• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.468-475
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• 2018

In medical ultrasound imaging, frequency-dependent attenuation downshifts and reduces a center frequency and a frequency bandwidth of received echo signals, respectively. This causes considerable errors in quadrature demodulation (QDM), result in lowering signal-to-noise ratio (SNR) and contrast resolution (CR). To address this problem, adaptive dynamic QDM (ADQDM) that estimates center frequencies along depth was introduced. However, the ADQDM often fails when imaging regions contain hypoechoic regions. In this paper, we introduce a valid region-based ADQDM (VR-ADQDM) method to reject the misestimated center frequencies to further improve SNR and CR. The valid regions are regions where the center frequency decreases monotonically along depth. In addition, as a low-pass filter of QDM, Gaussian wavelet based dynamic filtering was adopted. From the phantom experiments, average SNR improvements of the ADQDM and the VR-ADQDM over the traditional QDM were 1.22 and 5.27 dB, respectively, and the corresponding maximum SNR improvements were 2.56 and 10.58 dB. The contrast resolution of the VR-ADQDM was also improved by 0.68 compared to that of the ADQDM. Similar results were obtained from in vivo experiments. These results indicate that the proposed method would offer promises for imaging technically-difficult patients due to its capability in improving SNR and CR.

• Journal of Biomedical Engineering Research
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• v.32 no.3
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• pp.245-250
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• 2011

Parallel imaging technique can provide several advantages for a multitude of MRI applications. Especially, in SENSE technique, sensitivity maps were always required in order to determine the reconstruction matrix, therefore, a number of difference approaches using sensitivity information from coils have been demonstrated to improve of image quality. Moreover, many filtering methods were proposed such as adaptive matched filter and nonlinear diffusion technique to optimize the suppression of background noise and to improve of image quality. In this study, we performed SENSE reconstruction using computer simulations to confirm the most suitable method for the feasibility of filtering effect and according to changing order of polynomial fit that were applied on variation of spatial resolution of sensitivity map. The image was obtained at 0.32T(Magfinder II, Genpia, Korea) MRI system using spin-echo pulse sequence(TR/TE = 500/20 ms, FOV = 300 mm, matrix = $128{\times}128$, thickness = 8 mm). For the simulation, obtained image was multiplied with four linear-array coil sensitivities which were formed of 2D-gaussian distribution and the image was complex white gaussian noise was added. Image processing was separated to apply two methods which were polynomial fitting and filtering according to spatial resolution of sensitivity map and each coil image was subsampled corresponding to reduction factor(r-factor) of 2 and 4. The results were compared to mean value of geomety factor(g-factor) and artifact power(AP) according to r-factor 2 and 4. Our results were represented while changing of spatial resolution of sensitivity map and r-factor, polynomial fit methods were represented the better results compared with general filtering methods. Although our result had limitation of computer simulation study instead of applying to experiment and coil geometric array such as linear, our method may be useful for determination of optimal sensitivity map in a linear coil array.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.5 no.4
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• pp.267-275
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• 2000

In an effort to assess the reliability of satellite altimeter system, we conducted a comparative analysis of sea level data that were collected using the TOPEX/POSEIDON (T/P) altimeter and the 10 tide gauge (TG) stations in the satellite passing track. The analysis was made using data sets collected from marginal sea regions surrounding the Korean Peninsula at T/P cycles of 2 to 230, which correspond to October 1992 to December 1998. Because of strong tidal activity in the study area, treatment of tidal errors is a very critical step in data processing. Hence in the computation of dynamic heights from the Tn data, we adapted the procedures of Park and Gamberoni (1995) to reduce errors associated with it. When these T/P data were treated, the alias periods of M$_2$, S$_2$, and K$_1$ constitutions were found at 62.1, 58.7, and 173 days. The compatibility of the T/P and TG data sets were examined at various filtering periods. The results indicate that the low-frequency signal of Tn data can be interpreted more safely with longer filtering periods (such as up to the maximum selected values of 200 days). When RMS errors for 200-day low-pass filter period was compared among the whole 10 tidal stations, the values spanned in the range of 2.8 to 6.7 cm. The results of correlation analysis at this filtering period also showed a strong agreement between the Tn and TG data sets over the whole stations investigated (e.g., P values consistently less than 0.0001). According to our analysis, we conclude that the analysis of surface sea level using satellite altimeter data can be made safely and reasonably long filtering periods such as 200 days.