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Probabilistic instantaneous model-based signal processing applied to localization and tracking

ROBOTICS AND AUTONOMOUS SYSTEMS 2009年第3期57卷 249-258页

作者： Beutler, Frederik Huber, Marco F. Hanebeck, Uwe D. Univ Karlsruhe TH Inst Comp Sci & Engn Intelligent Sensor Actuator Syst Lab D-76128 Karlsruhe Germany

In this paper, a probabilistic approach for estimating time and space-variant parameters of a system, based on sequentially received discrete-time signal values, is presented. The system description is the solution of a linear partial differential equation (PDE). The PDE describes for example the wave propagation of an acoustic wave in a localization system. The solution of the PDE is given by a time-variant and space-variant impulse response. This impulse response is characterized by the time and space-variant parameters in order to track an object, which emits for example an acoustic signal. For estimating the position of the object in an instantaneous way a Bayesian approach has to be used, which considers the dynamic behavior of the parameters in a system model and uncertainties in a stochastic manner by means of probability density functions. Hence, the new approach provides a probabilistic instantaneous model-based signal processing, where the sequentially measured signal values are processed directly and known reference signal sequences are interpreted as part of a time-variant nonlinear measurement equation. (C) 2008 Elsevier B.V. All rights reserved.

关键词： model-based signal processing Bayesian estimator Localization Tracking

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Effects of non-nutritive sucking on heart rate, respiration and oxygenation:: a model-based signal processing approach

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COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY A-MOLECULAR AND INTEGRATIVE PHYSIOLOGY 2002年第1期132卷 97-106页

作者： Morren, G Van Huffel, S Helon, I Naulaers, G Daniëls, H Devlieger, H Casaer, P Katholieke Univ Leuven SISTA ESQT SCDDept Elect Engn B-3001 Louvain Belgium Univ Hosp Gasthuisberg Dept Paediat B-3000 Louvain Belgium

Several studies support the idea that the use of pacifiers can reduce the risk of Sudden Infant Death Syndrome. To investigate the effect of non-nutritive sucking (NNS), we measured heart rate, abdominal respiration, EMG and arterial oxygen saturation of 20 neonates. Also, in 10 of these neonates, changes in cerebral hemoglobin concentrations were acquired by means of near-infrared spectroscopy. Using a parametric technique to model the heart rate as a sum of exponentially damped sinusoids, two main frequency components were found in the heart rate during NNS: a frequency of approximately 0.08 Hz due to the alternation of sucking bursts and pauses, and a frequency of approximately 0.8 Hz that reflects the influence of the respiration. Our analysis shows that it is the alternation of bursts and pauses itself that causes the increased heart rate variability, and that this is not due to increased effort. This suggests that the neuronal mechanism regulating NNS also stimulates the heart rate. From our measurements, no effect of NNS on cerebral or peripheral oxygenation could be found. Furthermore, we show that our model-based signal processing technique is well suited for the analysis of non-stationary biomedical signals. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： heart rate variability model-based signal processing near-infrared spectroscopy non-nutritive sucking oxygenation polysomnography respiration Sudden Infant Death Syndrome

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A model-based signal processing Method for Fault Diagnosis in PMSM Machine 9

A Model-based Signal Processing Method for Fault Diagnosis i...

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2017 9th Annual IEEE Energy Conversion Congress & Exposition (ECCE)

作者： Heydarzadeh, Mehrdad Zafarani, Mohsen Ugur, Enes Akin, Bilal Nourani, Mehrdad Univ Texas Dallas Dept Elect & Comp Engn Richardson TX 75083 USA

ISBN: (纸本)9781509029983

In this paper, a model based signal processing method is proposed to diagnose two faults under different load condition for permanent magnet synchronous machines. This method captures the spectrum of stator current in a compact representation. Then, its dimensionality is reduced using the discriminant analysis. Finally a support vector machine automatically classifies the health state of the system. For testing the method, a customized test rig is built and data from different loads and faults are collected. An average accuracy of 93% in a 10-fold cross validation is obtained.

