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A mismatch in electrode impedance is a common and significant source of artefact in electrical impedance spectroscopy systems. We have analysed these artefacts, identifying key parameters in the optimisation of both current and voltage excited electrical impedance spectroscopy systems. We have used this information to develop a new clinical system for investigating muscle impedance anisotropy in human limbs, with the aim of developing biomarkers for various muscle pathologies. This new instrumentation integrates buffering at the front-end, which isolates the electrodes from both multiplexing and wiring parasitics. Modelling of the front-end instrumentation demonstrates an input impedance of over 1 $\text{G}\Omega $ in parallel with 0.4 pF. The clinical system currently has 16 electrodes, with the capability for more, and operates over a physiologically relevant frequency range of 76 Hz to 625 kHz. Results presented in this paper show that the new system operates with reduced levels of artefact, even in the presence of a significant mismatch in electrode impedances. [ABSTRACT FROM AUTHOR]

Despite significant advances in the remote sensing of fluid flows, light detection and ranging (LIDAR) measurement equipment still presents the problems of having only radial (line-of-sight) wind speed measurements (Cyclops’ dilemma). Substantial expanses of unmeasured flow still remain and range weighting errors have a considerable influence on LIDAR measurements. Clearly, more information needs to be extracted from LIDAR data. With this motivation in mind, this brief shows that it is possible to estimate the wind velocity, wind direction, and absolute pressure over the entire spatial region of interest. A key challenge is that most established estimation techniques cater for systems that are finite-dimensional and described by ordinary differential equations (ODEs). By contrast, many fluid flows are governed by the Navier–Stokes equations, which are partial differential-algebraic equations (PDAEs). We show how a basis function decomposition method in conjunction with a pressure Poisson equation (PPE) formulation yields a spatially continuous, strangeness-free, reduced-order dynamic model for which a modified DAE form of the unscented Kalman filter (UKF) algorithm is used to estimate unmeasured velocities and pressure using sparse measurements from wind turbine-mounted LIDAR instruments. The approach is validated for both synthetic data generated from large eddy simulations of the atmospheric boundary layer and real-world LIDAR measurement data. Results show that a reconstruction of the flow field is achievable, thus presenting a validated estimation framework for potential applications including wind gust prediction systems and the preview control of wind turbines. [ABSTRACT FROM AUTHOR]

In this paper, a two-degrees-of-freedom control algorithm based on uncertainty and disturbance estimator (UDE), aimed to minimize the total harmonic distortion of inverter output voltage is proposed, possessing enhanced robustness to fundamental frequency variations. A multiple-time-delay action is combined with a commonly utilized low-pass UDE filter to increase the range of output impedance magnitude minimization around odd multiples of fundamental frequency for enhanced rejection of typical single-phase nonlinear loads harmonics. Marginal robustness improvement achieved by increasing the number of time delays is quantified analytically and revealed to be independent of delay order. The performance of the proposed control approach and its superiority over two recently proposed methods is validated successfully by experimental results. [ABSTRACT FROM AUTHOR]

Instability problem is an important issue for dc/dc conversion cascaded systems (Cascaded system in short). Though most of the existing stabilization methods can stabilize the whole system very well, they may ignore their impacts on the dynamic performance of the original cascaded system. Unfortunately, these impacts are negative to some extent. Recently, an adaptive-series-virtual-impedance (ASVI) control strategy has been reported to address the above problem. It not only can stabilize the cascaded system via shaping the load input impedance, but also can reduce its impact on the original load converter. However, though the ASVI control strategy has already greatly reduced its impact on the load converter, its remaining impact is negative. To solve this problem, this paper moves the ASVI from the load side to the source side via a proposed source-side series-virtual-impedance (SSVI) control strategy for the source converter. This SSVI control strategy not only has the same stabilization function and adaptive characteristics as the ASVI control strategy, but also improves the performance of the source converter. In addition, since the SSVI control strategy is realized by changing the control block of the source converter, the performance of the load converter is not affected. Therefore, the SSVI control strategy can be treated as a supplement and expansion of the ASVI control strategy. Moreover, depending on the method of realization, the SSVI control strategy can be divided into the source stabilization methods of the cascaded system. Finally, a 100 W 48 V–32 V–24 V cascaded system has been fabricated to validate the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, a two-degrees-of-freedom control structure is proposed to minimize both total harmonic distortion and tracking error of inverter output voltage, adopting a resonant tracking controller and a modified uncertainty and disturbance estimator (UDE). Owing to the two-degrees-of-freedom feature of the proposed control strategy, tracking and disturbance rejection tasks are decoupled and treated almost independently. A time-delay action is introduced into a commonly adopted low-pass UDE filter to minimize the output impedance magnitude around the odd harmonics, which is typical to nonlinear loads. Once the disturbance is properly rejected, a tracking resonant controller is designed to force the output of the nominal system to follow a sinusoidal reference with near-zero amplitude and phase error. The performance of the proposed control structure is fully verified by the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a method to directly shape the output impedance of an inverter is proposed to reduce the total harmonic distortion of the output voltage, based on the uncertainty and disturbance estimator (UDE)-based robust control framework. It is shown that, because of the two-degree-of-freedom feature of the UDE-based control strategy, the UDE filter directly affects the inverter output impedance. A multi-band-stop filter instead of a commonly adopted low-pass filter is then proposed to directly minimize the output impedance around the harmonics to reduce the effect of nonlinear loads and assure robustness to frequency variations. Two tradeoffs are revealed: one between filter bandwidth and stability and the other between robustness and the number of harmonics suppressed. The effectiveness of the proposed control strategy is fully supported by experimental results. [ABSTRACT FROM PUBLISHER]

