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University of Colorado
1.
Anitha, Anila.
Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data.
Degree: MS, Electrical, Computer & Energy Engineering, 2010, University of Colorado
URL: https://scholar.colorado.edu/ecen_gradetds/15 
► Virtual restoration of underpaintings, paintings that have been painted over, has become realizable with data from non-invasive X-ray imaging techniques. With the advent of…
(more)
▼ Virtual restoration of underpaintings, paintings that have been painted over, has become realizable with data from non-invasive X-ray imaging techniques. With the advent of X-ray synchrotron method, developed by a team in Netherlands [10], it has become possible to collect very high resolution information of the individual chemical composition of any painting in great detail. The large amount of information thus collected can be combined with a variety of image processing algorithms to effectively recover the lost paintings.
In this thesis, we discuss the results of reconstructing underpaintings using X-ray synchrotron datasets of two paintings. The first painting is a Van Gogh and the other a Runge. These paintings are suspected to have been altered by the painters or have an entire underpainting below the surface image, based on traditional X-ray studies. Though previous work on these datasets [7] have yielded visually pleasing results, these algorithms have been painting/scenario specific. This thesis discusses three new methods for the underpainting reconstruction, which focus on delivering a generic and self-sustained solution.
First, a novel approach to source separation is presented to solve the underpainting recovery problem of separating underpainting information from the combined imaging data obtained. We then develop a method for identifying and inpainting areas from which information has been attenuated by particularly thick or X-ray absorbent features of the surface painting. In the end, results from reconstructing the color of the underpainting directly from the X-ray synchrotron imaging data are also presented. This is to our knowledge the first attempt at accurate color reconstruction from such data.
Advisors/Committee Members: Shannon Hughes, Peter Mathys, Youjian Liu.
Subjects/Keywords: art restoration; attenuating hue; color estimation; computerized analysis of art; source separation; underpainting recovery; Electrical and Computer Engineering; Fine Arts
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APA (6th Edition):
Anitha, A. (2010). Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/15
Chicago Manual of Style (16th Edition):
Anitha, Anila. “Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data.” 2010. Masters Thesis, University of Colorado. Accessed March 07, 2021.
https://scholar.colorado.edu/ecen_gradetds/15.
MLA Handbook (7th Edition):
Anitha, Anila. “Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data.” 2010. Web. 07 Mar 2021.
Vancouver:
Anitha A. Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data. [Internet] [Masters thesis]. University of Colorado; 2010. [cited 2021 Mar 07].
Available from: https://scholar.colorado.edu/ecen_gradetds/15.
Council of Science Editors:
Anitha A. Underpainting Recovery using Synchrotron-based X-ray Fluorescence Imaging Data. [Masters Thesis]. University of Colorado; 2010. Available from: https://scholar.colorado.edu/ecen_gradetds/15
University of Colorado
2.
Bernabe Miguel, Laura.
FMRI decoding using sparse neuronal networks.
Degree: MS, Electrical, Computer & Energy Engineering, 2012, University of Colorado
URL: https://scholar.colorado.edu/ecen_gradetds/55
► In this thesis we propose the use of Sparse Principal Component Analysis to recover neuronal areas in Brain Imaging. We work with functional magnetic…
(more)
▼ In this thesis we propose the use of Sparse Principal Component Analysis to recover neuronal areas in Brain Imaging. We work with functional magnetic resonance imaging data focusing our attention on the dimensionality reduction stage to represent the neuronal activation within the components that contain the maximum temporal variance, tightly related with the hemodynamic response of the neurons. The motivation for the sparse representation follows the idea of the massive modularity definition of the mind where "different neural circuits are specialized for solving adaptive problems''.
The results show that the new sparse low dimensional basis (
Eigenbrains) generated through novel unsupervised algorithms, such as
Augmented Sparse Principal Component Analysis, perform competitively in terms of neuronal activity prediction. We push the limits of the brain understanding by describing a neuronal network through each
Eigenbrain component and defining a prediction neuronal model using a linear combination of them.
Advisors/Committee Members: Francois G. Meyer, Shannon Hughes, Tor Wager.
Subjects/Keywords: neuronal activation; Sparse Principal Component Analysis; Eigenbrain; Electrical and Computer Engineering; Neuroscience and Neurobiology
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APA (6th Edition):
Bernabe Miguel, L. (2012). FMRI decoding using sparse neuronal networks. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/55
Chicago Manual of Style (16th Edition):
Bernabe Miguel, Laura. “FMRI decoding using sparse neuronal networks.” 2012. Masters Thesis, University of Colorado. Accessed March 07, 2021.
https://scholar.colorado.edu/ecen_gradetds/55.
MLA Handbook (7th Edition):
Bernabe Miguel, Laura. “FMRI decoding using sparse neuronal networks.” 2012. Web. 07 Mar 2021.
Vancouver:
Bernabe Miguel L. FMRI decoding using sparse neuronal networks. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2021 Mar 07].
Available from: https://scholar.colorado.edu/ecen_gradetds/55.
Council of Science Editors:
Bernabe Miguel L. FMRI decoding using sparse neuronal networks. [Masters Thesis]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/ecen_gradetds/55
University of Colorado
3.
Bertrand, Nicholas.
Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.
