Input Reconstruction Research Articles

Self-consistent atom probe tomography reconstructions utilizing electron microscopy

Atom probe tomography reconstructions provide valuable information on nanometer-scale compositional variations within materials. As such, the spatial accuracy of the reconstructions is of primary importance for the resulting conclusions to be valid. Here, the use of transmission electron microscopy images before and after atom probe analysis to provide additional information and constraints is examined for a number of different materials. In particular, the consistency between the input reconstruction parameters and the output reconstruction is explored. It is demonstrated that it is possible to generate reconstructions in which the input and known values are completely consistent with the output reconstructions. Yet, it is also found that for all of the datasets examined, a particular power law relationship exists such that, if the image compression factor or detection efficiency is not constrained, a series of similarly spatially accurate reconstructions results. However, if one of these values can be independently assessed, then the other is known as well. Means of incorporating these findings and this general methodology into reconstruction protocols are also discussed.

Unknown Inputs Nonlinear Observer for an Activated Sludge Process

This paper deals with the jointly estimation problem of unknown inputs and nonmeasured states of one altering aerated activated sludge process (ASP). In order to provide accurate and economic concentration measures during aerobic and anoxic phases, a cascade high gain observer (HGO) approach is developed. Only two concentrations are available; the other process’s states are assumed unavailable. The observer converges asymptotically and it leads to a good estimation of the unavailable states which are the ammonia and substrate concentration, as well as a quite reconstruction of the unknown inputs, which are the influent ammonia and the influent substrate concentrations. To highlight the efficiency of the proposed HGO with this MIMO system’s dynamics, simulation results are validated with experimental data.

Observer Design and Unknown Input Reconstruction for a Class of Switched Descriptor Systems

This correspondence paper is concerned with observer design problems for a class of unknown input switched descriptor systems under average dwell time switching signals. The switched descriptor system under consideration is first transformed into a general switched linear system, and then a switched reduced-order observer that can asymptotically estimate the system state without suffering the influence from the unknown inputs is developed. Besides, a high-order sliding mode observer is introduced to obtain the estimations of the output derivatives by using the system measured outputs. On the basis of estimations of the states and output derivatives, an unknown input reconstructing method is provided. Finally, two examples are presented to verify the effectiveness of the proposed approaches.

The Flash-Lag, Fröhlich and Related Motion Illusions Are Natural Consequences of Discrete Sampling in the Visual System.

The Fröhlich effect and flash-lag effect, in which moving objects appear advanced along their trajectories compared to their actual positions, have defied a simple and consistent explanation. Here, I show that these illusions can be understood as a natural consequence of temporal compression in the human visual system. Discrete sampling at some stage of sensory perception has long been considered, and if it were true, it would necessarily lead to these illusions of motion. I show that the discrete perception hypothesis, with a single free parameter, the perceptual moment or sampling rate, can quantitatively explain all of the scenarios of the Fröhlich and flash-lag effect. I interpret discrete perception as the implementation of data compression in the brain, and our conscious perception as the reconstruction of the compressed input.

Reduced-order switched UIO design for switched discrete-time descriptor systems

In this paper, a reduced-order switched unknown input observer design is investigated for switched discrete-time descriptor systems. By a series of equivalent transformations, the original descriptor system with unknown inputs is transformed into a reduced-order standard system without unknown inputs, and then a switched reduced-order unknown input observer is designed by using standard switched system stability analysis techniques. Besides, the sufficient condition for the existence of the switched observer is also discussed in detail. Moreover, based on the state estimation, a delayed unknown input reconstruction method is also proposed. Finally, two simulation examples are given to show the effectiveness of the proposed methods.

Inversion-based output tracking and unknown input reconstruction of square discrete-time linear systems

In this paper, we propose a framework for output tracking control of both minimum phase (MP) and non-minimum phase (NMP) systems as well as systems with transmission zeros on the unit circle. Towards this end, first the problem of unknown state and input reconstruction of non-minimum phase systems is addressed. An unknown input observer (UIO) is designed that accurately reconstructs the minimum phase states of the system. The reconstructed minimum phase states serve as inputs to an FIR filter that is designed for a delayed non-minimum phase state reconstruction. It is shown that a quantified upper bound of the reconstruction error exponentially decreases as the estimation delay is increased. Therefore, an almost perfect state and input reconstruction can be achieved by selecting the delay to be sufficiently large. Our proposed inversion scheme is then applied to solve the output-tracking control problem. Furthermore, we have also proposed a methodology to handle the output tracking problem of systems that have transmission zeros on the unit circle in addition to MP and NMP zeros. Simulation case studies are also presented to demonstrate and illustrate the merits and capabilities of our proposed methodologies.

On the practical estimation of unknown inputs for polytopic LTI systems

Reconstruction of unknown inputs is an important issue in real-time monitoring when they cannot be directly measured. This study proposes a coupled robust observer to estimate both the state vector and the unknown input of uncertain linear and time invariant (LTI) systems. A Luenberger observer is first considered to estimate the state vector and then, a super-twisting observer uses the precedent estimations to reconstruct the unknown input. Two semi-definite optimisation problems are proposed to compute the observer gains. The feasibility of the strategy is illustrated by the two examples, one of them a non-linear biological system.

