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

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 08, 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. 08 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 08]. 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

Texas A&M University

2. 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 08, 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. 08 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 08]. 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

Texas A&M University

3. 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 08, 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. 08 Dec 2019.

Vancouver:

Boddikurapati S. Sequential Monte Carlo Methods With Applications To Communication Channels. [Internet] [Thesis]. Texas A&M University; 2010. [cited 2019 Dec 08]. 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

4. 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 08, 2019. http://hdl.handle.net/2142/50708.

MLA Handbook (7^th Edition):

Yang, Tao. “Feedback particle filter and its applications.” 2014. Web. 08 Dec 2019.

Vancouver:

Yang T. Feedback particle filter and its applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 08]. 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

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

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 08, 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. 08 Dec 2019.

Vancouver:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2019 Dec 08]. 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

6. 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 08, 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. 08 Dec 2019.

Vancouver:

Krenek OFD. Particle Filtering for Location Estimation. [Internet] [Thesis]. University of Canterbury; 2011. [cited 2019 Dec 08]. 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 Newcastle

7. 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 08, 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. 08 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 08]. 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

8. 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 (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 08, 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. 08 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 08]. 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 Ottawa

9. 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 (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 08, 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. 08 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 08]. 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

10. 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 08, 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. 08 Dec 2019.

Vancouver:

Michaud NL. Bayesian models and inferential methods for forecasting disease outbreak severity. [Internet] [Thesis]. Iowa State University; 2016. [cited 2019 Dec 08]. 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

11. 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 08, 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. 08 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 08]. 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

12. 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 08, 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. 08 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 08]. 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

Princeton University

13. 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 08, 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. 08 Dec 2019.

Vancouver:

Girardi M. Liquid Transport and Agglomeration Tendency in Fluidized Beds . [Internet] [Doctoral dissertation]. Princeton University; 2015. [cited 2019 Dec 08]. 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

Indian Institute of Science

14. 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 (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 08, 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. 08 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 08]. 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

15. 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 08, 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. 08 Dec 2019.

Vancouver:

Ghiotto S. Comparison of nonlinear filtering techniques. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Dec 08]. 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

16. 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 08, 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. 08 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 08]. 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

The Ohio State University

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

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 08, 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. 08 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 08]. 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

18. 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 08, 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. 08 Dec 2019.

Vancouver:

Zhang X. Manifold Learning for Video-based Human Motion Estimation. [Internet] [Thesis]. Oklahoma State University; 2011. [cited 2019 Dec 08]. 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

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

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 08, 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. 08 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 08]. 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

University of Illinois – Urbana-Champaign

20. Yeong, Hoong Chieh. Dimensional reduction in nonlinear estimation of multiscale systems.

Degree: PhD, Aerospace Engineering, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99320

► State or signal estimation of stochastic systems based on measurement data is an important problem in many areas of science and engineering. The true signal… (more)

▼ State or signal estimation of stochastic systems based on measurement data is an important problem in many areas of science and engineering. The true signal is usually hidden, evolving according to its own dynamics, and observations are usually corrupted and possibly incomplete. The goal is to obtain optimal estimates of the signal based on noisy observations. When the dynamical model of the signal is completely known, the theory of filtering provides a recursive algorithm for estimating the conditional density (the filter) of the signal. Particle filters have been well established for the implementation of nonlinear filtering in applications. However, computational issues arise in high dimensions due to large number of particles being required to represent the signal density. The work done in this research attempts to address this issue by combining stochastic averaging with filtering techniques to develop a reduced-dimension particle filtering method for partially observed multiscale diffusion processes. When the dynamical model contains unknown parameters, the parameters need to be estimated along with the hidden states. The parameter estimation problem overlaps with the filtering problem for state estimation. In this research, the theory of maximum likelihood estimation is used to study dimensional reduction in the parameter estimation problem. The main contribution of this work are 1) a theoretical basis for a reduced-dimension filter, 2) a proposed numerical scheme for the reduced-dimension filter, 3) a theoretical basis for reduced-dimension parameter estimation in a special multiscale setting, and 4) a time-varying characterization of the information shared between signal and observations in the reduced-dimension filter. The results of this research are in the context of slow-fast stochastic systems driven by Brownian motion, in which the timescales of the rates of change of different state/signal components differ by orders of magnitude. The multiscale filtering problem is studied via the Zakai equation that describes the time evolution of the nonlinear filter. We construct a lower dimensional Zakai equation for estimation of the slow signal component and show that the solution of the lower dimensional equation converges to that of the original Zakai equation in the wide timescales separation limit. The convergence is shown to be at a rate proportional to the square root of the timescales separation factor (ratio of characteristic timescale of the fast component to that of the slow). A numerical scheme to approximate the reduced-dimension filter (the solution to the lower dimensional Zakai equation) is also constructed. This scheme combines a particle filtering algorithm with an existing multiscale numerical integration scheme. The reduced filter dimension can restore the feasibility of particle filters in certain high dimensional problems and lowers computational costs by appropriately averaging out fast scale components. The particle filtering scheme is adapted to discrete-, sparse-time observations by… Advisors/Committee Members: Namachchivaya, Navaratnam S (advisor), Namachchivaya, Navaratnam S (Committee Chair), Chen, Yuguo (committee member), Chew, Huck B (committee member), Perkowski, Nicolas (committee member), Rapti, Zoi (committee member), Voulgaris, Petros (committee member).

