• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.3
• /
• pp.188-193
• /
• 2011

In this letter, we have derived another exact performance analysis for the OADF(opportunistic adaptive decode-and-forward) relay systems over Rayleigh fading channels. Based on error-events at relay nodes, the received instantaneous SNR(signal-to-noise ratio) is presented and its PDF(probability density function) is expressed as a more tractable form in which the number of summations and the length of each summation are specified. Then, exact average error rate, outage probability, and average channel capacity are obtained as general forms. Simulation results are finally presented to validate that the proposed analytical expressions can be a unified frame work covering all Rayleigh fading channel conditions. Furthermore, it is confirmed that OADF schemes can outperform the other schemes on the average error rate, outage probability, and average channel capacity.

• The Journal of the Korea Contents Association
• /
• v.9 no.1
• /
• pp.159-166
• /
• 2009

In this paper, we analyse the noise robustness of MLPs(Multilayer perceptrons) through deriving the probability density function(p.d.f.) of output nodes with additive input noises and the misclassification ratio with the integral form of the p.d.f. functions. Also, we propose linear preprocessing methods to improve the noise robustness. As a preprocessing stage of MLPs, we consider ICA(independent component analysis) and PCA(principle component analysis). After analyzing the noise reduction effect using PCA or ICA in the viewpoints of SNR(Singal-to-Noise Ratio), we verify the preprocessing effects through the simulations of handwritten-digit recognition problems.

• Journal of Sensor Science and Technology
• /
• v.9 no.4
• /
• pp.316-326
• /
• 2000

A new signal processing algorithm for white light interferometry has been proposed and investigated theoretically. The goal of the algorithm is to determine the absolute optical path length of an interferometer with very high precision (<< one optical wavelength). The algorithm features cross-correlation of interferometer fringe scans and hypothesis testing. The hypothesis test looks for a zero order fringe peak candidate about which the cross-correlation is symmetric minimizing the uncertainty of misidentification. The shot noise limited performance of the proposed signal processing algorithm has been analyzed using computer simulations. Simulation results were extrapolated to predict the misidentification rate at Signal to-Shot noise ratio (SNR) higher than 31 dB. Root-mean-square phase error between the computer-generated zero order fringe peak and the estimated zero order fringe peak has been calculated for the changes of three different parameters (SNR, fringe scan sampling rate, coherence length of light source). Results of computer simulations showed the ability of the proposed signal processing algorithm to identify the zero order fringe peak correctly. The proposed signal processing algorithm uses a software approach, which is potentially inexpensive, simple and fast.

• Journal of radiological science and technology
• /
• v.40 no.3
• /
• pp.393-400
• /
• 2017

The purpose of this study is to suggest a method to reduce the dose by Analyzing the dose area product (DAP) and image quality according to the change of tube current using NEMA Phantom. The spatial resolution and low contrast resolution were used as evaluation criteria in addition to signal to noise ratio (SNR) and contrast to noise ratio (CNR), which are important image quality parameters of intervention. Tube voltage was fixed at 80 kVp and the amount of tube current was changed to 20, 30, 40, and 50 mAs, and the dose area product and image quality were compared and analyzed. As a result, the dose area product increased from $1066mGycm^2$ to $6160mGycm^2$ to 6 times as the condition increased, while the spatial resolution and low contrast resolution were higher than 20 mAs and 30 mAs, Spatial resolution and low contrast resolution were observed below the evaluation criteria. In addition, the SNR and CNR increased up to 30 mAs, slightly increased at 40 mAs, but not significantly different from the previous one, and decreased at 50 mAs. As a result, the exposure dose significantly increased due to overexposure of the test conditions and the image quality deteriorated in all areas of spatial resolution, low contrast resolution, SNR and CNR.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.4
• /
• pp.387-395
• /
• 2019

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

• Journal of information and communication convergence engineering
• /
• v.10 no.2
• /
• pp.162-167
• /
• 2012

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.

• Journal of the Korean Society of Radiology
• /
• v.15 no.1
• /
• pp.9-14
• /
• 2021

Recently, magnetic resonance imaging (MRI), which can acquire images with good contrast without exposure to radiation, has been widely used for diagnosis. However, noise that reduces the accuracy of diagnosis is essentially generated when acquiring the MR images, and by adjusting the parameters, the noise problem can be solved to obtain an image with excellent characteristics. Among the parameters, the number of excitation (NEX) can acquire images with excellent characteristics without additional degradation of image characteristics. In contrast, appropriate NEX setting is required since the scan time increases and motion artifacts may occur. Therefore, in this study, after fixing all MRI parameters through the MRiLab simulation program, we tried to evaluate the tendency of image characteristics according to changing NEX through quantitative evaluation of brain T2 weighted images acquired by adjusting only NEX. To evaluate the noise level and similarity of the acquired image, signal to noise ratio (SNR), contrast to noise ratio (CNR), root mean square error (RMSE) and peak signal to noise ratio (PSNR) were calculated. As a result, both noise level and similarity evaluation factors showed improved values as NEX increased, while the increasing width gradually decreased. In conclusion, we demonstrated that an appropriate NEX setting is important because an excessively large NEX does not affect image characteristics improvement and cause motion artifacts due to a long scan.

• Journal of information and communication convergence engineering
• /
• v.5 no.1
• /
• pp.1-6
• /
• 2007

In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.

• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.95-98
• /
• 2001

The adaptive line enhancer (ALE) is widely used for enhancing narrowband signals corrupted by broadband noise. In this paper, we propose novel ALE methods to improve the enhancing capability. The proposed methods are motivated by the fact that the output of the ALE is a fine estimate of the desired narrowband signal with the broadband noise component suppressed. The proposed methods preprocess the input signal using ALE filter to regenerate a finer input signal. Thus the proposed ALE is driven by the input signal with higher signal-to-noise ratio (SNR). The analysis and simulation results are presented to demonstrate that the proposed ALE has better performance than conventional ALE´s.

• The Journal of the Acoustical Society of Korea
• /
• v.43 no.1
• /
• pp.78-88
• /
• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.