Glottal Airflow Estimation using Neck Surface Acceleration and Low-Order Kalman Smoothing.

Phonatory function associated with hyperfunctionally related vocal fold lesions

Inverse filtering of oral airflow using closed-phase linear prediction is expected to preserve the effects of source-filter interactions in the glottal airflow pulse. Under incomplete glottal closure, the glottal airflow estimation is more challenging due to a lowered glottal impedance, increased subglottal coupling, and violated all-pole assumption. To account for these effects, a model-based inverse filtering scheme allowing for coupling between glottis and upper and lower airways was developed. Acoustic transmission in the tracts used a frequency-domain transmission line. A linearized, time-varying expression was used for the glottal impedance, along with a dipole representation. Synthetic vowels sounds and actual recordings were used to evaluate the proposed scheme. Subject-specific model parameters were obtained from simultaneous aerodynamic, acoustic, and high-speed videoendoscopic recordings of normal subjects uttering vowels with various degrees of glottal closure. Results illustrated that, even under incomplete glottal closure, the airflow entering the vocal tract preserved source-filter interactions and was comparable to that obtained using closed-phase linear prediction. The scheme also yielded an uncoupled glottal airflow that exhibited a clear pulse de-skewing, making it proportional to the glottal area. Cases with larger glottal gaps exhibited less pulse skewing, reduced unsteady flow, and a time-varying glottal impedance matching a parallel gap.

Being one of the major consequences of anthropogenic climate change, sea level rise forms a threat for many coastal areas and their inhabitants. Because all processes that cause sea-level changes have a spatially-varying fingerprint, local sea-level changes deviate substantially from the global mean. As a consequence, there is no single location on the earth that is subject to the global-mean sea-level change. To understand and forecast future changes in both global and regional sea level, a thorough understanding of its major underlying processes and their regional fingerprints is necessary. Nowadays, remote sensing from satellite altimetry provides an accurate estimate of changes in sea level on global and regional scales (Leuliette et al., 2004; Nerem et al., 2010; Ablain et al., 2017). The emergence of satellite gravimetry, in the form of the GRACE mission (Tapley et al., 2004), and the global coverage of in-situ subsurface temperature and salinity observations by the Argo programme (Roemmich et al., 2009; Roemmich and Gilson, 2009) has resulted in an extensive increase of our understanding of sea-level changes over the past decade, and the reliability of the estimates of the individual processes behind sea-level changes has reached the level where we can almost fully explain the observed sea-level changes from these contributors (Rietbroek et al., 2016; Leuliette and Miller, 2009; Dieng et al., 2015; Leuliette, 2015; Kleinherenbrink et al., 2016). However, before this period the spatially-varying signals have been sampled only sparsely by in-situ observations, mainly by means of tide gauges, which limits our current understanding of sea-level changes on global and regional scales. This thesis aims to find an answer to the question whether the sum of the underlying processes that cause sea-level changes can explain the observations, not only on a global scale, which has been assessed a multitude of times (Moore et al., 2011; Church et al., 2011; Gregory et al., 2013; Jevrejeva et al., 2016b), but also on scales of individual ocean basins and coastal regions. The assessment of this so-called sea-level budget has been done for two regional cases, and for the global ocean and individual basins. Furthermore, the effect of ocean bottom deformation on the difference between relative and geocentric observations has been quantified. Finally, we have applied an alternative approach to time-series analysis, in which the various contributors of sealevel variability are co-estimated with a time-varying trend using a Kalman filter and smoother approach, on tide gauge observations.

Low-cost adaptive square-root cubature Kalman filter for systems with process model uncertainty

Indirect observations of the vocal fold vibrations can be made from the glottal airflow waveform using the non-invasive technique of inverse filtering the oral airflow. The aim was to evaluate the automatic inverse filtering program ?Inverse?, that is, a pitch-asynchronous method based on linear prediction analysis. Oral airflow was recorded with a differential pressure transducer in a Rothenberg mask. Intraoral pressure, the electroglottographic (EGG) and acoustic signals were recorded simultaneously. Seventeen healthy women produced [ba:pa:pa:pa:p] in normal, soft and loud voice. Comparisons were made between results from inverse filtering performed using a manual procedure (Glottal Enterprises System) and the automatic program ?Inverse?. The glottal airflow waveform parameters peak flow, minimum flow, peak-to-peak flow, and the maximum negative flow derivative were extracted. High agreement between the two inverse filtering procedures was reflected by high correlation coefficients for each of the airflow parameters (r0.96). The absolute values of the parameters were generally lower for the automatic procedure (1.2-7.7%). This was thought to be due to the different low pass filter characteristics and the different number of anti-resonances used in the two methods. Inverse filtering requires careful control of the recording, and the results must be interpreted thoroughly also after automatic analysis. With those considerations, we conclude that the automatic program ?Inverse? can be used in clinical settings.

