subject:(particle filtering)

1. Hofman Radek. APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT .

Degree: 2011, Czech University of Technology

APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT; APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT Advisors/Committee Members: Pecha Petr (advisor).

Subjects/Keywords: data assimilation; particle filtering; dispersion modelling

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APA (6^th Edition):

Radek, H. (2011). APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT . (Thesis). Czech University of Technology. Retrieved from http://hdl.handle.net/10467/8017

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Radek, Hofman. “APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT .” 2011. Thesis, Czech University of Technology. Accessed December 09, 2019. http://hdl.handle.net/10467/8017.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Radek, Hofman. “APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT .” 2011. Web. 09 Dec 2019.

Vancouver:

Radek H. APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT . [Internet] [Thesis]. Czech University of Technology; 2011. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10467/8017.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Radek H. APPLICATION OF ADVANCED DATA ASSIMILATION METHODS IN OFF-SITE CONSEQUENCE ASSESSMENT . [Thesis]. Czech University of Technology; 2011. Available from: http://hdl.handle.net/10467/8017

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Tennessee – Knoxville

2. Li, Zhiqiang. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.

Degree: 2012, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_graddiss/1322

► Various particle filters have been proposed and their convergence to the optimal filter are obtained for finite time intervals. However, uniform convergence results have been… (more)

Subjects/Keywords: nonlinear filtering; branching particle aproximation; Probability

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APA (6^th Edition):

Li, Z. (2012). Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/1322

Chicago Manual of Style (16^th Edition):

Li, Zhiqiang. “Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.” 2012. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed December 09, 2019. https://trace.tennessee.edu/utk_graddiss/1322.

MLA Handbook (7^th Edition):

Li, Zhiqiang. “Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.” 2012. Web. 09 Dec 2019.

Vancouver:

Li Z. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2012. [cited 2019 Dec 09]. Available from: https://trace.tennessee.edu/utk_graddiss/1322.

Council of Science Editors:

Li Z. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2012. Available from: https://trace.tennessee.edu/utk_graddiss/1322

Cal Poly

3. Norris, Michael K. INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION.

Degree: MS, Computer Science, 2016, Cal Poly

URL: https://digitalcommons.calpoly.edu/theses/1629 ; 10.15368/theses.2016.96

► This work presents improvements to Monte Carlo Localization (MCL) for a mobile robot using computer vision. Solutions to the localization problem aim to provide… (more)

Subjects/Keywords: localization; computer vision; particle filtering; Computational Engineering

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APA (6^th Edition):

Norris, M. K. (2016). INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/1629 ; 10.15368/theses.2016.96

Chicago Manual of Style (16^th Edition):

Norris, Michael K. “INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION.” 2016. Masters Thesis, Cal Poly. Accessed December 09, 2019. https://digitalcommons.calpoly.edu/theses/1629 ; 10.15368/theses.2016.96.

MLA Handbook (7^th Edition):

Norris, Michael K. “INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION.” 2016. Web. 09 Dec 2019.

Vancouver:

Norris MK. INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION. [Internet] [Masters thesis]. Cal Poly; 2016. [cited 2019 Dec 09]. Available from: https://digitalcommons.calpoly.edu/theses/1629 ; 10.15368/theses.2016.96.

Council of Science Editors:

Norris MK. INCORPORATING HISTOGRAMS OF ORIENTED GRADIENTS INTO MONTE CARLO LOCALIZATION. [Masters Thesis]. Cal Poly; 2016. Available from: https://digitalcommons.calpoly.edu/theses/1629 ; 10.15368/theses.2016.96

University of Edinburgh

4. Zhong, Xionghu. Bayesian framework for multiple acoustic source tracking.

Degree: PhD, 2010, University of Edinburgh

URL: http://hdl.handle.net/1842/4752

► Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free… (more)

▼ Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a time-delay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised cross-correlation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed window-disjoint orthogonal (WDO) in the time-frequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely “nonconcurrent multiple acoustic tracking (NMAT)”. The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the tracking-lag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and time-varying number of acoustic sources. The DUET-GCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Rao-blackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data…

Subjects/Keywords: 621.382; Bayesian filter; particle filtering; tracking

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APA (6^th Edition):

Zhong, X. (2010). Bayesian framework for multiple acoustic source tracking. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/4752

Chicago Manual of Style (16^th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Doctoral Dissertation, University of Edinburgh. Accessed December 09, 2019. http://hdl.handle.net/1842/4752.

MLA Handbook (7^th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Web. 09 Dec 2019.

Vancouver:

Zhong X. Bayesian framework for multiple acoustic source tracking. [Internet] [Doctoral dissertation]. University of Edinburgh; 2010. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1842/4752.

Council of Science Editors:

Zhong X. Bayesian framework for multiple acoustic source tracking. [Doctoral Dissertation]. University of Edinburgh; 2010. Available from: http://hdl.handle.net/1842/4752

Texas A&M University

5. McClenny, Levi Daniel. Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise.

Degree: 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/157796

► Recently, state space signal models have been proposed to characterize the behavior of discrete-time boolean dynamical systems. The current system model is one in which… (more)

Subjects/Keywords: Boolean; Kalman; Filtering; Colored Noise; Correlated Noise; Particle Filtering

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APA (6^th Edition):

McClenny, L. D. (2016). Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157796

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

McClenny, Levi Daniel. “Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise.” 2016. Thesis, Texas A&M University. Accessed December 09, 2019. http://hdl.handle.net/1969.1/157796.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

McClenny, Levi Daniel. “Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise.” 2016. Web. 09 Dec 2019.

Vancouver:

McClenny LD. Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise. [Internet] [Thesis]. Texas A&M University; 2016. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1969.1/157796.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

McClenny LD. Optimal State Estimation for Partially Observed Boolean Dynamical Systems in the Presence of Correlated Observation Noise. [Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157796

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Syracuse University

6. ZHENG, YUJIAO. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.

Degree: PhD, Electrical Engineering and Computer Science, 2014, Syracuse University

URL: https://surface.syr.edu/etd/71

► Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate… (more)

▼ Distributed inference arising in sensor networks has been an interesting and promising discipline in recent years. The goal of this dissertation is to investigate several issues related to distributed inference in sensor networks, emphasizing parameter estimation and target tracking with resource-constrainted networks. To reduce the transmissions between sensors and the fusion center thereby saving bandwidth and energy consumption in sensor networks, a novel methodology, where each local sensor performs a censoring procedure based on the normalized innovation square (NIS), is proposed for the sequential Bayesian estimation problem in this dissertation. In this methodology, each sensor sends only the informative measurements and the fusion center fuses both missing measurements and received ones to yield more accurate inference. The new methodology is derived for both linear and nonlinear dynamic systems, and both scalar and vector measurements. The relationship between the censoring rule based on NIS and the one based on Kullback-Leibler (KL) divergence is investigated. A probabilistic transmission model over multiple access channels (MACs) is investigated. With this model, a relationship between the sensor management and compressive sensing problems is established, based on which, the sensor management problem becomes a constrained optimization problem, where the goal is to determine the optimal values of probabilities that each sensor should transmit with such that the determinant of the Fisher information matrix (FIM) at any given time step is maximized. The performance of the proposed compressive sensing based sensor management methodology in terms of accuracy of inference is investigated. For the Bayesian parameter estimation problem, a framework is proposed where quantized observations from local sensors are not directly fused at the fusion center, instead, an additive noise is injected independently to each quantized observation. The injected noise performs as a low-pass filter in the characteristic function (CF) domain, and therefore, is capable of recoverving the original analog data if certain conditions are satisfied. The optimal estimator based on the new framework is derived, so is the performance bound in terms of Fisher information. Moreover, a sub-optimal estimator, namely, linear minimum mean square error estimator (LMMSE) is derived, due to the fact that the proposed framework theoretically justifies the additive noise modeling of the quantization process. The bit allocation problem based on the framework is also investigated. A source localization problem in a large-scale sensor network is explored. The maximum-likelihood (ML) estimator based on the quantized data from local sensors and its performance bound in terms of Cramér-Rao lower bound (CRLB) are derived. Since the number of sensors is large, the law of large numbers (LLN) is utilized to obtain a closed-form version of the performance bound, which clearly… Advisors/Committee Members: Pramod k. Varshney, Ruixin Niu.

