Semper: Distortion Compensation, Selective Averaging, 3-D Reconstruction, and Transfer Function Correction in a Highly Programmable System

The data collected by ocean‐bottom seismometers are unique compared to that of land seismic stations and require preprocessing before use. In this study, we focus on preprocessing seismic data obtained from a passive‐source experiment in the Mariana subduction zone conducted by the Institute of Deep‐Sea Science and Engineering, Chinese Academy of Sciences from 2018 to 2019. We outline the steps involved in preprocessing the data, including time correction using the ocean background noise cross‐correlation function, tilt correction using a transfer function and orientation correction based on seismic wave polarization. Our results show that the maximum clock drifting was ∼4 s on two stations (K06 and K20), whereas the rest of the station waveforms have clock drifting of less than 2 s. Among stations, only the K06 station is tilted, and the tilt angle is 19.82°. We use the P‐wave and Rayleigh wave polarization methods to determine the orientation. Our results indicate that the error in the result obtained using the P‐wave polarization method is less than that of the Rayleigh wave polarization method. Additionally, we address the advantages and disadvantages of these preprocessing techniques. Our goal is to offer a comprehensive overview of the preprocessing process, to assist beginners in the field and to serve as a foundation for future research in passive‐source ocean‐bottom seismometers.

An analysis of the international tourism demand in Spain

Subtomogram analysis using the Volta phase plate.

Quantifying resolution limiting factors in subtomogram averaged cryo-electron tomography using simulations.

Electron tomography is a method for three-dimensional reconstruction of a specimen, based on its electron microscopical projections which are produced by tilting the specimen in the microscope about a fixed axis. After determining a common coordinate frame, a tomogram is computed by weighted backprojection. Higher resolution can be obtained by applying a contrast transfer function (CTF) correction to the projections. The tomographic data is further analyzed by extracting structural motifs from the raw tomograms, by selective alignment, multivariate statistical analysis, classification, and class-averaging, resulting in an increased signal-to-noise ratio and substantial data reduction. The volumetric data analysis facilitates the separation of a heterogeneous population of motifs into its constituents, and the visualization of the motifs in the raw tomograms by reassembly, replacing low signal-to-noise instances with higher signal-to-noise representations

Single particle tomography in EMAN2.

The approximation of a fractional order PIλDμ-controller transfer function using a chain fraction theory is considered. Analytical expressions for the approximation of s±α components of the transfer functions of PIλDμ-controllers were obtained through the application of the chain fraction theory. Graphs of transition functions and frequency characteristics of Dμ (α = μ = 0.5) and Iλ (α = λ = −0.5) parts for five different decomposition orders were obtained and analyzed. The results showed the possibility of applying the approximation of the PIλDμ-controller transfer function by the method of chain fractions with different valuesof λ and μ. For comparison, the transfer functions with the same order polynomials, obtained by the methods of Oustaloup transformation and chain fractions, were approximated for α = ±0.5. The analysis proved the advantages of using the chain fraction method to approximate the transfer function of the PIλDμ-controller. The performed approximation opens up the possibility of developing engineering methods for the technical implementation of PIλDμ-controllers. The accuracy of the same order transfer function approximation is higher when the method of chain fractions is used. It has been established that the adequacy of the frequency characteristics of the transfer functions obtained by the chain fraction method also depends on the approximation order.

SUMMARY In the presence of large and continuous correlated noise signals in measured electric and magnetic time-series, even robust remote-reference methods give erroneous estimates of MT transfer functions. If clean remote time-series are available, it is possible to separate MT and correlated noise signals and to derive unbiased MT transfer functions with the signal–noise separation method (SNS) (Larsen et al. 1996). In practice, the remote time series also contain some noise and the results can be improved by using a second remote data set and the SNS-remote-reference technique. We tested this method with data from the Saxonian Granulite Massif (SGM), Germany, where strong correlated noise signals were detected. We used remote data which were recorded 350 km away and, for short periods, data from sites of the profile across the SGM itself (distance 5 km). To show the efficiency of the signal–noise separation we first determined a ‘true’ MT transfer function from time-series with low noise level. In a second step we reproduced the results from processing very noisy data sections. We were able to determine useful MT transfer functions even when the MT variations have less than 10 per cent share of the measured variations. We identified dominant noise in the measured time-series from pipelines and trains.

Parameter estimation is one of the most challenging tasks in large‐scale distributed modeling, because of the high dimensionality of the parameter space. Relating model parameters to catchment/landscape characteristics reduces the number of parameters, enhances physical realism, and allows the transfer of hydrological model parameters in time and space. This study presents the first large‐scale application of automatic parameter transfer function (TF) estimation for a complex hydrological model. The Function Space Optimization (FSO) method can automatically estimate TF structures and coefficients for distributed models. We apply FSO to the mesoscale Hydrologic Model (mHM, mhm-ufz.org), which is the only available distributed model that includes a priori defined TFs for all its parameters. FSO is used to estimate new TFs for the parameters “saturated hydraulic conductivity” and “field capacity,” which both influence a range of hydrological processes. The setup of mHM from a previous study serves as a benchmark. The estimated TFs resulted in predictions in 222 validation basins with a median NSE of 0.68, showing that even with 5 years of calibration data, high performance in ungauged basins can be achieved. The performance is similar to the benchmark results, showing that the automatic TFs can achieve comparable results to TFs that were developed over years using expert knowledge. In summary, the findings present a step toward automatic TF estimation of model parameters for distributed models.