Subjects/Keywords: Nonlinear filtering; Homogenization; Stochastic partial differential equation; Particle filter; Maximum likelihood estimation; Mutual information

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

APA (6^th Edition):

Yeong, H. C. (2017). Dimensional reduction in nonlinear estimation of multiscale systems. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99320

Chicago Manual of Style (16^th Edition):

Yeong, Hoong Chieh. “Dimensional reduction in nonlinear estimation of multiscale systems.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 08, 2019. http://hdl.handle.net/2142/99320.

MLA Handbook (7^th Edition):

Yeong, Hoong Chieh. “Dimensional reduction in nonlinear estimation of multiscale systems.” 2017. Web. 08 Dec 2019.

Vancouver:

Yeong HC. Dimensional reduction in nonlinear estimation of multiscale systems. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2142/99320.

Council of Science Editors:

Yeong HC. Dimensional reduction in nonlinear estimation of multiscale systems. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99320

21. Spruyt, Vincent. Robust and real-time hand detection and tracking in monocular video.

Degree: 2015, Ghent University

URL: http://hdl.handle.net/1854/LU-5872175

► In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices… (more)

▼ In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices such as eyeglasses, wristwatches and smart televisions. With the advent of touchscreen technology, a new human-computer interaction (HCI) paradigm arose that allows users to interface with their device in an intuitive manner. Using simple gestures, such as swipe or pinch movements, a touchscreen can be used to directly interact with a virtual environment. Nevertheless, touchscreens still form a physical barrier between the virtual interface and the real world. An increasingly popular field of research that tries to overcome this limitation, is video based gesture recognition, hand detection and hand tracking. Gesture based interaction allows the user to directly interact with the computer in a natural manner by exploring a virtual reality using nothing but his own body language. In this dissertation, we investigate how robust hand detection and tracking can be accomplished under real-time constraints. In the context of human-computer interaction, real-time is defined as both low latency and low complexity, such that a complete video frame can be processed before the next one becomes available. Furthermore, for practical applications, the algorithms should be robust to illumination changes, camera motion, and cluttered backgrounds in the scene. Finally, the system should be able to initialize automatically, and to detect and recover from tracking failure. We study a wide variety of existing algorithms, and propose significant improvements and novel methods to build a complete detection and tracking system that meets these requirements. Hand detection, hand tracking and hand segmentation are related yet technically different challenges. Whereas detection deals with finding an object in a static image, tracking considers temporal information and is used to track the position of an object over time, throughout a video sequence. Hand segmentation is the task of estimating the hand contour, thereby separating the object from its background. Detection of hands in individual video frames allows us to automatically initialize our tracking algorithm, and to detect and recover from tracking failure. Human hands are highly articulated objects, consisting of finger parts that are connected with joints. As a result, the appearance of a hand can vary greatly, depending on the assumed hand pose. Traditional detection algorithms often assume that the appearance of the object of interest can be described using a rigid model and therefore can not be used to robustly detect human hands. Therefore, we developed an algorithm that detects hands by exploiting their articulated nature. Instead of resorting to a template based approach, we probabilistically model the spatial relations between different hand parts, and the centroid of the hand. Detecting hand parts, such as fingertips, is much easier than detecting a complete hand. Based on our model of the… Advisors/Committee Members: Philips, Wilfried, Ledda, Alessandro.

Subjects/Keywords: Technology and Engineering; object detection; hand tracking; Computer Vision; object tracking; particle filtering; hand detection

10 more images…

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APA (6^th Edition):

Spruyt, V. (2015). Robust and real-time hand detection and tracking in monocular video. (Thesis). Ghent University. Retrieved from http://hdl.handle.net/1854/LU-5872175

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):

Spruyt, Vincent. “Robust and real-time hand detection and tracking in monocular video.” 2015. Thesis, Ghent University. Accessed December 08, 2019. http://hdl.handle.net/1854/LU-5872175.

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

MLA Handbook (7^th Edition):

Spruyt, Vincent. “Robust and real-time hand detection and tracking in monocular video.” 2015. Web. 08 Dec 2019.

Vancouver:

Spruyt V. Robust and real-time hand detection and tracking in monocular video. [Internet] [Thesis]. Ghent University; 2015. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/1854/LU-5872175.