Subjects/Keywords: Bayesian estimation; Kalman filtering; Particle filtering; Sensor networks; Target tracking; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

ZHENG, Y. (2014). Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/71

Chicago Manual of Style (16^th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Doctoral Dissertation, Syracuse University. Accessed December 09, 2019. https://surface.syr.edu/etd/71.

MLA Handbook (7^th Edition):

ZHENG, YUJIAO. “Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks.” 2014. Web. 09 Dec 2019.

Vancouver:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Internet] [Doctoral dissertation]. Syracuse University; 2014. [cited 2019 Dec 09]. Available from: https://surface.syr.edu/etd/71.

Council of Science Editors:

ZHENG Y. Distributed Estimation and Performance Limits in Resource-constrained Wireless Sensor Networks. [Doctoral Dissertation]. Syracuse University; 2014. Available from: https://surface.syr.edu/etd/71

Texas A&M University

7. Boddikurapati, Sirish. Sequential Monte Carlo Methods With Applications To Communication Channels.

Degree: 2010, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537

► Estimating the state of a system from noisy measurements is a problem which arises in a variety of scientific and industrial areas which include signal… (more)

Subjects/Keywords: Particle filtering; Sequential Monte Carlo filtering; Markovian chains; Recursive Bayesian filtering; Continuous-Discrete particle filter; optical fiber propagation; capacity of optical fiber; information rate using particle filtering

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APA (6^th Edition):

Boddikurapati, S. (2010). Sequential Monte Carlo Methods With Applications To Communication Channels. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Boddikurapati, Sirish. “Sequential Monte Carlo Methods With Applications To Communication Channels.” 2010. Thesis, Texas A&M University. Accessed December 09, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Boddikurapati, Sirish. “Sequential Monte Carlo Methods With Applications To Communication Channels.” 2010. Web. 09 Dec 2019.

Vancouver:

Boddikurapati S. Sequential Monte Carlo Methods With Applications To Communication Channels. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Boddikurapati S. Sequential Monte Carlo Methods With Applications To Communication Channels. [Thesis]. Texas A&M University; 2010. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7537

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

8. Yang, Tao. Feedback particle filter and its applications.

Degree: PhD, 0133, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/50708

► The purpose of nonlinear filtering is to extract useful information from noisy sensor data. It finds applications in all disciplines of science and engineering, including… (more)

▼ The purpose of nonlinear filtering is to extract useful information from noisy sensor data. It finds applications in all disciplines of science and engineering, including tracking and navigation, traffic surveillance, financial engineering, neuroscience, biology, robotics, computer vision, weather forecasting, geophysical survey and oceanology, etc. This thesis is particularly concerned with the nonlinear filtering problem in the continuous-time continuous-valued state-space setting (diffusion). In this setting, the nonlinear filter is described by the Kushner-Stratonovich (K-S) stochastic partial differential equation (SPDE). For the general nonlinear non-Gaussian problem, no analytical expression for the solution of the SPDE is available. For certain special cases, finite-dimensional solution exists and one such case is the Kalman filter. The Kalman filter admits an innovation error-based feedback control structure, which is important on account of robustness, cost efficiency and ease of design, testing and operation. The limitations of Kalman filters in applications arise because of nonlinearities, not only in the signal models but also in the observation models. For such cases, Kalman filters are known to perform poorly. This motivates simulation-based methods to approximate the infinite-dimensional solution of the K-S SPDE. One popular approach is the particle filter, which is a Monte Carlo algorithm based on sequential importance sampling. Although it is potentially applicable to a general class of nonlinear non-Gaussian problems, the particle filter is known to suffer from several well-known drawbacks, such as particle degeneracy, curse of dimensionality, numerical instability and high computational cost. The goal of this dissertation is to propose a new framework for nonlinear filtering, which introduces the innovation error-based feedback control structure to the particle filter. The proposed filter is called the feedback particle filter (FPF). The first part of this dissertation covers the theory of the feedback particle filter. The filter is defined by an ensemble of controlled, stochastic, dynamic systems (the “particles”). Each particle evolves under feedback control based on its own state, and the empirical distribution of the ensemble. The feedback control law is obtained as the solution to a variational problem, in which the optimization criterion is the Kullback-Leibler divergence between the actual posterior, and the common posterior of any particle. The following conclusions are obtained for diffusions with continuous observations: 1) The optimal control solution is exact: The two posteriors match exactly, provided they are initialized with identical priors. 2) The optimal filter admits an innovation error-based gain feedback structure. 3) The optimal feedback gain is obtained via a solution of an Euler-Lagrange boundary value problem; the feedback gain equals the Kalman gain in the linear Gaussian case. The feedback particle filter offers significant variance improvements when compared to the… Advisors/Committee Members: Mehta, Prashant G. (advisor), Mehta, Prashant G. (Committee Chair), Basar, Tamer (committee member), Veeravalli, Venugopal V. (committee member), Moulin, Pierre (committee member).

Subjects/Keywords: Nonlinear filtering; estimation; particle filtering; statistical signal processing; optimal transportation; target tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yang, T. (2014). Feedback particle filter and its applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50708

Chicago Manual of Style (16^th Edition):

Yang, Tao. “Feedback particle filter and its applications.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 09, 2019. http://hdl.handle.net/2142/50708.

MLA Handbook (7^th Edition):

Yang, Tao. “Feedback particle filter and its applications.” 2014. Web. 09 Dec 2019.

Vancouver:

Yang T. Feedback particle filter and its applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2142/50708.

Council of Science Editors:

Yang T. Feedback particle filter and its applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50708

New Jersey Institute of Technology

9. Aunsri, Nattapol. Particle filtering for frequency estimation from acoustic time-series in dispersive media.

Degree: PhD, Mathematical Sciences, 2013, New Jersey Institute of Technology

URL: https://digitalcommons.njit.edu/dissertations/139

► Acoustic signals propagating in the ocean carry information about geometry and environmental parameters within the propagation medium. Accurately retrieving this information leads us to… (more)

Subjects/Keywords: Frequency estimation; Particle filtering; Bayesian filtering; Sequential Monte Carlo; Underwater acoustics; Signal processing; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Aunsri, N. (2013). Particle filtering for frequency estimation from acoustic time-series in dispersive media. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/139

Chicago Manual of Style (16^th Edition):

Aunsri, Nattapol. “Particle filtering for frequency estimation from acoustic time-series in dispersive media.” 2013. Doctoral Dissertation, New Jersey Institute of Technology. Accessed December 09, 2019. https://digitalcommons.njit.edu/dissertations/139.

MLA Handbook (7^th Edition):

Aunsri, Nattapol. “Particle filtering for frequency estimation from acoustic time-series in dispersive media.” 2013. Web. 09 Dec 2019.

Vancouver:

Aunsri N. Particle filtering for frequency estimation from acoustic time-series in dispersive media. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2013. [cited 2019 Dec 09]. Available from: https://digitalcommons.njit.edu/dissertations/139.

Council of Science Editors:

Aunsri N. Particle filtering for frequency estimation from acoustic time-series in dispersive media. [Doctoral Dissertation]. New Jersey Institute of Technology; 2013. Available from: https://digitalcommons.njit.edu/dissertations/139

10. Särkkä, Simo. Recursive Bayesian Inference on Stochastic Differential Equations.

Degree: 2006, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2006/isbn9512281279/

►

This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. The recursive real-time… (more)

▼

This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. The recursive real-time estimation algorithms for these continuous-discrete filtering problems are traditionally called optimal filters and the algorithms for recursively computing the estimates based on batches of observations are called optimal smoothers. In this thesis, new practical algorithms for approximate and asymptotically optimal continuous-discrete filtering and smoothing are presented. The mathematical approach of this thesis is probabilistic and the estimation algorithms are formulated in terms of Bayesian inference. This means that the unknown parameters, the unknown functions and the physical noise processes are treated as random processes in the same joint probability space. The Bayesian approach provides a consistent way of computing the optimal filtering and smoothing estimates, which are optimal given the model assumptions and a consistent way of analyzing their uncertainties. The formal equations of the optimal Bayesian continuous-discrete filtering and smoothing solutions are well known, but the exact analytical solutions are available only for linear Gaussian models and for a few other restricted special cases. The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting. The equations for the continuous-time unscented Kalman-Bucy filter are also derived. The estimation performance of the new filters and smoothers is tested using simulated data. Continuous-discrete filtering based solutions are also presented to the problems of tracking an unknown number of targets, estimating the spread of an infectious disease and to prediction of an unknown time series.

