Filtering Of Inputs Research Articles

An augmented complex-valued gradient-descent total least-squares algorithm for noncircular signals

In this paper, we propose a novel augmented complex-valued gradient-descent total least-squares (ACGDTLS) adaptive filter for processing noisy input and output noncircular complex-valued signals. First, a Rayleigh quotient cost function is formulated by incorporating augmented complex-valued statistics and the output-to-input-noise-ratio within the widely linear error-in-variable model, whereby the ACGDTLS is developed using the gradient-descent approach. Next, rigorous analysis is conducted to establish a conservative step-size bound guaranteeing mean convergence, a closed-form expression for the steady-state mean-squared deviation, and the algorithm’s computational complexity. Finally, through simulations conducted in system identification, wind/speech prediction, and stereophonic acoustic echo cancellation, the analytical findings are validated, and the proposed ACGDTLS filter demonstrates superior estimation accuracy compared to the augmented complex-valued least-mean-square algorithm and two state-of-the-art bias-compensated methods. Remarkably, this performance advantage persists across a wide range of step-sizes, input noise variances, and output noise variances.

A Novel Robust Variational Bayesian Filter for Unknown Time-Varying Input and Inaccurate Noise Statistics

Considering that, in many practical applications, the unknown time-varying input and heavy-tailed process and measurement noises induced by some unpredictable anomalous behaviors may degrade the performance of conventional filters seriously, this letter proposes a new robust variational Bayesian (VB) filter. First, the modified one-step prediction and measurement likelihood probability density function are constructed. Then, the VB method is utilized to jointly infer the system state, unknown time-varying input, and inaccurate noise covariance matrices. Finally, a new robust filter is derived, and its effectiveness is verified by the numerical simulations.

A linear Bayesian filter for input and state estimation of structural systems

AbstractThis paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. Also, in contrast with the existing algorithms in the latter category, such as the Gillijns and De Moor Filters (GDFs), the developed filter applies to systems with and without direct feedthrough, in particular, systems with a rank‐deficient feedforward matrix. Because of the latter features, the filter is referred to as universal filter (UF) for convenience. The numerical examples show that the UF performs better than the AKF. Due to its structure, the UF does not require the tuning of the hyperparameters for inputs, and therefore the problematic instability of the AKF is not encountered in the case of a large modeling error variance of the input. For systems with direct feedthrough, the error and covariance propagation terms differ due to the distinct state space. Consequently, the UF can enhance estimations due to the well‐conditionedness of the relevant inversion problem. Moreover, the UF can deal with systems with rank‐deficient feedforward matrix where these systems are not covered by GDFs.

An optimal filter for updated input of iterative learning controllers with multiplicative and additive noises

Multiplicative and additive noises, arising from both sensor-to-controller and controller-to-actuator channels, affect the convergence performance of wireless networked iterative learning control (ILC) systems. In order to guarantee the convergence performance of such ILC systems, this paper designs an input filter at the actuator side for estimating the controller updated input. Specifically, a P-type learning controller is considered firstly, and then a mathematical model is developed to describe the transmission processes of both measured output data and updated input data with the effect of those noises. On the basis of state augmentation, these two data transmission processes are further combined with the controller learning process to build a filtering model. Finally, according to this filtering model and the orthogonality projection theory, the optimal input filter in the sense of linear minimum variance is designed in front of actuators. The convergence performance of the filtering error covariance matrix is analysed theoretically. Furthermore, because the input filter is designed only with the controller learning process and the two data transmission processes, the convergence performance of any system with the considered controller can be improved by driving with the filtered input. Finally, numerical results are given to illustrate the effectiveness of the proposed method.

A hierarchical output-only Bayesian approach for online vibration-based crack detection using parametric reduced-order models

This contribution presents a hierarchical Bayesian filter for recursive input, state and parameter estimation using spatially incomplete and noisy output-only vibration measurements. The problem at hand is tailored to a dual-layered scheme, whereby the sought-after parameters are treated as random variables with a finite number of evolving states. For each one of these states an output-only Bayesian filter is employed for estimating the state and unknown input, creating thus a bank of filters, which are recursively weighted upon assimilation of the measurement data. The dynamics of parameter states are governed by an evolution strategy, which enables exploration of the parameter space and subsequent identification of the target values. The proposed scheme is numerically tested on crack identification problems using parametric reduced-order models (pROMs). The latter is a key element of the methodology in that it provides a generator of computationally efficient models, which can be evaluated at different parameter configurations, using mesh morphing. The performance of the algorithm is tested by means of simulated realistic components encountered in aerospace applications.