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

Council of Science Editors:

Spruyt V. Robust and real-time hand detection and tracking in monocular video. [Thesis]. Ghent University; 2015. Available from: http://hdl.handle.net/1854/LU-5872175

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

University of Arizona

22. Celik, Nurcin. INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) .

Degree: 2010, University of Arizona

URL: http://hdl.handle.net/10150/195427

► Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can… (more)

▼ Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising. Advisors/Committee Members: Son, Young-Jun (advisor), Son, Young-Jun (committeemember), Szidarovszky, Ferenc (committeemember), Bayraksan, Guzin (committeemember), Ram, Sudha (committeemember).

Subjects/Keywords: Adaptive simulations; Distributed simulation; Dynamic data driven simulations; Particle filtering; Resampling rules; Simulation-based control

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APA (6^th Edition):

Celik, N. (2010). INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/195427

Chicago Manual of Style (16^th Edition):

Celik, Nurcin. “INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) .” 2010. Doctoral Dissertation, University of Arizona. Accessed December 08, 2019. http://hdl.handle.net/10150/195427.

MLA Handbook (7^th Edition):

Celik, Nurcin. “INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) .” 2010. Web. 08 Dec 2019.

Vancouver:

Celik N. INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) . [Internet] [Doctoral dissertation]. University of Arizona; 2010. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10150/195427.

Council of Science Editors:

Celik N. INTEGRATED DECISION MAKING FOR PLANNING AND CONTROL OF DISTRIBUTED MANUFACTURING ENTERPRISES USING DYNAMIC-DATA-DRIVEN ADAPTIVE MULTI-SCALE SIMULATIONS (DDDAMS) . [Doctoral Dissertation]. University of Arizona; 2010. Available from: http://hdl.handle.net/10150/195427

University of Pretoria

23. De Freitas, Allan. A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar.

Degree: Electrical, Electronic and Computer Engineering, 2013, University of Pretoria

URL: http://hdl.handle.net/2263/33372

► In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A… (more)

▼ In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A target following this representation is commonly referred to as an extended target and results in more information about the target. However, extracting this information from the radar returns is challenging due to several complexities. These complexities include the single dimensional nature of the radar measurements, complexities associated with the scattering of electromagnetic waves, and complex environments in which radar systems are required to operate. There are several applications of HRR radar systems which extract target information with varying levels of success. A commonly used application is that of imaging referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques combine multiple single dimension measurements in order to obtain a single two dimensional image. These techniques rely on rotational motion between the target and the radar occurring during the collection of the single dimension measurements. In the case of ISAR, the radar is stationary while motion is induced by the target. There are several difficulties associated with the unknown motion of the target when standard Doppler processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties. The target and observations were modelled with a non-linear state space model. Several different Bayesian techniques were implemented to infer the hidden states of the model, which coincide with the unknown characteristics of the target. A simulation platform was designed in order to analyse the performance of the implemented techniques. The implemented techniques were capable of successfully tracking a randomly generated target in a controlled environment. The influence of varying several parameters, related to the characteristics of the target and the implemented techniques, was explored. Finally, a comparison was made between standard Doppler processing and the Bayesian methods proposed. Advisors/Committee Members: De Villiers, J.P.R. (advisor), Nel, W. A. J. (coadvisor).

Subjects/Keywords: Extended target; Tracking; High range-resolution radar; High rangeresolution profile; Particle filtering; ISAR; Particle Markov chain Monte-Carlo; Particle marginal Metropolis-Hastings sampler; Static parameter estimation; UCTD

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

APA (6^th Edition):

De Freitas, A. (2013). A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/33372

Chicago Manual of Style (16^th Edition):

De Freitas, Allan. “A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar.” 2013. Masters Thesis, University of Pretoria. Accessed December 08, 2019. http://hdl.handle.net/2263/33372.

MLA Handbook (7^th Edition):

De Freitas, Allan. “A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar.” 2013. Web. 08 Dec 2019.

Vancouver:

De Freitas A. A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar. [Internet] [Masters thesis]. University of Pretoria; 2013. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2263/33372.

