subject:( sparse sampling )

Université de Grenoble

1. Coppa, Bertrand. Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation.

Degree: Docteur es, Sciences et technologie industrielles, 2013, Université de Grenoble

URL: http://www.theses.fr/2013GRENT009

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Le codage parcimonieux permet la reconstruction d'un signal à partir de quelques projections linéaires de celui-ci, sous l'hypothèse que le signal se décompose de manière… (more)

Subjects/Keywords: Codage parcimonieux; Problème inverse; Minimisation L1; Sparse sampling; Compressed sampling; Inverse problem; L1-minimization

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APA (6^th Edition):

Coppa, B. (2013). Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation. (Doctoral Dissertation). Université de Grenoble. Retrieved from http://www.theses.fr/2013GRENT009

Chicago Manual of Style (16^th Edition):

Coppa, Bertrand. “Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation.” 2013. Doctoral Dissertation, Université de Grenoble. Accessed January 22, 2021. http://www.theses.fr/2013GRENT009.

MLA Handbook (7^th Edition):

Coppa, Bertrand. “Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation.” 2013. Web. 22 Jan 2021.

Vancouver:

Coppa B. Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation. [Internet] [Doctoral dissertation]. Université de Grenoble; 2013. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2013GRENT009.

Council of Science Editors:

Coppa B. Sur quelques applications du codage parcimonieux et sa mise en oeuvre : On compressed sampling applications and its implementation. [Doctoral Dissertation]. Université de Grenoble; 2013. Available from: http://www.theses.fr/2013GRENT009

Rochester Institute of Technology

2. Liu, Dengyu. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.

Degree: MS, Chester F. Carlson Center for Imaging Science (COS), 2015, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/8906

► Cameras face a fundamental tradeoff between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video… (more)

Subjects/Keywords: Computational camera; Dictionary learning; Space-time sampling; Sparse reconstruction

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APA (6^th Edition):

Liu, D. (2015). Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8906

Chicago Manual of Style (16^th Edition):

Liu, Dengyu. “Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.” 2015. Masters Thesis, Rochester Institute of Technology. Accessed January 22, 2021. https://scholarworks.rit.edu/theses/8906.

MLA Handbook (7^th Edition):

Liu, Dengyu. “Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging.” 2015. Web. 22 Jan 2021.

Vancouver:

Liu D. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. [Internet] [Masters thesis]. Rochester Institute of Technology; 2015. [cited 2021 Jan 22]. Available from: https://scholarworks.rit.edu/theses/8906.

Council of Science Editors:

Liu D. Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging. [Masters Thesis]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8906

Penn State University

3. Wilson, Scott Alden. Compressive Microwave Radar Holography.

Degree: 2014, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/23274

► Radar holography has been established as an effective image reconstruction process by which the measured diffraction pattern across an aperture provides information of a three-dimensional… (more)

Subjects/Keywords: Compressive sensing; sparse sampling; near-field imaging; radar imaging; microwave holography

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APA (6^th Edition):

Wilson, S. A. (2014). Compressive Microwave Radar Holography. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/23274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wilson, Scott Alden. “Compressive Microwave Radar Holography.” 2014. Thesis, Penn State University. Accessed January 22, 2021. https://submit-etda.libraries.psu.edu/catalog/23274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wilson, Scott Alden. “Compressive Microwave Radar Holography.” 2014. Web. 22 Jan 2021.

Vancouver:

Wilson SA. Compressive Microwave Radar Holography. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Jan 22]. Available from: https://submit-etda.libraries.psu.edu/catalog/23274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wilson SA. Compressive Microwave Radar Holography. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/23274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

McMaster University

4. Chinta, Venkateswarao Yogesh. Sparse Sampling of Velocity MRI.

Degree: MASc, 2011, McMaster University

URL: http://hdl.handle.net/11375/10439

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Standard MRI is used to image objects at rest. In addition to standard MRI images, which measure tissues at rest, Phase Contrast MRI can… (more)

Subjects/Keywords: Optimization; Velocity MRI; Sparse sampling; k-space; Computational Engineering; Computational Engineering

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APA (6^th Edition):

Chinta, V. Y. (2011). Sparse Sampling of Velocity MRI. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/10439

Chicago Manual of Style (16^th Edition):

Chinta, Venkateswarao Yogesh. “Sparse Sampling of Velocity MRI.” 2011. Masters Thesis, McMaster University. Accessed January 22, 2021. http://hdl.handle.net/11375/10439.

MLA Handbook (7^th Edition):

Chinta, Venkateswarao Yogesh. “Sparse Sampling of Velocity MRI.” 2011. Web. 22 Jan 2021.

Vancouver:

Chinta VY. Sparse Sampling of Velocity MRI. [Internet] [Masters thesis]. McMaster University; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11375/10439.

Council of Science Editors:

Chinta VY. Sparse Sampling of Velocity MRI. [Masters Thesis]. McMaster University; 2011. Available from: http://hdl.handle.net/11375/10439

5. bi, xiaofei. Compressed Sampling for High Frequency Receivers Applications.

Degree: Mathematics and Natural Sciences, 2011, University of Gävle

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10877

► In digital signal processing field, for recovering the signal without distortion, Shannon sampling theory must be fulfilled in the traditional signal sampling. However, in… (more)

Subjects/Keywords: Compressive Sampling (CS); sparse representation; measurement matrix; signal reconstruction.

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APA (6^th Edition):

bi, x. (2011). Compressed Sampling for High Frequency Receivers Applications. (Thesis). University of Gävle. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10877

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

bi, xiaofei. “Compressed Sampling for High Frequency Receivers Applications.” 2011. Thesis, University of Gävle. Accessed January 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10877.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

bi, xiaofei. “Compressed Sampling for High Frequency Receivers Applications.” 2011. Web. 22 Jan 2021.