Helsinki University of Technology Laboratory of Computational Engineering publications. Report B, ISSN 1457-0404; 54

Advisors/Committee Members: Helsinki University of Technology, Department of Electrical and Communications Engineering, Laboratory of Computational Engineering.

Subjects/Keywords: Bayesian inference; continuous-discrete filtering; unscented Kalman filter; particle filter

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APA (6^th Edition):

Särkkä, S. (2006). Recursive Bayesian Inference on Stochastic Differential Equations. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2006/isbn9512281279/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Särkkä, Simo. “Recursive Bayesian Inference on Stochastic Differential Equations.” 2006. Thesis, Helsinki University of Technology. Accessed December 09, 2019. http://lib.tkk.fi/Diss/2006/isbn9512281279/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Särkkä, Simo. “Recursive Bayesian Inference on Stochastic Differential Equations.” 2006. Web. 09 Dec 2019.

Vancouver:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2019 Dec 09]. Available from: http://lib.tkk.fi/Diss/2006/isbn9512281279/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Thesis]. Helsinki University of Technology; 2006. Available from: http://lib.tkk.fi/Diss/2006/isbn9512281279/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Canterbury

11. Krenek, Oliver Francis Daley. Particle Filtering for Location Estimation.

Degree: Electrical and Computer Engineering, 2011, University of Canterbury

URL: http://hdl.handle.net/10092/5805

► Vehicle location and tracking has a variety of commercial applications and none of the techniques currently used can provide accurate results in all situations. This… (more)

Subjects/Keywords: Particle filtering; vehicle location; vehicle tracking; location estimation

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APA (6^th Edition):

Krenek, O. F. D. (2011). Particle Filtering for Location Estimation. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/5805

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Krenek, Oliver Francis Daley. “Particle Filtering for Location Estimation.” 2011. Thesis, University of Canterbury. Accessed December 09, 2019. http://hdl.handle.net/10092/5805.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Krenek, Oliver Francis Daley. “Particle Filtering for Location Estimation.” 2011. Web. 09 Dec 2019.

Vancouver:

Krenek OFD. Particle Filtering for Location Estimation. [Internet] [Thesis]. University of Canterbury; 2011. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10092/5805.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Krenek OFD. Particle Filtering for Location Estimation. [Thesis]. University of Canterbury; 2011. Available from: http://hdl.handle.net/10092/5805

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

12. Singh, Vivek Kumar. Monocular human pose tracking and action recognition in dynamic environments.

Degree: PhD, Computer Science, 2011, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4209

► The objective of this work is to develop an efficient method to find human in videos captured from a single camera, and recognize the action… (more)

▼ The objective of this work is to develop an efficient method to find human in videos captured from a single camera, and recognize the action being performed. Automatic detection of humans in a scene and understanding the ongoing activities has been extensively studied, as solution to this problem finds applications in diverse areas such as surveillance, video summarization, content mining and human computer interaction, among others. ❧ Though significant advances have made towards finding human in specific poses such as upright pose in cluttered scenes, the problem of finding a human in an arbitrary pose in an unknown environment is still a challenge. We address the problem of estimating human pose using a part based approach, that first finds body part candidates using part detectors and then enforce kinematic constraints using a tree-structured graphical model. For inference, we present a collaborative branch and bound algorithm that uses branch and bound method to search for each part and use kinematics from neighboring parts to guide the branching behavior and compute bounds on the best part estimate. We use multiple, heterogeneous part detectors with varying accuracy and computation requirements, ordered in a hierarchy, to achieve more accurate and efficient pose estimation. ❧ While the above approach deals well with pose articulations, it still fails to find human in poses with heavy self occlusion such as crouch, as it does not model inter part occlusion. Thus, recognizing actions from inferred poses would be unreliable. In order to deal with this issue, we propose a joint tracking and recognition approach which tracks the actor pose by sampling from 3D action models and localizing each pose sample; this also allows view-invariant action recognition. We model an action as a sequence of transformations between keyposes. These action models can be obtained by annotating only a few keyposes in 2D; this avoids large training data and MoCAP. For efficiently localizing a sampled pose, we generate a Pose-Specific Part Model (PSPM) which captures appropriate kinematic and occlusion constraints in a tree-structure. In addition, our approach also does not require pose silhouettes and thus also works well in presence of background motion. We show improvements to previous results on two publicly available datasets as well as on a novel, augmented dataset with dynamic backgrounds. ❧ Since the poses are sampled from action models, the above activity driven approach works well if the actor only performs actions for which models are available, and does not generalize well to unseen poses and actions. We address this by proposing an activity assisted tracking framework that combines the activity driven tracking with the bottom up pose estimation by using pose samples obtained using part models, in addition to those sampled from action models. We demonstrate the effectiveness of our approach on long video sequences with hand gestures. Advisors/Committee Members: Nevatia, Ramakant (Committee Chair), Medioni, Gerard G. (Committee Member), Medioni, Gérard G. (Committee Member), Ortega, Antonio K. (Committee Member).

Subjects/Keywords: pictorial structures; branch and bound; conditional random fields; particle filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Singh, V. K. (2011). Monocular human pose tracking and action recognition in dynamic environments. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4209

Chicago Manual of Style (16^th Edition):

Singh, Vivek Kumar. “Monocular human pose tracking and action recognition in dynamic environments.” 2011. Doctoral Dissertation, University of Southern California. Accessed December 09, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4209.

MLA Handbook (7^th Edition):

Singh, Vivek Kumar. “Monocular human pose tracking and action recognition in dynamic environments.” 2011. Web. 09 Dec 2019.

Vancouver:

Singh VK. Monocular human pose tracking and action recognition in dynamic environments. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2019 Dec 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4209.

Council of Science Editors:

Singh VK. Monocular human pose tracking and action recognition in dynamic environments. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/175429/rec/4209

University of Newcastle

13. Newman, Amanda K. A particle filter for efficient recursive BATEA analysis of hydrological models.

Degree: PhD, 2017, University of Newcastle

URL: http://hdl.handle.net/1959.13/1333789

►

Research Doctorate - Doctor of Philosophy (PhD)

The Bayesian Total Error Analysis (BATEA) framework permits model calibration and prediction to be informed by estimates of… (more)

▼

Research Doctorate - Doctor of Philosophy (PhD)

The Bayesian Total Error Analysis (BATEA) framework permits model calibration and prediction to be informed by estimates of data and model uncertainty, and allows assessment of the relative contribution of various sources of error to the total uncertainty within the conceptual hydrologic modelling system. However, full BATEA applications are presently limited to studies with relatively short record lengths. This is because batch calibration rapidly becomes computationally infeasible as the number of inferred input and/or model structural errors grows. This thesis presents the development of a recursive implementation of the BATEA framework based on particle filtering techniques. Particle filtering techniques, traditionally used in automatic control and signal processing, are a group of sequential Monte Carlo methods which can be adapted to provide a robust recursive implementation of the BATEA framework within the non-linear and non-Gaussian conditions presented by conceptual hydrologic models. The particle filter developed in this thesis is designed to preserve the constraints and relationships between time-invariant parameters and latents which exist in most conceptual hydrologic models. This is achieved in a fully recursive manner through careful selection of appropriate Importance Sampling proposals, design and selection of Markov Chain Monte Carlo (MCMC) proposals which permit efficient regeneration of time-invariant parameters and the construction of an approximation to the Metropolis-Hasting acceptance probability which avoids the need for batch evaluation. The resulting particle filter is capable of efficiently performing an approximate recursive BATEA analysis for a conceptual hydrological model subject to observation, structural and parameter uncertainty with the parameters of both the error model and the hydrological model requiring inference. The performance of the approximate BATEA analysis technique is demonstrated with synthetic case studies ranging from well-posed to highly ill-posed problems and is shown to produce practically useful results at a small fraction of the computational effort required in batch calibration.

Advisors/Committee Members: University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering.

