• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.814-821
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• 2009

In an ultrawide band (UWB) indoor wireless localization, time of arrival (TOA) parameter estimation techniques have some difficulties in acquiring a reasonable TOA estimate because of the clustered multipath components overlapping or random time intervals mainly due to non line-of-sight (NLOS) environment. In order to solve that problem and achieve an excellent UWB indoor wireless localization, we propose a UWB signal model and a robust TOA parameter estimation technique that has little effect on the clustered problems unlike the conventional technique. Through simulation studies, the validity of the proposed model and the TOA estimation technique are examined. The performance of estimation error is also analyzed.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.31-40
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• 2008

There have been a lot of efforts to find more accurate evaluation methods for traffic signal control effectiveness for a long period of time. Nowadays a newly advanced method called HILSS, 'Hardware-in-the-Loop-Simulation System', is used to evaluate the overall traffic control's effectiveness including physical control environments like communication conditions, hardware performance, controller's mechanical operations and so on. In this study, an Online-HILSS model has been developed, which runs on CORSIM(5.0) micro traffic simulation model on-lined to COSMOS. For the verification of the model, three tests are performed as follows; (1) a comparison of TMC's timing plan with the simulated green interval, (2) as a case study, a delay distribution comparison of the online simulation with the CORSIM stand-alone simulation. The result of the first test shows that the model can run the simulation green interval by TMC's timing plan correctly. The result of second test shows that the online simulation of the model brings the same simulation results with the CORSIM offline simulation in case of the same timing plan. These results mean that the online evaluation model could be a reliable tool to measure a real-time signal control effectiveness of a wide area street network with the HILSS method.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.15-22
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• 2000

The analysis of power spectrum based on linear AR model is applied widely to quantize the response of autonomic nerve noninvasively, In this paper, we estimate the power spectrum density for heartrate variability of the electrocadiogram and pulse wave for short term data(less than two minute), The time series of heart rate variability is obtained from the time interval(RRI, PPI) between the feature point of the electrocadiogram and pulse wave for normal person, The generated time series reconstructed into new time series through polynomial interpolation to apply to the AR mode. The power spectrum density for AR model is calculated by Burg algorithm, After applying AR model, the power spectrum density for heart rate variability of the electrocadiogram and the pulse wave is shown smooth spectrum power at the region of low frequence and high frequence, and that the power spectrum density of electrocadiogram and pulse wave has similar form for same subject.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.23-28
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• 2006

Pitch period that is a important factor in speech signal processing is used in various applications such as speech recognition, speaker identification, speech analysis and synthesis. So many pitch detection algorithms have been studied until now. AMDF which is one of pitch period detection algorithms chooses the time interval from valley point to valley point as pitch period. In selection of valley point to detect pitch period, complexity of the algorithm is increased. So in this paper we proposed the simple algorithm using rotation transform of AMDF that detects global minimum valley point as pitch period of speech signal and compared it with existing methods through simulation.

• The Journal of the Korea Contents Association
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• v.7 no.8
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• pp.41-49
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• 2007

BCI system integrates control or telecommunication system with generating electric signals in scalp itself after signal acquisition. This system detect a movement of EEG at real time, can control an electron equipment using a generated signal through EEG movement or software-based processor. In this paper, we deal with removing and separating artifacts induceced from measurement when brain-computer interface system that analyzes recognizes EEG signals occurred from various mental states. In this paper, we proposed a method of EEG classification and an artifact interval detection using bisection mathematical modeling in the EEG classification process for BCI system implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.181-186
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• 2006