Spatiotemporal models are ubiquitous in science and engineering, yet estimation in these models from discrete observations remains computationally challenging. We propose a practical novel approach to inference in spatiotemporal processes, both from continuous and from discrete (point-process) observations. The method is based on a finite-dimensional reduction of the spatiotemporal model, followed by a mean field variational approximate inference approach. To cater for the point-process case, a variational-Laplace approach is proposed which yields tractable computations of approximate variational posteriors. Results show that variational Bayes is a viable and practical alternative to statistical methods such as expectation maximization or Markov chain Monte Carlo. [ABSTRACT FROM PUBLISHER]

A state space model of the stochastic spatio-temporal Integro-Difference Equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial band- width of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense. [ABSTRACT FROM AUTHOR]

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the Integro-Difference Equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples. [ABSTRACT FROM AUTHOR]

Fast sampling is desirable to describe signal transmission through wide-bandwidth systems. The delta-operator provides an ideal discrete-time modeling description for such fast-sampled systems. However, the estimation of delta-domain model parameters is usually biased by directly applying the delta-transformations to a sampled signal corrupted by additive measurement noise. This problem is solved here by expectation-maximization, where the delta-transformations of the true signal are estimated and then used to obtain the model parameters. The method is demonstrated on a numerical example to improve on the accuracy of using a shift operator approach when the sample rate is fast. [ABSTRACT FROM AUTHOR]

System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue. [ABSTRACT FROM AUTHOR]

The maximum likelihood estimation of a dynamic spatiotemporal model is introduced, centred around the inclusion of a prior arbitrary spatiotemporal neighborhood description. The neighborhood description defines a specific parameterization of the state transition matrix, chosen on the basis of prior knowledge about the system. The model used is inspired by the spatiotemporal ARMA (STARMA) model, but the representation used is based on the standard state-space model. The inclusion of the neighborhood into an expectation-maximization based joint state and parameter estimation algorithm allows for accurate characterization of the spatiotemporal model. The process of including the neighborhood, and the effect it has on the maximum likelihood parameter estimate is described and demonstrated in this paper. [ABSTRACT FROM AUTHOR]

Previous stability analysis of the particle swarm optimizer was restricted to the assumption that all parameters are nonrandom, in effect a deterministic particle swarm optimizer. We analyze the stability of the particle dynamics without this restrictive assumption using Lyapunov stability analysis and the concept of passive systems. Sufficient conditions for stability are derived, and an illustrative example is given. Simulation results confirm the prediction from theory that stability of the particle dynamics requires increasing the maximum value of the random parameter when the inertia factor is reduced. [ABSTRACT FROM AUTHOR]

An adaptive receiver based on hybrid system theory is developed for a multiuser multiple-input multiple-output (MIMO) fading code-division multiple-access (CDMA) system. The basic idea is to treat the transmitted symbols and channel gains as unknown states (discrete and continuous) within a hybrid systems framework. The Bayesian-inference-based state estimation is derived using multiple model theory resulting in an optimal joint sequence estimator, which is shown to be intractable in its computational complexity. A suboptimal receiver (IMM-SIC) is then derived based on the well-known Interacting Multiple Model (IMM) algorithm and successive interference cancellation (SIC) scheme. This paper shows the specific approximations made to the probability densities of the optimal receiver in deriving the IMM-SIC receiver with complexity linear in number of users. This receiver design is well suited for online recursive processing of space-time coded CDMA system, where the decoding stage is incorporated within the multiple model framework. [ABSTRACT FROM AUTHOR]

A novel and more robust implementation is proposed for sequence estimation in uncertain environments with additive non-Gaussian ambient noise and intersymbol interference. This is based on a deterministic performance index that minimizes the effect of worst-case disturbances on the estimation error. The decoder has multiple H∞ filters and is in the fashion of per-survivor processing with a Viterbi trellis for decoding. There is a substantial performance improvement over maximum-likelihood sequence estimation, as shown by simulation results obtained for joint channel estimation and symbol decoding in non-Gaussian channels. [ABSTRACT FROM AUTHOR]

Focuses on the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. Formulation of the FDI problem in a multiple model environment; Creation of an FDI scheme by combining likelihood ratio test with the PF; Provision of a highly nonlinear systems for the efficacy of the methods.

Presents an adaptive control technique that grow in time according to the location of the system's state in space. Use of dynamic structure Gaussian radial basis function neural networks; Control of affine nonlinear systems; Dynamic network adaptive controller.