Modified Ensemble Kalman filter for reservoir parameter and state estimation in the presence of model uncertainty

Combined impulse-response/Kalman filtering (CIRKF) for input/state estimation

This work investigates the problem of estimating the flue gas flow signal in a heat exchanger using the Kalman filter. The methodology of this work comprises two steps: the mathematical modeling step and the state estimation step. In the first step, the Autoregressive models with Exogenous Inputs (ARX) are obtained, which describe the dynamics of the HE-3 heat exchanger system. In the second step, the estimation of the flue gas flow signal is obtained using the Kalman filter from the identified ARX model and a set of measurements. The results obtained suggest that the Kalman filter is a promising method for the estimation of signals involved in the reduction step in steelmaking, making it a useful tool for applications in steelmaking processes.

This paper present the development of Kalman filter that allows evaluation in the estimation of resistance (R), inductance (L), and capacitance (C) values for Universiti Malaysia Pahang (UMP) short transmission line. To overcome the weaknesses of existing system such as power losses in the transmission line, Kalman Filter can be a better solution to estimate the parameters. The aim of this paper is to estimate RLC values by using Kalman filter that in the end can increase the system efficiency in UMP. In this research, matlab simulink model is developed to analyse the UMP short transmission line by considering different noise conditions to reprint certain unknown parameters which are difficult to predict. The data is then used for comparison purposes between calculated and estimated values. The results have illustrated that the Kalman Filter estimate accurately the RLC parameters with less error. The comparison of accuracy between Kalman Filter and Least Square method is also presented to evaluate their performances.

Nonlinear Kalman filters for aircraft engine gas path health estimation with measurement uncertainty

Aiming at the problem of low filtering accuracy and even divergence caused by model mismatch when using extended Kalman filter in ship GPS navigation and positioning state estimation, a positioning ship state estimation algorithm based on the fusion of improved unscented Kalman filter and particle filter is proposed. Compared with the traditional particle filtering algorithm, the algorithm has two improvements: first, the algorithm uses untraced Kalman as the main framework, and uses the optimal estimation of particle updating state by particle algorithm; Secondly, in the resampling process, a resampling algorithm based on weight optimization is proposed to increase the diversity of particles. The simulation results show that not only the particle degradation degree of the particle filter is reduced, but also the particle tracking accuracy is improved.

Accurate comparison between INRIM and a secondary calibration laboratory using a top-class multifunction electrical calibrator

This letter presents an algorithm for reducing measurement uncertainty of one physical quantity when oversampling measurements of two physical quantities with correlated noise. The algorithm assumes that the aleatoric measurement uncertainty in both physical quantities follows a Gaussian distribution and relies on sampling faster than it is possible for the measurand (the true value of the physical quantity that we are trying to measure) to change (due to the system thermal time constant) to calculate the parameters of the noise distribution. In contrast to the Kalman and particle filters, which, respectively, require state update equations and a map of one physical quality, our algorithm requires only the oversampled sensor measurements. When applied to temperature-compensated humidity sensors, it provides reduced uncertainty in humidity estimates from correlated temperature and humidity measurements. In an experimental evaluation, the algorithm achieves average uncertainty reduction of 10.3%. The algorithm incurs an execution time overhead of 5.3% when compared to the minimum algorithm required to measure and calculate the uncertainty. Detailed instruction-level emulation of a C-language implementation compiled to the RISC-V architecture shows that the uncertainty reduction program required 0.05% more instructions per iteration than the minimum operations required to calculate the uncertainty.

Most of the present navigation sensor integration techniques are based on Kalman filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multi-sensor integration, it has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of Artificial Neural Network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multi-sensor integration approach for fusing data from an Inertial Navigation system (INS) and Differential Global Positioning System (DGPS) utilizing multi-layer feed-forward neural networks with a back propagation learning algorithm. The performance of the proposed architecture was tested using two different INS systems(tactical grade IMU and navigation grade IMU) in a land vehicle.

In this paper an on-site accuracy comparison of capacitor and inductive voltage transformers is presented. A 400/110 kV transformer substation in Croatia has been chosen as an appropriate location for the comparison as both types of transformers (capacitor and inductive) operate there simultaneously. Each transformer type is a part of one of the two existing measuring chains for measurement of electrical energy. Their accuracy comparison is, therefore, carried out indirectly, by actually comparing the energies measured by the first and the second measuring chain and then estimating the measurement uncertainty of their difference. Without measurement uncertainty, a parameter that shows the quality of any measurement result, it is generally not possible to conclude about the accuracy of measurement or to compare measurement results mutually. In this measurement uncertainty estimation real on-site conditions in transformer substation, especially those which effect voltage transformer accuracy, have been taken into account (voltage, transformer load, frequency, temperature, etc.)