关键词： Autoregressive moving average model Dimension reduction Fault diagnosis model-based signal processing Parametric spectral analysis Permanent magnet synchronous machine Support vector machine

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signal processing aspects of fusion plasma broadband reflectometry

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IEEE TRANSACTIONS ON signal processing 1999年第2期47卷 378-388页

作者： Nunes, FD Leitao, JMN Univ Tecn Lisboa Inst Telecomunicacoes EURATOM IST AssocInst Super Tecn Lisbon Portugal Univ Tecn Lisboa Ctr Fusao Nucl EURATOM IST AssocInst Super Tecn Lisbon Portugal

Microwave reflectometry is a radar technique utilized by nuclear fusion diagnostics to evaluate the plasma electron density distribution (density profile) and its local fluctuations in experimental devices, e.g., tokamaks, It exploits the fact that an electromagnetic wave launched into the plasma is reflected at the layer where the refractive index vanishes. By mixing the incident and reflected waves, a phase-modulated reflectometric signal is produced. In O-mode broadband reflectometry, the density profile is determined by sweeping the frequency of the incident wave, estimating the phase-rate of the reflectometric signal and computing its Abel inversion integral. In this paper, a stochastic nonlinear filtering approach is adopted for the estimation problem. The joint phase and phase-rate dynamics is modeled as a vector Gauss-Markov process from which only the first component is observed. A suboptimal nonlinear filter tailored to the features of the problem under study is developed and tested by simulation and applied to real data. This estimator exhibits significant advantages over the extended Kalman-Bucy filter, which is used in this work as a benchmark.

关键词： broadband reflectometry model-based signal processing phase/phase-rate estimation stochastic nonlinear filtering

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SPEECH DEREVERBERATION WITH MULTI-CHANNEL LINEAR PREDICTION AND SPARSE PRIORS FOR THE DESIRED signal

SPEECH DEREVERBERATION WITH MULTI-CHANNEL LINEAR PREDICTION ...

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4th Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA)

作者： Jukic, Ante van Waterschoot, Toon Gerkmann, Timo Doclo, Simon Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26111 Oldenburg Germany Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS ETC Leuven Belgium

ISBN: (纸本)9781479931095

The quality of recorded speech signals can be substantially affected by room reverberation. In this paper we focus on a blind method for speech dereverberation based on the multi-channel linear prediction model in the short-time Fourier domain, where the parameters of the model are estimated using a maximum-likelihood procedure. Contrary to the conventional approach, we propose to model the desired speech signal using a general sparse prior that can be represented as a maximization over scaled complex Gaussians. Experimental evaluation, employing a parametric complex generalized Gaussian prior for the desired speech signal, shows that instrumentally predicted speech quality can be improved compared to the conventional approach.

关键词： Dereverberation speech enhancement model-based signal processing sparse priors

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An adaptively tunable model of the cardiac signal for the bio-impedance signal decomposer (BISD)

An adaptively tunable model of the cardiac signal for the bi...

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IEEE International Workshop on Medical Measurements and Applications (MeMeA 2008)

作者： Krivoshei, Andrei Kukk, Vello Min, Mart Tallinn Univ Technol Dept Elect Ehitajate Tee 5 EE-19086 Tallinn Estonia Tallinn Uinv Techol Dept Comp Control Tallinn 19086 Estonia

ISBN: (纸本)9781424419371

The paper presents the further development of the bio-impedance signal decomposer (BISD) of the total bio-impedance (BI) signal to its cardiac and respiratory components. The Jacobi orthonormal polynomials based adaptively tunable model of the cardiac BI signal is proposed in the paper, which plays very important role in the decomposition task The importance arises from the fact, that the BISD must be reliable and have to correct operate with signals taken from different persons, and in such cases, when the cardiac BI signal of a person is changing in time. For the proposed system the reliability significantly depends on the difference between the model of the cardiac signal and the real cardiac signal (the reference signal). Tie averaged through several periods version of the already separated cardiac BI signal is used as reference signal in the proposed algorithm for tuning the parameters of the cardiac BI signal model using a modified Newton adaptation algorithm. After the model is elaborated, the system separates the cardiac and the respiratory components more accurately by tracking the cardiac BI signal.

关键词： adaptive signal processing adaptive parameter estimation bio-impedance model-based signal processing heart rate monitoring

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SPEECH DEREVERBERATION USING WEIGHTED PREDICTION ERROR WITH LAPLACIAN model OF THE DESIRED signal

SPEECH DEREVERBERATION USING WEIGHTED PREDICTION ERROR WITH ...

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IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Jukic, Ante Doclo, Simon Carl von Ossietzky Univ Oldenburg Dept Med Phys & Acoust D-26111 Oldenburg Germany

ISBN: (纸本)9781479928934

Reverberation has a considerable impact on the quality and intelligibility of captured speech signals. In this paper we present an approach for blind multi-microphone speech dereverberation based on the weighted prediction error method, where the reverberant observations are modeled using multi-channel linear prediction in the short-time Fourier transform domain. Instead of using the commonly employed Gaussian distribution for the desired speech signal, the proposed approach uses a Laplacian distribution which is known to be more accurate in modeling speech signals. Maximum-likelihood estimation is used for estimating the model parameters, leading to a linear programming optimization problem. Experimental results, obtained using measured impulse responses, indicate that the proposed approach could be used to improve the dereverberation performance compared to the classical technique.