Vancouver:

bi x. Compressed Sampling for High Frequency Receivers Applications. [Internet] [Thesis]. University of Gävle; 2011. [cited 2021 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10877.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

bi x. Compressed Sampling for High Frequency Receivers Applications. [Thesis]. University of Gävle; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10877

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Duke University

6. Werner-Allen, Jonathan. Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins .

Degree: 2011, Duke University

URL: http://hdl.handle.net/10161/5022

► Ssu72 is a protein phosphatase that selectively targets phosphorylated serine residues at the 5th position (pS5) in the heptad repeats of the C-terminal domain… (more)

▼ Ssu72 is a protein phosphatase that selectively targets phosphorylated serine residues at the 5th position (pS5) in the heptad repeats of the C-terminal domain (CTD) of RNA polymerase II, in order to regulate the CTD-mediated coupling between eukaryotic transcription and co-transcriptional events. The biological importance of Ssu72 is underscored by (1) the requirement of its activity for viability in yeast, and (2) the numerous phenotypes - affecting all three stages of the transcription cycle - that result from its mutation in yeast. Despite limited homology to the low molecular weight (LMW) subclass of protein tyrosine phosphatases (PTPs), several lines of evidence suggest that Ssu72 represents the founding member of a new class of enzymes, including its unique substrate specificity and an in vivo connection with the activity of proline isomerase Ess1. The main focus of this thesis has been to structurally and kinetically characterize Ssu72, in order to define its relation to known enzyme families, to provide biochemical explanations for extant in vivo observations, and to allow future structure-guided investigations of its role in coordinating transcription with co-transcriptional events. To this end, we solved the structure of Ssu72 in complex with its pS5 CTD substrate, revealing an enzyme fold with unique structural features and a surprising substrate conformation with the pS5-P6 motif of the CTD adopting the cis configuration. Together with kinetic assays, the structure provides a new interpretation of the role of proline isomers in regulating the CTD phosphorylation state, with broad implications for CTD biology. The second goal of this thesis has been to develop new methods for NMR studies of large proteins, which present unique challenges to conventional methods, including fast signal decay and severe signal degeneracy. The first of these new methods, the `just-in-time' HN(CA)CO, improves the sensitivity of a common backbone assignment experiment. The next two methods, the 4-D diagonal-suppressed TROSY-NOESY-TROSY and the 4-D time-shared NOESY, were designed for use with sparse sampling techniques that allow the acquisition of high-resolution, high-dimensionality datasets. These efforts culminate with global fold calculations for large proteins, including the 23 kDa Ssu72, with accurate and unambiguous automated assignment of NOE crosspeaks. We expect that the methods presented here will be particularly useful as the NMR community continues to push toward higher molecular weight targets. Advisors/Committee Members: Zhou, Pei (advisor).

Subjects/Keywords: Biochemistry; cis proline; NMR; RNAPII CTD; SCRUB; sparse sampling; Ssu72

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APA (6^th Edition):

Werner-Allen, J. (2011). Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/5022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Werner-Allen, Jonathan. “Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins .” 2011. Thesis, Duke University. Accessed January 22, 2021. http://hdl.handle.net/10161/5022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Werner-Allen, Jonathan. “Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins .” 2011. Web. 22 Jan 2021.

Vancouver:

Werner-Allen J. Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins . [Internet] [Thesis]. Duke University; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10161/5022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Werner-Allen J. Structural and Kinetic Characterization of RNA Polymerase II C-Terminal Domain Phosphatase Ssu72 and Development of New Methods for NMR Studies of Large Proteins . [Thesis]. Duke University; 2011. Available from: http://hdl.handle.net/10161/5022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

7. Kim, Youngchun. Signal acquisition challenges in mobile systems.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/68089

► In recent decades, the advent of mobile computing has changed human lives by providing information that was not available in the past. The mobile computing… (more)

▼ In recent decades, the advent of mobile computing has changed human lives by providing information that was not available in the past. The mobile computing platform opens a new door to the connected world in which various forms of hand-held and wearable systems are ubiquitous. A single mobile device plays multiple roles and shapes human lives towards a better future. In these systems, sensor-based data acquisition plays an essential role in generating and providing useful information. The increased number of sensors is embedded in a single device in order to process various signal modalities. In practice, more than 30 data converters are required in designing a mobile system in which the data-converting blocks become among the most power-hungry components in battery-operated systems. Due to the increased variety of sensors, mobile systems are meant to face several obstacles. For example, the increased number of sensors increase system power consumption during the system operation. The increased power consumption directly affects operation time because mobile systems are powered by a limited energy source. Moreover, an increased amount of information also gives rise to bandwidth problems in communication due to the increased volume of data transmission. Also, this system design requires a larger area in a silicon die so that multiple signal paths can be placed without cross-channel interference. Therefore, the system design has presented a challenge in terms of trying to resolve the design constraints such as power consumption, bandwidth usage, storage space, and design complexity issues. To overcome these obstacles, in this dissertation, efficient data acquisition and processing methods are investigated. Specifically, this thesis considers the problems of energy-efficient sampling and binary event detection. This dissertation begins by presenting a new signal sampling scheme that enables higher precision signal conversion in compressed-sensing-based signal acquisition. The proposed scheme is based on the popular successive approximation register and employs a modified compressive sensing technique to increase the resolution of successive-approximation-register (SAR) analog-to-digital converter (ADC) architecture. Circuit-level architecture is discussed to implement the proposed scheme using the SAR ADC architecture. A non-uniform quantization scheme is proposed and it improves data quality after data acquisition. The proposed scheme is expected to be used for medium- or high- frequency data conversion. Secondly, the possibility of using fewer ADCs than channels is studied by leveraging sparse-signal representation and blind-source-separation (BSS) techniques. In particular, this dissertation examines the problem of using a single ADC or quantizer system for digitizing multi-channel inputs. Mixing and de-mixing strategies are extensively studied for sampling frequency-sparse signals and the proposed multi-channel architecture can be easily implemented using today's analog/mixed-signal circuits. The third part of… Advisors/Committee Members: Tewfik, Ahmed (advisor), Evans, Brian L (committee member), Orshansky, Michael E (committee member), Sun, Nan (committee member), Gilbert, John E (committee member).

Subjects/Keywords: Sparse signal processing; Compressed sensing; Random sampling; Data converter; Sequential detection

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APA (6^th Edition):

Kim, Y. (2018). Signal acquisition challenges in mobile systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68089

Chicago Manual of Style (16^th Edition):

Kim, Youngchun. “Signal acquisition challenges in mobile systems.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed January 22, 2021. http://hdl.handle.net/2152/68089.

