• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.203-210
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• 2013

To look into site characteristics of the Hwacheon borehole seismic station, we analyzed property of earthquake and microtremor recorded on surface and borehole seismometers. Acoording to analysis result of microtremor, the surface-to-borehole energy ratio was approximately 15 times greater during the daytime than during the nighttime, and the surface-to-borehole ratios of spectral amplitudes as frequency increases. For earthquake data, amplitude spectra and dominant frequency were computed using surface and borehole data. As a result, small earthquakes with short distance recorded on surface seismometer peaked at 8 Hz, 46 Hz. This result corresponds to resonance frequencies (7.4 Hz, 46 Hz) calculated by H/V spectral ratio. We confirmed amplification effect by site characteristics of overburden. Background noise level was approximately 20,000 times smaller at borehole seismic station than surface seismic station. These results provide strong evidence for the superior recording of earthquakes using borehole seismometers instead of surface seismometers.

• 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.

• Geophysics and Geophysical Exploration
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• v.27 no.2
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• pp.91-107
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• 2024

Single-channel seismic exploration has proven effective in delineating subsurface geological structures using small-scale survey systems. The seismic data acquired through zero- or near-offset methods directly capture subsurface features along the vertical axis, facilitating the construction of corresponding seismic sections. However, substantial noise in single-channel seismic data hampers precise interpretation because of the low signal-to-noise ratio. This study introduces a novel approach that integrate noise reduction and signal enhancement via matrix rank optimization to address this issue. Unlike conventional rank-reduction methods, which retain selected singular values to mitigate random noise, our method optimizes the entire singular value spectrum, thus effectively tackling both random and erratic noises commonly found in environments with low signal-to-noise ratio. Additionally, to enhance the horizontal continuity of seismic events and mitigate signal loss during noise reduction, we introduced an adaptive weighting factor computed from the eigenimage of the seismic section. To access the robustness of the proposed method, we conducted numerical experiments using single-channel Sparker seismic data from the Chukchi Plateau in the Arctic Ocean. The results demonstrated that the seismic sections had significantly improved signal-to-noise ratios and minimal signal loss. These advancements hold promise for enhancing single-channel and high-resolution seismic surveys and aiding in the identification of marine development and submarine geological hazards in domestic coastal areas.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.45-55
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• 1999

In this paper, we used the WLMS(Wavelet domain Least Mean Square) adaptive filter based on the wavelet transform to cancel grain noise. Usually, grain noise occurs in changes of the crystalline structure of metals in high temperature environment. It makes the detection of flaw difficult. The WLMS adaptive filtering algorithm establishes the faster convergence rate by orthogonalizaing the input vector of adaptive filter as compared with that of LMS adaptive filtering algorithm in time domain. We implemented the WLMS adaptive filter by using the delayed version of the primary input vector as the reference input vector and then implemented the CA-CFAR(Cell Averaging- Constant False Alarm Rate) threshold estimator. CA-CFAR threshold estimator enables to detect the flaw and back echo signals automatically. Here, we used the output signals of adaptive filter as its input signal. To Cow the statistical characteristic of ultrasonic signals corrupted by grain noise, we performed run test. The results showed that ultrasonic signals are nonstationary signal, that is, signals whose statistical properties vary with time. The performance of each filter is appreciated by the signal-to-noise ratio. After LMS adaptive filtering in time domain, SNR improves to about 2-3㏈ but after WLMS adaptive filtering in wavelet domain, SNR improves to about 4-6㏈.

• Journal of Digital Contents Society
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• v.11 no.2
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• pp.217-224
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• 2010

The purpose of this study was to examine the usefulness of IDEAL technique in breast MRI by performing a quantitative comparative analysis in patients diagnosed with DCIS. On a 3.0T MR scanner, fat-suppressed T2-weighted images and T1-weighted images before and after contrast enhancement were obtained from 20 patients histologically diagnosed with ductal carcinoma in situ (DCIS). The findings from the quantitative image analysis are the following: 1) On T2-weighted images, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 2) On T1-weighted images before enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group. In addition, the CNR were higher for the IDEAL group in those regions. 3) On T1-weighted images after enhancement, SNR were not significantly different in the lesion area itself between the CHESS and IDEAL groups, while the IDEAL group showed higher SNR at the ductal area and fat area than the CHESS group.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.283-290
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• 2010

The effectiveness of 3.0T phase array coil images was tested by comparing signal-to-noise ratios for the same coil images relative to 1.5T endorectal coil images. Signal intensities were measured in the three regions of prostate, central and peripheral (right and left) after 40 patients with prostate cancer were imaged during the period between Jan. 2008 and Oct. 2009 with T2 W, T1 W, and DW images obtained respectively using endorectal coil on a 1.5T MR scanner and phase array coil on a 3.0T MR scanner. For quantitative analysis, comparisons of average SNRs for the same ROIs were made between groups scanned with a 1.5T and a 3.0T MR scanner. The signal-to-noise ratios were shown to increase more sharply when using a phase array coil at a 3.0T MR scanner compared to using an endorectal coil at a 1.5T MR scanner.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1102-1109
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• 2013

Blind interference alignment (BIA) scheme has demonstrated a way of interference alignment (IA) without channel state information at transmitter (CSIT). While it shows superior performance in high signal-to-noise ratio (SNR) regime stemming from the maximal degrees of freedom (DoF) gain, BIA scheme achieves inferior sum-rate performance in low SNR regime. This paper proposes a dual-mode transmission strategy which switches between single user (SU) SISO with receive mode selection and the BIA scheme depending upon the range of SNR. First, we derive a closed-form achievable rate for each transmission-mode. Secondly, we propose a low-complex transmission-mode selection algorithm.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.159-169
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• 2017

The Satellite Electro-Optic Payload System needsspecial requirements with the conditions of limited power consumption and the space environment of solar radiation. The acquired image quality should be mainly depend on the GSD (Ground Sampled Distance), SNR (Signal to Noise Ratio), and MTF (Modulation Transfer Function). On the well-manufactured sensor level, the thermal noise is removed on ASP (Analog Signal Processing) using the CDS (Corrective Double Sampling); the noise signal from the image sensor can be reduced from the offset signals based on the pre-pixels and the dark-pixels. The non-uniformity shall be corrected with gain, offset, and correction parameter of the image sensor pixel characteristic on the sensor control system. This paper describes the SNR enhancement method of the satellite EOS payload using the mentioned noise remove processes on the system design and operation, which is verified by tests and simulations.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1061-1068
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• 2012

This paper deals with a noise reduction algorithm which uses the binary masking approach in the time-frequency domain to improve speech intelligibility. In the binary masking approach, the noise-corrupted speech is decomposed into time-frequency units. Noise-dominant time-frequency units are removed by setting the corresponding binary masks as "0"s and target-dominant units are retained untouched by assigning mask "1"s. We propose a binary mask estimation by comparing the local signal-to-noise ratio (SNR) to a threshold. The local SNR is estimated by a training-based approach. An optimal threshold is proposed, which is obtained from observing the distribution of the training database. The proposed method is evaluated by normal-hearing subjects and the intelligibility scores are computed by counting the number of words correctly recognized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1886-1891
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• 2017

Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.