• ETRI Journal
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• v.44 no.5
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• pp.826-836
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• 2022

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational results show that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classification among state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.50-57
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• 1994

In the non-destructive evaluation techniques using ultrasonic signal, backscattering noise from grain interface decreases the SNR of received signal. In this paper, SSP(split-spectrum processing) based on the constant FBR decomposition method has been applied to enhance the SNR. This algorithm helps to find optimal parameters of filter bank through a simple theory and has an advantage that reduce the signal processing time compared with the conventional constant bandwidth decomposition method. In this experiment, the 304 stainless steel sample is heat-treated and received ultrasonic signal is processed by SSP using the constand bandwidth decomposition method and the constand FBR decomposition method enhanced the SNR by 1.4 dB and reduced the required number of filters by 4 compared with the constant bandwidth decomposition method.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.109-117
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• 2008

In this study, a method predicting the displacement responseof structures from the measured dynamic strain signal is proposed by using a mode decomposition technique. Dynamic loadings including wind and seismic loadings could be exerted to the bridge. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. To overcome such a problem, a mode decomposition technique was used in this study. The measured strain signal is decomposed into each modal component by using the empirical mode decomposition(EMD) as one of mode decomposition techniques. Then, the decomposed strain signals on each modal component are transformed into the modal displacement components. And the corresponding mode shapes can be also estimated by using the proper orthogonal decomposition(POD) from the measured strain signal. Thus, total displacement response could be predicted from combining the modal displacement components.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.2
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• pp.65-72
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• 2006

In digital signal processing, the polyphase decomposition of signal has been often used in the implementation of multirate system. Especially, in the design of digital filter and so forth the method in very useful to improve the performance of various algorithms because it provides the multi-channel for paralled processing. Generally, the polyphase-decomposed signals tend to expand the frequency band by including more high frequencies than original signal from decimation for down sampling. This property brings about the significant limitation in the structure or the performance of digital polyphase signal processing system. In this paper we theoretically propose a perfect band compression and reconstruction method for polyphase component signals, then experimentally show its effectiveness through Matlab simulation.

• ETRI Journal
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• v.45 no.1
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• pp.138-149
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• 2023

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2671-2676
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• 2009

Empirical mode decomposition (EMD) is new time-frequency analysis method to decompose the signal adaptively and efficiently. The key idea of EMD is to decompose the signal into a set of functions defined by the signal itself, named Intrinsic Mode Functions (IMFs), which preserve the inherent properties of the original signal. Since the decomposition is based on the local time scale of the signal, it is not only applicable to nonlinear and non-stationary processes but also useful in biomedical signals like electrocardiogram (ECG). Traditional low-pass filter uses fourier transform to analysis signal in frequency domain, but EMD is filtered to maintain signal properties in time domain. This paper performed signal decomposition and filtering for noisy ECGs using EMD method. The proposed method is presented and compared with traditional low-pass filter by two performance indices. Our results show effectiveness for enhancement of the noisy ECG waveforms.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.281-288
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• 1994

In this paper, a new decomposition method of the interference EMG signal using MAMDF filtering and digital signal processor. The efficient software and hardware signal processing techniques are employed. The MAMDF filter is employed in order to estimate the presence and likely location of the respective templates which may include in the observed mixture, and high-resolution waveform alignment is employed in order to provide the optimal combination set and time delays of the selected templates. The TMS320C25 digital signal processor chip is employed in order to execute the intensive calculation part of the software. The method is verified through a simulation with real templates which are obtain ed from needle EMG. As a result, the proposed method provides an overall speed improvement of 32-40 times.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.438-443
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• 2006

In this paper, We had proposed SDR module, and designed FPGA to compose with channel separation of broadband CDMA signal what have multiple FA. At decomposition and composition process of multiple FA CDMA signal, system only progress decomposition and composition of channel selected by software. Therefore, proposed system can manage base station transceiver system very effectively than the other way what send on all band of multiple CDMA signal. Also, it is possible that system sets again coefficient of each filter because it is consisted of SDR module. Therefore, we can easily control coefficient each filter according to base station transceiver system environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.279-286
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• 2015

EMD is a fully data-driven signal processing method without using any predetermined basis function and requiring any user parameters setting. However EMD experiences a problem of mode mixing which interferes with decomposing the signal into similar oscillations within a mode. To overcome the problem, EEMD method was introduced. The algorithm performs the EMD method over an ensemble of the signal added independent identically distributed white noise of the same standard deviation. Even so EEMD created problems when the decomposition is complete. The ensemble of different signal with added noise may produce different number of modes and the reconstructed signal includes residual noise. This paper propose an modified EEMD method to overcome mode mixing of EMD, to provide an exact reconstruction of the original signal, and to separate modes with lower cost than EEMD's. The experimental results show that the proposed method provides a better separation of the modes with less number of sifting iterations, costs 20.87% for a complete decomposition of the signal and demonstrates superior performance in the signal reconstruction, compared with EEMD.

• Speech Sciences
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• v.8 no.4
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• pp.45-58
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• 2001

Modulated components of a speech signal are frequently used for speech coding, speech recognition, and speech synthesis. Time-frequency representation (TFR) reveals some information about instantaneous frequency, instantaneous bandwidth and boundary of each component of the considering speech signal. In many cases, the extraction of AM-FM components corresponding to instantaneous frequencies is difficult since the Fourier spectra of the components with time-varying instantaneous frequency are overlapped each other in Fourier frequency domain. In this paper, an efficient method decomposing speech signal into AM-FM components is proposed. A variable bandwidth filter is developed for the decomposition of speech signals with time-varying instantaneous frequencies. The variable bandwidth filter can extract AM-FM components of a speech signal whose TFRs are not overlapped in timefrequency domain. Also, amplitude and instantaneous frequency of the decomposed components are estimated by using Hilbert transform.