• The Journal of Engineering Geology
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• v.25 no.1
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• pp.103-113
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• 2015

This study presents a novel algorithm for determining the P-wave arrival time using amoving window function to improve source localization in low-SNR (signal-to-noise ratio)acoustic emissions. The proposed algorithm was applied to low-SNR signals to verify the accuracy of measurements against existing algorithms. When other algorithms were applied, the test results revealed that SNR decreased and accuracy was reduced, especially where SNR wasless than 2.14. The proposed algorithm using amoving window function considers the frequency characteristic and signal amplitude simultaneously, and produced reliable results where SNR was 2.14.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8A
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• pp.671-678
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• 2005

This paper proposed symbol rate method which does not require a priori knowledge on the symbol rate and simplified modulation identification method to classify BPSK, QPSK, 8PSK signal. In order to estimate the unknown symbol rate, sliding FFT and simple moving average to estimate the spectrum of the signals is utilized, and sliding window and decimation, LPF blcok to estimate the proper symbol rate is used. Although conventional modulation ID method must use SNR value as the test statistics, the receiver cannot estimate the SNR value since the receiver cannot know the modulation type at the start of communication, and bit resolution is high due to using nonlinear function such as log, cosh. Therefore, we proposed the simplified fixed SNR value method. The performance of symbol rate estimation and modulation ID is shown using Monte Carlo computer simulation. This paper show that symbol rate estimation also has good performance in low SNR, and proposed simplified fixed SNR method has almost equivalent performance compared to conventional method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1083-1096
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• 2021

Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value u_op by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and u_op. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1287-1296
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• 2009

It is very important to estimate the Signal to Noise Ratio(SNR) of received signal in time varying channel state. Most SNR estimation techniques derive the SNR estimates solely from the samples of the received signal after the matched filter. In the severe distorted wireless channel, the performance of these estimators become unstable and degraded. LP-based SNR estimator which can operate on data samples collected at the front-end of a receiver shows more stable performance than other SNR estimator. In this paper, we study an efficient SNR estimation algorithm based on LP and propose a new estimation method to decrease the computation complexity. Proposed algorithm accomplishes the SNR estimation process efficiently because it uses the forward prediction error and its conjugate value during the linear prediction error update. Via the computer simulation, the performance of this proposed estimation method is compared and discussed with other conventional SNR estimators in digital communication channels.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.169-174
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• 2010

This paper explains end-point detection method for better speech recognition rates. The proposed method determines speech and non-speech region with the entropy and the harmonic detection of speech. The end-point detection using entropy on the speech spectral energy has good performance at the high SNR(SNR 15dB) environments. At the low SNR environment(SNR 0dB), however, the threshold level of speech and noise varies, so the precise end-point detection is difficult. Therefore, this paper introduces the end-point detection methods which uses speech spectral entropy and harmonics. Experiment shows better performance than the conventional entropy methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1756-1760
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• 2015

This paper focuses on a priori signal to noise ratio (SNR) estimation method for the speech enhancement. There are many researches for speech enhancement with several ambient noise cancellation methods. The method based on spectral subtraction (SS) which is widely used in noise reduction has a trade-off between the performance and the distortion of the signals. So the need of adaptive method like an estimated a priori SNR being able to making a high performance and low distortion is increasing. The decision directed (DD) approach is used to determine a priori SNR in noisy speech signals. A priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a modified a priori SNR estimator and the weighted rational transfer function for speech enhancement with wind noises. The experimental result shows the performance of our proposed estimator is better Perceptual Evaluation of Speech Quality scores (PESQ, ITU-T P.862) compare to the conventional DD approach-based systems and different noise reduction methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1121-1126
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• 2017

Various studies such as INS(: Inertial Navigation System) are conducting to estimate the position of UAV, because the GPS information of UAV is at risk like the GPS jamming. The position estimation using DoA and RTT are used to apply many radar systems, and that process can be applied in datalink of UAV. The general monopulse feed in UAV datalink is Multi-horn, because of the wide BW(: Band Width) and frequency range. And it needs wide SNR range of tracking because of the limited transmit power of airborne unit. The estimation error of position increase at low SNR, and the DoA is valid in only 3dB beam width but high SNR causes false of mainlobe detection because of large sidelobe. In this paper, We propose the method to achieve higher accuracy of DoA estimation on low SNR and review some idea that able to detect mainlobe.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1571-1576
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• 2013

This paper proposes an improvement algorithm of signal-to-noise ratios (SNRs) for speech signals under noisy environments. The proposed algorithm first estimates the SNRs in a low SNR, mid SNR and high SNR areas, in order to improve the SNRs in the speech signal from background noise, such as white noise and car noise. Thereafter, this algorithm subtracts the noise signal from the noisy speech signal at each bands using a spectrum sharpening method. In the experiment, good signal-to-noise ratios (SNR) are obtained for white noise and car noise compared with a conventional spectral subtraction method. From the experiment results, the maximal improvement in the output SNR results was approximately 4.2 dB and 3.7 dB better for white noise and car noise compared with the results of the spectral subtraction method, in the background noisy environment, respectively.

• Journal of IKEEE
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• v.19 no.1
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• pp.87-93
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• 2015

In this paper, a $145{\mu}W$, 87dB SNR, Low power 3rd order Sigma-Delta Modulator with Op-amp sharing is proposed. Conventional architecture with analog path and digital path is improved by adding a delayed feed -forward path for disadvantages that coefficient value of the first integrator is small. Proposed architecture has a larger coefficient value of the first integrator to remove the digital path. Power consumption of proposed architecture using op-amp sharing is lower than conventional architecture. Simulation results for the proposed SDM designed in $0.18{\mu}m$ CMOS technology with power supply voltage 1.8V, signal bandwidth 20KHz and sampling frequency 2.8224MHz shows SNR(Signal to Noise Ratio) of 87dB, the power consumption of $145{\mu}W$.