• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.26-32
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• 2003

We propose new time-frequency (TF) tools for analyzing linear time-varying (LTV) systems and nonstationary random processes showing hyperbolic TF structure. Obtained through hyperbolic warping the narrowband Weyl symbol (WS) and spreading function (SF) in frequency, the new TF tools are useful for analyzing LTV systems and random processes characterized by hyperbolic time shifts. This new TF symbol, called the hyperbolic WS, satisfies the hyperbolic time-shift covariance and scale covariance properties, and is useful in wideband signal analysis. Using the new, hyperbolic time-shift covariant WS and 2-D TF kernels, we provide a formulation for the hyperbolic time-shift covariant TF symbols, which are 2-D smoothed versions of the hyperbolic WS. We also propose a new interpretation of linear signal transformations as weighted superposition of hyperbolic time shifted and scale changed versions of the signal. Application examples in signal analysis and detection demonstrate the advantages of our new results.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.507-516
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• 1995

The nonstationary identifier in the DCT domain is suggested in this study for the identification of AR parameters of above-lesion upper-trunk electromyographic (EMG) signals as a means of developing a reliable real time signal to control functional electrical stimulation (FES) in paraplegics to enable primitive walking. As paraplegic shifts his posture from one attitude to another, there is transition period where the signal is clearly nonstationary. Also as muscle fatigues, nonstationarities become more prevalent even during stable postures. So, it requires a develpment of time varying nonstationary EMG signal identifier. In this paper, time varying nonstationary EMG signals are transformed into DCT domain and the transformed EMG signals are modeled and analyzed in the transform domain. In the DCT domain, we verified reduction of condition number and increment of the smallest eigenvalue of input correlation matrix that influences numerical properties and mean square error were compared with SLS algorithm, and the proposed algorithm is implemented using IMS T-805 parallel processing computer for real time application.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.674-679
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• 1994

Acoustic signal such as speech and scattered sound, are generally a nonstationary process whose frequency contents vary at any instant of time. For time-varying signal, whether a nonstationary or a deterministic transient signal, a traditional frequency domain representation does not reveal the contents of signal characteristics and may lead to erroneous results such as the loss of desired characteristics features or the mis-interpretation for a wrong conclusion. A time-frequency domain representation is needed to characterize such signatures. Pseudo Wigner-Ville distribution (PWVD) is ideally suited for portraying nonstationary signal time-frequency domain and carried out by adapting the fast Fourier transform algorithm. In this paper, the important properties of PWVD were investigated using both stationary and nonstationry signatures by numerical examples PWVD was applied to acoustic sigtnatures to demonstrate its application for time-ferquency domain analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1278-1285
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• 2008

An optimization-based real-time joint angle extraction method of human elbow is proposed by processing the biomedical signal of surface EMG (electromyogram) measured at the center point of biceps brachii. The EMG signal is known as non-stationary (time-varying) signal, but we assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from raw EMG signal is firstly suggested, and then an optimization method to minimize the error between the pre-angle and real joint angle is proposed in this paper. Finally, we suggest the experimental results showing the effectiveness of the proposed algorithm.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.05
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• pp.213-217
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• 1992

An inductively coupled magnetical signal (pulse wave, 0.7 to 60Hz, eighteen volts peak to Peak) that was applied non-invasively on the skin surface overlying the approximate site(measure position). In the group with unipolar pulse signal currents produced smaller than in the group with bipolar pulse signal. The signal was transmitted to the active coil, including a time-varying magnetic field: this in turn induced a the-varying electrical field in the field in the bone. It is very important to determine system parameters due to treatment time(healing) and the simplicity. This paper investigation was designed to compare the relative effects of pulsed unipolar currents with the effects of an identical pulsed bipolar currents. Since Inductive coupling is non-invasive and involves portable equipment, it is easy to apply and requires precise localization, it has distinct advantages and field characteristics along the bone for each different signal.

• Smart Structures and Systems
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• v.33 no.2
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• pp.145-163
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• 2024

The evaluation of dynamic characteristics of bridges under operational traffic loads is a crucial aspect of bridge structural health monitoring. In the vehicle-bridge interaction (VBI) system, the vibration responses of bridge exhibit time-varying characteristics. To address this issue, an accurate time-frequency analysis method that combines the autoregressive power spectrum based empirical wavelet transform (AR-EWT) and local maximum synchrosqueezing transform (LMSST) is proposed to identify the time-varying instantaneous frequencies (IFs) of the bridge in the VBI system. The AR-EWT method decomposes the vibration response of the bridge into mono-component signals. Then, LMSST is employed to identify the IFs of each mono-component signal. The AR-EWT combined with the LMSST method (AR-EWT+LMSST) can resolve the problem that LMSST cannot effectively identify the multi-component signals with weak amplitude components. The proposed AR-EWT+LMSST method is compared with some advanced time-frequency analysis techniques such as synchrosqueezing transform (SST), synchroextracting transform (SET), and LMSST. The results demonstrate that the proposed AR-EWT+LMSST method can improve the accuracy of identified IFs. The effectiveness and applicability of the proposed method are validated through a multi-component signal, a VBI numerical model with a four-degree-of-freedom half-car, and a VBI model experiment. The effect of vehicle characteristics, vehicle speed, and road surface roughness on the identified IFs of bridge are investigated.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.202-208
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• 2012

Fetal heart rate monitoring is important information to assess fetal well-being. Non-invasive fetal ECG (electrocardiography) can be derived from maternal abdominal signal. And various promising signal processing methods have been introduced to extract fetal ECG from mother's composite abdominal signal. However, non-invasive fetal ECG monitoring still has not been widely used in clinical practice due to insufficient reliable measurement and difficulty of signal processing. In application of signal processing method to extract fetal ECG, it might be lower signal to noise ratio due to time varying white Gaussian noise. In this paper, time varying Kalman smoother is proposed to remove white noise in fetal ECG and its feasibility is confirmed. Wiener process was set as Kalman system model and covariance matrix was modified according to white Gaussian noise level. Modified error covariance matrix changed Kalman gain and degree of smoothness. Optimal covariance matrix according to various amplitude in Gaussian white noise was extracted by 5 channel fetal ECG model, and feasibility of proposed method could be confirmed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.7 no.1
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• pp.79-86
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• 2003

A myoelectric signal, under sustained isometric contraction of muscle the modelled as the output of a linear time-varying system whose input is constant number of pulse train. The proposed model considered localized muscle fatigue by metabolic by-products during sustained fatiguing contraction. To characterize muscle fatiguing model of myoelectric signal, We calculated median frequency of generated signal as fatiguing index of muscle during sustained isometric contraction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.226-229
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• 2001

Radio signal tracking has been developed especially in military as well as in other industries. It is necessary that an adaptive system trace the signal varying its PRI and frequency. In this paper we proposed a system to adapt various PRI and frequency using a neural network model named Error Back Propagation. Fist we prepared learning data by separating signal into time intervals and did some experiments with the teaming data. We found that the system had good effectiveness in tracing varying PRI and frequency signals.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.832-844
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• 2020

The degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases or on the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multi-step recursive algorithm for variance calculation of the time-varying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multi-step recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for time-varying data, which demonstrates the potential value for time-consumption data analysis or high speed signal processing.