Subjects/Keywords: recursive estimation; particle filtering; BATEA; hydrologic model calibration

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Newman, A. K. (2017). A particle filter for efficient recursive BATEA analysis of hydrological models. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/1333789

Chicago Manual of Style (16^th Edition):

Newman, Amanda K. “A particle filter for efficient recursive BATEA analysis of hydrological models.” 2017. Doctoral Dissertation, University of Newcastle. Accessed December 09, 2019. http://hdl.handle.net/1959.13/1333789.

MLA Handbook (7^th Edition):

Newman, Amanda K. “A particle filter for efficient recursive BATEA analysis of hydrological models.” 2017. Web. 09 Dec 2019.

Vancouver:

Newman AK. A particle filter for efficient recursive BATEA analysis of hydrological models. [Internet] [Doctoral dissertation]. University of Newcastle; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/1959.13/1333789.

Council of Science Editors:

Newman AK. A particle filter for efficient recursive BATEA analysis of hydrological models. [Doctoral Dissertation]. University of Newcastle; 2017. Available from: http://hdl.handle.net/1959.13/1333789

University of Otago

14. Javed, Adeel. Train Localisation using Wireless Sensor Networks .

Degree: University of Otago

URL: http://hdl.handle.net/10523/6910

► Safety and reliability have always been concerns for railway transportation. Knowing the exact location of a train enables the railway system to react to an… (more)

▼ Safety and reliability have always been concerns for railway transportation. Knowing the exact location of a train enables the railway system to react to an unusual situation for the safety of human lives and properties. Generally, the accuracy of localisation systems is related with their deployment and maintenance costs, which can be on the order of millions of dollars a year. Despite a lot of research efforts, existing localisation systems based on different technologies are still limited because most of them either require expensive infrastructure (ultrasound and laser), have high database maintenance, computational costs or accumulate errors (vision), offer limited coverage (GPS-dark regions, Wi-Fi, RFID) or provide low accuracy (audible sound). On the other hand, wireless sensor networks (WSNs) offer the potential for a cheap, reliable and accurate solutions for the train localisation system. This thesis proposes a WSN-based train localisation system, in which train location is estimated based on the information gathered through the communication between the anchor sensors deployed along the track and the gateway sensor installed on the train, such as anchor sensors' geographic coordinates and the Received Signal Strength Indicator (RSSI). In the proposed system, timely anchor-gateway communication implies accurate localisation. How to guarantee effective communication between anchor sensors along the track and the gateway sensor on the train is a challenging problem for WSN-based train localisation. I propose a beacon driven sensors wake-up scheme (BWS) to address this problem. BWS allows each anchor sensor to run an asynchronous duty-cycling protocol to conserve energy and establishes an upper bound on the sleep time in one duty cycle to guarantee their timely wake-up once a train approaches. Simulation results show that the BWS scheme can timely wake up the anchor sensors at a very low energy consumption cost. To design an accurate scheme for train localisation, I conducted on-site experiments in an open field, a railway station and a tunnel, and the results show that RSSI can be used as an estimator for train localisation and its applicability increases with the incorporation of another type of data such as location information of anchor sensors. By combining the advantages of RSSI-based distance estimation and Particle Filtering techniques, I designed a Particle-Filter-based train localisation scheme and propose a novel Weighted RSSI Likelihood Function (WRLF) for particle update. The proposed localisation scheme is evaluated through extensive simulations using the data obtained from the on-site measurements. Simulation results demonstrate that the proposed scheme can achieve significant accuracy, where average localisation error stays under 30 cm at the train speed of 40 m=s, 40% anchor sensors failure rate and sparse deployment. In addition, the proposed train localisation scheme is robust to changes in train speed, the deployment density and reliability of anchor… Advisors/Committee Members: Huang, Zhiyi (advisor).

Subjects/Keywords: Localisation; WSN; Trains; Particle Filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Javed, A. (n.d.). Train Localisation using Wireless Sensor Networks . (Doctoral Dissertation). University of Otago. Retrieved from http://hdl.handle.net/10523/6910

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16^th Edition):

Javed, Adeel. “Train Localisation using Wireless Sensor Networks .” Doctoral Dissertation, University of Otago. Accessed December 09, 2019. http://hdl.handle.net/10523/6910.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7^th Edition):

Javed, Adeel. “Train Localisation using Wireless Sensor Networks .” Web. 09 Dec 2019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Javed A. Train Localisation using Wireless Sensor Networks . [Internet] [Doctoral dissertation]. University of Otago; [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10523/6910.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Javed A. Train Localisation using Wireless Sensor Networks . [Doctoral Dissertation]. University of Otago; Available from: http://hdl.handle.net/10523/6910

Note: this citation may be lacking information needed for this citation format:
No year of publication.

University of Melbourne

15. KARUNARATNE, BENTARAGE SACHINTHA. Statistical signal processing for target tracking with multipath radar.

Degree: 2014, University of Melbourne

URL: http://hdl.handle.net/11343/55278

► Until recently the applications of radar technology have been limited to environments with large open space. One of the reasons for such confinement is the… (more)

▼ Until recently the applications of radar technology have been limited to environments with large open space. One of the reasons for such confinement is the radar system's dependence on line of sight communication for detection and/or tracking; this is easily accomplished in an open space environment. Additionally, it has been long believed that multipath reflections hinder the functions of a radar system. Modern research efforts, however, have radically challenged the belief that multipath reflections are nuisances. Various studies suggest that multipath reflections contain information that could be exploited for tracking and/or detecting objects. This paradigm shift has enabled potential new applications in radar technology, particularly pertaining to multipath rich dense urban environments. The main focus of this thesis is the study of radar tracking using multipath in an urban environment. We have particularly emphasised accounting for uncertainty inherent in urban environments. In doing so, we have adapted a Bayesian probabilistic framework for inferential tasks. After introducing a robust model for a multipath environment, we derive performance bounds for tracking a moving target in such an environment. Recent developments in nonlinear statistical signal processing, as well as the availability of powerful computing resources have enabled us to design statistical filters for challenging tracking problems. Consequently, we propose a novel Markov Chain Monte Carlo based particle filter to address the tracking problem pertaining to our multipath model. We then address the multipath tracking problem where much larger uncertainty exists on the locations of the building in the urban environment; that is, when a map of the environment is not available. We also study a particular generalisation of a multivariate von-Mises distribution, which was encountered while addressing the tracking problem. A comprehensive Bayesian conjugate analysis of this distribution is provided.

Subjects/Keywords: radar; multipath; target tracking; Bayesian; Monte Carlo; particle filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

KARUNARATNE, B. S. (2014). Statistical signal processing for target tracking with multipath radar. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/55278

Chicago Manual of Style (16^th Edition):

KARUNARATNE, BENTARAGE SACHINTHA. “Statistical signal processing for target tracking with multipath radar.” 2014. Doctoral Dissertation, University of Melbourne. Accessed December 09, 2019. http://hdl.handle.net/11343/55278.

MLA Handbook (7^th Edition):

KARUNARATNE, BENTARAGE SACHINTHA. “Statistical signal processing for target tracking with multipath radar.” 2014. Web. 09 Dec 2019.

Vancouver:

KARUNARATNE BS. Statistical signal processing for target tracking with multipath radar. [Internet] [Doctoral dissertation]. University of Melbourne; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/11343/55278.

Council of Science Editors:

KARUNARATNE BS. Statistical signal processing for target tracking with multipath radar. [Doctoral Dissertation]. University of Melbourne; 2014. Available from: http://hdl.handle.net/11343/55278

University of Ottawa

16. Wang, Jing. Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things .

Degree: 2017, University of Ottawa

URL: http://hdl.handle.net/10393/36051

► Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First,… (more)

▼ Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First, the proximity-based methods are less affected by multi-path distortion and dynamic changes of the indoor environment compared to the traditional range-based localization methods. The objective of this dissertation is to use tag-to-tag backscattering communication link in augmented UHF RFID system (AURIS) for proximity-based indoor localization solution. Tag-to-tag backscattering communication in AURIS has an obvious advantage over the conventional reader-to-tag link for proximity-based indoor localization by keeping both landmark and mobile tags simple and inexpensive. This work is the very first thesis evaluating proximity-based localization solution using tag-to-tag backscattering communication.Our research makes the contributions in terms of phase cancellation effect, the improved mathematical models and localization algorithm. First, we investigate the phase cancellation effect in the tag-to-tag backscattering communication, which has a significant effect on proximity-based localization. We then present a solution to counter such destructive effect by exploiting the spatial diversity of dual antennas. Second, a novel and realistic detection probability model of ST-to-tag detection is proposed. In AURIS, a large set of passive tags are placed at known locations as landmarks, and STs are attached mobile targets of interest. We identify two technical roadblocks of AURIS and existing localization algorithms as false synchronous detection assumption and state evolution model constraints. With the new and more realistic detection probability model we explore the use of particle filtering methodology for localizing ST, which overcomes the aforementioned roadblocks. Last, we propose a landmark-based sequential localization and mapping framework (SQLAM) for AURIS to locate STs and passive tags with unknown locations, which leverages a set of passive landmark tags to localize ST, and sequentially constructs a geographical map of passive tags with unknown locations while ST is moving in the environment. Mapping passive tags with unknown locations accurately leads to practical advantages. First, the localization capability of AURIS is not confined to the objects carrying STs. Second, the problem of failed landmark tags is addressed by including passive tags with resolved locations into landmark set. Each of the contributions is supported by extensive computer simulation to demonstrate the performance of enhancements.