Council of Science Editors:

De Freitas A. A Monte-Carlo approach to dominant scatterer tracking of a single extended target in high range-resolution radar. [Masters Thesis]. University of Pretoria; 2013. Available from: http://hdl.handle.net/2263/33372

Indian Institute of Science

24. Paresh, A. Experiential Sampling For Object Detection In Video.

Degree: 2008, Indian Institute of Science

URL: http://hdl.handle.net/2005/843

► The problem of object detection deals with determining whether an instance of a given class of object is present or not. There are robust, supervised… (more)

▼ The problem of object detection deals with determining whether an instance of a given class of object is present or not. There are robust, supervised learning based algorithms available for object detection in an image. These image object detectors (image-based object detectors) use characteristics learnt from the training samples to find object and non-object regions. The characteristics used are such that the detectors work under a variety of conditions and hence are very robust. Object detection in video can be performed by using such a detector on each frame of the video sequence. This approach checks for presence of an object around each pixel, at different scales. Such a frame-based approach completely ignores the temporal continuity inherent in the video. The detector declares presence of the object independent of what has happened in the past frames. Also, various visual cues such as motion and color, which give hints about the location of the object, are not used. The current work is aimed at building a generic framework for using a supervised learning based image object detector for video that exploits temporal continuity and the presence of various visual cues. We use temporal continuity and visual cues to speed up the detection and improve detection accuracy by considering past detection results. We propose a generic framework, based on Experiential Sampling [1], which considers temporal continuity and visual cues to focus on a relevant subset of each frame. We determine some key positions in each frame, called attention samples, and object detection is performed only at scales with these positions as centers. These key positions are statistical samples from a density function that is estimated based on various visual cues, past experience and temporal continuity. This density estimation is modeled as a Bayesian Filtering problem and is carried out using Sequential Monte Carlo methods (also known as Particle Filtering), where a density is represented by a weighted sample set. The experiential sampling framework is inspired by Neisser’s perceptual cycle [2] and Itti-Koch’s static visual attention model[3]. In this work, we first use Basic Experiential Sampling as presented in[1]for object detection in video and show its limitations. To overcome these limitations, we extend the framework to effectively combine top-down and bottom-up visual attention phenomena. We use learning based detector’s response, which is a top-down cue, along with visual cues to improve attention estimate. To effectively handle multiple objects, we maintain a minimum number of attention samples per object. We propose to use motion as an alert cue to reduce the delay in detecting new objects entering the field of view. We use an inhibition map to avoid revisiting already attended regions. Finally, we improve detection accuracy by using a particle filter based detection scheme [4], also known as Track Before Detect (TBD). In this scheme, we compute likelihood of presence of the object based on current and past frame data. This… Advisors/Committee Members: Ramakrishnan, K R.

Subjects/Keywords: Video Image Processing; Sampling Techniques; Experiential Sampling; Image Object Detectors; Video - Object Detection; Object Detection; Image Object Detector; Bayesian Filtering; Track Before Detect (TBD); Particle Filtering; Applied Optics

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

APA (6^th Edition):

Paresh, A. (2008). Experiential Sampling For Object Detection In Video. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/843

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):

Paresh, A. “Experiential Sampling For Object Detection In Video.” 2008. Thesis, Indian Institute of Science. Accessed December 08, 2019. http://hdl.handle.net/2005/843.

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

MLA Handbook (7^th Edition):

Paresh, A. “Experiential Sampling For Object Detection In Video.” 2008. Web. 08 Dec 2019.

Vancouver:

Paresh A. Experiential Sampling For Object Detection In Video. [Internet] [Thesis]. Indian Institute of Science; 2008. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/2005/843.

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

Council of Science Editors:

Paresh A. Experiential Sampling For Object Detection In Video. [Thesis]. Indian Institute of Science; 2008. Available from: http://hdl.handle.net/2005/843

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

Cranfield University

25. Wileman, Andrew John. An investigation into the prognosis of electromagnetic relays.

Degree: 2016, Cranfield University

URL: http://dspace.lib.cranfield.ac.uk/handle/1826/13665

► Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications.… (more)

▼ Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made.

Subjects/Keywords: Electromagnetic relays; Integrated vehicle health management; Prognosics and health management; Condition based maintenance; Hybrid prognostics; Physics-based prognostics; Data-driven prognostics; Kalman filtering; Particle filtering

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APA (6^th Edition):

Wileman, A. J. (2016). An investigation into the prognosis of electromagnetic relays. (Thesis). Cranfield University. Retrieved from http://dspace.lib.cranfield.ac.uk/handle/1826/13665

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):

Wileman, Andrew John. “An investigation into the prognosis of electromagnetic relays.” 2016. Thesis, Cranfield University. Accessed December 08, 2019. http://dspace.lib.cranfield.ac.uk/handle/1826/13665.

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

MLA Handbook (7^th Edition):

Wileman, Andrew John. “An investigation into the prognosis of electromagnetic relays.” 2016. Web. 08 Dec 2019.

Vancouver:

Wileman AJ. An investigation into the prognosis of electromagnetic relays. [Internet] [Thesis]. Cranfield University; 2016. [cited 2019 Dec 08]. Available from: http://dspace.lib.cranfield.ac.uk/handle/1826/13665.