A digital twin is a promising evolving tool for prognostic health monitoring. However, in rotating machinery, the transfer function between the rotating components and the sensor distorts the vibration signal, hence, complicating the ability to apply a digital twin to new systems. This paper demonstrates the importance of estimating the transfer function for a successful transfer across different machines (TDM). Furthermore, there are few algorithms in the literature for transfer function estimation. The current algorithms can estimate the magnitude of the transfer function without its original phase. In this study, a new approach is presented that enables the estimation of the transfer function with its phase for a gear signal. The performance of the new algorithm is demonstrated by measured signals and by a simulated transfer function.

Significant arterio-venous differences in nicotine concentrations have been observed during and after cigarette smoking, nicotine nasal spray, and intravenous nicotine administration. In this paper we describe a novel mathematical method for estimating arterial blood levels from venous blood level data. The model allows to quantify: (i) the influence of the microcirculation in the hands and forearm on the distribution of nicotine, and (ii) the influence of disregarding the venous to arterial circulation in the estimate of systemic inputs. We also (iii) propose a general method to predict arterial concentrations and inputs given venous data. The basic model we adopt is based on the relationship Cv = T * Ca, where Cv and Ca are the concentration in the venous and arterial site, respectively, T is the arterio-venous transfer function and * indicates convolution. We use empirical data to estimate T. We then compare estimates of systemic inputs to the venous site obtained taking into account the transfer function or, as usually done, disregarding it. The relationship we use to compare estimated inputs are: Cv = T * ka * A (where Ka is the arterial disposition function and A the systemic input), and Cv = Kv * A (where Kv is the venous disposition function), respectively. Finally, the estimated transfer function allows to estimate (average) Ca or A given arbitrary venous data. (i) Our analysis suggests that a bi-exponential T is needed to describe observed arterial-venous differences. The estimated transfer function indicates that no elimination of nicotine is involved in the forearm. (ii) Disregarding T, as usually done, erroneously obtains too complex venous input functions (because these input functions incorporate T). (iii) Disregarding T erroneously estimates significantly higher total inputs. (iv) Using the proposed model and previously published venous nicotine level data we predict substantial arterial-venous differences in blood nicotine levels for smokeless tobacco and nicotine gum. The use of disposition functions obtained from venous data may lead to erroneous estimation of the rates of entry into the circulation and systemic bioavailability for many drugs.

The problem of transfer function estimation is considered. We will discuss the problem in two (related) aspects: firstly, how to obtain a good black-box transfer function model; secondly, how to derive a suitable description of the model uncertainty (modelling errors). We will emphasize the second point. We shall discuss the asymptotic theory on the properties of transfer function estimates, developed recently by Ljung and Yuan. This new theory shows that the transfer function estimate is consistent, the error of the estimate is asymptotically normal, with a very simple expression for its variance. Their results will be extended here to cases where spectral analysis is used. Based on this theory, we will propose the method for obtaining a low-order nominal model of linear processes, and define and estimate an upper bound of the model uncertainty. An upper bound of the model uncertainty is a suitable description of the modelling errors for the robust controller design.

This paper addresses the blind dereverberation problem of a single-input multiple-output acoustic system. Many conventional approaches require a precise order of the transfer functions. In this paper, we propose an equalization algorithm that is less sensitive to the order misadjustment of the transfer functions. First, the transfer functions are estimated using an overestimated order, and the inverse filter set for these estimated transfer functions is calculated. Since the estimated transfer functions contain a common polynomial, the signal processed by the inverse filter set suffers from the effect of this common polynomial. Then, we extract this polynomial to compensate for the distortion. The proposed algorithm recovers input signal as long as the channel is overestimated. Simulation results show that the proposed method works well even when the order is highly overestimated.

Optimal Approximation, Simulation and Analog Realization of the Fundamental Fractional Order Transfer FunctionThis paper provides an optimal approximation of the fundamental linear fractional order transfer function using a distribution of the relaxation time function. Simple methods, useful in systems and control theories, which can be used to approximate the irrational transfer function of a class of fractional systems for a given frequency band by a rational function are presented. The optimal parameters of the approximated model are obtained by minimizing simultaneously the gain and the phase error between the irrational transfer function and its rational approximation. A simple analog circuit which can serve as a fundamental analog fractional system is obtained. Illustrative examples are presented to show the quality and usefulness of the approximation method.

This paper presents a detailed study on the impact source identification of a plate structure using time-reversal (T-R) method. Prior to impact monitoring, the plate is calibrated (or characterized) by transfer functions at discrete locations on the plate surface. Both impact location and impact loading time-history are identified using T-R technique and associated signal processing algorithms. Numerical verification for finite-size isotropic plates under low velocity impacts is performed to demonstrate the versatility of T-R method for impact source identification. The tradeoff between accuracy of the impact location detection and calibration spacing is studied in detail. In particular, the effect of plate thickness on calibration spacing has been examined. A number of parameters selected for determining the impact location, approximated transfer functions and steps taken for reconstructing the impact loading time-history are also examined. Two types of noise with various intensities contaminated in strain response and/or transfer functions are investigated for demonstrating the stability and reliability of the T-R method. The results show that T-R method is robust against noise in impact location detection and force reconstruction in circumventing the inherent ill-conditioned inverse problem. Only transfer functions are needed to be calibrated and four sensors are requested in T-R method for impact identification.