Subjects/Keywords: Internet-of-Things; UHF RFID; Indoor localization; Phase cancellation; Particle filtering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, J. (2017). Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things . (Thesis). University of Ottawa. Retrieved from http://hdl.handle.net/10393/36051

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Jing. “Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things .” 2017. Thesis, University of Ottawa. Accessed December 09, 2019. http://hdl.handle.net/10393/36051.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Jing. “Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things .” 2017. Web. 09 Dec 2019.

Vancouver:

Wang J. Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things . [Internet] [Thesis]. University of Ottawa; 2017. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10393/36051.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang J. Indoor Localization Using Augmented UHF RFID System for the Internet-of-Things . [Thesis]. University of Ottawa; 2017. Available from: http://hdl.handle.net/10393/36051

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

17. Michaud, Nicholas Lorenz. Bayesian models and inferential methods for forecasting disease outbreak severity.

Degree: 2016, Iowa State University

URL: https://lib.dr.iastate.edu/etd/15773

► Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients… (more)

▼ Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The U.S. Outpatient Influenza-like Illness Surveillance Network, or ILINet, collects data on influenza-like illnesses from over 3,300 health care providers, and uses these data to produce indicators of current influenza epidemic severity. ILINet data provide an unbiased estimate of the severity of a season's influenza epidemic, and are typically reported at a lag of about two weeks. Other sources of influenza severity, such as indices calculated from search engine query data from Google, Twitter, and Wikipeida, are provided in near-real time. However, these sources of data are less direct measurements of influenza severity than ILINet indicators, and are likely to suffer from bias. We begin by describing general methods for inference on state space models implemented in the NIMBLE R package, and demonstrate these inferential methods as applied to influenza outbreak forecasting. We then examine model specifications to estimate epidemic severity which incorporate data from both ILINet and other real-time, possibly biased sources. We fit these models using Google Flu Trends data, which uses the number of Google searches for influenza related keywords to calculate an estimate of epidemic severity. We explicitly model the possible bias of the Google Flu Trends data, which allows us to make epidemic severity predictions which take advantage of the recency of Google Flu Trends data and the accuracy of ILINet data, and we preform estimation using Bayesian methods. Models with and without explicit bias modeling are compared to models using only ILINet data, and it is found that including GFT data significantly improves forecasting accuracy of epidemic severity. We also propose hierarchical models which incorporate multiple seasons of influenza data, and evaluate the forecasting benefits that hierarchical modeling confers.

Subjects/Keywords: Bayesian; Disease outbreaks; Inference; MCMC; Particle Filtering; Statistics and Probability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Michaud, N. L. (2016). Bayesian models and inferential methods for forecasting disease outbreak severity. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/15773

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Michaud, Nicholas Lorenz. “Bayesian models and inferential methods for forecasting disease outbreak severity.” 2016. Thesis, Iowa State University. Accessed December 09, 2019. https://lib.dr.iastate.edu/etd/15773.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Michaud, Nicholas Lorenz. “Bayesian models and inferential methods for forecasting disease outbreak severity.” 2016. Web. 09 Dec 2019.

Vancouver:

Michaud NL. Bayesian models and inferential methods for forecasting disease outbreak severity. [Internet] [Thesis]. Iowa State University; 2016. [cited 2019 Dec 09]. Available from: https://lib.dr.iastate.edu/etd/15773.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Michaud NL. Bayesian models and inferential methods for forecasting disease outbreak severity. [Thesis]. Iowa State University; 2016. Available from: https://lib.dr.iastate.edu/etd/15773

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

18. Medarametla, Krishna Kalyan. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.

Degree: MS, 0133, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/50584

► In a recent work it has been shown that importance sampling can be avoided in particle filter through an innovation structure inspired by traditional nonlinear… (more)

Subjects/Keywords: Extended Kalman filter; Feedback particle filter; Comparison; Nonlinear filtering

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APA (6^th Edition):

Medarametla, K. K. (2014). Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed December 09, 2019. http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Web. 09 Dec 2019.

Vancouver:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

19. Chikkerur, Vishal. Model reduction of linear and nonlinear systems using balancing methods.

Degree: MS, 4048, 2010, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/16914

► This thesis presents a review of the existing body of knowledge pertaining to model reduction using balancing techniques. A simple linear system is studied in… (more)

Subjects/Keywords: Nonlinear; Balancing; Systems; Vortices; Filtering; Particle; Fokker-Planck; Control; Noise

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APA (6^th Edition):

Chikkerur, V. (2010). Model reduction of linear and nonlinear systems using balancing methods. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/16914

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chikkerur, Vishal. “Model reduction of linear and nonlinear systems using balancing methods.” 2010. Thesis, University of Illinois – Urbana-Champaign. Accessed December 09, 2019. http://hdl.handle.net/2142/16914.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chikkerur, Vishal. “Model reduction of linear and nonlinear systems using balancing methods.” 2010. Web. 09 Dec 2019.

Vancouver:

Chikkerur V. Model reduction of linear and nonlinear systems using balancing methods. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2010. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2142/16914.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chikkerur V. Model reduction of linear and nonlinear systems using balancing methods. [Thesis]. University of Illinois – Urbana-Champaign; 2010. Available from: http://hdl.handle.net/2142/16914

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Saskatchewan

20. Safarishahrbijari, Anahita. Particle filtering in compartmental projection models.

Degree: 2018, University of Saskatchewan

URL: http://hdl.handle.net/10388/11776

► Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution.… (more)

▼ Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution. However, models usually diverge from the real observations. Stochastics involved in pandemic systems, such as changes in human contact patterns play a substantial role in disease transmissions and are not usually captured in traditional dynamic models. In addition, models of emerging diseases face the challenge of limited epidemiological knowledge about the natural history of disease. Even when the information about natural history is available – for example for endemic seasonal diseases – transmission models are often simplified and are involved with omissions. Availability of data streams can provide a view of early days of a pandemic, but fail to predict how the pandemic will evolve. Recent developments of computational statistics algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo, provide the possibility of creating models based on historical data as well as re-grounding models based on ongoing data observations. The objective of this thesis is to combine particle filtering – a Sequential Monte Carlo algorithm – with system dynamics models of pandemics. We developed particle filtering models that can recurrently be re-grounded as new observations become available. To this end, we also examined the effectiveness of this arrangement which is subject to specifics of the configuration (e.g., frequency of data sampling). While clinically-diagnosed cases are valuable incoming data stream during an outbreak, new generation of geo-spatially specific data sources, such as search volumes can work as a complementary data resource to clinical data. As another contribution, we used particle filtering in a model which can be re-grounded based on both clinical and search volume data. Our results indicate that the particle filtering in combination with compartmental models provides accurate projection systems for the estimation of model states and also model parameters (particularly compared to traditional calibration methodologies and in the context of emerging communicable diseases). The results also suggest that more frequent sampling from clinical data improves predictive accuracy outstandingly. The results also present that assumptions to make regarding the parameters associated with the particle filtering itself and changes in contact rate were robust across adequacy of empirical data since the beginning of the outbreak and inter-observation interval. The results also support the use of data from Google search API along with clinical data. Advisors/Committee Members: Stanley, Kevin, Mondal, Debajyoti, Page, Andrew, Neudorf, Cory.

