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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 34 for “"Reduced Rank"”.
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Reduced Rank Classification and Estimation of the Actual Error Rate
Made available in DSpace on 2014-12-13T18:22:03Z (GMT). No. of bitstreams: 1 7913434.pdf: 5498771 bytes, checksum: f5d8da72e5428f4044ed642e1497ff0f (MD5) Previous issue date: 1978
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Reduced rank filtering in chaotic systems with application in geophysical sciences
… As a result, most applications use suboptimal reduced rank algorithms. Successful implementation of reduced rank filters depends on the dynamical properties of the underlying system. Here, the focus is on geophysical systems with chaotic behavior defined as extreme sensitivity of the dynamics …
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Robust Steering Vector Mismatch Techniques for Reduced Rank Adaptive Array Signal Processing
… dissertation is on the development of advanced reduced rank adaptive signal processing for airborne radar space-time adaptive processing (STAP) and steering vector mismatch robustness. This is an important area of research in the field of airborne radar signal processing since practical STAP …
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Reduced Rank Adaptive Filtering Applied to Interference Mitigation in Wideband CDMA Systems
… on the development and application of advanced reduced rank adaptive signal processing techniques for high data rate wireless code division multiple access (CDMA) communications systems. This is an important area of research in the field of wireless communications. Current systems are moving …
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Interference suppression and diversity for CDMA systems
… impacting performance. In this dissertation, reduced-rank minimum mean square error (MMSE) receiver and reduced-rank minimum variance receiver are investigated to suppress interference; transmit diversity is applied to multicarrier CDMA (MC-CDMA) systems to combat fading; packet combing is …
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Array Signal Processing Algorithms for Beamforming and Direction Finding
… constant modulus (CCM) criteria to propose full-rank and reduced-rank adaptive algorithms. Specifically, for the full-rank algorithms, we present two low-complexity adaptive step size mechanisms with the CCM criterion for the step size adaptation of the stochastic gradient (SG) algorithms. The …
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Space/time/frequency methods in adaptive radar
… of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of …
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Modeling Strategies for Large Dimensional Vector Autoregressions
… of a large dimensional VAR model is based on a reduced-rank estimator for the white noise covariance matrix. We first derive the reduced-rank covariance estimator under the setting of independent observations and give the analytical form of its maximum likelihood estimate. Then we describe how …
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Physically constrained maximum likelihood (PCML) mode filtering and its application as a pre-processing method for underwater acoustic communication
… compares the performance of the pseudoinverse, reduced rank pseudoinverse, sampled mode shape, PCML minimum power distortionless response (MPDR), PCML-MAP, and MAP mode filters. The PCML-MAP filter performs as well as the MAP filter without the need for a priori data statistics. The PCML-MPDR …
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Commonality in Two-Dimensions: An Empirical Investigation
… follow Hasbrouck and Seppi (2001)’s work and use reduced-rank regression to model the commonality in Chapter Two. The literature on the study of return commonality generally attributes its source to the order flow. But I find that return and order flows are endogenous and use the new exogenous …
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Physically constrained maximum likelihood (PCML) mode filtering and its application as a pre-processing method for underwater acoustic communication
… compares the performance of the pseudoinverse, reduced rank pseudoinverse, sampled mode shape, PCML minimum power distortionless response (MPDR), PCML-MAP, and MAP mode filters. The PCML-MAP filter performs as well as the MAP filter without the need for a priori data statistics. The PCML-MPDR …
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Adaptive detection in ultrawide bandwidth wireless communication systems
… the complexity of the RLS adaptive detector, rank-reduction techniques are introduced. With the aid of reduced-rank techniques, the filter size can be efficiently reduced, which in turn reduces the number of parameters required to be estimated. Consequently, the convergence speed, tracking …
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Computation of multiscale time-harmonic electromagnetic radiation
… ray-physics behaviors, which raises questions of reduced rank in the discretized operators. These questions are addressed by identifying the wave- to ray-physics transition and observing reduced rank in the space of plane wave functions.
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Adaptive interference suppression algorithms for DS-UWB systems
… are considered. We first investigate a generic reduced-rank scheme based on the concept of joint and iterative optimization (JIO) that jointly optimizes a projection vector and a reduced-rank filter by using the minimum mean-squared error (MMSE) criterion. A low-complexity scheme, named Switched …
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Robust Implementations of the Multistage Wiener Filter
The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of …
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Transceiver design and system optimization for ultra-wideband communications
… and ISI resulting in higher information rates. A reduced-rank adaptive filtering technique is applied to the problem of interference suppression and optimum combining in UWB communications. The reduced-rank combining method, in particular the eigencanceler, is proposed and compared with a minimum …
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Applications of low-rank approximation: complex networks and inverse problems
The use of low-rank approximation is crucial when one is interested in solving problems of large dimension. In this case, the matrix with reduced rank can be obtained starting from the singular value decomposition considering only the largest components. This thesis describes how the use of the …
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Prediction and Anomaly Detection Techniques for Spatial Data
… inference. Here, we presents a novel robust and reduced Rank spatial kriging Model (R$^3$-SKM), which is resilient to the influences of outliers and allows for fast spatial inference. Second, this research introduces a flexible hierarchical Bayesian framework that permits the simultaneous …
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Sensor Array Processing with Manifold Uncertainty
… non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same …
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