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 56 for “"signal reconstruction"”.
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Constrained signal reconstruction
… reported in this dissertation addresses the reconstruction of signals and images from linear measurements subject to convex constraints. The objectives are to describe the existence and uniqueness of solutions, to characterize reconstructions, and to develop algorithms for efficiently …
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Signal reconstruction from phase
Typescript (photocopy).
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Signal reconstruction from phase or magnitude
Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981.
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Approximate signal reconstruction from partial information
… not represent an optimal way in which to code a signal in terms of theoretical rate distortion bounds. A signal may be coded more efficiently if side information is included with the signal during transmission. This side information can then be used to reconstruct the image at some later time. In …
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Inverse filtering by signal reconstruction from phase
A common problem that arises in image processing is that of performing inverse filtering on an image that has been blurred. Methods for doing this have been developed, but require fairly accurate knowledge of the magnitude of the Fourier transform of the blurring function and are sensitive to noise …
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Signal reconstruction from discrete-time Wigner distribution
… for time-frequency analysis of rumvstationary signals. Wigner distribution is a bilinear signal transformation which provides two dimensional time-frequency characterization of one dimensional signals. Although much work has been done recently in signal analysis and applications using Wigner …
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Tensor Methods for Signal Reconstruction and Network Embedding
… increased interest in machine learning (ML) and signal processing (SP) research. How do we fuse and complete multi-dimensional signals? What is a concise and informative representation of entities in multi-dimensional networks? How do we develop efficient lightweight algorithms that handle very …
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A sparse signal reconstruction perspective for source localization with sensor arrays
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
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From Homogeneous To Heterogeneous: Statistical 3-D Signal Reconstruction Of Macromolecular Complexes
… a virus. The problem is treated as a stochastic signal in noise problem with the goal of estimating the statistics of the signal by a maximum likelihood estimator. The signal model includes both discrete and continuous heterogeneity, specifically, within each class of the discrete heterogeneity, …
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Data Analysis in Global 21-cm Experiments: Chromatic Effects and Signal Reconstruction
… effects that can distort the global 21-cm signal and introduces a flexible signal model designed to capture potential features arising from unexpected physics. The work is carried out within the framework of the REACH experiment and its data analysis pipeline. Part I includes two chapters. …
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Signal reconstruction in distributed sampling systems with application to time-interleaved A/D converters
… methods for calibration have used training signals or expensive circuitry to overcome these problems. In this work, we investigate alternative approaches for signal recovery. In particular, we develop blind calibration techniques that focus on the estimation of the associated unknown gain …
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Iterative algorithms for optimal signal reconstruction and parameter identification given noisy and incomplete data
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.
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Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques
… detecting the presence of interference in weak signal measurements. This thesis presents a new method for confirming the source of detected energy in weak signal measurements by sampling them directly, then estimating their expected effects. First, we assume that the detected signal is located …
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A system identification approach to non-invasive central cardiovascular monitoring
… analyze blind system identification and input signal reconstruction algorithms for a class of 2-channel IIR and Wiener systems. In particular, this thesis will present blind identifiability conditions for a class of 2-channel IIR and Wiener wave propagation systems and develop the associated …
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Opportunistic Sampling by Level-Crossing
… is an ideal enabler of reliable and perfect signal reconstruction, it is not always economical and efficient. LC is a threshold-based sampling that is particularly suitable for processing bursty signals, which exist in a diverse range of settings. The motivations for this work are twofold: …
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Universal Minimum-Rate Sampling and Spectrum-Blind Reconstruction for Multiband Signals
… The proposed spectrum-blind sampling and reconstruction theory addresses issues like existence and optimal design of universal sampling patterns and their conditioning, algorithms and uniqueness conditions for optimal spectral support recovery, as well as algorithms, uniqueness conditions …
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Statistical Signal Processing and Detector Optimization in Project 8
… the statistically motivated limits to CRES signal detection and parameter estimation, as well as the resultant consequences on optimal detector configuration. I implement and test an application of the Viterbi algorithm for CRES signal reconstruction, yielding the first derived limits on the …
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Sparse Representation and its Application to Multivariate Time Series Classification
In signal processing field, there are various measures that can be employed to analyse and represent the signal in order to obtain meaningful outcome. Sparse representation (SR) has continued to receive great attention as one of the well-known tools in statistical theory which among others, is used …
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Sparse Representation and its Application to Multivariate Time Series Classification
In signal processing field, there are various measures that can be employed to analyse and represent the signal in order to obtain meaningful outcome. Sparse representation (SR) has continued to receive great attention as one of the well-known tools in statistical theory which among others, is used …
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Image reconstruction through polyfiltered variation minimization
<p>There has been considerable interest in reconstruction of remotely sensed imagery from incomplete frequency measurements for some time now. Given the nature of the collection process, it may be that portions of the spectrum are either missing or corrupted such that one is left with an incomplete …
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