MLA Handbook (7^th Edition):

Kim, Youngchun. “Signal acquisition challenges in mobile systems.” 2018. Web. 22 Jan 2021.

Vancouver:

Kim Y. Signal acquisition challenges in mobile systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2152/68089.

Council of Science Editors:

Kim Y. Signal acquisition challenges in mobile systems. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68089

University of Florida

8. Xu, Xie. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

URL: https://ufdc.ufl.edu/UFE0046527

► Sampling and reconstruction of volumetric data are ubiquitous throughout biomedical imaging, scientific simulation, and visualization applications. In this dissertation, we focus on the reconstruction of… (more)

Subjects/Keywords: Approximation; Boxes; Conceptual lattices; Datasets; Face centered cubic lattices; Interpolation; Mathematical lattices; Sampling rates; Signals; Supernova remnants; box-splines – compressed-sensing – reconstruction – sampling – sparse-approximation – sparse-representation – volumetric-data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xu, X. (2014). Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0046527

Chicago Manual of Style (16^th Edition):

Xu, Xie. “Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.” 2014. Doctoral Dissertation, University of Florida. Accessed January 22, 2021. https://ufdc.ufl.edu/UFE0046527.

MLA Handbook (7^th Edition):

Xu, Xie. “Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.” 2014. Web. 22 Jan 2021.

Vancouver:

Xu X. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2021 Jan 22]. Available from: https://ufdc.ufl.edu/UFE0046527.

Council of Science Editors:

Xu X. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. [Doctoral Dissertation]. University of Florida; 2014. Available from: https://ufdc.ufl.edu/UFE0046527

McMaster University

9. Pournaghi, Reza. Coded Acquisition of High Speed Videos with Multiple Cameras.

Degree: PhD, 2015, McMaster University

URL: http://hdl.handle.net/11375/16887

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High frame rate video (HFV) is an important investigational tool in sciences, engineering and military. In ultrahigh speed imaging, the obtainable temporal, spatial and spectral… (more)

Subjects/Keywords: Compressive Sensing; High Speed Video; Coded Acquisition; Random Sampling; Sparse Representation; Digital Cameras; Rolling Shutter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pournaghi, R. (2015). Coded Acquisition of High Speed Videos with Multiple Cameras. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/16887

Chicago Manual of Style (16^th Edition):

Pournaghi, Reza. “Coded Acquisition of High Speed Videos with Multiple Cameras.” 2015. Doctoral Dissertation, McMaster University. Accessed January 22, 2021. http://hdl.handle.net/11375/16887.

MLA Handbook (7^th Edition):

Pournaghi, Reza. “Coded Acquisition of High Speed Videos with Multiple Cameras.” 2015. Web. 22 Jan 2021.

Vancouver:

Pournaghi R. Coded Acquisition of High Speed Videos with Multiple Cameras. [Internet] [Doctoral dissertation]. McMaster University; 2015. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11375/16887.

Council of Science Editors:

Pournaghi R. Coded Acquisition of High Speed Videos with Multiple Cameras. [Doctoral Dissertation]. McMaster University; 2015. Available from: http://hdl.handle.net/11375/16887

Delft University of Technology

10. Ortiz Jimenez, Guillermo (author). Multidomain Graph Signal Processing: Learning and Sampling.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:6fd6b441-1694-473e-a317-b60d168f19a7

► In this era of data deluge, we are overwhelmed with massive volumes of extremely complex datasets. Data generated today is complex because it lacks a… (more)

Subjects/Keywords: deep learning; graph signal processing; sparse sampling; product graphs; submodular optimization; tensors

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APA (6^th Edition):

Ortiz Jimenez, G. (. (2018). Multidomain Graph Signal Processing: Learning and Sampling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6fd6b441-1694-473e-a317-b60d168f19a7

Chicago Manual of Style (16^th Edition):

Ortiz Jimenez, Guillermo (author). “Multidomain Graph Signal Processing: Learning and Sampling.” 2018. Masters Thesis, Delft University of Technology. Accessed January 22, 2021. http://resolver.tudelft.nl/uuid:6fd6b441-1694-473e-a317-b60d168f19a7.

MLA Handbook (7^th Edition):

Ortiz Jimenez, Guillermo (author). “Multidomain Graph Signal Processing: Learning and Sampling.” 2018. Web. 22 Jan 2021.

Vancouver:

Ortiz Jimenez G(. Multidomain Graph Signal Processing: Learning and Sampling. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 22]. Available from: http://resolver.tudelft.nl/uuid:6fd6b441-1694-473e-a317-b60d168f19a7.

Council of Science Editors:

Ortiz Jimenez G(. Multidomain Graph Signal Processing: Learning and Sampling. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6fd6b441-1694-473e-a317-b60d168f19a7

University of Illinois – Urbana-Champaign

11. Lam, Fan. A subspace approach to high-resolution magnetic resonance spectroscopic imaging.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/78611

► With its unique capability to obtain spatially resolved biochemical profiles from the human body noninvasively, magnetic resonance spectroscopic imaging (MRSI) has been recognized as a… (more)

