Showing papers on "Impulse response published in 2019"

Adaptive Shrinkage in Bayesian Vector Autoregressive Models

Abstract: Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article, we apply the Normal-Gamma shrinkage prior to the VAR with stochastic volatility case and derive its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariance parameters of the VAR along with Gamma priors on a set of local and global prior scaling parameters. In a second step, we modify this prior setup by introducing another layer of shrinkage with scaling parameters that push certain regions of the parameter space to zero. Two simulation exercises show that the proposed framework yields more precise estimates of model parameters and impulse response functions. In addition, a forecasting exercise applied to U.S. data shows that this prior performs well relative to other commonly used specifications in terms of p...

Impulse Response Estimation by Smooth Local Projections

Abstract: Local projections (LP) is a popular methodology for the estimation of impulse responses (IR). Compared to the traditional VAR approach, LP allow for more flexible IR estimation by imposing weaker a...

Simultaneous confidence bands: Theory, implementation, and an application to SVARs

Abstract: Simultaneous confidence bands are versatile tools for visualizing estimation uncertainty for parameter vectors, such as impulse response functions. In linear models, it is known that that the sup‐t confidence band is narrower than commonly used alternatives—for example, Bonferroni and projection bands. We show that the same ranking applies asymptotically even in general nonlinear models, such as vector autoregressions (VARs). Moreover, we provide further justification for the sup‐t band by showing that it is the optimal default choice when the researcher does not know the audience's preferences. Complementing existing plug‐in and bootstrap implementations, we propose a computationally convenient Bayesian sup‐t band with exact finite‐sample simultaneous credibility. In an application to structural VAR impulse response function estimation, the sup‐t band—which has been surprisingly overlooked in this setting—is at least 35% narrower than other off‐the‐shelf simultaneous bands.

Bayesian inference on structural impulse response functions

Abstract: I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly on the impulse responses in a flexible and transparent manner. Second, it can handle noninvertible impulse response functions, which are often encountered in applications. Rapid simulation of the posterior distribution of the impulse responses is possible using an algorithm that exploits the Whittle likelihood. The impulse responses are partially identified, and I derive the frequentist asymptotics of the Bayesian procedure to show which features of the prior information are updated by the data. The procedure is used to estimate the effects of technological news shocks on the U.S. business cycle. Bayesian inference Hamiltonian Monte Carlo impulse response function news shock nonfundamental noninvertible partial identification structural vector autoregression structural vector moving average Whittle likelihood C11 C32

Lamb Wave Mode Decomposition Based on Cross-Wigner-Ville Distribution and Its Application to Anomaly Imaging for Structural Health Monitoring

Abstract: Lamb waves are characterized by their multimodal and dispersive propagation, which often complicates analysis. This paper presents a method for separation of the mode components and reflected components in sensor signals in an active structural health monitoring (SHM) system. The system is trained using linear chirp signals but works for arbitrary excitation signals. The training process employs the cross-Wigner-Ville distribution (xWVD) of the excitation signal and the sensor signal to separate the temporally overlapped modes in the time-frequency domain. The mode decomposition method uses a ridge extraction algorithm to separate each signal component in the time-frequency distribution. Once the individual modes are separated in the time-frequency domain, they are reconstructed in the time domain using the inverse xWVD operation. The propagation impulse response associated with each component can be directly estimated for chirp inputs. The estimated propagation impulse response can be used to separate the modes resulting from arbitrary excitation signals as long as their frequency components fall in the range of the chirp signal. The usefulness of the mode decomposition algorithm is demonstrated on a new health monitoring system for composite structures. This system performs anomaly imaging using the first arriving mode extracted from sensor array signals acquired from the structure. The anomaly maps are computed using a sparse tomographic reconstruction algorithm. The reconstructed map can locate anomalies on the structure and estimate their boundaries. Comparisons with methods that do not employ mode decomposition and/or sparse reconstruction techniques indicate a substantially better performance for the method of this paper.