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

Council of Science Editors:

Wileman AJ. An investigation into the prognosis of electromagnetic relays. [Thesis]. Cranfield University; 2016. Available from: http://dspace.lib.cranfield.ac.uk/handle/1826/13665

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

University of New South Wales

26. Vu, Phuong Anh. Multivariate stochastic loss reserving with common shock approaches.

Degree: Actuarial Studies, 2019, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/61430 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55470/SOURCE02?view=true

► Outstanding claims liability is usually one of the largest liabilities on the balance sheet of a general insurer. Therefore, it is critical for insurers to… (more)

▼ Outstanding claims liability is usually one of the largest liabilities on the balance sheet of a general insurer. Therefore, it is critical for insurers to accurately estimate their outstanding claims. Furthermore, a general insurer typically operates in multiple business lines whose risks are not perfectly dependent. This results in "diversification benefits", the consideration of which is crucial due to their effects on the aggregate reserves and capital. It is then essential to consider the dependence across business lines in the estimation of outstanding claims. The goal of this thesis is to develop new approaches to assess outstanding claims for portfolios of dependent lines. We explore the common shock technique for model developments, a very popular dependence modelling technique with distinctive strengths, such as explicit dependence structure, ease of interpretation, and parsimonious construction of correlation matrices. We also aim to enhance the practicality of our approaches by incorporating realistic and desirable model features. Motivated by the richness of the Tweedie distribution family which covers Poisson distributions, gamma distributions and many more, we introduce a common shock Tweedie framework with dependence across business lines. Desirable properties of this framework are studied, including its marginal flexibility, tractable moments, and ability to handle masses at 0. To overcome the complex distributional structure of the Tweedie framework, we formulate a Bayesian approach for model estimation and perform a real data illustration. Remarks on practical features of the framework are drawn. Loss reserving data possesses an unbalanced nature, that is, claims from different positions within and between loss triangles can vary widely as more claims typically develop in early development periods. We account for this feature explicitly in common shock models with a parsimonious common shock adjustment. Theoretical and real data illustrations are performed using the multivariate Tweedie framework. Finally, in the last part of this thesis, we develop a dynamic framework with evolutionary factors to account for claims development patterns that change over time. Calendar year dependence is introduced using common shocks. We also formulate an estimation approach that is tailored to the structure of loss reserving data and perform a real data illustration. Advisors/Committee Members: Avanzi, Benjamin, Actuarial Studies, Australian School of Business, UNSW, Taylor, Gregory, Actuarial Studies, Australian School of Business, UNSW, Wong, Bernard, Actuarial Studies, Australian School of Business, UNSW.

Subjects/Keywords: Common shock; Stochastic reserving; Loss triangles; Evolutionary modelling; Robotic reserving; Tweedie family of distributions; Bayesian estimation; Unbalanced data; Kalman filtering; Particle filtering

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

APA (6^th Edition):

Vu, P. A. (2019). Multivariate stochastic loss reserving with common shock approaches. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/61430 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55470/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Vu, Phuong Anh. “Multivariate stochastic loss reserving with common shock approaches.” 2019. Doctoral Dissertation, University of New South Wales. Accessed December 08, 2019. http://handle.unsw.edu.au/1959.4/61430 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55470/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Vu, Phuong Anh. “Multivariate stochastic loss reserving with common shock approaches.” 2019. Web. 08 Dec 2019.

Vancouver:

Vu PA. Multivariate stochastic loss reserving with common shock approaches. [Internet] [Doctoral dissertation]. University of New South Wales; 2019. [cited 2019 Dec 08]. Available from: http://handle.unsw.edu.au/1959.4/61430 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55470/SOURCE02?view=true.

Council of Science Editors:

Vu PA. Multivariate stochastic loss reserving with common shock approaches. [Doctoral Dissertation]. University of New South Wales; 2019. Available from: http://handle.unsw.edu.au/1959.4/61430 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55470/SOURCE02?view=true

27. Μακρής, Αλέξανδρος. A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups.

Degree: 2010, National and Kapodistrian University of Athens; Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (ΕΚΠΑ)

URL: http://hdl.handle.net/10442/hedi/23750

►

The thesis concerns the problems of visual tracking and violence detection in video sequences. For the visual tracking problem, two feature fusion frameworks are presented.… (more)

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The thesis concerns the problems of visual tracking and violence detection in video sequences. For the visual tracking problem, two feature fusion frameworks are presented. The first tracking framework called 'Model Fusion via Proposal' (MFP) framework, provides a way to efficiently fuse visual cues using independent trackers to construct an improved proposal distribution for the main tracker. The fusion method results in reduced computational requirements due to the better proposal and the gradual exploitation of the state space. The 'Hierarchical Model Fusion' (HMF) framework, extends the MFP framework by integrating the multiple models into a single tracker which exploits all the visual cues. This way the robustness of the approach is further increased. The frameworks are implemented and tested in various challenging sequences and proved robust with significant reduction of the computational cost. For violence detection, a system that classifies movie segments as violent or non-violent is proposed. The system fuses audio and visual information. The audio module uses state-of-the-art methods. The visual features concern the general motion in the scene, the detection of gunshots, and the motion of the detected people. The method is tested in a dataset comprised of segments from several movies.