Variational Bayesian-Based Filter for Inaccurate Input in Underwater Navigation

Autonomous underwater vehicle (AUV) has been employed in oceanography applications based on a reliable navigation. The complex underwater environment leads to more velocity measurement errors of AUV, so it is difficult to determine the accurate navigation and positioning information. To solve the problem, a novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information under the complex marine condition of inaccurate input. Firstly, the velocities are assumed to follow the Gaussian distribution, which can better describe the model of input information. Secondly, the augmentation method is used to augment the state vector and error covariance matrix to simplify calculation. The augmented state vector, the augmented predicted error covariance and measurement error noise matrices are derived more accurately based on the variational Bayesian (VB) approach. The experiment results show that the proposed VBFII has better estimation accuracy and robustness than other comparison algorithms.

Tensor Recursive Least Squares Filters for Multichannel Interrelational Signals

It is well known that recursive least squares (RLS) filter is a prevalent adaptive filter that recursively updates the filter-coefficients to minimize a weighted cost function related to an input signal. Conventionally, such an input signal is a sequence of scaler numbers. Multichannel signals generated from one or more sources are often encountered nowadays. Therefore, there emerges a urgent demand to generalize the conventional RLS filter to accommodate multichannel signals with implicit interrelations. The key issue of this new generalization is to convert the correlation matrix involved in the conventional RLS filter for one-dimensional input to the correlation tensor required by the new RLS filter for multi-dimensional input. Due to lack of the sufficient mathematical framework of the required tensor operations, here we make the first attempt to established the new tensor recursive least squares filter (TRLS) theory based on our newly derived Woodbury tensor identity. We apply the Woodbury tensor identity to design a recursive algorithm for updating the weight-tensor. Furthermore, the performance analysis of the proposed new TRLS algorithm, including mean-squared deviation and misadjustment, are facilitated using the established tensor calculus theory. Numerical experiments are also presented to investigate the convergence speed, mean-squared deviation, and misadjustment for input signals with various statistical characteristics and different dimensions.

Study of Random Forest to Identify Wiener–Hammerstein System

The Wiener–Hammerstein (W–H) system is the most popular type of the Volterra nonlinear dynamical system. It is a combination of two dynamical subsystems, separated by a static nonlinearity. The best linear approximation (BLA) technique assembles two linear filters and the nonlinearity into a single filter for input and output. The main identification challenge resides in separating two filters. This work proposes an iterative random forest as an alternative to select the dynamics combinatorially. It is like the iterative selection of holiday destinations based on the recommendations of random travelers. The proposed technique supports reasonably high noise level and requires the optimization of a single model. Thus, a speedup in processing time is achieved without any prior knowledge about the model configuration both on simulated examples and benchmark data.

The Enlightenment of Affective Filter Hypothesis and Risk-Taking on English Learning

Affective filter hypothesis reveals that learners with different emotional learning attitudes have different filter capability for language learning input. Learners with positive emotional attitude have a low filter of language learning input, while learners with negative emotional attitudes have a high filter of language learning input. Risk-taking means that the learner dares to take risks. He is not afraid of making mistakes and the unknown situation. The risk taker will seize every opportunity to use learned knowledge into practice, which is a positive emotional attitude. In the meantime, the adventurous students’ affective filter is relatively low. In actual classroom, teachers usually pay little attention to the emotional state of students, and teachers rarely realize the impact of motivation, self-confidence, anxiety, and risk taking on students’ English learning. More attention is still paid to the training of students’ basic skills. Therefore, this paper first explains the connotation of the affective filter hypothesis and risk-taking. After analyzing and explaining the actual teaching situation and the current situation of students, four suggestions are put forward from the perspectives of teachers and students. Among them, the first three suggestions are for teachers, and the last one is for students. The first suggestion is about teaching methods. Teachers should get rid of the obstacles of traditional teaching methods, use multimedia and other technology to assist teaching and give students more opportunities to speak in class. The second suggestion is about teaching atmosphere. Creating a relaxation and pleasant classroom environment is conducive to reducing students’ anxiety and good for students to take risks. The third suggestion is about self-confidence. In order to build students’ confidence in English learning, teachers should encourage students, discover the highlights of each student and praise students when they make progress. The fourth suggestion is about the significance of affective factors. Students should recognize the role of emotional factors in English learning and adopt effective methods to self-regulate.

Electrically induced verbal perseveration: A striatal deafferentation model.