关键词： Dereverberation speech enhancement model-based signal processing

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Pseudo-Bayesian model-based Noninvasive Intracranial Pressure Estimation and Tracking

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2020年第6期67卷 1604-1615页

作者： Imaduddin, Syed M. Fanelli, Andrea Vonberg, Frederick W. Tasker, Robert C. Heldt, Thomas MIT Inst Med Engn & Sci Res Lab Elect Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA MIT Inst Med Engn & Sci Cambridge MA 02139 USA MIT Res Lab Elect Cambridge MA 02139 USA Boston Childrens Hosp Dept Anesthesiol Crit Care & Pain Med Boston MA USA Boston Childrens Hosp Dept Neurol Boston MA USA

Objective: A noninvasive intracranial pressure (ICP) estimation method is proposed that incorporates a model-based approach within a probabilistic framework to mitigate the effects of data and modeling uncertainties. Methods: A first-order model of the cerebral vasculature relates measured arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) to ICP. The model is driven by the ABP waveform and is solved for a range of mean ICP values to predict the CBFV waveform. The resulting errors between measured and predicted CBFV are transformed into likelihoods for each candidate ICP in two steps. First, a baseline ICP estimate is established over five data windows of 20 beats by combining the likelihoods with a prior distribution of the ICP to yield an a posteriori distribution whose median is taken as the baseline ICP estimate. A single-state model of cerebral autoregulatory dynamics is then employed in subsequent data windows to track changes in the baseline by combining ICP estimates obtained with a uniform prior belief and model-predicted ICP. For each data window, the estimated model parameters are also used to determine the ICP pulse pressure. Results: On a dataset of thirteen pediatric patients with a variety of pathological conditions requiring invasive ICP monitoring, the method yielded for mean ICP estimation a bias (mean error) of 0.6 mmHg and a root-mean-squared error of 3.7 mmHg. Conclusion: These performance characteristics are well within the acceptable range for clinical decision making. Significance: The method proposed here constitutes a significant step towards robust, continuous, patient-specific noninvasive ICP determination.

关键词： Estimation Microsoft Windows Biomedical measurement Biological system modeling Data models Blood Extraterrestrial measurements Bayesian estimation model-based signal processing intracranial pressure brain injury

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Thermal Drifts of Capacitive Flow Meters: Analysis of Effects and model-based Compensation

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-11页

作者： Suppan, Thomas Neumayer, Markus Bretterklieber, Thomas Wegleiter, Hannes Graz Univ Technol Inst Elect Measurement & Sensor Syst A-8010 Graz Austria

Capacitive sensing has become a favorable measurement technology for flow metering in pneumatic conveying systems. Multielectrode sensing structures and tomographic signal evaluations enable spatially resolved flow parameter estimation, which is of particular interest for pneumatically conveyed solids due to inhomogeneous particle distributions within the pipeline. The noninvasive working principle of capacitive sensors is an important feature for the application in industrial processes with harsh environments. However, cross sensitivities of the capacitive probe cause effects, such as temperature drifts of the measurements. For a reliable operation of capacitive flow meters in harsh environments, induced drifts have to be compensated. In this article, we present the detailed analyses of thermal effects within capacitive sensors. based on the findings, a model-based temperature compensation approach is developed within the Bayesian framework. The performance of the proposed compensation approach is analyzed by a measurement-based validation within a climate chamber and by a simulation-based uncertainty quantification. The capability to obtain temperature-independent estimates with calibration measurements acquired at room temperature is demonstrated.

关键词： Capacitive sensing flow measurement harsh environments model-based signal processing

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Varied excitation for the improving of information extraction from signals of single sensors

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TM-TECHNISCHES MESSEN 2006年第7-8期73卷 382-392页

作者： Kanoun, Olfa Universität der Bundeswehr München Germany Institut für Mess- und Automatisierungstechnik Universität der Bundeswehr München 85577 Neubiberg Germany

Different approaches can be pursued in order to improve sensory information. The approach of this work is using one single sensor element under varied excitation, such as current or frequency. The dependence of the sensor output on the excitation can be used to separate the effects according to their different reactions on the excitation. Changes in the dependence of the sensor output on excitation can be identified and utilized for self-test and self-calibration. This kind of sensor systems called Varied Input Sensor systems (VIS) closes the gap between single sensors and multi-sensor systems. The development process of this kind of sensor systems needs special care for a robust and undisturbed function.

关键词： sensor signal processing model-based signal processing control parameter impedance spectroscopy

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