▼ With its unique capability to obtain spatially resolved biochemical profiles from the human body noninvasively, magnetic resonance spectroscopic imaging (MRSI) has been recognized as a powerful tool for in vivo metabolic studies. However, research and clinical applications of in vivo MRSI have been progressing more slowly than expected. The main reasons for this situation are the problems of long data acquisition time, poor spatial resolution and low signal-to-noise ratio (SNR) for this imaging modality. In the last four decades, significant efforts have been made to improve MRSI, resulting in a large number of fast pulse sequences and advanced image reconstruction methods. However, the existing techniques have yet to offer the levels of improvement in imaging time, spatial resolution and SNR necessary to significantly impact in vivo applications of MRSI. This thesis work develops a new subspace imaging approach to address these technical challenges to enable fast, high-resolution MRSI with high SNR. The proposed approach, coined SPICE (Spectroscopic Imaging by Exploiting Spatiospectral Correlation), is characterized by using a subspace model for integrative data acquisition, processing and image reconstruction. More specifically, SPICE represents the spectroscopic signals in MRSI using the partial separability (PS) model. The PS model implies that the high-dimensional spectroscopic signals reside in a low-dimensional subspace, which enables the design of special sparse sampling strategies for accelerated spatiospectral encoding and special image reconstruction strategies for determining the subspace and reconstructing the underlying spatiospectral function of interest from the sparse data. Using the SPICE framework, new data acquisition and image reconstruction methods are developed to enable high-resolution 1H-MRSI of the brain. We have evaluated SPICE using theoretical analysis, numerical simulations, phantom and in vivo experimental studies. Results obtained from these experiments demonstrate the unprecedented capability of SPICE in achieving accelerated MRSI with simultaneously very high resolution and SNR. We expect SPICE to provide a powerful tool for in vivo metabolic studies with many exciting applications. Furthermore, the SPICE framework also presents new opportunities for future developments in subspace-driven signal generation, signal encoding, data processing and image reconstruction methods to advance the research and clinical applications of high-resolution in vivo MRSI. Advisors/Committee Members: Liang, Zhi-Pei (advisor), Liang, Zhi-Pei (Committee Chair), Boppart, Stephen A. (committee member), Do, Minh N. (committee member), Sutton, Brad P. (committee member).

Subjects/Keywords: Magnetic resonance spectroscopic imaging; Partial separability; Subspace modeling; Low-rank model; Sparse sampling

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APA (6^th Edition):

Lam, F. (2015). A subspace approach to high-resolution magnetic resonance spectroscopic imaging. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78611

Chicago Manual of Style (16^th Edition):

Lam, Fan. “A subspace approach to high-resolution magnetic resonance spectroscopic imaging.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 22, 2021. http://hdl.handle.net/2142/78611.

MLA Handbook (7^th Edition):

Lam, Fan. “A subspace approach to high-resolution magnetic resonance spectroscopic imaging.” 2015. Web. 22 Jan 2021.

Vancouver:

Lam F. A subspace approach to high-resolution magnetic resonance spectroscopic imaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2142/78611.

Council of Science Editors:

Lam F. A subspace approach to high-resolution magnetic resonance spectroscopic imaging. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78611

12. Ajamian, Tzila. Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry.

Degree: Docteur es, Signal, Image, Vision, 2019, Ecole centrale de Nantes

URL: http://www.theses.fr/2019ECDN0040

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La réflectométrie, une technique utilisée en diagnostic filaire, permet la détection et la localisation de défauts des câbles. Alors que, les Convertisseurs Analogique-Numérique sont indispensables… (more)

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La réflectométrie, une technique utilisée en diagnostic filaire, permet la détection et la localisation de défauts des câbles. Alors que, les Convertisseurs Analogique-Numérique sont indispensables dans les architectures de la réflectométrie, la nécessité du respect de la condition de Shannon et le besoin d’effectuer des traitements en temps réel limitent les fréquences maximales des signaux injectés, ainsi la précision de localisation. Pour une première fois dans le système de diagnostic filaire, cette étude s’inscrit l’échantillonnage comprimé du signal réfléchi afin d’améliorer ces performances. Pour cela en utilisant les signaux multi-porteuses de la réflectométrie, nous proposons les dictionnaires adaptées induisant la parcimonie. Ainsi, grâce à l’acquisition comprimée et son encodeur analogique nous arrivons à reconstruire le réflectogramme avec une fréquence d’échantillonnage moins élevée et également d’identifier les défauts à proximité.

Reflectometry, a wire diagnosis technique, allows to detect and localize electrical defects in networks efficiently. In order to achieve a very precise online diagnosis without altering the functioning of a network, reflectometry methods should use specific wideband Multi- Carrier signals, whose generation requires dedicated numerical tools. The underlying architecture of any reflectometry system for the injection of the signal requires appropriate Digital-to-Analog Converters. Yet, measuring the reflected signal should be performed using Analog-to-Digital Converters capable of reaching high sampling frequencies together with sufficient resolution. Such converter are either extremely expensive or beyond nowadays state of the art. In that respect, this study addresses a new architectural approach for designing such reflectometry systems based on Compressive Sampling method bypassing the Nyquist rate. Thus, thanks to the compressed acquisition and its analog encoder, we succeed in reconstructing the reflectogram with a lower sampling frequency and also in identifying nearby defects.

Advisors/Committee Members: Moussaoui, Saïd (thesis director).

Subjects/Keywords: Diagnostic filaire; Acquisition comprimée; Matrices parcimonieuses; Wire diagnosis; Compressive Sampling; Sparse matrices

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ajamian, T. (2019). Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry. (Doctoral Dissertation). Ecole centrale de Nantes. Retrieved from http://www.theses.fr/2019ECDN0040

Chicago Manual of Style (16^th Edition):

Ajamian, Tzila. “Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry.” 2019. Doctoral Dissertation, Ecole centrale de Nantes. Accessed January 22, 2021. http://www.theses.fr/2019ECDN0040.

MLA Handbook (7^th Edition):

Ajamian, Tzila. “Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry.” 2019. Web. 22 Jan 2021.

Vancouver:

Ajamian T. Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry. [Internet] [Doctoral dissertation]. Ecole centrale de Nantes; 2019. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2019ECDN0040.

Council of Science Editors:

Ajamian T. Exploration de l’Acquisition Comprimée appliquée à la Réflectométrie : Exploration of Compressive Sampling for Wire Diagnosis Systems Based on Reflectometry. [Doctoral Dissertation]. Ecole centrale de Nantes; 2019. Available from: http://www.theses.fr/2019ECDN0040

University of Maryland

13. Reddy, Nagilla Dikpal. SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS.

Degree: Electrical Engineering, 2011, University of Maryland

URL: http://hdl.handle.net/1903/11984

► Sparse representation, acquisition and reconstruction of signals guided by theory of Compressive Sensing (CS) has become an active research research topic over the last few… (more)