Calibration of Stochastic Radio Propagation Models Using Machine Learning

Abstract: This letter proposes a machine learning-based method for the calibration of stochastic radio propagation models. Model calibration is cast as a regression problem involving mapping of the channel transfer function or impulse response to the model parameters. A multilayer perceptron is trained with summary statistics computed from synthetically generated channel realizations using the model. To calibrate the model, the trained network is used to estimate the model parameters from channel statistics obtained from measurements. The performance of the proposed method is evaluated with propagation graph and Saleh–Valenzuela models using both simulated data and in-room channel measurements. Results show accurate estimation of the parameters of both models.

Application of stochastic fractal search in approximation and control of LTI systems

Abstract: The present work deals with the application of evolutionary computation in approximation and control of linear time invariant (LTI) systems. Stochastic fractal search algorithm (SFS) has been proposed to obtain low order system (LOS) from LTI higher order system (HOS) as well as in speed control of DC motor with PID controller. SFS is quite simple to use in control system and employs the diffusion property present in random fractals to discover the search space. In approximation of LTI systems, the integral square error (ISE) while in control of DC motor, the integral of time multiplied absolute error has been taken as an objective/fitness functions. In system’s approximation, the results show that the proposed SFS based LOS preserves both the transient and steady state properties of original HOS. The simulation results have also been compared in terms of; ISE, integral absolute error and impulse response energy with well known familiar and recently published works in the literature which shows the superiority of SFS algorithm. In control of DC motor, the obtained results are satisfactory having no overshoot and less rise and settling times in comparison to existing techniques.

Pulse Compression in Nondestructive Testing Applications: Reduction of Near Sidelobes Exploiting Reactance Transformation

Abstract: Pulse compression (PuC) is exploited in several applications, where the impulse response of a linear system must be estimated in a noisy environment In nondestructive testing (NDT), PuC based on frequency-modulated signals is applied with sensors of different types The presence of sidelobes in the impulse response retrieved after the PuC is a major drawback of the method as it degrades the quality of the estimation To limit this effect, the matched filter is usually shaped by means of windows Here, a windowing function based on reactance transformation is proposed that outperforms the most used ones in terms of near-sidelobes level reduction and sidelobes oscillations suppression Numerical and experimental data were used to validate the technique It is shown that the proposed approach is particularly suitable for those NDT techniques, such as eddy current and thermography that use very broadband excitations The proposed window function is first introduced and applied to linear frequency-modulated signals and then extended to nonlinear frequency-modulated signals, which are needed in some applications The quite general framework introduced in this paper for designing frequency-modulated signals and optimizing sidelobes is of general validity and can be successfully exploited in any application relying on chirp PuC

Coherent structures in the linearized impulse response of turbulent channel flow

Abstract: We study the evolution of velocity fluctuations due to an isolated spatio-temporal impulse using the linearized Navier–Stokes equations. The impulse is introduced as an external body force in incompressible channel flow at . Velocity fluctuations are defined about the turbulent mean velocity profile. A turbulent eddy viscosity is added to the equations to fix the mean velocity as an exact solution, which also serves to model the dissipative effects of the background turbulence on large-scale fluctuations. An impulsive body force produces flow fields that evolve into coherent structures containing long streamwise velocity streaks that are flanked by quasi-streamwise vortices; some of these impulses produce hairpin vortices. As these vortex–streak structures evolve, they grow in size to be nominally self-similar geometrically with an aspect ratio (streamwise to wall-normal) of approximately 10, while their kinetic energy density decays monotonically. The topology of the vortex–streak structures is not sensitive to the location of the impulse, but is dependent on the direction of the impulsive body force. All of these vortex–streak structures are attached to the wall, and their Reynolds stresses collapse when scaled by distance from the wall, consistent with Townsend’s attached-eddy hypothesis.

Calculation of the impulse response and phase noise of a high-current photodetector using the drift-diffusion equations.