The present study aimed to elucidate the neural correlates of the deafferentation cognitive model of verbal perseveration (VP) by analyzing the connectomics of the sites where electric stimulation elicited VP during awake left glioma surgery. We retrospectively reviewed the anatomic sites that generated VP when electrically stimulated in a series of 21 patients operated on while awake for a left glioma. Each stimulation point was manually located on the postoperative MRI and then registered to the Montreal Neurological Institute template. Connectomics of these sites were further analyzed with Tractotron and disconnectome maps. VP stimulation sites were located within the white matter surrounding the posterosuperior head of the caudate nucleus, as well as within the white matter of the external capsule and the superolateral wall of the temporal horn of the ventricle. Furthermore, Tractotron and disconnectome maps revealed the connectome of these stimulation sites: the inferior fronto-occipital fasciculus, frontostriatal tract, and anterior thalamic radiations. On the basis of these results and other data, we propose the following anatomic implementation of the deafferentation cognitive model: the lexico-semantic system, comprising different areas linked together through direct cortico-cortical connections, sends information to the striatum; the striato-thalamic system acts as a tunable filter of this lexico-semantic input; and the thalamus projects back to the lexico-semantic system, amplifying the targeted response and inhibiting its competitors.

Discrete‐time sliding mode control with an input filter for an electro‐hydraulic actuator

This study presents a discrete-time sliding mode control (DSMC) with an input filter for a low damping electro-hydraulic actuator (EHA) suffering from large control volume variation. Non-switching type reaching law and one-step delayed estimation of disturbance are combined in the DSMC. Different from existing DSMC strategies which view parametric uncertainty as a lumped bounded disturbance, the influence of parametric uncertainty on the system stability is analysed, which results to a two terms iterative expression of state. The stability criterion for this iteration is put forward. To stabilise the EHA that has a large control volume variation, a first-order input filter for the control input is introduced. The disturbance resulting from the input filter is expressed and the stability criterion with the input filter is derived. Both simulation and experimental results show the effectiveness of the proposed approach on the position tracking of the EHA with large volume variation.

Differential Impairment of Thalamocortical Structural Connectivity in Amyotrophic Lateral Sclerosis.

The thalamus is a major relay station that modulates input from many cortical areas and a filter for sensory input and is involved in the pathophysiology of amyotrophic lateral sclerosis (ALS). However, it still remains unclear whether all thalamocortical networks are affected or whether there is selective vulnerability. In this study, we aimed to study the selective vulnerability of different thalamocortical structural connections in ALS and to test the hypothesis of a specific impairment in motor-related thalamocortical connectivity. Diffusion tensor imaging (DTI) tractography was used to identify thalamocortical structural pathways in 38 individuals with ALS and 35 gender/age-matched control subjects. Thalami of both groups were parcellated into subregions based on local patterns of thalamocortical connectivity. DTI measures of these distinct thalamocortical connections were derived and compared between groups. The analysis of probabilistic tractography showed that the structural connectivity between bilateral pre/primary motor cortices and associated thalamic subregions was specifically impaired in patients with ALS, while the other thalamocortical connections remained relatively intact. In addition, fractional anisotropy values of the impaired thalamocortical motor pathway were inversely correlated with the disease duration. Our findings provide direct evidence for selective impairment of the thalamocortical structural connectivity in ALS.

Spin inverter and polarizer curved nanowire driven by Rashba and Dresselhaus spin–orbit interactions

We propose in theory a curved nanowire structure that can both serve as a spin inverter and a spin polarizer driven by a periodic Rashba spin–orbit coupling (SOC) and a uniform Dresselhaus SOC. The curved section of the U-shaped quasi-one dimensional nanowire with an arc of radius R and circumferential length πR is divided into segments of equal length initially having only its inherent homogeneous Dresselhaus SOC. Then a Rashba-type SOC is applied at every alternating segment. By tuning the Rashba SOC strength and the incident electron energy, this device can flip the spin at the output of an incoming spin-polarized electron. On the other hand, this same device acts as a spin filter for an unpolarized input for which an outgoing electron with a non-zero polarization can be achieved without the application of an external magnetic field. Moreover, the potential modulation caused by the periodic Rashba SOC enables this device to function as an attenuator for a certain range of incident electron energies that can make the probability current density drop to 10−4 of its otherwise magnitude in other regimes.

A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems

In this paper, we present a unified optimal and exponentially stable filter for linear discrete-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense, without making any assumptions on the direct feedthrough matrix. We also provide the connection between the stability of the estimator and a system property known as strong detectability, and discuss the global optimality of the proposed filter. Finally, an illustrative example is given to demonstrate the performance of the unified unbiased minimum-variance filter.