Η διατριβή αφορά στα προβλήματα της οπτικής παρακολούθησης και του εντοπισμού βίας σε βίντεο. Για την οπτική παρακολούθηση αναπτύχθηκαν δύο πλάισια συγκερασμού οπτικών χαρακτηριστικών. Το πρώτο πλαίσιο παρακολούθησης, που τιτλοφορείται «Πλαίσιο Συγκερασμού μοντέλων μέσω κατανομής Πρότασης» (MFP), παρέχει μία μέθοδο για να γίνει αποτελεσματικός συγκερασμός οπτικών χαρακτηριστικών μέσω ανεξάρτητων ιχνηλατών με σκοπό την κατασκευή καλύτερης κατανομής proposal για τον κυρίως ιχνηλάτη. Η μέθοδος επιτυγχάνει μείωση της υπολογιστικής πολυπλοκότητας και σταδιακή αναζήτηση στο χώρο κατάστασης. Το «Ιεραρχικό Πλαίσιο Συγκερασμού Μοντέλων» (HMF) επεκτείνει το πλαίσιο MFP ενσωματώνοντας τα μοντέλα αντικειμένων σε ένα συνολικό μοντέλο που χρησιμοποιεί όλα τα διαθέσιμα οπτικά χαρακτηριστικά. Με αυτόν τον τρόπο αυξάνεται η ευρωστία της μεθόδου. Τα πλαίσια υλοποιήθηκαν και δοκιμάστηκαν σε απαιτητικά βίντεο όπου αποδείχθηκαν εύρωστα και πέτυχαν μείωση του υπολογιστικού κόστους. Για την αναγνώριση βίας προτείνεται ένα σύστημα που ταξινομεί τμήματα βίντεο σαν βίαια ή μη. Το σύστημα χρησιμοποιεί οπτική και ακουστική πληροφορία. Το μέρος που αφορά την ακουστική πληροφορία χρησιμοποιήθηκαν σύγχρονες μέθοδοι από τη βιβλιογραφία. Τα οπτικά χαρακτηριστικά αφορούν τη γενική κίνηση στη σκηνή, την αναγνώριση των πυροβολισμών και την κίνηση των ατόμων που εντοπίζονται. Η μέθοδος δοκιμάστηκε σε ένα σετ από τμήματα διάφορων ταινιών.

Subjects/Keywords: Φίλτρα σωματιδίων; Οπτική παρακολούθηση; Φιλτράρισμα; Εντοπισμός βίας; Οπτικοακουστικός συγκερασμός; Particle filters; Visual tracking; Bayesian filtering; Violence detection; Audio-visual fusion

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

APA (6^th Edition):

Μακρής, . �. (2010). A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups. (Thesis). National and Kapodistrian University of Athens; Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (ΕΚΠΑ). Retrieved from http://hdl.handle.net/10442/hedi/23750

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):

Μακρής, Αλέξανδρος. “A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups.” 2010. Thesis, National and Kapodistrian University of Athens; Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (ΕΚΠΑ). Accessed December 08, 2019. http://hdl.handle.net/10442/hedi/23750.

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

MLA Handbook (7^th Edition):

Μακρής, Αλέξανδρος. “A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups.” 2010. Web. 08 Dec 2019.

Vancouver:

Μακρής �. A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups. [Internet] [Thesis]. National and Kapodistrian University of Athens; Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (ΕΚΠΑ); 2010. [cited 2019 Dec 08]. Available from: http://hdl.handle.net/10442/hedi/23750.

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

Council of Science Editors:

Μακρής �. A multimedia content modeling and classification methodology using visual information for the protection of sensitive user groups. [Thesis]. National and Kapodistrian University of Athens; Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών (ΕΚΠΑ); 2010. Available from: http://hdl.handle.net/10442/hedi/23750

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

Wright State University

28. Hlinomaz, Peter V. Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking.

Degree: PhD, Engineering PhD, 2008, Wright State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=wright1226586660

► Hlinomaz, Peter Vladimir, Ph.D., Engineering Ph.D. Program, Wright State University, 2008. Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications… (more)