Abstract: We describe a procedure to calculate the impulse response and phase noise of high-current photodetectors using the drift-diffusion equations while avoiding computationally expensive Monte Carlo simulations. We apply this procedure to a modified uni-traveling-carrier (MUTC) photodetector. In our approach, we first use the full drift-diffusion equations to calculate the steady-state photodetector parameters. We then perturb the generation rate as a function of time to calculate the impulse response. We next calculate the fundamental shot noise limit and cut-off frequency of the device. We find the contributions of the electron, hole, and displacement currents. We calculate the phase noise of an MUTC photodetector. We find good agreement with experimental and Monte Carlo simulation results. We show that phase noise is minimized by having an impulse response with a tail that is as small as possible. Since, our approach is much faster computationally than Monte Carlo simulations, we are able to carry out a broad parameter study to optimize the device performance. We propose a new optimized structure with less phase noise and reduced nonlinearity.

Low probability of intercept-based distributed MIMO radar waveform design against barrage jamming in signal-dependent clutter and coloured noise

Abstract: This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction

Abstract: Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

Modified Least-Mean Mixed-Norm Algorithms For Adaptive Sparse System Identification Under Impulsive Noise Environment

Abstract: In this paper, new algorithms robust to a mix of Gaussian and impulsive noises that approximate an unknown sparse impulse response of an LTI system are proposed. They are using the sigmoid cost function and based on the Least-Mean Mixed-Norm (LMMN) adaptive algorithm. It is shown by simulations that the proposed sigmoid LMMN (SLMMN) algorithms that exploit sparsity-enforcing penalties achieve superior performance to other competing algorithms in the sparse system identification context.

Kernel formalism applied to Fourier-based wave-front sensing in presence of residual phases

Abstract: In this paper, we describe Fourier-based Wave Front Sensors (WFS) as linear integral operators, characterized by their Kernel. In a first part, we derive the dependency of this quantity with respect to the WFS's optical parameters: pupil geometry, filtering mask, tip/tilt modulation. In a second part we focus the study on the special case of convolutional Kernels. The assumptions required to be in such a regime are described. We then show that these convolutional kernels allow to drastically simplify the WFS's model by summarizing its behavior in a concise and comprehensive quantity called the WFS's Impulse Response. We explain in particular how it allows to compute the sensor's sensitivity with respect to the spatial frequencies. Such an approach therefore provides a fast diagnostic tool to compare and optimize Fourier-based WFSs. In a third part, we develop the impact of the residual phases on the sensor's impulse response, and show that the convolutional model remains valid. Finally, a section dedicated to the Pyramid WFS concludes this work, and illustrates how the slopes maps are easily handled by the convolutional model.

Optimization algorithms, an effective tool for the design of digital filters; a review

Abstract: Nowadays, optimal and intelligent design approaches are vital in almost all areas of engineering. Scientists and engineers are attempting to make frameworks and models more proficient and intelligent. This paper deals with a detailed investigation on design of various digital filters using optimization algorithms. Generally digital filters are classified into two types which are FIR and IIR filters and are again classified into one dimensional, two dimensional and three dimensional filters for signal, image and video respectively. The design of a digital filter that satisfies all the required conditions perfectly is a challenging factor. So, apart from the conventional mathematical methods, optimization algorithms can be used to design optimal digital filters. IIR Filters are infinite impulse response filter; they have impulse response of infinite duration. FIR Filters are finite impulse response filters; they have impulse response of finite duration. In this paper we have discussed the design of various optimal digital filters based on various optimization algorithms, for processing of signal, image and video. The design of digital filters based on Evolutionary algorithms and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Cuckoo Search Algorithm, Differential Evolution, Gravitational Search, Harmony Search, Spiral Optimization, teaching–learning based optimization, wind driven optimization, hybridization of optimization algorithm are presented.

Enhancing signal-to-noise ratio and resolution in low-field NMR relaxation measurements using post-acquisition digital filters.

