• Title/Summary/Keyword: Nonstationary Signal

Search Result 74, Processing Time 0.035 seconds

A Study on an Automatic FES Control System for Paraplegic Walking Against Muscle Fatigue (근육피로도를 고려한 하반신 마비환자의 보행 자동제어 FES 시스템에 관한 연구)

  • Min, Byoung-Gwan;Kim, Jong-Weon;Kim, Sung-Hwan
    • Journal of Biomedical Engineering Research
    • /
    • v.15 no.2
    • /
    • pp.167-174
    • /
    • 1994
  • In this paper, a DSP and microcomputer-based EMG controlled functional electrical stimulation (FES) system, for restoring walking of paraplegics at the patients' own command, is presented. The above-lesion EMG is a time-varying nonstationary signal and its autoregressive (AR) parameters are identified by the nonstationary identification algorithm using a DSP chip. The identified AR parameters are used for the cloassification of the function and the control of the movement. The below-lesion response-EMG signal is used as a measure of muscle fatigue. This FES system is designed to measure muscle fatigue and control the stimulation intensity according to the amplitude of the response-EMG signal. While the automatic electrical intensity control is obtained by identifying the movement, the proposed FES system is suitable for the automatic control of paraplegic walking.

  • PDF

Time-Frequency Analysis Using Linear Combination Wavelet Transform and Its Application to Diagnostic Monitoring System (선형조합 웨이브릿 변환을 사용한 시간-주파수 분석 및 진단 모니터링 시스템의 적용)

  • 김민수;권기룡;김석태
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.3 no.1
    • /
    • pp.83-95
    • /
    • 1999
  • Wavelet transform has localization for time or frequency. It is useful to analyze a nonstationary signal. Basic function on wavelet transform is generated dilating and translating the original wavelet(mother wavelet). In this paper, time-frequency analysis method using linear combination wavelet transform is proposed. And it is applied to diagnostic monitoring system using the proposed linear combination wavelet transform. The stationary and nonstationary signal is used linear chirp signal, fan noise signal, a sinusoid signal from revolution body, electronic signal. Transform applied to signal analysis use fast Fourier transform (FFT), Daubechies, Haar and proposed linear combination method. The result of time-frequency analysis using linear combination wavelet transform is suited for portraying nonstationary time signal as well as stationary signal. Also the diagnostic monitoring system carry out the effective the signal analysis.

  • PDF

Speech Enhancement Using Multiple Kalman Filter (다중칼만필터를 이용한 음성향상)

  • 이기용
    • Proceedings of the Acoustical Society of Korea Conference
    • /
    • 1998.08a
    • /
    • pp.225-230
    • /
    • 1998
  • In this paper, a Kalman filter approach for enhancing speech signals degraded by statistically independent additive nonstationary noise is developed. The autoregressive hidden markov model is used for modeling the statistical characteristics of both the clean speech signal and the nonstationary noise process. In this case, the speech enhancement comprises a weighted sum of conditional mean estimators for the composite states of the models for the speech and noise, where the weights equal to the posterior probabilities of the composite states, given the noisy speech. The conditional mean estimators use a smoothing spproach based on two Kalmean filters with Markovian switching coefficients, where one of the filters propagates in the forward-time direction with one frame. The proposed method is tested against the noisy speech signals degraded by Gaussian colored noise or nonstationary noise at various input signal-to-noise ratios. An app개ximate improvement of 4.7-5.2 dB is SNR is achieved at input SNR 10 and 15 dB. Also, in a comparison of conventional and the proposed methods, an improvement of the about 0.3 dB in SNR is obtained with our proposed method.

  • PDF

Online Probability Density Estimation of Nonstationary Random Signal using Dynamic Bayesian Networks

  • Cho, Hyun-Cheol;Fadali, M. Sami;Lee, Kwon-Soon
    • International Journal of Control, Automation, and Systems
    • /
    • v.6 no.1
    • /
    • pp.109-118
    • /
    • 2008
  • We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.

