Determination of Propagation Times of Finite Ultrasonic Signals in the UFM Measuring Path

This article provides a brief analysis of the error in calculating the discrete crosscorrelation function of the transit-time ultrasonic flowmeter signals. Special attention is paid to the study of the influence of the obtained discrete correlation function inaccuracy on the ultrasonic flowmeter’s propagation times determining error. It is known that for real time-limited acoustic signals, the discrete correlation function values are calculated with a significant error. The consequence of this is the appearance of the measurement error of the time delay between signals. The reason for this phenomenon is incorrect truncation of finite length digital sequences of the received acoustic signals. The report presents and describes an improved cross-correlation method for determining the time difference. The new algorithm takes into account the existing discretizing parameters of the received UPS – sampling frequency, sequence size and the truncated signal’s shape. Theoretical analytical expressions for the signals discrete cross-correlation function estimating are obtained as an approximation of a continuous function (the method of trapezoids and Simpson is used as an integral replacement). The numerical simulation by MatLab explains the error formation essence in the signal times difference calculating.

An Adaptive Method Based on Multiscale Dilated Convolutional Network for Binaural Speech Source Localization

Most binaural speech source localization models perform poorly in unprecedentedly noisy and reverberant situations. Here, this issue is approached by modelling a multiscale dilated convolutional neural network (CNN). The time-related crosscorrelation function (CCF) and energy-related interaural level differences (ILD) are preprocessed in separate branches of dilated convolutional network. The multiscale dilated CNN can encode discriminative representations for CCF and ILD, respectively. After encoding, the individual interaural representations are fused to map source direction. Furthermore, in order to improve the parameter adaptation, a novel semiadaptive entropy is proposed to train the network under directional constraints. Experimental results show the proposed method can adaptively locate speech sources in simulated noisy and reverberant environments.

Three-dimensional data-domain full traveltime inversion using a practical workflow of early arrival selection

A new method of data-domain full traveltime inversion (FTI) is proposed to estimate the near-surface velocity model using early arrivals in seismic shot gathers. Data-domain FTI is capable of generating a background velocity model from which the predicted early arrivals can kinematically match the observed ones. Such a match is measured and quantified in terms of the crosscorrelation function between the computed and observed traces. Our method aims to find an optimal estimated velocity model that minimizes the crosscorrelation computed from the selected early arrivals. The early arrivals are isolated via a sequence of operations, including the [Formula: see text]-[Formula: see text] scan, autopicking, multidomain quality control, and guide interpolation. Because windows, rather than exact arrival times, are constructed, the difficulties encountered while picking precise arrivals are reduced. In addition, the gradient of data-domain FTI is derived based on an amplitude-constrained optimization problem, which makes the gradient essentially different from that derived with the Born approximation in which no constraint is used. The constraint requires the inversion to honor traveltime information only, and it thus ignores any amplitude changes caused by velocity variations. This method is validated using 3D synthetic as well as field data sets. The results show that data-domain FTI, combined with the early arrival selection workflow, is able to generate reasonable background velocities that kinematically match the predicted early arrivals with the observed ones, and the associated depth-domain images are clearly improved.

A technique for detecting concealed objects in terahertz images based on information measure

In this paper, a new technique for detecting concealed objects in the images acquired by a passive THz imaging system is proposed. The technique is based on a method for mutual information maximization successfully used for image matching. For reducing computational expenses, we propose to analyze the mutual information at local maxima of the crosscorrelation function computed in the Fourier domain. The proposed technique does not require parameter tuning. A computing experiment approved the efficiency of the proposed technique and the possibility of its implementation in security systems.