Abstract: The traditional way to enhance signal-to-noise ratio (SNR) of nuclear magnetic resonance (NMR) signals is to increase the number of scans. However, this procedure increases the measuring time that can be prohibitive for some applications. Therefore, we have tested the use of several post-acquisition digital filters to enhance SNR up to one order of magnitude in time domain NMR (TD-NMR) relaxation measurements. The procedures were studied using continuous wave free precession (CWFP-T1 ) signals, acquired with very low flip angles that contain six times more noise than the Carr-Purcell-Meiboom-Gill (CPMG) signal of the same sample and experimental time. Linear (LI) and logarithmic (LO) data compression, low-pass infinity impulse response (LP), Savitzky-Golay (SG), and wavelet transform (WA) post-acquisition filters enhanced the SNR of the CWFP-T1 signals by at least six times. The best filters were LO, SG, and WA that have high enhancement in SNR without significant distortions in the ILT relaxation distribution data. Therefore, it was demonstrated that these post-acquisition digital filters could be a useful way to denoise CWFP-T1 , as well as CPMG noisy signals, and consequently reducing the experimental time. It was also demonstrated that filtered CWFP-T1 method has the potential to be a rapid and nondestructive method to measure fat content in beef and certainly in other meat samples.

Rapid acquisition of the 3D MRI gradient impulse response function using a simple phantom measurement.

Abstract: Purpose To provide a simple tool for rapid measurement of the 3D gradient modulation transfer function (GMTF) of clinical MRI systems using a phantom. Knowledge of the transfer function is useful for gradient chain characterization, system calibration, and improvement of image reconstruction results. Methods Starting from the well-established thin slice method used for phantom-based measurement of the 1D GMTF, we add phase encoding to partition the thin slices into voxels that act as localized field probes. From the signal phase evolution measured at the 3D voxel positions, the GMTF can be derived for cross and higher order spatial terms represented by spherical harmonics up to 3rd order. Results Using spherical phantoms, 16 GMTFs representing all terms up to 3rd order harmonics can be determined in a scan time of 1 mL yields high SNR, enabling signal acquisition using the system's body coil. The method is applied for improving system calibration and for characterizing the effect of additional hardware in the bore. Conclusion The presented method seems well-suited for rapid measurement of the GMTF of a clinical system, as it delivers high-quality results in a short scan time.

Dynamic Audio Reproduction with Linear Loudspeaker Arrays

Abstract: This paper describes the creation of dynamic filters for listener-adaptive reproduction with loudspeaker arrays. The proposed filters allow the delivery of two independent personalized signals to a pair of listeners or a binaural signal for a single listener. The filters are modified in real-time according to the listener position. This is obtained by expressing the impulse response of each filter as a network of variable gain-delay elements that are modified so that the filters adapt the reproduction to the listener position, assuming that each loudspeaker behaves as a point-source free-field monopole. The paper introduces the filter formulation, together with the signal processing scheme for a real-time implementation and measured performance for a listener-adaptive 28 loudspeaker linear array using an optical head-tracking system.

A General Analytical Approximation to Impulse Response of 3-D Microfluidic Channels in Molecular Communication

Abstract: In this paper, the impulse response for a 3-D microfluidic channel in the presence of Poiseuille flow is obtained by solving the diffusion equation in radial coordinates. Using the radial distribution, the axial distribution is then approximated accordingly. Since Poiseuille flow velocity changes with radial position, molecules have different axial properties for different radial distributions. We, therefore, present a piecewise function for the axial distribution of the molecules in the channel considering this radial distribution. We lay evidence for our theoretical derivations for impulse response of the microfluidic channel and radial distribution of molecules through comparing them using various Monte Carlo simulations. Finally, the communication performance of the channel is examined.

3D Reconstruction Using Single-photon Lidar Data Exploiting the Widths of the Returns

Abstract: Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illumination), can be acquired by systems with a broad instrumental response (non-parallel laser beam with respect to the target surface) and with possibly high attenuating media (underwater conditions). The existing methods generally tackle only a subset of these problems and can fail in a more general scenario. In this paper, we propose a new 3D reconstruction algorithm that can handle all the aforementioned difficulties. The novel algorithm estimates the broadening of the impulse response, considers the attenuation induced by scattering media, while allowing for multiple surfaces per pixel. A series of experiments performed in real long-range and underwater Lidar datasets demonstrate the performance of the proposed method.