Subjects/Keywords: machine learning; particle filtering; system dynamics; projection; outbreaks

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APA (6^th Edition):

Safarishahrbijari, A. (2018). Particle filtering in compartmental projection models. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/11776

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Safarishahrbijari, Anahita. “Particle filtering in compartmental projection models.” 2018. Thesis, University of Saskatchewan. Accessed December 09, 2019. http://hdl.handle.net/10388/11776.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Safarishahrbijari, Anahita. “Particle filtering in compartmental projection models.” 2018. Web. 09 Dec 2019.

Vancouver:

Safarishahrbijari A. Particle filtering in compartmental projection models. [Internet] [Thesis]. University of Saskatchewan; 2018. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/10388/11776.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Safarishahrbijari A. Particle filtering in compartmental projection models. [Thesis]. University of Saskatchewan; 2018. Available from: http://hdl.handle.net/10388/11776

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

21. Girardi, Matthew. Liquid Transport and Agglomeration Tendency in Fluidized Beds .

Degree: PhD, 2015, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp013b591b95d

► Wet gas-solid fluidization is an essential component in a wide range of industrial processes, particularly those in the energy and pharmaceutical industries. In gas-solid fluidized… (more)

▼ Wet gas-solid fluidization is an essential component in a wide range of industrial processes, particularly those in the energy and pharmaceutical industries. In gas-solid fluidized beds, solid particles are suspended against gravity by a drag force from upward flowing gas. When liquid is added to the bed, particles become coated in a thin liquid film, which gives rise to the formation of pendular liquid bridges upon particle-particle and particle-wall collisions. These liquid bridges provide a cohesive force that results in the formation of agglomerates that influence the flow behavior within the bed. The focus of this thesis is to understand how agglomerate formation and fluidization behavior are dependent on the wetting properties. Data is collected from a wide range of Euler-Lagrange simulations that allow for dynamic liquid bridge formation and rupture. The dependence of fluidization behavior on wetting parameters is described using a key dimensionless group, referred to here as a modified Bond number. The modified Bond number, which accounts for both surface tension effects and liquid loading level, correlates strongly with average liquid bridge coordination number, demonstrating its origins relating to agglomeration strength. While the domain size used in the study is suitable for the use of microscale computational grids, a filtering methodology is proposed for use in larger coarse-grained systems in which a liquid phase is present on the particles. A filtered drag coefficient is provided as a function of filter size, solid loading, and the wetting parameters. Liquid in the fluidized bed exists both on the surface of particles, where it reacts to form higher valued products, and also within pendular liquid bridges, where it binds particles together to form agglomerates. The distribution of liquid on the particles and in the bridges is assessed as a function of the wetting properties. The study concludes with an analysis of liquid spreading in an inhomogeneously wetted bed, relating the rate of liquid dispersion to the surface tension forces present in the system. The results of the entire study allow for prediction of fluidization behavior based on the wetting parameters, allowing for simulation of large-scale systems appropriate for use in industrial application. Advisors/Committee Members: Sundaresan, Sankaran (advisor).

Subjects/Keywords: Euler Lagrange; Filtering; Fluidized bed; Liquid transport; Multiphase flow; Particle wetting

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Girardi, M. (2015). Liquid Transport and Agglomeration Tendency in Fluidized Beds . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp013b591b95d

Chicago Manual of Style (16^th Edition):

Girardi, Matthew. “Liquid Transport and Agglomeration Tendency in Fluidized Beds .” 2015. Doctoral Dissertation, Princeton University. Accessed December 09, 2019. http://arks.princeton.edu/ark:/88435/dsp013b591b95d.

MLA Handbook (7^th Edition):

Girardi, Matthew. “Liquid Transport and Agglomeration Tendency in Fluidized Beds .” 2015. Web. 09 Dec 2019.

Vancouver:

Girardi M. Liquid Transport and Agglomeration Tendency in Fluidized Beds . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Dec 09]. Available from: http://arks.princeton.edu/ark:/88435/dsp013b591b95d.

Council of Science Editors:

Girardi M. Liquid Transport and Agglomeration Tendency in Fluidized Beds . [Doctoral Dissertation]. Princeton University; 2015. Available from: http://arks.princeton.edu/ark:/88435/dsp013b591b95d

22. El Mokhtari, Karim. Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas.

Degree: Docteur es, Génie informatique, Automatique et Traitement du signal, 2015, Littoral; Université Abdelmalek Essaadi (Tanger, Maroc). Faculté des Sciences et Techniques

URL: http://www.theses.fr/2015DUNK0367

►

La navigation dans les environnements contraints tels que les zones portuaires ou les zones urbainesdenses est souvent exposée au problème du masquage des satellites GPS.… (more)

▼

La navigation dans les environnements contraints tels que les zones portuaires ou les zones urbainesdenses est souvent exposée au problème du masquage des satellites GPS. Dans ce cas, le recours auxcapteurs proprioceptifs est généralement la solution envisagée pour localiser temporairement le véhiculesur une carte. Cependant, la dérive de ces capteurs met rapidement en défaut le système de navigation.Pour localiser le véhicule, on utilise dans cette thèse, un magnétomètre pour la mesure du cap dans unrepère absolu, un capteur de vitesse et une carte numérique du réseau de routes.Dans ce contexte, le premier apport de ce travail est de proposer la mise en correspondance desmesures de cap avec la carte numérique (map matching) pour localiser le véhicule. La technique proposéefait appel à un filtre particulaire défini dans le domaine circulaire et à un préfiltrage circulairedes mesures de cap. On montre que cette technique est plus performante qu’un algorithme de map matchingtopologique classique et notamment dans le cas problématique d’une jonction de route en Y. Ledeuxième apport de ce travail est de proposer un filtre circulaire multi-modèles CIMM défini dans uncadre bayésien à partir de la distribution circulaire de von Mises. On montre que l’intégration de cettenouvelle approche dans le préfiltrage et l’analyse des mesures de cap permet d’améliorer la robustesse del’estimation de la direction pendant les virages ainsi que d’augmenter la qualité du map matching grâce àune meilleure propagation des particules du filtre sur le réseau de routes. Les performances des méthodesproposées sont évaluées sur des données synthétiques et réelles.

Navigation in constrained areas such as ports or dense urban environments is often exposed to theproblem of non-line-of-sight to GPS satellites. In this case, proprioceptive sensors are generally used totemporarily localize the vehicle on a map. However, the drift of these sensors quickly cause the navigationsystem to fail. To localize the vehicle, a magnetometer is used in this thesis for heading measurementunder an absolute reference together with a velocity sensor and a digital map of the road network.In this context, the first contribution of this work is to provide a matching of the vehicle’s headingwith the digital map (map matching) to localize the vehicle. The proposed technique uses a particle filterdefined in the circular domain and a circular pre-filtering on the heading measurements. It is shown thatthis technique is more efficient than a conventional topological map matching algorithm, particularly inambiguous cases like a Y-shape road junction. The second contribution of this work is to propose a circularmultiple model filter CIMM defined in a Bayesian framwork from the von Mises circular distribution.It is shown that the integration of this new approach in the pre-filtering and analysis of the heading observationsimproves the robustness of the heading’s estimation during cornering and increases the mapmatching’s quality through a better propagation of the particles on…

Advisors/Committee Members: Amami, Benaissa (thesis director), Reboul, Serge (thesis director).

Subjects/Keywords: Map matching; EStimation circulaire; Filtrage particulaire; Filtrage multi-modèles; Map matching; Circular estimation; Particle filtering; Multiple model filtering

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APA (6^th Edition):

El Mokhtari, K. (2015). Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas. (Doctoral Dissertation). Littoral; Université Abdelmalek Essaadi (Tanger, Maroc). Faculté des Sciences et Techniques. Retrieved from http://www.theses.fr/2015DUNK0367

Chicago Manual of Style (16^th Edition):

El Mokhtari, Karim. “Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas.” 2015. Doctoral Dissertation, Littoral; Université Abdelmalek Essaadi (Tanger, Maroc). Faculté des Sciences et Techniques. Accessed December 09, 2019. http://www.theses.fr/2015DUNK0367.

MLA Handbook (7^th Edition):

El Mokhtari, Karim. “Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas.” 2015. Web. 09 Dec 2019.

Vancouver:

El Mokhtari K. Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas. [Internet] [Doctoral dissertation]. Littoral; Université Abdelmalek Essaadi (Tanger, Maroc). Faculté des Sciences et Techniques; 2015. [cited 2019 Dec 09]. Available from: http://www.theses.fr/2015DUNK0367.