Noise Reduction Using the Standard Deviation of the Time-Frequency Bin and Modified Gain Function for Speech Enhancement in Stationary and Nonstationary Noisy Environments

  • Lee, Soo-Jeong;Kim, Soon-Hyob
    • The Journal of the Acoustical Society of Korea
    • /
    • v.26 no.3E
    • /
    • pp.87-96
    • /
    • 2007
  • In this paper we propose a new noise reduction algorithm for stationary and nonstationary noisy environments. Our algorithm classifies the speech and noise signal contributions in time-frequency bins, and is not based on a spectral algorithm or a minimum statistics approach. It relies on calculating the ratio of the standard deviation of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. We show that good quality can be achieved for enhancement speech signal by choosing appropriate values for ${\delta}_t\;and\;{\delta}_f$. The proposed method greatly reduces the noise while providing enhanced speech with lower residual noise and somewhat higher mean opinion score (MOS), background intrusiveness (BAK) and signal distortion (SIG) scores than conventional methods.

A Study of Data Compression of Power Quality Disturbance Signal (전력품질 왜곡 신호 압축에 관한 연구)

  • Chung Young Sik;Park Chan Woong
    • Proceedings of the KIEE Conference
    • /
    • summer
    • /
    • pp.336-338
    • /
    • 2004
  • This paper introduces a compression algorithm for power quality disturbance signal via the discrete wavelet transform, DWT. Fundamental signal or stationary signal is estimated and then subtracted from a given signal to obtain a difference signal or nonstationary signal. DWT is applied to a difference signal to get coefficients that are thresholded to reduce a number of coefficients. Simulation results show the resonable compression ratio while keep low signal distortion.

  • PDF

CASA Based Approach to Estimate Acoustic Transfer Function Ratios (CASA 기반의 마이크간 전달함수 비 추정 알고리즘)

  • Shin, Minkyu;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
    • /
    • v.33 no.1
    • /
    • pp.54-59
    • /
    • 2014
  • Identification of RTF (Relative Transfer Function) between sensors is essential to multichannel speech enhancement system. In this paper, we present an approach for estimating the relative transfer function of speech signal. This method adapts a CASA (Computational Auditory Scene Analysis) technique to the conventional OM-LSA (Optimally-Modified Log-Spectral Amplitude) based approach. Evaluation of the proposed approach is performed under simulated stationary and nonstationary WGN (White Gaussian Noise). Experimental results confirm advantages of the proposed approach.

Real-time Implementation of an Identifier for Nonstationary Time-varying Signals and Systems

  • Kim, Jong-Weon;Kim, Sung-Hwan
    • The Journal of the Acoustical Society of Korea
    • /
    • v.15 no.3E
    • /
    • pp.13-18
    • /
    • 1996
  • A real-time identifier for the nonstationary time-varying signals and systems was implemented using a low cost DSP (digital signal processing) chip. The identifier is comprised of I/O units, a central processing unit, a control unit and its supporting software. In order t estimate the system accurately and to reduce quantization error during arithmetic operation, the firmware was programmed with 64-bit extended precision arithmetic. The performance of the identifier was verified by comparing with the simulation results. The implemented real-time identifier has negligible quantization errors and its real-time processing capability crresponds to 0.6kHz for the nonstationary AR (autoregressive) model with n=4 and m=1.

  • PDF

Speech Enhancement Based on Mixture Hidden Filter Model (HFM) Under Nonstationary Noise (혼합 은닉필터모델 (HFM)을 이용한 비정상 잡음에 오염된 음성신호의 향상)

  • 강상기;백성준;이기용;성굉모
    • The Journal of the Acoustical Society of Korea
    • /
    • v.21 no.4
    • /
    • pp.387-393
    • /
    • 2002
  • The enhancement technique of noise signal using mixture HFM (Midden Filter Model) are proposed. Given the parameters of the clean signal and noise, noisy signal is modeled by a linear state-space model with Markov switching parameters. Estimation of state vector is required for estimating original signal. The estimation procedure is based on mixture interacting multiple model (MIMM) and the estimator of speech is given by the weighted sum of parallel Kalman filters operating interactively. Simulation results showed that the proposed method offers performance gains relative to the previous results with slightly increased complexity.

Digital Signal Processing Based on Fuzzy Rules

  • Arakawa, Kaoru
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 1993.06a
    • /
    • pp.1305-1308
    • /
    • 1993
  • A novel digital signal processing technique based on fuzzy rules is proposed for estimating nonstationary signals, such as image signals, contaminated with additive random noises. In this filter, fuzzy rules are utilized to set the filter parameters, taking the local characteristics of the signal into consideration. The introduction of the fuzzy rules is effective, since the rules to set the filter parameters is usually expressed ambiguously. Computer simulations verify its high performance.

  • PDF