Study on longitudinal vibration of a pile with variable sectional acoustic impedance by integral transformation

Abstract: A study on longitudinal vibration of a pile with variable sectional acoustic impedance subjected to arbitrary form of external excitation is conducted. The analytical solution of transfer function is primarily solved by Laplace transform, and then its corresponding impulse response function is solved by residue method of inverse Laplace transformation. With impulse response function, the analytical solution of response at pile top can be obtained by convolution calculation, which overcomes the limit of previous analytical solutions subjected to prescribed time-harmonic load. The accuracy of presented solution is verified by compared with existing solutions subjected to semi-sine excitation and low-strain test of model piles. To take advantage of presented solution, this paper also proposes a case study of imposed excitation considering superimposed high-frequency interference component, and some conclusions derived from this case can provide practical guidance for engineering implementation.

Multivariate Quantile Impulse Response Functions

Abstract: A reduced form multivariate quantile autoregressive model is developed to study heterogeneity in the effects of macroeconomic shocks. This framework is used for forecasting and for constructing quantile impulse response functions that explore dynamic heterogeneity in the response of endogenous variables to different shocks. The methodology allows evaluating different quantile paths, defined as the dynamic effects for a fix collection of quantile indexes. The model is applied to study monetary shocks in a three‐variable macroeconomic model (output gap, inflation, Fed Funds rate) for the USA for the period 1980q1–2010q1.

Is Tollmien-Schlichting wave necessary for transition of zero pressure gradient boundary layer flow?

Abstract: Comprehensive understanding of the routes of instability and transition for many flows is not complete yet. For a zero pressure gradient (ZPG) boundary layer, linear spatial theory predicted Tollmien-Schlichting (TS) waves, which have been experimentally verified by vortically exciting the flow by a monochromatic source. This is the well-known frequency response of dynamical system theory. Natural transition in real flows occurs due to polychromatic excitation, and to simulate such transition, the ZPG boundary layer has been excited via an impulse response in some of our recent direct numerical simulations. Such impulse responses cause transition even when TS waves are not excited. In the present exercise, we show the theoretical basis of natural transition by spatiotemporal stability analysis, as used in the work of Sengupta et al. [“Spatiotemporal growing wave fronts in spatially stable boundary layers,” Phys. Rev. Lett. 96(22), 224504 (2006)], by invoking finite start-up of the frequency response to wall excitation. There appear to be different instability mechanisms active for the frequency and the impulse responses to localized wall excitation. Here, we show that in both the frequency and impulse responses, the spatiotemporal wave-front (STWF) is the common element. Additionally, we also consider cases, where following different start-ups, the wall excitation remains constant, which also show the presence of the STWF. The presented results for the ZPG boundary layer show that the TS wave is not necessary for transition to turbulence and help us to re-evaluate our understanding of the transition mechanism for this canonical flow.

Detection of spatially sparse damage using impulse response sensitivity and LASSO regularization

Abstract: This paper presents an impulse response sensitivity approach enhanced with a least absolute shrinkage and selection operator regularization in order to detect spatially sparse (localized) damage. The analytical expression for impulse response sensitivity was derived using Vetter calculus. The proposed algorithm exploits the fact that when damage is sparse, an -norm regularization is more suitable than the common least squares ( -norm) minimization. The proposed methodology is successfully applied in the context of a simulated 21 degree of freedom non-uniform shear beam with noise-contaminated measurements, limited modal parameters and limited sensor locations. Single input–single output and single input-two output cases are investigated.

Deep Neural Network Based Regression Approach for Acoustic Echo Cancellation

Abstract: An acoustic echo canceller (AEC) aims to remove the acoustic echo in the mixture signal received by the near-end microphone. The conventional method uses an adaptive finite impulse response (FIR) filter to identify a room impulse response (RIR)which is not robust to various wild scenarios. In this paper, we propose a deep neural network-based regression approach that directly estimates the amplitude spectrum of the near-end target signal from features extracted from the mixtures of near-end and far-end signals. Depend on the powerful modelling and generalizing ability of deep learning, the complex echo signal can be well eliminated. Experimental results show the effectiveness of the proposed method for echo removal in double-talk, background noise, RIR variation and nonlinear distortion scenarios. In addition, the proposed method generalizes well to real-life acoustic echoes recorded in vehicles.