Council of Science Editors:

El Mokhtari K. Estimation circulaire multi-modèles appliquée au Map matching en environnement contraint : Circular estimation multiple models applied to Map matching in constrained areas. [Doctoral Dissertation]. Littoral; Université Abdelmalek Essaadi (Tanger, Maroc). Faculté des Sciences et Techniques; 2015. Available from: http://www.theses.fr/2015DUNK0367

Indian Institute of Science

23. Ahmed, Nasrellah Hassan. Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models.

Degree: 2009, Indian Institute of Science

URL: http://hdl.handle.net/2005/675

► The thesis outlines the development and application of a few novel dynamic state estimation based methods for estimation of parameters of vibrating engineering structures. The… (more)

▼ The thesis outlines the development and application of a few novel dynamic state estimation based methods for estimation of parameters of vibrating engineering structures. The study investigates strategies for data fusion from multiple tests of possibly different types and different sensor quantities through the introduction of a common pseudo-time parameter. These strategies have been developed within the framework of Kalman and particle filtering techniques. The proposed methods are applied to a suite of problems that includes laboratory and field studies with a primary focus on finite element model updating of bridge structures and vehicle structure interaction problems. The study also describes how finite element models residing in commercially available softwares can be made to communicate with database of measurements via a particle filtering algorithm developed on the Matlab platform. The thesis is divided into six chapters and an appendix. A review of literature on problems of structural system identification with emphasis on methods on dynamic state estimation techniques is presented in Chapter 1. The problem of system parameter idenfification when measurements originate from multiple tests and multiple sensors is considered in Chapter 2. and solution based on Neumann expansion of the structural static/dynamic stiffness matrix and Kalman filtering is proposed to tackle this problem. The question of decoupling the problem of parameter estimation from state estimation is also discussed. The avoidance of linearization of the stiffness matrix and solution of the parameter problems by using Monte Carlo filters is examined in Chapter 3. This also enables treatment of nonlinear structural mechanics problems. The proposed method is assessed using synthetic and laboratory measurement data. The problem of interfacing structural models residing in professional finite element analysis software with measured data via particle filtering algorithm developed on Matlab platform is considered in Chapter 4. Illustrative examples now cover laboratory studies on a beam structure and also filed studies on an existing multi-span masonry railway arch bridge. Identification of parameters of systems with strong nonlinearities, such, as a rectangular rubber sheet with a concentric hole, is also investigated. Studies on parameter identification in beam moving oscillator problem are reported in Chapter 5. The efficacy of particle filtering strategy in identifying parameters of this class of time varying system is demonstrated. A resume of contributions made and a few suggestions for further research are provided in Chapter 6. The appendix contains details of development of interfaces among finite element software(NISA), data base of measurements and particle filtering algorithm (developed on Matlab platform). Advisors/Committee Members: Manohar, C S.

Subjects/Keywords: Structural Analysis; Finite Element Method; State Estimation; Parameter Estimation; Extended Kalman Filter (EKF); Structural System Identification; Particle Filter; Kalman Filtering; Particle Filtering Algorithm; Structural Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ahmed, N. H. (2009). Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ahmed, Nasrellah Hassan. “Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models.” 2009. Thesis, Indian Institute of Science. Accessed December 09, 2019. http://hdl.handle.net/2005/675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ahmed, Nasrellah Hassan. “Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models.” 2009. Web. 09 Dec 2019.

Vancouver:

Ahmed NH. Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models. [Internet] [Thesis]. Indian Institute of Science; 2009. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2005/675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ahmed NH. Dynamic State Estimation Techniques For Identification Of Parameters Of Finite Element Structural Models. [Thesis]. Indian Institute of Science; 2009. Available from: http://hdl.handle.net/2005/675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Ghiotto, Shane. Comparison of nonlinear filtering techniques.

Degree: MS, 0133, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/49437

► In a recent work it is shown that importance sampling can be avoided in the particle filter through an innovation structure inspired by traditional nonlinear… (more)

Subjects/Keywords: Filtering; state estimation; particle filtering; Kalman filter; feedback particle filter

…other case evaluated is a comparison of the resampling based particle filtering techniques… …many important advances in importance sampling based approaches for particle filtering; cf… …CHAPTER 1 INTRODUCTION Filtering and state estimation from noisy measurements is a… …vision and even economics, the applications of filtering are diverse and widespread. The… …primary objective in filtering is to provide an estimate of the hidden state of a dynamical…

4 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ghiotto, S. (2014). Comparison of nonlinear filtering techniques. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/49437

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ghiotto, Shane. “Comparison of nonlinear filtering techniques.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed December 09, 2019. http://hdl.handle.net/2142/49437.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ghiotto, Shane. “Comparison of nonlinear filtering techniques.” 2014. Web. 09 Dec 2019.

Vancouver:

Ghiotto S. Comparison of nonlinear filtering techniques. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/2142/49437.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ghiotto S. Comparison of nonlinear filtering techniques. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/49437

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Florida

25. Yoon, Jae Myung. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.

Degree: MS, Mechanical Engineering - Mechanical and Aerospace Engineering, 2012, University of Florida

URL: http://ufdc.ufl.edu/UFE0044219

► In this thesis, we present a comparative study of conventional particle filtering (PF) algorithms for tracking applications. Through the review from the generic PF to… (more)

Subjects/Keywords: Approximation; Covariance; Graphics; Kalman filters; Noise measurement; Particle density; Quantum statistics; Standard deviation; State estimation; Trajectories; filter – filtering – mcmc – particle – tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yoon, J. M. (2012). A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044219

Chicago Manual of Style (16^th Edition):

Yoon, Jae Myung. “A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.” 2012. Masters Thesis, University of Florida. Accessed December 09, 2019. http://ufdc.ufl.edu/UFE0044219.

MLA Handbook (7^th Edition):

Yoon, Jae Myung. “A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.” 2012. Web. 09 Dec 2019.

Vancouver:

Yoon JM. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. [Internet] [Masters thesis]. University of Florida; 2012. [cited 2019 Dec 09]. Available from: http://ufdc.ufl.edu/UFE0044219.

Council of Science Editors:

Yoon JM. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. [Masters Thesis]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044219

University of New Orleans

26. Wu, Jiande. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.

Degree: PhD, Electrical Engineering, 2014, University of New Orleans

URL: https://scholarworks.uno.edu/td/1953

► Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found… (more)

Subjects/Keywords: nonlinear filtering, particle filter, particle flow filter, parallel and distributed computing, GPU, computer cluster, FPGA, target tracking; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wu, J. (2014). Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. (Doctoral Dissertation). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/1953

Chicago Manual of Style (16^th Edition):

Wu, Jiande. “Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.” 2014. Doctoral Dissertation, University of New Orleans. Accessed December 09, 2019. https://scholarworks.uno.edu/td/1953.

MLA Handbook (7^th Edition):

Wu, Jiande. “Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.” 2014. Web. 09 Dec 2019.

Vancouver:

Wu J. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. [Internet] [Doctoral dissertation]. University of New Orleans; 2014. [cited 2019 Dec 09]. Available from: https://scholarworks.uno.edu/td/1953.

Council of Science Editors:

Wu J. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. [Doctoral Dissertation]. University of New Orleans; 2014. Available from: https://scholarworks.uno.edu/td/1953

University of Colorado

27. Jennings, Dale Kurtis. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.

Degree: PhD, Applied Mathematics, 2016, University of Colorado

URL: http://scholar.colorado.edu/appm_gradetds/81

► Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a desired probability distribution. While there exist many algorithms that attempt… (more)

Subjects/Keywords: Applied Probability; Bayesian Networks; Birth and Death Process; Kac Model; MCMC; Particle Filtering; Applied Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Jennings, D. K. (2016). Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/appm_gradetds/81

Chicago Manual of Style (16^th Edition):

Jennings, Dale Kurtis. “Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.” 2016. Doctoral Dissertation, University of Colorado. Accessed December 09, 2019. http://scholar.colorado.edu/appm_gradetds/81.

MLA Handbook (7^th Edition):

Jennings, Dale Kurtis. “Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks.” 2016. Web. 09 Dec 2019.

Vancouver:

Jennings DK. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2019 Dec 09]. Available from: http://scholar.colorado.edu/appm_gradetds/81.