Geostationary Orbit satellite, unlike other sun-synchronous polar-orbit satellites, will be able to take a picture of a large region several times a day (almost with everyone hour interval). For geostationary satellite, the target region is fixed though the location of sun is changed always. However, Sun-synchronous polar-orbit satellites able to take a picture of target region same time a everyday. Thus Ocean signal is almost same. Accordingly, the ocean signal of a given target point is largely dependent on time. In other words, the ocean signal detected by geostationary satellite sensor must translate to the signal of target when both sun and satellite are located in nadir, using another correction model. This correction is performed with a standardization of signal throughout relative geometric relationship among satellite-sun-target points. This relative ratio called bidirectional factor. To find relationship between time and $[L_w]_N$/Bidirectional Factor differences, we are calculate solar position, geometry parameters. And reflectance, total radiance at the top of atmosphere(). And water leaving radiance, normalized water leaving radiance. And calculate bidirectional factor, that is the ratio of $[L_w]_N$ between target region and aiming the point. Then, we can make the bidirectional factor lookup table for one year imaging. So, we suggested for necessary to simulation experiment bidirectional factor in more various condition(wavelength and ocean/air condition).

• Journal of Korean Neurosurgical Society
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• v.64 no.3
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• pp.418-426
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• 2021

Objective : A role of diffusion-weighted imaging (DWI) in the diagnosis of cerebral venous thrombosis (CVT) is not well-understood. This study evaluates the effectiveness of DWI in the diagnosis of CVT. Methods : Literature search was conducted in electronic databases for the identification of studies which reported the outcomes of patients subjected to DWI for CVT diagnosis. Random-effects meta-analyses were performed to achieve overall estimates of important diagnostic efficiency indices including hyperintense signal rate, the sensitivity and specificity of DWI in diagnosing CVT, and the apparent diffusion coefficient (ADC) of DWI signal areas and surrounding tissue. Results : Nineteen studies (443 patients with 856 CVTs; age 40 years [95% confidence interval (CI), 33 to 43]; 28% males [95% CI, 18 to 38]; symptom onset to DWI time 4.6 days [95% CI, 2.3 to 6.9]) were included. Hyperintense signals on DWI were detected in 40% (95% CI, 26 to 55) of the cases. The sensitivity of DWI for detecting CVT was 22% (95% CI, 11 to 34) but specificity was 98% (95% CI, 95 to 100). ADC values were quite heterogenous in DWI signal areas. However, generally the ADC values were lower in DWI signal areas than in surrounding normal areas (mean difference-0.33×10-3 ㎟/s [95% CI, -0.44 to -0.23]; p<0.00001). Conclusion : DWI has a low sensitivity in detecting CVT and thus has a high risk of missing many CVT cases. However, because of its high specificity, it may have supporting and exploratory roles in CVT diagnosis.

• Korean Journal of Radiology
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• v.2 no.4
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• pp.183-191
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• 2001

Objective: To document the signal characteristics of intracerebral hemorrhage (ICH) at evolving stages on diffusion-weighted images (DWI) by comparison with conventional MR images. Materials and Methods: In our retrospective study, 38 patients with ICH underwent a set of imaging sequences that included DWI, T1-and T2-weighted imaging, and fluid-attenuated inversion recovery (FLAIR). In 33 and 10 patients, respectively, conventional and echo-planar T2^* gradient-echo images were also obtained. According to the time interval between symptom onset and initial MRI, five stages were categorized: hyperacute (n=6); acute (n=7); early subacute (n=7); late subacute (n=10); and chronic (n=8). We investigated the signal intensity and apparent diffusion coefficient (ADC) of ICH and compared the signal intensities of hematomas at DWI and on conventional MR images. Results: DWI showed that hematomas were hyperintense at the hyperacute and late subacute stages, and hypointense at the acute, early subacute and chronic stages. Invariably, focal hypointensity was observed within a hyperacute hematoma. At the hyperacute, acute and early subacute stages, hyperintense rims that corresponded with edema surrounding the hematoma were present. The mean ADC ratio was 0.73 at the hyperacute stage, 0.72 at the acute stage, 0.70 at the early subacute stage, 0.72 at the late subacute stage, and 2.56 at the chronic stage. Conclusion: DWI showed that the signal intensity of an ICH may be related to both its ADC value and the magnetic susceptibility effect. In patients with acute stroke, an understanding of the characteristic features of ICH seen at DWI can be helpful in both the characterization of intracranial hemorrhagic lesions and the differentiation of hemorrhage from ischemia.