Variability in transport of terrigenous material on the shelves and the deep Arctic Ocean during the Holocene

Arctic coastal zones serve as a sensitive filter for terrigenous matter input onto the shelves via river discharge and coastal erosion. This material is further distributed across the Arctic by ocean currents and sea ice. The coastal regions are particularly vulnerable to changes related to recent climate change. We compiled a pan-Arctic review that looks into the changing Holocene sources, transport processes and sinks of terrigenous sediment in the Arctic Ocean. Existing palaeoceanographic studies demonstrate how climate warming and the disappearance of ice sheets during the early Holocene initiated eustatic sea-level rise that greatly modified the physiography of the Arctic Ocean. Sedimentation rates over the shelves and slopes were much greater during periods of rapid sea-level rise in the early and middle Holocene, as a result of the relative distance to the terrestrial sediment sources. However, estimates of suspended sediment delivery through major Arctic rivers do not indicate enhanced delivery during this time, which suggests enhanced rates of coastal erosion. The increased supply of terrigenous material to the outer shelves and deep Arctic Ocean in the early and middle Holocene might serve as analogous to forecast changes in the future Arctic.Keywords: Arctic; riverine input; coastal erosion; land–ocean interaction; Holocene.(Published: 9 December 2015)To access the supplementary material for this article, please see supplementary files in the column to the right (under Article Tools).Citation: Polar Research 2015, 34, 24964, http://dx.doi.org/10.3402/polar.v34.24964

Optimised conversion efficiency of a HTS MMIC Josephson down-converter

A high-Tc superconducting (HTS) monolithic microwave integrated circuit (MMIC) Josephson down-converter that approaches zero conversion loss is reported. The all-HTS YBa2Cu3O7−x thin-film circuit consists of a step-edge Josephson junction mixer, a 10–12 GHz bandpass filter for the RF input, a lowpass filter for the IF output and a resonant strip line for local oscillator isolation; all are integrated on a single 10 mm × 20 mm MgO substrate. The DC characteristics of the junction and its mixing properties have been experimentally studied and compared to the results of (a) a single Josephson mixer without the on-chip HTS filters, and (b) our previously reported MMIC down-converter which had very different junction characteristics. The Josephson junction parameters are analysed to give insight into their effect on the mixer performance.

Observer‐based decentralized adaptive control for large‐scale pure‐feedback systems with unknown time‐delayed nonlinear interactions

SummaryThis paper presents an approximation design for a decentralized adaptive output‐feedback control of large‐scale pure‐feedback nonlinear systems with unknown time‐varying delayed interconnections. The interaction terms are bounded by unknown nonlinear bounding functions including unmeasurable state variables of subsystems. These bounding functions together with the algebraic loop problem of virtual and actual control inputs in the pure‐feedback form make the output‐feedback controller design difficult and challenging. To overcome the design difficulties, the observer‐based dynamic surface memoryless local controller for each subsystem is designed using appropriate Lyapunov‐Krasovskii functionals, the function approximation technique based on neural networks, and the additional first‐order low‐pass filter for the actual control input. It is shown that all signals in the total controlled closed‐loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, simulation examples are provided to illustrate the effectiveness of the proposed decentralized control scheme. Copyright © 2013 John Wiley & Sons, Ltd.

A 7–8.5 GHz High Performance MMIC HTS Josephson Mixer

A low-loss, low power consumption monolithic high-temperature superconducting (HTS) Josephson junction mixer at 7-8.5 GHz is presented. The mixer consists of a HTS YBa2Cu3O7-x (YBCO) bandpass filter for RF input, a lowpass filter for IF output and a LO resonator integrated with a single Josephson junction. All the passive and active devices are fabricated on a 20 mm×10 mm MgO substrate. Measurement result shows a conversion gain of -7 dB at 40 K, and -4.7 dB at 20 K. The IF output versus the RF input exhibits a wide linear range of conversion gains. The mixer has an extremely low LO power requirement at -32 dBm and a 50 nW power consumption.

On the asymptotic stability of minimum-variance unbiased input and state estimation

In this note, we investigate the asymptotic stability of the filter for minimum-variance unbiased input and state estimation developed by Gillijns and De Moor. Sufficient conditions for the stability are proposed and proven, with inspiration from the Kalman filter stability analysis.

Monolithic high-temperature superconducting heterodyne Josephson frequency down-converter

A monolithic microwave integrated circuit (MMIC) frequency down-converter based on a compact high-Tc superconducting (HTS) device is demonstrated. The on-chip integrated HTS down-converter consists of a 7–9 GHz bandpass filter for RF input, a lowpass filter for intermediate frequency output, and a self-pumped Josephson heterodyne mixer. All the above passive and active components are fabricated on a single 10 mm × 20 mm chip of YBa2Cu3O7−x film on MgO substrate. Characterization of this MMIC HTS down-converter in terms of frequency response, conversion gain, frequency-tuneability, bias dependence, dynamic range, linearity, and intrinsic noise are presented in this paper.