Council of Science Editors:

Jennings DK. Advances in MCMC Methods with Applications to Particle Filtering, DSMC, and Bayesian Networks. [Doctoral Dissertation]. University of Colorado; 2016. Available from: http://scholar.colorado.edu/appm_gradetds/81

The Ohio State University

28. Zhu, Ting. Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method.

Degree: MS, Electrical and Computer Engineering, 2011, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1306863054

► Various kinds of algorithms have been developed for object tracking, which can be divided into two categories, probabilistic and non-probabilistic, respectively. In either case,… (more)

Subjects/Keywords: Electrical Engineering; Fingertip Tracking; Particle Filtering; High-order Priori Motion Model; Finger Mouse

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APA (6^th Edition):

Zhu, T. (2011). Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1306863054

Chicago Manual of Style (16^th Edition):

Zhu, Ting. “Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method.” 2011. Masters Thesis, The Ohio State University. Accessed December 09, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306863054.

MLA Handbook (7^th Edition):

Zhu, Ting. “Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method.” 2011. Web. 09 Dec 2019.

Vancouver:

Zhu T. Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method. [Internet] [Masters thesis]. The Ohio State University; 2011. [cited 2019 Dec 09]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1306863054.

Council of Science Editors:

Zhu T. Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method. [Masters Thesis]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1306863054

Oklahoma State University

29. Zhang, Xin. Manifold Learning for Video-based Human Motion Estimation.

Degree: School of Electrical & Computer Engineering, 2011, Oklahoma State University

URL: http://hdl.handle.net/11244/7884

► This dissertation presents a generative model-based gait representation framework for video-based human motion estimation. In this work, we advocate an important newconcept, gait manifold, which… (more)

▼ This dissertation presents a generative model-based gait representation framework for video-based human motion estimation. In this work, we advocate an important newconcept, gait manifold, which is used to capture the gait variability among different individuals and enables gait interpolation for an unknown subject from a limited training gaits. Specifically, we propose two manifold structures, a closed-loop and a torus , along which the continuous gait variable is defined. Both gait manifolds are used to develop visual gait generative models for human motion estimation.The learning of the closed-loop gait manifold involves a non-linear tensor decomposition by which we can learn a kinematic gait generative model (KGGM) and a visual gait generative model (VGGM). KGGM and VGGM represent gait kinematics and appearances by a few latent variables, respectively. Then a new manifold learning technique is proposed to learn a closed-loop gait manifold by which KGGM and VGGM can be integrated together to estimate gait kinematics from gait appearances. Additionally, we extend this framework to the part-level gait modeling that involves two gait manifolds, one for the lower body and one for the upper-body. Experimental results show our algorithms are competitive with state-of-art algorithms considering that a single camera is used, and the part-level gait modeling further improves results.The toroid joint gait-pose manifold (JGPM) is proposed to jointly represent the pose and gait factors into a single latent space that captures the motion variability across gaits and poses simultaneously. We propose a new Gaussian processes (GP) based dimensionality reduction (DR) algorithm to learn a torus-like JGPM that balances the desired manifold structure with the actual intrinsic structure among data. JGPM is further used to learn a visual gait generative model for motion estimation. Experimental results show that the proposed JGPM provides superior performance on human motion modeling compared with other GP-based methods, and the results on video-based motion estimation are also among the best in the literature.The major contribution of this dissertation is on the structure-guided manifold learning. It is a critical issue when we are dealing with a sparse and unorganized (without explicit topology) data set and when the prior knowledge of the expected manifold structure is involved. This idea can be applied to other manifold learning applications that may be encumbered by a limited training data set without a clear topology. Advisors/Committee Members: Fan, Guoliang (advisor), Hagan, Martin (committee member), Cheng, Qi (committee member), Shen, Weihua (committee member), Li, Xiaolin (committee member).

Subjects/Keywords: dimensionality reduction; gaussian processes; manifold learning; particle filtering inference; video-based human

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, X. (2011). Manifold Learning for Video-based Human Motion Estimation. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/7884

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhang, Xin. “Manifold Learning for Video-based Human Motion Estimation.” 2011. Thesis, Oklahoma State University. Accessed December 09, 2019. http://hdl.handle.net/11244/7884.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhang, Xin. “Manifold Learning for Video-based Human Motion Estimation.” 2011. Web. 09 Dec 2019.

Vancouver:

Zhang X. Manifold Learning for Video-based Human Motion Estimation. [Internet] [Thesis]. Oklahoma State University; 2011. [cited 2019 Dec 09]. Available from: http://hdl.handle.net/11244/7884.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang X. Manifold Learning for Video-based Human Motion Estimation. [Thesis]. Oklahoma State University; 2011. Available from: http://hdl.handle.net/11244/7884

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

30. Das, Samarjit. Particle filtering on large dimensional state spaces and applications in computer vision.

Degree: 2010, Iowa State University

URL: https://lib.dr.iastate.edu/etd/11811

► Tracking of spatio-temporal events is a fundamental problem in computer vision and signal processing in general. For example, keeping track of motion activities from video… (more)

▼ Tracking of spatio-temporal events is a fundamental problem in computer vision and signal processing in general. For example, keeping track of motion activities from video sequences for abnormality detection or spotting neuronal activity patterns inside the brain from fMRI data. To that end, our research has two main aspects with equal emphasis - first, development of efficient Bayesian filtering frameworks for solving real-world tracking problems and second, understanding the temporal evolution dynamics of physical systems/phenomenon and build statistical models for them. These models facilitate prior information to the trackers as well as lead to intelligent signal processing for computer vision and image understanding. The first part of the dissertation deals with the key signal processing aspects of tracking and the challenges involved. In simple terms, tracking basically is the problem of estimating the hidden state of a system from noisy observed data(from sensors). As frequently encountered in real-life, due to the non-linear and non-Gaussian nature of the state spaces involved, Particle Filters (PF) give an approximate Bayesian inference under such problem setup. However, quite often we are faced with large dimensional state spaces together with multimodal observation likelihood due to occlusion and clutter. This makes the existing particle filters very inefficient for practical purposes. In order to tackle these issues, we have developed and implemented efficient particle filters on large dimensional state spaces with applications to various visual tracking problems in computer vision. In the second part of the dissertation, we develop dynamical models for motion activities inspired by human visual cognitive ability of characterizing temporal evolution pattern of shapes. We take a landmark shape based approach for the representation and tracking of motion activities. Basically, we have developed statistical models for the shape change of a configuration of ``landmark" points (key points of interest) over time and to use these models for automatic landmark extraction and tracking, filtering and change detection from video sequences. In this regard, we demonstrate superior performance of our Non-Stationary Shape Activity(NSSA) model in comparison to other existing works. Also, owing to the large dimensional state space of this problem, we have utilized efficient particle filters(PF) for motion activity tracking. In the third part of the dissertation, we develop a visual tracking algorithm that is able to track in presence of illumination variations in the scene. In order to do that we build and learn a dynamical model for 2D illumination patterns based on Legendre basis functions. Under our problem formulation, we pose the visual tracking task as a large dimensional tracking problem in a joint motion-illumination space and thus use an efficient PF algorithm called PF-MT(PF with Mode Tracker) for tracking. In addition, we also demonstrate the use of change/abnormality detection framework for tracking across…

Subjects/Keywords: Compressive sensing; Computer vision; Deformable shape models; Particle filtering; Visual tracking; Electrical and Computer Engineering

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APA (6^th Edition):

Das, S. (2010). Particle filtering on large dimensional state spaces and applications in computer vision. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/11811

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Das, Samarjit. “Particle filtering on large dimensional state spaces and applications in computer vision.” 2010. Thesis, Iowa State University. Accessed December 09, 2019. https://lib.dr.iastate.edu/etd/11811.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Das, Samarjit. “Particle filtering on large dimensional state spaces and applications in computer vision.” 2010. Web. 09 Dec 2019.

Vancouver:

Das S. Particle filtering on large dimensional state spaces and applications in computer vision. [Internet] [Thesis]. Iowa State University; 2010. [cited 2019 Dec 09]. Available from: https://lib.dr.iastate.edu/etd/11811.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Das S. Particle filtering on large dimensional state spaces and applications in computer vision. [Thesis]. Iowa State University; 2010. Available from: https://lib.dr.iastate.edu/etd/11811

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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