▼ Sparse representation, acquisition and reconstruction of signals guided by theory of Compressive Sensing (CS) has become an active research research topic over the last few years. Sparse representations effectively capture the idea of parsimony enabling novel acquisition schemes including sub-Nyquist sampling. Ideas from CS have had significant impact on well established fields such as signal acquisition, machine learning and statistics and have also inspired new areas of research such as low rank matrix completion. In this dissertation we apply CS ideas to low-level computer vision problems. The contribution of this dissertation is to show that CS theory is an important addition to the existing computational toolbox in computer vision and pattern recognition, particularly in data representation and processing. Additionally, in each of the problems we show how sparse representation helps in improved modeling of the underlying data leading to novel applications and better understanding of existing problems. In our work, the impact of CS is most felt in the acquisition of videos with novel camera designs. We build prototype cameras with slow sensors capable of capturing at an order of magnitude higher temporal resolution. First, we propose sub-Nyquist acquisition of periodic events and then generalize the idea to capturing regular events. Both the cameras operate by first acquiring the video at a slower rate and then computationally recovering the desired higher temporal resolution frames. In our camera, we sense the light with a slow sensor after modulating it with a fluttering shutter and then reconstruct the high speed video by enforcing its sparsity. Our cameras offer a significant advantage in light efficiency and cost by obviating the need to sense, transfer and store data at a higher frame rate. Next, we explore the applicability of compressive cameras for computer vision applications in bandwidth constrained scenarios. We design a compressive camera capable of capturing video using fewer measurements and also separate the foreground from the background. We model surveillance type videos with two processes, a slower background and a faster but spatially sparse foreground such that we can recover both of them separately and accurately. By formulating the problem in a distributed CS framework we achieve state-of-the-art video reconstruction and background subtraction. Subsequently we show that if the camera geometry is provided in a multi-camera setting, the background subtracted CS images can be used for localizing the object and tracking it by formulating its occupancy in a grid as a sparse reconstruction problem. Finally, we apply CS to robust estimation of gradients obtained through photometric stereo and other gradient-based techniques. Since gradient fields are often not integrable, the errors in them need to be estimated and removed. By assuming the errors, particularly the outliers, as sparse in number we accurately estimate and remove them. Using conditions on sparse recovery in CS we… Advisors/Committee Members: Chellappa, Ramalingam (advisor).

Subjects/Keywords: Electrical engineering; Background Subtraction; Compressive Sensing; High Speed Cameras; Sampling; Sparse Representation; Video Reconstrcution

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APA (6^th Edition):

Reddy, N. D. (2011). SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/11984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Reddy, Nagilla Dikpal. “SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS.” 2011. Thesis, University of Maryland. Accessed January 22, 2021. http://hdl.handle.net/1903/11984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Reddy, Nagilla Dikpal. “SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS.” 2011. Web. 22 Jan 2021.

Vancouver:

Reddy ND. SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS. [Internet] [Thesis]. University of Maryland; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/1903/11984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Reddy ND. SPARSE ACQUISITION AND RECONSTRUCTION FOR SOME COMPUTER VISION PROBLEMS. [Thesis]. University of Maryland; 2011. Available from: http://hdl.handle.net/1903/11984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oklahoma State University

14. Mamun, S. M. Abdullah Al. Data-driven sparse estimation of nonlinear fluid flows.

Degree: Mechanical and Aerospace Engineering, 2020, Oklahoma State University

URL: http://hdl.handle.net/11244/325505

► Estimation of full state fluid flow from limited observations is central for many practical applications in physics and engineering science. Fluid flows are manifestations of… (more)

▼ Estimation of full state fluid flow from limited observations is central for many practical applications in physics and engineering science. Fluid flows are manifestations of nonlinear multiscale partial differential equations (PDE) dynamical systems with inherent scale separation. Although the Navier-stokes equations can successfully model fluid flows, there are only limited cases of flows for which it is feasible to acquire exact analytical or numerical solutions. For many real-life fluid flow problems, extremely complex boundary conditions limit accurate modeling and simulations. In such situations, data from experiments or field measurements represents the absolute truth and very few in numbers thus limiting the potential of in-depth analysis. Consequently different data-driven techniques have been critical in active research in recent days. The ability to reconstruct important fluid flows from limited data is critical in applications extending from active flow control to as diverse as cardiac blood flow modeling and climate science. In this work, we investigated both (1) linear estimation method by leveraging data specific proper orthogonal decomposition (POD) technique, and (2) nonlinear estimation method on the ground of machine learning using deep neural network (DNN) algorithm. Given that sparse reconstruction is an inherently ill-posed problem, to generate well-posedness our linear sparse estimation (LSE) approach encodes the physics into the underlying sparse basis obtained from POD. On the other hand, for nonlinear sparse estimation (NLSE) we tried to find an optimal neural network model working over different ranges of hyperparameters through a systematic implementation. Our NLSE approach learns an end-to-end mapping between the sensor measurements and the high dimensional fluid flow field. We demonstrate the performance of both approaches for low and high dimensional examples in fluid mechanics. We also assess the interplay between sensor quantity and their placements introducing some greedy-smart sensor placement methods such as Discrete Empirical Interpolation Method (DEIM), QR-pivoting, etc. The LSE method needs the knowledge of low dimensional sparse basis to be known a priori, whereas the NLSE requires no prior knowledge to be available. The estimation algorithm of NLSE is purely data-driven with a comparable level of performance. To make our neural network optimization more robust we implemented Latin Hypercube Sampling (LHS) algorithm to ensure that each hyperparameter sample has all portions of its distribution in the considered range of analysis instead of sampling them randomly. Throughout the thesis, we demonstrate a comparison of each approach taken into consideration to conclude on their performances. A special focus has been placed to learn high dimensional multiscale system such as the near-wall turbulent channel flow using the NLSE method to evaluate the advantages and limitations of the nonlinear approach in comparison to the traditional linear estimation. Advisors/Committee Members: Jayaraman, Balaji (advisor), Bai, He (committee member), Kamalapurkar, Rushikesh (committee member).

Subjects/Keywords: deep neural network; latin hypercube sampling; linear estimation; nonlinear estimation; pod; sparse estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mamun, S. M. A. A. (2020). Data-driven sparse estimation of nonlinear fluid flows. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/325505

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mamun, S M Abdullah Al. “Data-driven sparse estimation of nonlinear fluid flows.” 2020. Thesis, Oklahoma State University. Accessed January 22, 2021. http://hdl.handle.net/11244/325505.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mamun, S M Abdullah Al. “Data-driven sparse estimation of nonlinear fluid flows.” 2020. Web. 22 Jan 2021.

