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 59 for “"Sparseness"”.
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Bayesian and Information-Theoretic Learning of High Dimensional Data
<p>The concept of sparseness is harnessed to learn a low dimensional representation of high dimensional data. This sparseness assumption is exploited in multiple ways. In the Bayesian Elastic Net, a small number of correlated features are identified for the response variable. In the sparse Factor …
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Poverty mapping : what can we really learn?
… of small area estimation arising from data sparseness and argue that any attempt to overcome this fundamental problem has to take the form of a conditional homogeneity assumption.
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The Effect of Environmental Control Costs on u.s. Trade
… to analyze the problem were hampered by the sparseness of data for pollution abatement expenditures on a disaggregated level and an inadequate theoretical framework.
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SPARSE RECOVERY BY NONCONVEX LIPSHITZIAN MAPPINGS
… The first are aimed at uniformly enhancing the sparseness level by shrinking effects, the latter to project back into the feasible space of solutions. In the second part of this thesis we study two applications in which sparseness has been successfully applied in recent areas of the signal and …
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Adaptive filters for sparse system identification
… and lower complexity than previous ones. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter's coefficients is combined with row action projections (RAP) to significantly reduce the …
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Efficient Structure and Motion: Path Planning, Uncertainty and Sparsity
… tree decomposition is presented, exploiting the sparseness patterns in typical structure-and-motion input data.
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Spectrum-Aware Orthogonal Frequency Division Multiplexing
… completed and tested over-the-air. Sub-carrier sparseness assumptions were validated under practical implementation and performance considerations. A novel algorithm for frame detection and synchronization with mutual interference rejection applicable to the FBMC case was proposed and tested.
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Estimation of Intrinsic Gravity Wave Parameters From Multiple, Ground-Based Observations of a Single Mesopheric Airglow Emission
… advantage of the wave perturbation's Fourier sparseness. Then, a parameter estimation (PE) technique is developed to infer the key AGW parameters directly from the data, followed by an in-depth analysis of the error from this estimation method. Finally, the PE method is applied to real data …
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A statistical learning framework for data mining of large-scale systems : algorithms, implementation, and applications
… machine learning. It also takes advantage of the sparseness of support vectors and this allows for parallelization and online training to further speed-up of the computation. The solver can be integrated into existing systems, embedded into databases, or exposed as a web service. Understanding the …
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Estimation of channelized features in geological media using sparsity constraint
… spatially continuous parameters that exhibit sparseness in an incoherent basis (e.g. a Fourier basis). The solution is constrained to be sparse in the transform domain and the dimension of the search space is effectively reduced to a low frequency subspace to improve estimation efficiency. The …
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Assessing the benefits of DCT compressive sensing for computational electromagnetics
… problems have a property known as sparseness. It has been proven that through under sampling, computation runtimes can be substantially decreased while maintaining sufficient accuracy. Lawrence Carin and his team of researchers at Duke University developed an in situ compressive …
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Spatio-temporal analysis in functional brain imaging
… estimator. The underlying model captures the sparseness of the active areas in space while encouraging smooth temporal dynamics. We compute the current source estimates efficiently by solving a second-order cone programming problem. By considering all time points simultaneously, we achieve …
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Exploring the visual pathway and its applications to image reconstruction, contrast enhancement and object recognition
… in Sparse Representation Classification (SRC). Sparseness is a key feature of the brain's internal representation whereby it achieves its robustness and adaptability. This work replaces the mean square error measure for similarity comparison of images with a perceptually compatible structural …
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Exploring the visual pathway and its applications to image reconstruction, contrast enhancement and object recognition
… in Sparse Representation Classification (SRC). Sparseness is a key feature of the brain's internal representation whereby it achieves its robustness and adaptability. This work replaces the mean square error measure for similarity comparison of images with a perceptually compatible structural …
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Speech perception in a sparse domain
… order statistics affect our speech perception. Sparseness can be defined by the fourth order statistics, kurtosis, and it is hypothesised that greater kurtosis should be reflected by better speech recognition performance in noise. Based on a corpus of speech material, kurtosis was found to be …
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Opportunistic Sampling by Level-Crossing
… and 4. a numerical index that measures signal sparseness which is used to analytically show the relationship between rate of transmission and signal characteristics. The framework established in this work aims to capture the full potential of LC. Insights gained from the analytical work will …
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Monopolistic insider trading in a stationary market
… than that at which trading takes place. The sparseness of signals induces insiders to trade patiently before the next signal comes in, as in the finite horizon model of Kyle (1985). Furthermore, the degree of market efficiency declines as signals arrive more sparsely. Finally, we assume that …
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Topics in Genomic Signal Processing
… principle, the maximum parsimony principle, to a sparseness condition.
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Entity recognition for multi-modal socio-technical systems
… learning for this project. To overcome data sparseness issues that results from considering a large number of entity classes, we built two separate classifiers for predicting labels for entity boundary and class. We herein investigate rules for merging both labels while minimizing the loss of …
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Large-area visually augmented navigation for autonomous underwater vehicles
… canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsication methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the …
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