Unbiased Filtering for State and Unknown Input with Delay

Abstract In this paper, we consider linear network systems with unknown inputs. We present an unbiased recursive algorithm that simultaneously estimates states and inputs. We focus on delay-l left invertible systems with intrinsic delay l ≥ 1, where the input reconstruction is possible only by using outputs up to l time steps later in the future. By showing an equivalence with a descriptor system, we state conditions under which the time-varying filter converges to a stationary stable filter, involving the solution of a discrete-time algebraic Riccati equation.

Space climate and space weather over the past 400 years: 2. Proxy indicators of geomagnetic storm and substorm occurrence

Using the reconstruction of power input to the magnetosphere presented in Paper 1 Lockwood et al. [J Space Weather Space Clim 7 (2017a)], we reconstruct annual means of the geomagnetic Ap and AE indices over the past 400 years to within a 1-sigma error of ±20%. In addition, we study the behaviour of the lognormal distribution of daily and hourly values about these annual means and show that we can also reconstruct the fraction of geomagnetically-active (storm-like) days and (substorm-like) hours in each year to accuracies of to accuracies of ~50%, including the large percentage uncertainties in near-zero values. The results are the first physics-based quantification of the space weather conditions in both the Dalton and Maunder minima. Looking to the future, the weakening of Earth's magnetic moment means that the terrestrial disturbance levels during a future repeats of the solar Dalton and Maunder minima will be weaker and we here quantify this effect for the first time.

Reconstruction of Signals From Level-Crossing Samples Using Implicit Information

This paper focuses on the recovery of bandlimited signals from level-crossing samples by exploiting not only the knowledge of when given levels are crossed by the input, but also the implicit information that the signal stays between neighboring levels in the time intervals between the level crossings. We propose to use the technique of projection onto convex sets (POCS) for perfect signal reconstruction from either the level crossings or the associated implicit information. Two POCS algorithms are proposed: iterative POCS and one-step POCS. While the one-step POCS is based on matrix inversion, the iterative projections can be implemented using a chain of standard circuit operations: a low-pass filter and a clipping circuit, respectively. The perfect signal recovery of the infinite projection iteration can be viewed as the completion of the nonperfect input reconstruction achieved in continuous-time digital signal processing from level crossings. The comparative analysis of simulation results for both iterative and one-step POCS algorithms show the importance of a good selection of the initial guess for the POCS reconstruction.

An Explicit Fourth-Order Runge–Kutta Method for Dynamic Force Identification

This paper presents a new technique for input reconstruction based on the explicit fourth-order Runge–Kutta (RK4) method. First, the state-space representation of the dynamic system is discretized by the explicit RK4 method under the assumption of linear interpolation for the dynamic load, leading to a recurrence equation between the current state and the previous state. Then, the mapping from the sequences of input to output is established through the recursive operation of the system equation and observation equation. Finally, the stabilized force information is recovered using the Tikhonov regularization method. This approach makes use of the good stability and high precision of the RK4 method; in addition, the computational efficiency is enhanced by avoiding the computation of the inverse stiffness matrix. The proposed method is numerically illustrated and validated with various excitations on a simple four-story shear building and a more complicated 2D truss structure, along with a detailed parametric study. The simulation studies show that the external loads can be reconstructed with high efficiency and accuracy under a low noise environment.

Optimal state filter from sequential unknown input reconstruction: Application to distributed state filtering

SummaryThis paper generalizes Kalman filtering with an intermittent unknown input problem to be left invertible discrete‐time stochastic linear systems with zero, one, or more structural delays. Contrary to the state filtering–based system inversion where the unknown input vector is reconstructed with a time delay that is equal to the structural delay of the plant, we propose an optimal state filtering by reconstructing some linear combinations of the unknown input vector with a time delay less than the structural delay. Designed under a sequential unknown input decoupling constraint, which has never been previously studied in the literature, all presented filters are very computationally efficient. The proposed state filtering is used to solve the autonomous distributed state filtering problem in large‐scale networked control systems when the unknown input vector represents interactions between subsystems and when each subsystem receives intermittent information about the interaction from unreliable networks. The stochastic stability conditions of the extended intermittent unknown input Kalman filter are established when the arrival binary sequence of packet dropouts follows a random Bernoulli process.

Simultaneous state and unknown input reconstruction using cascaded high-gain observers

ABSTRACTThis paper presents a systematic routine for jointly reconstruct the state variables and the unknown inputs (UIs) for a large class of nonlinear MIMO systems. After an appropriate change of state coordinates, a set of cascade high-gain observers (CHGO) are designed in such a way that each of them provides an estimation of only one component of the UI vector, except the last one which gives a final adjustment of the whole state variables. Such design achieves a boundary of the state estimation error which can be arbitrarily small by properly specifying the sole synthesis parameter. An illustrative example verifies the effectiveness of the proposed scheme.

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.