▼ Hlinomaz, Peter Vladimir, Ph.D., Engineering Ph.D. Program, Wright State University, 2008. Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking The majority of deployed target tracking systems use some variant of the Kalman filter for their state estimation algorithm. In order for a Kalman filter to be optimal, the measurement and state equations must be linear and the process and measurement noises must be Gaussian random variables (or vectors). One problem arises when the state or measurement function becomes a multi-modal Gaussian mixture. This typically occurs with the interactive multiple model (IMM) technique and its derivatives and also with probabilistic and joint probabilistic data association (PDA/JPDA) algorithms. Another common problem in target tracking is that the target’s signal-to-noise ratio (SNR) at the sensor is often low. This situation is often referred to as the dim target tracking or track-before-detect (TBD) scenario. When this occurs, the probability density function (PDF) of the measurement likelihood function becomes non-Gaussian and often has a Rayleigh or Ricean distribution. In this case, a Kalman filter variant may also perform poorly. The common solution to both of these problems is the particle filter (PF). A key drawback of PF algorithms, however, is that they are computationally expensive. This dissertation, thus, concentrates on developing PF algorithms that provide comparable performance to conventional PFs but at lower particle costs and presents the following four research efforts.1. A multirate multiple model particle filter (MRMMPF) is presented in Section-3. The MRMMPF tracks a single, high signal-to-noise-ratio, maneuvering target in clutter. It coherently accumulates measurement information over multiple scans via discrete wavelet transforms (DWT) and multirate processing. This provides the MRMMPF with a much stronger data association capability than is possible with a single scan algorithm. In addition, its particle filter nature allows it to better handle multiple modes that arise from multiple target motion models. Consequently, the MRMMPF provides substantially better root-mean-square error (RMSE) tracking performance than either a full-rate or multirate Kalman filter tracker or full-rate multiple model particle filter (MMPF) with a same particle count. 2. A full-rate multiple model particle filter for track-before-detect (MMPF-TBD) and a multirate multiple model particle filter for track-before-detect (MRMMPF-TBD) are presented in Section-4. These algorithms extend the areas mentioned above and track low SNR targets which perform small maneuvers. The MRMMPF-TBD and MMPF-TBD both use a combined probabilistic data association (PDA) and maximum likelihood (ML) approach. The MRMMPF-TBD provides equivalent RMSE performance at substantially lower particle counts than a full-rate MMPF-TBD. In addition, the MRMMPF-TBD tracks very dim constant velocity targets that the MMPF-TBD cannot. 3. An… Advisors/Committee Members: Hong, Lang (Advisor).

Subjects/Keywords: Electrical Engineering; particle filtering; multirate tracking; MRMMPF-TBD; MMPF-TBD; MRIMM; dim target; multi-modal; multiresolutional tracking

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

APA (6^th Edition):

Hlinomaz, P. V. (2008). Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1226586660

Chicago Manual of Style (16^th Edition):

Hlinomaz, Peter V. “Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking.” 2008. Doctoral Dissertation, Wright State University. Accessed December 08, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1226586660.

MLA Handbook (7^th Edition):

Hlinomaz, Peter V. “Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking.” 2008. Web. 08 Dec 2019.

Vancouver:

Hlinomaz PV. Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking. [Internet] [Doctoral dissertation]. Wright State University; 2008. [cited 2019 Dec 08]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1226586660.

Council of Science Editors:

Hlinomaz PV. Study of Multi-Modal and Non-Gaussian Probability Density Functions in Target Tracking with Applications to Dim Target Tracking. [Doctoral Dissertation]. Wright State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1226586660

University of Alberta

29. Sahar, Movaghati. Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks.

Degree: PhD, Department of Electrical and Computer Engineering, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/cxd07gs82z

► In distributed sensing systems, measurements from a random process or parameter are usually not available in one place. Also, the processing resources are distributed over… (more)

▼ In distributed sensing systems, measurements from a random process or parameter are usually not available in one place. Also, the processing resources are distributed over the network. This distributed characteristic of such sensing systems demands for special attention when an estimation or inference task needs to be done. In contrast to a centralized case, where the raw measurements are transmitted to a fusion centre for processing, distributed processing resources can be used for some local processing, such as data compression or estimation according to distributed quantization or estimation algorithms. Wireless sensor networks (WSNs) consist of small sensor devices with limited power and processing capability, which cooperate through wireless transmission, in order to fulfill a common task. These networks are currently employed on land, underground, and underwater, in a wide range of applications including environmental sensing, industrial and structural monitoring, medical care, etc. However, there are still many impediments that hold back these networks from being pervasive, some of which are characteristics of WSNs, such as scarcity of energy and bandwidth resources and limited processing and storage capability of sensor nodes. Therefore, many challenges still need to be overcome before WSNs can be extensively employed. In this study, we concentrate on developing algorithms that are useful for estimation tasks in distributed sensing systems, such as wireless sensor networks. In designing these algorithms we consider the special constraints and characteristics of such systems, i.e., distributed nature of the measurements and the processing resources, as well as the limited energy of wireless and often small devices. We first investigate a general stochastic inference problem. We design a non-parametric algorithm for tracking a random process using distributed and noisy measurements. Next, we narrow down the problem to the distributed parameter estimation, and design distributed quantizers to compress measurement data while maintaining an accurate estimation of the unknown parameter. The contributions of this thesis are as follows. In Chapter 3, we design an algorithm for the distributed inference problem. We first use factor graphs to model the stochastic dependencies among the variables involved in the problem and factorize the global inference problem to a number of local dependencies. A message passing algorithm called the sum-product algorithm is then used on the factor graph to determine local computations and data exchanges that must be performed by the sensing devices in order to achieve the estimation goal. To tackle the nonlinearities in the problem, we combine the particle filtering and Monte-Carlo sampling in the sum-product algorithm and develop a distributed non-parametric solution for the general nonlinear inference problems. We apply our algorithm to the problem of distributed target tracking and show that even with a few number of particles the algorithm can efficiently track the target. In the…

