Selection of optimal window length using STFT for quantitative SNR analysis of LFM signal

An adaptive approach to select analysis window param- eters for linear frequency modulated （LFM） signals is proposed to obtain the optimal 3 dB signal-to-noise ratio （SNR） in the short- time Fourier transform （STFT） domain. After analyzing the instan- taneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaus- sian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.

The Influence of Window Length Analysis on the Time and Frequency Domain of Mechanomyographic and Electromyographic Signals of Submaximal Fatiguing Contractions

Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physiological events could be compromised due to imprecision in the determination of parameters. Therefore, this study investigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contractions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint. Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly. Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110° and 90°, respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation was calculated to determine whether parameters that monitor similar physiological events would have strong correlation. The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong correlation between parameters of different domains.

Multiple subtraction method based on a non-causal matching filter

The filter operator used in normal multichannel matching filter is physically realizable. This filter operator only delays seismic data in the filtering process. A non- causal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is later than real data. The differences between causal and non-causal multichannel matching filters are compared using a synthetic shot gather, which demonstrates the validity of the non-causal matching filter. In addition, a variable length sliding window which changes with offset and layer velocity is proposed to solve the count of events increasing with increasing offset in a fixed length sliding window. This variable length sliding window is also introduced into the modified and expanded multichannel matching filter. This method is applied to the Pluto1.5 synthetic data set. The benefits of the non-causal filter operator and variable length sliding window are demonstrated by the good multiple attenuation result.

Estimation of structural modal parameters by fourier transform with an optimal window

An adaptive Fourier Transform （FT） with an optimal window has been proposed for the time-frequency analysis of nonstationary time series. The method allows for a good estimation of both frequency and amplitude of the spectrum and can be easily applied to the general case of time-varying signals. The evaluation of the proposed approach has been performed on measured time-varying signals from a suspension bridge model and a steel frame model whose data have the typical non-stationary characteristics. The numerical results show that the proposed approach can overcome some of the difficulties encountered in the classic Fourier transform technique and can achieve higher computation accuracy.

The Coherence Cube Computing Method with Self-adaptive Time Window Based on Wavelet Transform

The coherence cube technology has become an important technology for the seismic attribute interpretation, which extracts the discontinuities of the events through analyzing the similarities of adjacent seismic channels to identify the fault form. The coherence cube technology which uses constant time window lengths can not balance the shallow layers and the deep layers, because the frequency band of seismic data varies with time. When analyzing the shallow layers, the time window will crossover a lot of events, which will lead to weak focusing ability and failure to delineate the details. While the time window will not be long enough for analyzing deep layers, which will lead to low accuracy because the coherences near the zero points of the events are heavily influenced by noise. For solving the problem, we should make a research on the coherence cube technology with self-adaptive time window. This paper determines the sample points＆#39; time window lengths in real time by computing the instantaneous frequency bands with Wavelet Transformation, which gives a coherence computing method with the self-adaptive time window lengths. The result shows that the coherence cube technology with self-adaptive time window based on Wavelet Transformation improves the accuracy of fault identification, and supresses the noise effectively. The method combines the advantages of long time window method and short time window method.