Vancouver:

Mamun SMAA. Data-driven sparse estimation of nonlinear fluid flows. [Internet] [Thesis]. Oklahoma State University; 2020. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11244/325505.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mamun SMAA. Data-driven sparse estimation of nonlinear fluid flows. [Thesis]. Oklahoma State University; 2020. Available from: http://hdl.handle.net/11244/325505

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

15. Peng, Ji. Uncertainty Quantification via Sparse Polynomial Chaos Expansion.

Degree: PhD, Mechanical Engineering, 2015, University of Colorado

URL: https://scholar.colorado.edu/mcen_gradetds/112

► Uncertainty quantification (UQ) is an emerging research area that aims to develop methods for accurate predictions of quantities of interest (QoI's) from complex engineering… (more)

▼ Uncertainty quantification (UQ) is an emerging research area that aims to develop methods for accurate predictions of quantities of interest (QoI's) from complex engineering systems, as well as quantitative validation of the associated mathematical models, with presence of random inputs. To perform a comprehensive UQ analysis, polynomial chaos expansion (PCE) is now a commonly used approach in which the QoI is represented in a series of multi-variate polynomials that are orthogonal with respect to the measure of the inputs. Traditional methods for PCE, such as Monte Carlo, stochastic collocation, least-squares regression, are known to suffer from either slow convergence rate or rapid growth of the computational cost (as the number of random inputs increases) in identifying the PCE coefficients. When the PCE coefficients are sparse, i.e., many of them are negligible, it has been shown that compressive sampling is an effective technique to identify the coefficients with smaller number of system simulations. In the context of compressive sampling, this thesis presents new approaches which improve the accuracy of identifying PCE coefficients, and therefore the PCE itself. In detail, a weighted L_1-minimization including a priori information about the PCE coefficients, a bi-fidelity L_1-minimization, a bi-fidelity orthogonal matching pursuit (OMP), and an L_1-minimization including the derivatives of QoI with respect to the random inputs are proposed. Both theoretical analyses and numerical experiments are presented to demonstrate that all the proposed approaches reduce the cost of computing a PCE. % We use various numerical experiments to show that all the proposed approaches improve the accuracy in PCE approximation. For a QoI whose PCE with respect to the measure of the underlying random inputs is not sparse, a polynomial basis design is proposed where, in addition to the coefficients, the basis functions are also learned from the simulation data. The approach has been empirically shown to find the optimal basis which makes the PCE converge more rapidly, and enhances the accuracy of the PCE approximation. Advisors/Committee Members: Alireza Doostan, Daven Henze, Brandon Jones, Kurt Maute, Oleg Vasilyev.

Subjects/Keywords: Basis design; Compressive sampling; Polynomial chaos expansion; Sparse approximation; Uncertainty quantification; Applied Mathematics; Mechanical Engineering; Statistics and Probability

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APA (6^th Edition):

Peng, J. (2015). Uncertainty Quantification via Sparse Polynomial Chaos Expansion. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/mcen_gradetds/112

Chicago Manual of Style (16^th Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Doctoral Dissertation, University of Colorado. Accessed January 22, 2021. https://scholar.colorado.edu/mcen_gradetds/112.

MLA Handbook (7^th Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Web. 22 Jan 2021.

Vancouver:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2021 Jan 22]. Available from: https://scholar.colorado.edu/mcen_gradetds/112.

Council of Science Editors:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/mcen_gradetds/112

16. Khaled, Yassine. Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements.

Degree: Docteur es, Génie électrique et électronique - Cergy, 2014, Cergy-Pontoise; Université Abou Bekr Belkaid (Tlemcen, Algérie)

URL: http://www.theses.fr/2014CERG0706

►

Les travaux de cette thèse portent sur l'analyse de stabilité des systèmes dynamiques impulsionnels et la synthèse d'observateurs pour les systèmes dynamiques continus avec mesures… (more)

▼

Les travaux de cette thèse portent sur l'analyse de stabilité des systèmes dynamiques impulsionnels et la synthèse d'observateurs pour les systèmes dynamiques continus avec mesures discrètes.On considère que les mesures sont prises d'une façon aléatoire pour éviter la perte d'observabilité et on montre que la synthèse d'un observateur impulsionnel couplé avec un observateur classique continu via un gain est une solution pertinente pour reconstruire l'état continu du système et commander et stabiliser ces systèmes par un retour d'état basé sur ces observateurs. De plus, ce nouveau schéma d'observateur (impulsionnel couplé avec observateur classique continu) permet de reconstruire le vecteur de sortie même si les mesures prises ne vérifient pas les conditions du Shannon-Nyquist. Ensuite, un chapitre est dédié à la détection de mode actif et à la reconstruction de son état associé, ceci pour une classe de systèmes linéaires hybrides sous mesures clairsemées. La solution que nous avons apportée à ce problème est d'une part l'analyse d'observabilité des systèmes sous échantillonnage aléatoire et d'autre part la synthèse d'observateurs impulsionnels. Ici, la première approche est basée sur le concept d'échantillonnage compressif bien connu en théorie du traitementdu signal. Une synthèse d'observateurs impulsionnels a été présentée pourquelques cas particuliers.D'autre part, une nouvelle méthode de synthèse d'observateurs spécifique aux systèmes non linéaire continus avec mesures discrètes est également proposée. Cette méthode utilise la condition de Lipchitz pour la transformation d'un système non linéaire à un système linéaire à paramètres variants basée sur l'utilisation du théorème des accroissements finis afin de synthétiser des observateurs impulsionnels.Enfin, les observateurs proposés sont testés sur une application à la synchronisation de systèmes chaotiques dédiés à la communication sécurisée.

