Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 32 for “"Probabilistic Data"”.
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Probabilistic data analysis with probabilistic programming
Probabilistic techniques are central to data analysis, but dierent approaches can be challenging to apply, combine, and compare. This thesis introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and …
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Aerial reconstructions via probabilistic data fusion
In this thesis we propose a probabilistic model that incorporates multi-modal noisy measurements: aerial images and Light Detection and Ranging (LiDAR) to recover scene geometry and appearance in order to build a 3D photo-realistic model of a given scene. In urban environments, these …
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Ballistic missile tracking using the interacting multiple model joint probabilistic data association filter
… of the interacting multiple model joint probabilistic data association filter to effectively track a ballistic missile and detect decoys and maneuvers is the focus of this thesis. Model development and data association schemes are discussed along with optimized values for selected …
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Probabilistic multiple kernel learning
… to address parts of that direction by proposing probabilistic data integration algorithms for multiclass decisions where an observation of interest is assigned to one of many categories based on a plurality of information channels.
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Bloom Filters for Filesystem Forensics
… become more time consuming as the amount of data to be investigated grows. Secular growth trends between hard drive and memory capacity just exacerbate the problem. Bloom filters are space-efficient, probabilistic data structures that can represent data sets with quantifiable false positive …
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Machine Learning and Bayesian Statistics for Seismic Compressive Sensing
… receiver’s output as unusable. These gaps in the data cause problems in later stages of the seismic signal processing work flow via aliasing or incoherent noise and thus signal reconstruction is necessary. Modern algorithms utilise the principle of Compressive Sensing (CS) for reconstruction which …
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Multimodal Data Fusion for Estimating Electricity Access and Demand
… chapter, we employ machine learning systems for probabilistic data fusion to the problem of forecasting annual electricity demand at the countrylevel for all African countries. We provide a novel set of probabilistic forecasts for the continent while addressing missing data issues and employing a …
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Radar and LiDAR Fusion for Scaled Vehicle Sensing
… an extended Kalman filter (EKF) and the joint probabilistic data association (JPDA). Second, a 1/5th scaled vehicle performed the same vehicle maneuvers but scaled to approximately 1/5th the distance and speed. When taking the scaling factor into consideration, the RTS' positional error at …
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Terrain-relative navigation for autonomous underwater vehicles
… represented through a covariance matrix. A probabilistic data association filter with amplitude information (PDAFAI), grounded in the Kalman Filter framework, probabilistically weights each good match that lies within the validation gate. Weights are a function of both the match quality and …
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Adaptive electricity access planning
… are presented. In addition, a novel model for probabilistic data fusion and other machine learning methods are compared for electrification status estimation. Inference tools such as these allow for the cost-effective provision of granular data required by techno-economic models. We also …
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Multichannel source separation and tracking with phase differences by random sample consensus
… IPD features compose a noisy circular-linear dataset. This data is clustered with the RANdom SAmple Consensus (RANSAC) algorithm in the presence of strong reverberation to simultaneously localize and separate speakers. The remarkable performance of RANSAC is due to its natural tendency to …
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Efficient Indexing for Structured and Unstructured Data
The collection of digital data is growing at an exponential rate. Data originates from wide range of data sources such as text feeds, biological sequencers, internet traffic over routers, through sensors and many other sources. To mine intelligent information from these sources, users have to query …
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Best Linear Unbiased Estimation Fusion with Constraints
<p>Estimation fusion, or data fusion for estimation, is the problem of how to best utilize useful information contained in multiple data sets for the purpose of estimating an unknown quantity — a parameter or a process. Estimation fusion with constraints gives rise to challenging theoretical …
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Two-Stage Stochastic Mixed Integer Nonlinear Programming: Theory, Algorithms, and Applications
… one of the powerful modeling tools that allows probabilistic data parameters in mixed integer programming, a well-known tool for optimization modeling with deterministic input data. However, akin to the mixed integer programs, these stochastic models are theoretically intractable and …
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Monitoring uncertain data for sensor-based real-time systems
… of user-defined constraints on time-varying data is a fundamental functionality in various sensor-based real-time applications such as environmental monitoring, process control, location-based surveillance, etc. In general, these applications track real-world objects and constantly evaluate …
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Understanding and improving people’s judgments of synergistic risks
… the studies presented in the four papers employs data obtained via questionnaires specifically designed to address each research question. In the first paper, two studies are presented that examine whether people believe that combined hazards can present synergistic risks. In the second paper, …
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A Methodology for the Development of a Production Experience Database for Earthmoving Operations Using Automated Data Collection
Automated data acquisition has revolutionized the reliability of product design in recent years. A noteworthy example is the improvement in the design of aircrafts through field data. This research proposes a similar improvement in the reliability of process design of earthmoving operations through …
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Dynamic model-based safety analysis: from state machines to temporal fault trees
… not encouraged; here the focus is primarily on probabilistic analysis. Qualitative analysis is particularly important when probabilistic data are not available (e.g., at early stages of design). In an alternative approach though, the generation of combinatorial, Boolean FTs has been applied to …
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A framework for promotion analysis in multi-dimensional space
… thesis, we motivate and discuss a novel class of data mining problems, called promotion analysis, for promoting a given object in a multi-dimensional space by leveraging object ranking information. The key observation is that most objects may not be highly ranked in the global space, where all …
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Coping Uncertainty in Wireless Network Optimization
… statistics, symmetric properties, or limited data samples. Therefore, CCP is more flexible to handle different network settings, which is important to address problems in 5G/next-G networks. Further, worst-case optimization assumes upper or lower bounds (i.e., worst cases) for the uncertain …
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