Subjects/Keywords: distributed quantization; assignment problem; bayesian networks; distributed algorithm; distributed estimation; optimization; particle filtering; factor graph; wireless sensor network; mutual information

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

APA (6^th Edition):

Sahar, M. (2014). Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cxd07gs82z

Chicago Manual of Style (16^th Edition):

Sahar, Movaghati. “Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks.” 2014. Doctoral Dissertation, University of Alberta. Accessed December 08, 2019. https://era.library.ualberta.ca/files/cxd07gs82z.

MLA Handbook (7^th Edition):

Sahar, Movaghati. “Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks.” 2014. Web. 08 Dec 2019.

Vancouver:

Sahar M. Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2019 Dec 08]. Available from: https://era.library.ualberta.ca/files/cxd07gs82z.

Council of Science Editors:

Sahar M. Distributed Estimation and Quantization Algorithms for Wireless Sensor Networks. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/cxd07gs82z

University of California – Berkeley

30. Shi, Lei. Hierarchical Bayesian inference in the brain: psychological models and neural implementation.

Degree: Neuroscience, 2009, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/6rx845s0

► The human brain effortlessly solves problems that still pose a challenge for modern computers, such as recognizing patterns in natural images. Many of these problems… (more)

▼ The human brain effortlessly solves problems that still pose a challenge for modern computers, such as recognizing patterns in natural images. Many of these problems can be formulated in terms of Bayesian inference, including planning motor movements, combining cues from different modalities, and making predictions. Recent work in psychology and neuroscience suggests that human behavior is often consistent with Bayesian inference. However, most research using probabilistic models has focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than considering mechanisms that could implement these solutions. Therefore, it is critical to understand the psychological models and neural implementations that carry out these notoriously challenging computations.Exemplar models are a successful class of psychological process models that use an inventory of stored examples to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling, and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference.The goal of perception is to infer the hidden states in the hierarchical process by which sensory data are generated, a problem that can be solved optimally using Bayesian inference. Here we propose a simple mechanism for Bayesian inference which involves averaging over a few feature detection neurons which fire at a rate determined by their similarity to a sensory stimulus. This mechanism is again based on importance sampling. Moreover, many cognitive and perceptual tasks involve multiple levels of abstraction, which results in ``hierarchical'' models. We show that a simple extension to recursive importance sampling can be used to perform hierarchical Bayesian inference. We identify a scheme for implementing importance sampling with spiking neurons, and show that this scheme can account for human behavior in sensorimotor integration, cue combination, and orientation perception.Another important function of nervous system is to process temporal information in the dynamical environment, such as motion coordination where the system's state is estimated sequentially based on the constant perceptual feedback. Our study suggests that a neural network structure similar to recursive importance sampling can solve the sequential estimation problem by approximating the posterior updates. This algorithm performs as well as the state-of-the-art sequential Monte Carlo methods know as…

Subjects/Keywords: Biology, Neuroscience; Psychology, Cognitive; Statistics; Bayesian inference; exemplar models; hierarchical model; importance sampling; neural implementation; particle filtering

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

APA (6^th Edition):

Shi, L. (2009). Hierarchical Bayesian inference in the brain: psychological models and neural implementation. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/6rx845s0

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):

Shi, Lei. “Hierarchical Bayesian inference in the brain: psychological models and neural implementation.” 2009. Thesis, University of California – Berkeley. Accessed December 08, 2019. http://www.escholarship.org/uc/item/6rx845s0.

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

MLA Handbook (7^th Edition):

Shi, Lei. “Hierarchical Bayesian inference in the brain: psychological models and neural implementation.” 2009. Web. 08 Dec 2019.

Vancouver:

Shi L. Hierarchical Bayesian inference in the brain: psychological models and neural implementation. [Internet] [Thesis]. University of California – Berkeley; 2009. [cited 2019 Dec 08]. Available from: http://www.escholarship.org/uc/item/6rx845s0.

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

Council of Science Editors:

Shi L. Hierarchical Bayesian inference in the brain: psychological models and neural implementation. [Thesis]. University of California – Berkeley; 2009. Available from: http://www.escholarship.org/uc/item/6rx845s0

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

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Open Access Theses and Dissertations