Degree: MS, Applied Mathematics, 2012, University of Colorado
URL: https://scholar.colorado.edu/appm_gradetds/55
► We investigate the problem of finding a parameterization of a smooth, low-dimensional manifold based on noisy observations from a high-dimensional ambient space. The formulation…
(more)
▼ We investigate the problem of finding a parameterization of a smooth, low-dimensional manifold based on noisy observations from a high-dimensional ambient space. The formulation of such parameterizations sees applications in a variety of areas such as data denoising and image segmentation.
We introduce algorithms inspired by the existing k-svd algorithm for training dictionaries for sparse data representation, and the local best-fit at algorithm for hybrid linear modeling. The output of our algorithm is an assignment of input data points to locally linear models. To demonstrate the applicability of our algorithm, we discuss experiments performed on synthetic datasets.
Advisors/Committee Members: Francois Meyer, James Curry, Shannon Hughes.
Subjects/Keywords: k-svd algorithm; MLBF; Applied Mathematics
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APA (6th Edition):
Bertrand, N. (2012). Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/55
Chicago Manual of Style (16th Edition):
Bertrand, Nicholas. “Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.” 2012. Masters Thesis, University of Colorado. Accessed March 07, 2021.
https://scholar.colorado.edu/appm_gradetds/55.
MLA Handbook (7th Edition):
Bertrand, Nicholas. “Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations.” 2012. Web. 07 Mar 2021.
Vancouver:
Bertrand N. Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2021 Mar 07].
Available from: https://scholar.colorado.edu/appm_gradetds/55.
Council of Science Editors:
Bertrand N. Sparse Encoding of Observations from a Smooth Manifold via Locally Linear Approximations. [Masters Thesis]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/appm_gradetds/55
University of Colorado
4.
Vaze, Chinmay Shankar.
Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation.
Degree: PhD, Electrical, Computer & Energy Engineering, 2012, University of Colorado
URL: https://scholar.colorado.edu/ecen_gradetds/49
► A multiple-input multiple-output (MIMO) communication network consists of a set of multi-antenna transmitters and receivers that communicate over a common noisy medium. Each transmitter…
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▼ A multiple-input multiple-output (MIMO) communication network consists of a set of multi-antenna transmitters and receivers that communicate over a common noisy medium. Each transmitter has access to a set of messages, each of which it needs to deliver to one of the receivers. Since the transmitter(s) transmit multiple messages over such networks, each receiver encounters interference due to undesired transmissions. This interference seen by the receivers is, in fact, the main factor that limits the capacity regions of such networks. Hence, to attain high data-rates, efficient interference management is crucial, and for the same reason, these networks are also known as interference networks. A common and by far the most important MIMO interference network is a wireless cellular network.
A wireless signal after its transmission undergoes attenuation or fading. The set of all channel fading coefficients between different pairs of transmit-receive antennas is called the channel state. If this channel state is known to all terminals of the network, sophisticated interference management schemes can be implemented to achieve high data-rates. While, in practice, channel state information (CSI) can be obtained at the receivers via pilot transmissions, there is no natural way for acquiring CSI at transmitters (CSIT). Unfortunately, the lack of CSIT severely affects the capacity regions of almost all MIMO networks. To avoid this capacity loss, the next-generation cellular standards are making a provision for having feedback links from the receivers to the transmitters over which the latter can be informed about the channel state. However, due to the dynamic nature of the wireless environment, the channel state is time-varying, which makes it difficult for the transmitters to obtain feedback in a timely manner. Specifically, by the time feedback is available to the transmitters, the channel state may have already changed to a significantly different value. This motivates the study of MIMO interference networks with strictly delayed feedback, which is the main topic of this thesis.
We analyze various feedback models depending upon whether the channel state or the channel outputs (i.e., the received signals) or both or a function of the two is fed back. We further consider the worst-case scenario, where the channel state changes independently across time and feedback is available with some delay. Under such a setting, feedback is rather outdated because the information obtained via feedback is completely irrelevant as far as the current channel state is concerned. It may seem here that outdated feedback can not be of much use, which is indeed true for the simplest MIMO network with a single transmitter-receiver pair.
Surprisingly, we prove here for the MIMO broadcast channel (BC, a one-to-two, generally, one-to-many system) and for the MIMO interference channel (IC, a system with two transmit-receive pairs) that outdated feedback can significantly improve their capacity regions, relative to the…
Advisors/Committee Members: Mahesh K. Varanasi, Youjian Liu, Shannon Hughes, Peter Mathys, Brian Rider.
Subjects/Keywords: cooperation; degrees of freedom; feedback; information theory; interference alignment; wireless communication networks; Electrical and Computer Engineering
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Record Details
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APA ·
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APA (6th Edition):
Vaze, C. S. (2012). Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/49
Chicago Manual of Style (16th Edition):
Vaze, Chinmay Shankar. “Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation.” 2012. Doctoral Dissertation, University of Colorado. Accessed March 07, 2021.
https://scholar.colorado.edu/ecen_gradetds/49.
MLA Handbook (7th Edition):
Vaze, Chinmay Shankar. “Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation.” 2012. Web. 07 Mar 2021.
Vancouver:
Vaze CS. Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation. [Internet] [Doctoral dissertation]. University of Colorado; 2012. [cited 2021 Mar 07].
Available from: https://scholar.colorado.edu/ecen_gradetds/49.
Council of Science Editors:
Vaze CS. Degrees of Freedom of Single-hop and Multi-hop MIMO Interference Networks with Feedback and Cooperation. [Doctoral Dissertation]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/ecen_gradetds/49
. 
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