411 research outputs found

    A Kalman Filter Approach for Biomolecular Systems with Noise Covariance Updating

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    An important part of system modeling is determining parameter values, particularly for biomolecular systems, where direct measurements of individual parameters are typically hard. While Extended Kalman Filters have been used for this purpose, the choice of the process noise covariance is generally unclear. In this chapter, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise covariance on the states and parameters, as given in the Langevin framework. We adapt a Hybrid Extended Kalman Filtering technique by updating the process noise covariance at each time step based on estimates. We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured data for a negative transcriptional feedback circuit. We find that the Extended Kalman Filter with such process noise covariance update is closer to the optimality condition in the sense that the innovation sequence becomes white and in achieving a balance between the mean square estimation error and parameter convergence time. The results of this chapter may help in the use of Extended Kalman Filters for systems where process noise covariance depends on states and/or parameters.Comment: 23 pages, 9 figure

    Digital signal processing algorithms and structures for adaptive line enhancing

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    Predictive Shutdown Systems for Nuclear Power Plants

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    This dissertation investigates the use of a Kalman filter (KF) to predict, within the shutdown system (SDS) of a nuclear power plant (NPP), whether a safety parameter measurement will reach a corresponding trip set-point (TSP). The proposed predictive SDS (PSDS) designs aim to initiate shutdown actions at a time which is earlier than conventional shutdown initiation. These early detections are, in turn, expected to improve safety and productivity margins within the NPP. The KF-based point-PSDS design utilizes a linear time-varying (LTV) system model to predict mean safety parameter measurements for comparison against the TSP. The KF considers noise covariances that either have assumed predetermined values, or are estimated online using an adaptive limited memory filter (ALMF). The PSDS is enhanced to consider, by recursive least squares (RLS) estimation, conditions that are uncertain with respect to the assumed system model and noise properties. The result is a KF⁄RLS-based PSDS that compensates for prediction error by RLS-based estimation of deterministic disturbances to the system state and measurement. The PSDS is further enhanced to calculate confidence intervals for the predictions as a function of the propagated error covariance. This enhancement results in interval-PSDS designs that consider confidence in an impending condition by comparing predetermined confidence interval bounds against the TSP. Finally, an optimal-PSDS design is formulated to adapt the effective prediction, e.g. horizon or bias, by limiting and minimizing the probability of missed and false trip occurrences respectively using hypothesis testing methods and optimal alarm theory. In this manner, the optimal-PSDS is made aware of the quality of past predictions. The PSDS designs are compared, through simulation and experiment, against a conventional SDS in terms of response time or time-to-trip for the steam generator level low (SGLL) safety parameter under various conditions of uncertainty, e.g. parameter error or unmeasurable signals. MATLAB-based simulations demonstrate that the PSDS designs are able to reduce time-to-trip. The PSDS designs are then implemented within a Tricon v9 safety-PLC with a scan time that adheres to current nuclear industry regulations. The experimental results reveal that a reduced time-to-trip can be achieved for a real-world system with unknown system-model mismatch

    Improving Channel Estimation and Tracking Performance in Distributed MIMO Communication Systems

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    This dissertation develops and analyzes several techniques for improving channel estimation and tracking performance in distributed multi-input multi-output (D-MIMO) wireless communication systems. D-MIMO communication systems have been studied for the last decade and are known to offer the benefits of antenna arrays, e.g., improved range and data rates, to systems of single-antenna devices. D-MIMO communication systems are considered a promising technology for future wireless standards including advanced cellular communication systems. This dissertation considers problems related to channel estimation and tracking in D-MIMO communication systems and is focused on three related topics: (i) characterizing oscillator stability for nodes in D-MIMO systems, (ii) the development of an optimal unified tracking framework and a performance comparison to previously considered sub-optimal tracking approaches, and (iii) incorporating independent kinematics into dynamic channel models and using accelerometers to improve channel tracking performance. A key challenge of D-MIMO systems is estimating and tracking the time-varying channels present between each pair of nodes in the system. Even if the propagation channel between a pair of nodes is time-invariant, the independent local oscillators in each node cause the carrier phases and frequencies and the effective channels between the nodes to have random time-varying phase offsets. The first part of this dissertation considers the problem of characterizing the stability parameters of the oscillators used as references for the transmitted waveforms. Having good estimates of these parameters is critical to facilitate optimal tracking of the phase and frequency offsets. We develop a new method for estimating these oscillator stability parameters based on Allan deviation measurements and compare this method to several previously developed parameter estimation techniques based on innovation covariance whitening. The Allan deviation method is validated with both simulations and experimental data from low-precision and high-precision oscillators. The second part of this dissertation considers a D-MIMO scenario with NtN_t transmitters and NrN_r receivers. While there are NtimesNrN_t imes N_r node-to-node pairwise channels in such a system, there are only Nt+NrN_t + N_r independent oscillators. We develop a new unified tracking model where one Kalman filter jointly tracks all of the pairwise channels and compare the performance of unified tracking to previously developed suboptimal local tracking approaches where the channels are not jointly tracked. Numerical results show that unified tracking tends to provide similar beamforming performance to local tracking but can provide significantly better nullforming performance in some scenarios. The third part of this dissertation considers a scenario where the transmit nodes in a D-MIMO system have independent kinematics. In general, this makes the channel tracking problem more difficult since the independent kinematics make the D-MIMO channels less predictable. We develop dynamics models which incorporate the effects of acceleration on oscillator frequency and displacement on propagation time. The tracking performance of a system with conventional feedback is compared to a system with conventional feedback and local accelerometer measurements. Numerical results show that the tracking performance is significantly improved with local accelerometer measurements

    Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method

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    In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RRBP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant K25-NS05758)National Institutes of Health (U.S.) (Grant DP2- OD006454)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant R01-DA015644)Massachusetts General Hospital (Clinical Research Center, UL1 Grant RR025758

    PREDICTION OF RESPIRATORY MOTION

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    Radiation therapy is a cancer treatment method that employs high-energy radiation beams to destroy cancer cells by damaging the ability of these cells to reproduce. Thoracic and abdominal tumors may change their positions during respiration by as much as three centimeters during radiation treatment. The prediction of respiratory motion has become an important research area because respiratory motion severely affects precise radiation dose delivery. This study describes recent radiotherapy technologies including tools for measuring target position during radiotherapy and tracking-based delivery systems. In the first part of our study we review three prediction approaches of respiratory motion, i.e., model-based methods, model-free heuristic learning algorithms, and hybrid methods. In the second part of our work we propose respiratory motion estimation with hybrid implementation of extended Kalman filter. The proposed method uses the recurrent neural network as the role of the predictor and the extended Kalman filter as the role of the corrector. In the third part of our work we further extend our research work to present customized prediction of respiratory motion with clustering from multiple patient interactions. For the customized prediction we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the fourth part of our work we retrospectively categorize breathing data into several classes and propose a new approach to detect irregular breathing patterns using neural networks. We have evaluated the proposed new algorithm by comparing the prediction overshoot and the tracking estimation value. The experimental results of 448 patients’ breathing patterns validated the proposed irregular breathing classifier
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