• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1727-1730
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• 2016

Magnetic resonance imaging has a potential to produce clear anatomical as well as functional images of human body. However, the ability to diagnose is limited by signal to noise ratio (SNR) and the resolution of current medical systems. To remove the challenges prevalent due to the use of high field scanners, dedicated radio frequency coils are used. Transverse electromagnetic coils have an advantage of providing homogeneous magnetic field throughout the region but with low signal to noise ratio while surface coils have an advantage of providing higher signal to noise ratio but with low homogeneity. This research combines both the advantage into one by utilizing transmit only transverse electromagnetic radio frequency coils (8 channel) along with receive only surface coils (by varying the number) for better imaging of brain. A 7 Tesla 32-channel close fitting helmet shaped phased-array surface coils along with the combination of 8 channel transmit only transverse electromagnetic coils provided good homogeneity as well as significant SNR improvements throughout the human brain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12A
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• pp.949-961
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• 2009

This paper analyses BER (Bit Error Rate) performance of 2-hop wireless communications networks with hybrid decode-and-forward (HDF) relays. The conventional HDF method is usually based on the receive signal-to-noise ratio (SNR) for the relay to decide whether to forward the decoded data in order to obviate the erroneous detection at the relay. In contrast, we propose a new solution of using log-likelihood ratio (LLR) as an efficient alternative to SNR. The approximate BER expressions of different HDF schemes are also derived and verified by Monte-Carlo simulations. In addition, we compute the optimum thresholds for HDF schemes. A variety of numerical results demonstrate that the new LLR-based HDF significantly outperforms the SNR-based HDF for any threshold level and relay location under flat Rayleigh fading channel plus AWGN (Additive White Gaussian Noise).

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.1
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• pp.75-80
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• 2005

A new antenna shuffling scheme for double space time transmit diversity is proposed. The proposed method obtains the shuffling pattern directly from the estimated channel by maximizing minimum post-processing signal to noise ratio(SNR), while the conventional method minimizes channel correlation. Since the minimum post-processing SNR is directly related with error performance, the proposed method shows better bit error rate performance than the conventional method. Monte Carlo simulations showed that the proposed scheme has more 3 dB SNR gain than the conventional scheme for 10/sup -3/ bit error rate in spatially correlated fadingcaused by a single cluster model.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.230-235
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• 2005

In this paper, we propose an algorithm that improves a tone quality of a noisy audio signal in order to enhance a performance of perceptual filter using intelligent estimation of noise pattern from a band degraded by additive noise. The proposed method doesn't use the estimated noise which is obtained from silent range. Instead new estimated noise according to the power of signal and effect of noise variation is considered for each frame. So the noisy audio signal is enhanced by the method which controls a estimation of noise Pattern effectively in a noise corruption band. To show the performance of the proposed algorithm, various input signals which had a different signal-to-noise ratio(SNR) such as $5\cal{dB},\;10\cal{dB},\;15\cal{dB}\;and\;20\cal{dB}$ were used to test the proposed algorithm. we carry out SSNR and NMR of objective measurement and MOS test of subjective measurement. An approximate improvement of $7.4\cal{dB},\;6.8\cal{dB},\;5.7\cal{dB},\;5.1\cal{dB}$ in SSNR and $15.7\cal{dB},\;15.5\cal{dB},\;15.2\cal{dB},\;14.8\cal{dB}$ in NMR is achieved with the input signals, respectively. And we confirm the enhancement of tone quality in terms of mean opinion score(MOS) test which is result of subjective measurement.

• Journal of the Korean Magnetics Society
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• v.26 no.3
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• pp.92-98
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• 2016

To aim of this study was to assess the full scan and half scan of imaging with half scan factor. Patients without a cerebral vascular disease (n = 30) and were subject to the full scan half scan, and set a region of interest in the cerebral artery from the three regions (C1, C2, C3) in the range of 7 to 8 mm. MIP (maximum intensity projection) to reconstruct the images in signal strength SNR (signal to noise ration), PSNR (peak signal noise to ratio), RMSE (root mean square error), MAE (mean absolute error) and calculated by paired t-test for use by statistics were analyzed. Scan time was half scan (4 minutes 53 seconds), the full scan (6 minutes 04 seconds). The mean measurement range (7.21 mm) of all the ROI in the brain blood vessel, was the SNR of the first C1 is completely scanned (58.66 dB), half-scan (62.10 dB), a positive correlation ($r^2=0.503$), for the second C2 SNR is completely scanned (70.30 dB), half-scan (74.67 dB) the amount of correlation ($r^2=0.575$), third C3 of a complete scan SNR (70.33 dB), half scan SNR (74.64 dB) in the amount of correlation between the It was analyzed with ($r^2=0.523$). Comparative full scan with half of SNR ($4.75{\pm}0.26dB$), PSNR ($21.87{\pm}0.28dB$), RMSE ($48.88{\pm}1.61$), was calculated as MAE ($25.56{\pm}2.2$). SNR is also applied to examine the half-scans are not many differences in the quality of the two scan methods were not statistically significant in the scan (p-value > .05) image takes less time than a full scan was used.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.3-9
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• 2015

In this paper, we propose an initial timing and frequency synchronization scheme for low probability detection (LPD) systems with cyclic code shift keying (CCSK). The performance of the LPD system with CCSK highly depend on initial timing and frequency offset. On the other hand, the operating SNR (Signal-to-Noise Ratio) of LPD systems is usually very low. Hence, to guarantee a reliable performance of the LPD system, it is crucial to develop suitable initial synchronization algorithms. In this paper, we propose an initial timing and frequency synchronization scheme suitable for CCSK based LPD system using a repeated preamble pattern.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.87-94
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• 2009

Noise estimation and suppression are a crucial factor of many speech communication and recognition systems. In this paper, proposed algorithm is based on the ratio of variance normalized of noisy power spectrum in time-frequency domain. Our proposed algorithm tracks the threshold and controls the trade-off between residual noise and distortion. This algorithm is evaluated by the ITU-T P.835 signal distortion (SIG) and segment signal to noise ratio (SNR), and is superior to the conventional methods.

• Progress in Medical Physics
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• v.13 no.2
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• pp.85-89
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• 2002

We authors developed a new small-size birdcage RF coil for animal MR images. And we compared signal-to-noise ratio (SNR) of the new small coil with a conventional knee coil. The dimension of the low-pass type birdcage coil with 12 elements at 37 MRI system are 13 cm outer diameter, 12 cm inner diameter and 20 cm length. For each element, the width of copper tape is 0.05 mm, thickness is 8 mm and length is 20 cm. The small birdcage coil with 12 elements exhibited 7 resonance modes. The isolation of the quadrature channel could be achieved more than 20 ㏈. The coil quality factor (Q value) was 98.6. The SNR of the animal coil was 243.2 on the average and was about twice as high as the conventional knee coil. The present study successfully demonstrated that the small birdcage coil could provide high quality animal MR images with the improved SNR. Therefore, it is expected that the small birdcage coil could be used in the clinical diagnosis and research studies for veterinary medicine in the near future.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.41-49
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• 2024

Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.