• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.611-616
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• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. Then we can find defect frequency component in the envelope spectrum of the bearing signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.362.2-362
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• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) Is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. (omitted)

• Journal of information and communication convergence engineering
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• v.2 no.3
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• pp.145-148
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• 2004

The performances of M-ary signals using L-branch maximum ratio combining (MRC) diversity reception in correlated Nakagami fading channels are derived theoretically. The coherent reception of M-ary differential phase shift keying (MDPSK), phase shift keying (MPSK), and quadrature amplitude modulation (MQAM) is considered. It is assumed that the fading parameters in each diversity branch are identical. The general formula for evaluating symbol error rate (SER) of M-ary signals in the independent branch diversity system is presented using the integral-form expressions.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.283-290
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• 2021

Recently, correlated superposition coding (CSC) has been proposed to implement non-orthogonal multiple access (NOMA) without successive interference cancellation (SIC), without loss of spectral efficiency, in contrast to conventional independent superposition coding (ISC). However, correlation between signals has reduced the average total allocated power, which results in degraded performance. Thus, in order to avoid the reduction of the average total allocated power owing to correlation between signals, this paper proposes a cross-correlated quadrature amplitude modulation (QAM) NOMA scheme under Rayleigh fading channel surroundings. First, we design the cross-correlated QAM NOMA scheme. Then, simulations demonstrate that for the weaker channel gain's user, the symbol error rate (SER) performance of the proposed cross-correlated QAM NOMA improves largely, whereas for the stronger channel gain's user, the SER performance of the proposed cross-correlated QAM CSM NOMA degrades little, compared to that of the conventional QAM NOMA.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.136-140
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• 2009

The performances of M-ary signals using L-branch maximum ratio combining (MRC) diversity reception in correlated Nakagami fading channels are derived theoretically. The coherent reception of M-ary differential phase shift keying (MDPSK), phase shift keying (MPSK), and quadrature amplitude modulation (MQAM) is considered. It is assumed that the fading parameters in each diversity branch are identical. The general formula for evaluating symbol error rate (SER) of M-ary signals in the independent branch diversity system is presented using the integral-form expressions. Until now, results did not extend to the various M-ary case for a coherent reception. The numerical results presented in this paper are expected to provide information for the design of radio system using M-ary modulation method for above mentioned channel environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.765-774
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• 2013

In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

• Progress in Superconductivity
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• v.4 no.2
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• pp.114-120
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• 2003

We employed a method eliminating a temporally partial principal component (PC) of multichannel-recorded neuromagnetic fields for excluding spatially correlated noises from event-evoked signals. The noises in magnetoencephalography (MEG) are considered to be mainly spontaneous neuromagnetic fields which are spatially correlated. In conventional MEG experiments, the amplitude of the spontaneous neuromagnetic field is much lager than that of the evoked signal and the synchronized characteristics of the correlated rhythmic noise makes it possible for us to extract the correlation noises from the evoked signal by means of the general PC analysis. However, the whole-time PC of the fields still contains a little projection component of the evoked signal and the elimination of the PC results in the distortion of the evoked signal. Especially, the distortion will not be negligible when the amplitude of the evoked signal is relatively large or when the evoked signals have a spatially-asymmetrical distribution which does not cancel out the corresponding elements of the covariance matrix. In the period of prestimulus, there are only the spontaneous fields and we can find the pure noise PC that is not including the evoked signal. Besides that, we propose a method, called the extended temporal decorrelation method (ETDM), to suppress the distortion of the noise PC from remanent evoked signal components. In this study, we applied the Partial Principal component elimination method (PPCE) and ETDM to simulated signals and the auditory evoked signals that had been obtained with our homemade 37-channel magnetometer-based SQUID system. We demonstrate here that PPCE and ETDM reduce the number of epochs required in averaging to about half of that required in conventional averaging.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.34-41
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• 2021

Recently, a lossless non-successive interference cancellation (SIC) non-orthogonal multiple access (NOMA) implementation has been proposed. Such lossless NOMA without SIC is achieved via correlated superposition coding (SC), in comparison with conventional independent SC. However, only high-correlated SC was investigated in the lossless non-SIC NOMA implementation. Thus, this paper investigates low-correlated SC, especially a lossless interval, owing to low-correlation between signals. First, for the low-correlated SC scheme, we derive the closed-form expressions for the two roots with which the lossless interval is defined. Then, simulations demonstrate that the lossless interval of low-correlated SC NOMA is enlarged, with a degraded middle interval, compared to that of high-correlated SC NOMA. Moreover, we also show that such tendency becomes stronger as the value of the correlation coefficient varies. As a result, the proposed low-correlated SC scheme could be considered as a promising correlated SC scheme, with the enlarged lossless interval in NOMA toward the future sixth-generation (6G) ultra-reliable low-latency communications (URLLC).

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.35-41
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• 1993

In this paper, a new speech enhancement method for speech signal corrupted by environmental noise is proposed. Two signals are obtained from the microphone and from the accelerometer attached to the neck, respectively. Since two signals are generated from same source signal, both signals are closely correlated. And environmental noise has no effect on the accelerometer signal. The speech enhancement system identifies the optimum linear system between two signals on the basis of the dependence between the signals. The enhanced speech can be obtained by filtering the noise-free accelerometer signal. Since the characteristcs of the speech signal and environmental noise are changing with time, adaptive filtering system has to be used for characterizing the time-varing system. Simulation results show 7dB enhancement with 0dB speech signal level relative to the white noise.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.76.1-76.1
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• 2012

One of the main challenges for future 21-cm observations is to remove foregrounds which are several orders of magnitude more intense than the HI signal. We propose a new technique for removing foregrounds of the redshifted 21-cm observations. We consider multi-frequency interferometer observations. We assume that the 21-cm signals in different frequency channels are uncorrelated and the foreground signals change slowly as a function of frequency. When we add the visibilities of all channels, the foreground signals increase roughly by a factor of N because they are highly correlated. However, the 21-cm signals increase by a factor of sqrt{N} because the signals in different channels contribute randomly. This enables us to obtain an accurate shape of the foreground angular power spectrum. Then, we obtain the 21-cm power spectrum by subtracting the foreground power spectrum obtained this way. We describe how to obtain the average power spectrum of the 21-cm signal.