Rate-Distortion Bounds for Kernel-Based Distortion Measures

Kernel methods have been used for turning linear learning algorithms into nonlinear ones. These nonlinear algorithms measure distances between data points by the distance in the kernel-induced feature space. In lossy data compression, the optimal tradeoff between the number of quantized points and the incurred distortion is characterized by the rate-distortion function. However, the rate-distortion functions associated with distortion measures involving kernel feature mapping have yet to be analyzed. We consider two reconstruction schemes, reconstruction in input space and reconstruction in feature space, and provide bounds to the rate-distortion functions for these schemes. Comparison of the derived bounds to the quantizer performance obtained by the kernel K -means method suggests that the rate-distortion bounds for input space and feature space reconstructions are informative at low and high distortion levels, respectively.

Cascade High Gain Observers Based State and Unknown Input Reconstruction

This paper presents a systematic routine for jointly reconstruct the state variables and the unknown inputs for a large class of nonlinear MIMO systems. After an appropriate change of state coordinates, a set of cascade high gain observers are designed in such a way that each of them provides an estimation of only one component of the unknown input vector, except the last one which gives a final adjustment of the whole state variables. Such design achieves a boundary of the state estimation error which can be arbitrarily small by properly specifying the sole synthesis parameter. An illustrative example verifies the effectiveness of the proposed scheme.

LOW-DOSE CT PROTOCOL OPTIMIZATION FOR THE ASSESSMENT OF ACUTE APPENDICITIS: THE OPTICAP PHANTOM STUDY.

ClinicalTrials.gov number, NCT01022567.

Nonlinear Integral Type Observer Design for State Estimation and Unknown Input Reconstruction

This paper is concerned with model-based robust observer designs for state observation and its application for unknown input reconstruction. Firstly, a sliding mode observer (SMO), which provides exponential convergence of estimation error, is designed for a class of multivariable perturbed systems. Observer gain matrices subject to specific structures are going to be imposed such that the unknown perturbation will not affect estimate precision during the sliding modes; Secondly, to improve discontinuous control induced in the SMO as well as pursue asymptotic estimate precision, a proportional-integral type observer (PIO) is further developed. Both the design procedures of the SMO and PIO algorithms are characterized as feasibility issues of linear matrix inequality (LMI) and thus the computations of the control parameters can be efficiently solved. Compared with the SMO, it will be demonstrated that the PIO is capable of achieving better estimation precision as long as the unknown inputs are continuous. Finally, a servo-drive flexible robot arm is selected as an example to demonstrate the applications of the robust observer designs.

Sensory Flow as a Basis for a Novel Distance Cue in Freely Behaving Electric Fish

The sensory input that an animal receives is directly linked to its motor activity. Behavior thus enables animals to influence their sensory input, a concept referred to as active sensing. How such behavior can serve as a scaffold for generating sensory information is of general scientific interest. In this article, we investigate how behavior can shape sensory information by using some unique features of the sensorimotor system of the weakly electric fish. Based on quantitative behavioral characterizations and computational reconstruction of sensory input, we show how electrosensory flow is actively created during highly patterned, spontaneous behavior in Gnathonemus petersii. The spatiotemporal structure of the sensory input provides information for the computation of a novel distance cue, which allows for a continuous estimation of distance. This has significant advantages over previously known nondynamic distance estimators as determined from electric image blur. Our investigation of the sensorimotor interactions in pulsatile electrolocation shows, for the first time, that the electrosensory flow contains behaviorally relevant information accessible only through active behavior. As patterned sensory behaviors are a shared feature of (active) sensory systems, our results have general implications for the understanding of (active) sensing, with the proposed sensory flow-based measure being potentially pertinent to a broad range of sensory modalities. <b>SIGNIFICANCE STATEMENT</b> Acquisition of sensory information depends on motion, as either an animal or its sensors move. Behavior can thus actively influence the sensory flow; and in this way, behavior can be seen as a manifestation of the brain9s integrative functions. The properties of the active pulsatile electrolocation system in Gnathonemus petersii allow for the sensory input to be computationally reconstructed, enabling us to link the informational content of spatiotemporal sensory dynamics to behavior. Our study reveals a novel sensory cue for estimating depth that is actively generated by the fishes9 behavior. The physical and behavioral similarities between electrolocation and other active sensory systems suggest that this may be a mechanism shared by (active) sensory systems.

Sensory Flow as a Basis for a Novel Distance Cue in Freely Behaving Electric Fish.

Acquisition of sensory information depends on motion, as either an animal or its sensors move. Behavior can thus actively influence the sensory flow; and in this way, behavior can be seen as a manifestation of the brain's integrative functions. The properties of the active pulsatile electrolocation system in Gnathonemus petersii allow for the sensory input to be computationally reconstructed, enabling us to link the informational content of spatiotemporal sensory dynamics to behavior. Our study reveals a novel sensory cue for estimating depth that is actively generated by the fishes' behavior. The physical and behavioral similarities between electrolocation and other active sensory systems suggest that this may be a mechanism shared by (active) sensory systems.