071-6.450, p = 0.035) were independent predictors of ISR after DES implantation. In patients who underwent DES implantation for significant coronary stenosis on MDCT, ISR was associated with left main stem disease, male sex, and the presence of diabetes. However, CAC score by MDCT was not a predictor of ISR in this study population.