This thesis deals with the stability analysis of impulsive systems and the design of impulsive observers for systems under sparse measurements.The measures are sparse but random in order to avoid the loss of observability.Moreover, it is highlighted that the synthesis of an impulsive observer coupled with a classical continuous observer via an observer gain is an appropriate solution to reconstruct the continuous system state and to stabilize this system by state feedback based on these observers. In addition, this new scheme (impulsive observer coupled with classical observer) can reconstruct the output vector, even if the available measurement do not verify the Nyquist-Shannon conditions. Another part is dedicated to the detection of the active mode and to the estimation of the associated continuous state for a class of linear hybrid systems under sparse measurements. The solution we found to this problem is firstly the observability of systems under random sampling and secondly the design of an impulsive observer. Here, the first approach is based on the concept of compressive sensing theory well known in signal…

Advisors/Committee Members: Barbot, Jean-Pierre (thesis director), Benmerzouk, Djamila (thesis director).

Subjects/Keywords: Observateurs impulsionnels; Mesures clairsemées; Systèmes sous échantillonnage; Acquisition compressive; Systèmes dynamiques impulsionnels; Chaos; Impulsive observer; Sparse measurement; System under sampling; System under sampling; Impulsive dynamical systems; Chaos

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Khaled, Y. (2014). Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements. (Doctoral Dissertation). Cergy-Pontoise; Université Abou Bekr Belkaid (Tlemcen, Algérie). Retrieved from http://www.theses.fr/2014CERG0706

Chicago Manual of Style (16^th Edition):

Khaled, Yassine. “Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements.” 2014. Doctoral Dissertation, Cergy-Pontoise; Université Abou Bekr Belkaid (Tlemcen, Algérie). Accessed January 22, 2021. http://www.theses.fr/2014CERG0706.

MLA Handbook (7^th Edition):

Khaled, Yassine. “Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements.” 2014. Web. 22 Jan 2021.

Vancouver:

Khaled Y. Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements. [Internet] [Doctoral dissertation]. Cergy-Pontoise; Université Abou Bekr Belkaid (Tlemcen, Algérie); 2014. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2014CERG0706.

Council of Science Editors:

Khaled Y. Contribution à la commande et l'observation des systèmes dynamiques continus sous mesures clairsemées : Contribution to the observation and control of continuous systems under sparse measurements. [Doctoral Dissertation]. Cergy-Pontoise; Université Abou Bekr Belkaid (Tlemcen, Algérie); 2014. Available from: http://www.theses.fr/2014CERG0706

17. Wajer, F.T.A.W. Non-Cartesian MRI scan time reduction through sparse sampling.

Degree: 2001, Ponsen & Looijen

URL: http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec

► Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI… (more)

▼ Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI signal can not be taken at will, but lie along k-space trajectories determined by the magnetic field gradients. MRI measurements are usually Cartesian, where the trajectories are parallel and equidistant and sampling along the trajectories is also equidistant. This allows fast reconstruction using the inverse Fast Fourier transform (IFFT). However, this thesis focuses on non-Cartesian MRI. Typical trajectories in this case are radial and spiral, but there exists a multitude of other possibilities. Chapter 2 a basic introduction on MRI relevant for this thesis. Image reconstruction in the non-Cartesian case can not be accomplished by IFFT. In certain cases, however, dedicated reconstruction algorithms are available. For example, for radial scanning there exists the Filtered Back Projection algorithm. Another possibility, aiming at maintaining the IFFT for the transformation to the image domain, is the gridding algorithm. This algorithm, which is capable of image reconstruction from a k-space sampled along arbitrary trajectories, is given extensive attention in this thesis. A major complication in non-Cartesian sampling is the compensation for the non-uniformity of the sampling density. In case the trajectories are rather regular, an analytical expression for the density may be derived or Voronoi triangulation can be applied. Another more recent approach is the Pipe-Menon algorithm. However, all these approaches fail in more irregular cases. The above-mentioned image reconstruction algorithms are described in chapter 3. They are based on the inverse Fourier transform and therefore require k-space sampling to obey the Nyquist criterion. These algorithms cannot cope with k-space undersampling. However, in certain cases there may not be enough time to fully sample k-space; or scan-time is deliberately reduced by omitting trajectories, the total scan-time being proportional to the number of trajectories. Under these circumstances we still want to be able to reconstruct an image. Chapter 4 presents two algorithms that are able to cope with undersampled k-space data, and still reconstruct artefact free images. The first, based on work by G.J. Marseille, who worked on Cartesian scans, aims on estimating values for the missing data. In this approach the missing data are estimated iteratively by shuttling back and forth between image and k-space, while smoothing the image with an edge-preserving filter and resetting the measured data to their original values. This algorithm has a major drawback in that it requires density compensation. This means that this algorithm is only applicable if the trajectories are regular. Moreover, the algorithm requires user input on which k-space points are missing. In certain cases, especially when sampling is irregular, this may be impossible or not desirable. Note that this difficulty is absent in undersampled… Advisors/Committee Members: Mehlkopf, A.F..

Subjects/Keywords: magnetic resonance; k-space; gridding; bayesian reconstruction; sparse sampling; irregular sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wajer, F. T. A. W. (2001). Non-Cartesian MRI scan time reduction through sparse sampling. (Doctoral Dissertation). Ponsen & Looijen. Retrieved from http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec

Chicago Manual of Style (16^th Edition):

Wajer, F T A W. “Non-Cartesian MRI scan time reduction through sparse sampling.” 2001. Doctoral Dissertation, Ponsen & Looijen. Accessed January 22, 2021. http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec.

MLA Handbook (7^th Edition):

Wajer, F T A W. “Non-Cartesian MRI scan time reduction through sparse sampling.” 2001. Web. 22 Jan 2021.

Vancouver:

Wajer FTAW. Non-Cartesian MRI scan time reduction through sparse sampling. [Internet] [Doctoral dissertation]. Ponsen & Looijen; 2001. [cited 2021 Jan 22]. Available from: http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec.

Council of Science Editors:

Wajer FTAW. Non-Cartesian MRI scan time reduction through sparse sampling. [Doctoral Dissertation]. Ponsen & Looijen; 2001. Available from: http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; urn:NBN:nl:ui:24-uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec ; http://resolver.tudelft.nl/uuid:60b5f3ca-4700-4822-8f58-92c6987cd5ec

Delft University of Technology

18. Chepuri, S.P. Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications.

Degree: 2016, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9

► In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors.… (more)

▼ In today's society, we are flooded with massive volumes of data in the order of a billion gigabytes on a daily basis from pervasive sensors. It is becoming increasingly challenging to locally store and transport the acquired data to a central location for signal/data processing (i.e., for inference). To alleviate these problems, it is evident that there is an urgent need to significantly reduce the sensing cost (i.e., the number of expensive sensors) as well as the related memory and bandwidth requirements by developing unconventional sensing mechanisms to extract as much information as possible yet collecting fewer data. The first aim of this thesis is to develop theory and algorithms for data reduction. We develop a data reduction tool called sparse sensing, which consists of a deterministic and structured sensing function (guided by a sparse vector) that is optimally designed to achieve a desired inference performance with the reduced number of data samples. The first part of this thesis is dedicated to the development of sparse sensing mechanisms and convex programs to efficiently design sparse sensing functions. We design sparse sensing functions under the assumption that the data is not yet available and the model information is perfectly known. Sparse sensing offers a number of advantages over compressed sensing (a state-of-the-art data reduction method for sparse signal recovery). One of the major differences is that in sparse sensing the underlying signals need not be sparse. This allows us to consider general signal processing tasks (not just sparse signal recovery) under the proposed sparse sensing framework. Specifically, we focus on fundamental statistical inference tasks, like estimation, filtering, and detection. In essence, we present topics that transform classical (e.g., random or uniform) sensing methods to low-cost data acquisition mechanisms tailored for specific inference tasks. The developed framework can be applied to sensor selection, sensor placement, or sensor scheduling, for example. In the second part of this thesis, we focus on some applications related to distributed sampling using sensor networks. Sensor networks can be used as a spatial sampling device, that is, to faithfully represent distributed signals (e.g., a spatially varying phenomenon such as a temperature field). On top of that, the distributed signals can exist in space and time, where the temporal sampling is achieved using analog-to-digital converters, for example. Each sensor has an independent sample clock, and its stability essentially determines the alignment of the temporal sampling grid across the sensors. Due to imperfections in the oscillator, the sample clocks drift from each other, resulting in the misalignment of the temporal sampling grids. To overcome this issue, we devise a mechanism to distribute the sample clock wirelessly. Specifically, we perform wireless clock synchronization based on the time-of-flight measurements of broadcast messages. In addition, clock synchronization also plays a central role in… Advisors/Committee Members: Leus, G.J.T., van der Veen, A.J..

Subjects/Keywords: sparse sensing; sensor networks; sampling; estimation; detection; filtering; localization; clock synchronization

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APA (6^th Edition):

Chepuri, S. P. (2016). Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications. (Doctoral Dissertation). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9

Chicago Manual of Style (16^th Edition):

Chepuri, S P. “Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications.” 2016. Doctoral Dissertation, Delft University of Technology. Accessed January 22, 2021. http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9.

MLA Handbook (7^th Edition):

Chepuri, S P. “Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications.” 2016. Web. 22 Jan 2021.

Vancouver:

Chepuri SP. Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications. [Internet] [Doctoral dissertation]. Delft University of Technology; 2016. [cited 2021 Jan 22]. Available from: http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9.

Council of Science Editors:

Chepuri SP. Sparse Sensing for Statistical Inference: Theory, Algorithms, and Applications. [Doctoral Dissertation]. Delft University of Technology; 2016. Available from: http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; urn:NBN:nl:ui:24-uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9 ; http://resolver.tudelft.nl/uuid:7776e6e3-5ea8-4aaa-892f-c26011bf96b9

19. Schwartz, Tal Shimon. Data-guided statistical sparse measurements modeling for compressive sensing.

Degree: 2013, University of Waterloo

URL: http://hdl.handle.net/10012/7418

► Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of… (more)

▼ Digital image acquisition can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). In this work, a CS-based data-guided statistical sparse measurements method is presented, implemented and evaluated. This method significantly improves image reconstruction from sparse measurements. In the data-guided statistical sparse measurements approach, signal sampling distribution is optimized for improving image reconstruction performance. The sampling distribution is based on underlying data rather than the commonly used uniform random distribution. The optimal sampling pattern probability is accomplished by learning process through two methods - direct and indirect. The direct method is implemented for learning a nonparametric probability density function directly from the dataset. The indirect learning method is implemented for cases where a mapping between extracted features and the probability density function is required. The unified model is implemented for different representation domains, including frequency domain and spatial domain. Experiments were performed for multiple applications such as optical coherence tomography, bridge structure vibration, robotic vision, 3D laser range measurements and fluorescence microscopy. Results show that the data-guided statistical sparse measurements method significantly outperforms the conventional CS reconstruction performance. Data-guided statistical sparse measurements method achieves much higher reconstruction signal-to-noise ratio for the same compression rate as the conventional CS. Alternatively, Data-guided statistical sparse measurements method achieves similar reconstruction signal-to-noise ratio as the conventional CS with significantly fewer samples.

Subjects/Keywords: compressive sensing; compressed sampling; digital image acquisition; sparse measurements

…Sparse Measurements Model and Framework 17 3.1 Model for Constructing a Sampling Pattern… …x7B;φk }M k=1 ϕk : k th column in sampling basis Φ ϕT,k : k th sparse column in… …for a greatly reduced sampling rate through the use of sparse measurements (samples… …to the CS sparse measurement model (2.22, 2.23) using sampling basis {φk… …near the origin and sparse sampling in the periphery. The coherence between the sparsity and…

10 more images…

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APA (6^th Edition):

Schwartz, T. S. (2013). Data-guided statistical sparse measurements modeling for compressive sensing. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7418

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Schwartz, Tal Shimon. “Data-guided statistical sparse measurements modeling for compressive sensing.” 2013. Thesis, University of Waterloo. Accessed January 22, 2021. http://hdl.handle.net/10012/7418.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Schwartz, Tal Shimon. “Data-guided statistical sparse measurements modeling for compressive sensing.” 2013. Web. 22 Jan 2021.

Vancouver:

Schwartz TS. Data-guided statistical sparse measurements modeling for compressive sensing. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/10012/7418.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Schwartz TS. Data-guided statistical sparse measurements modeling for compressive sensing. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7418

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Cambridge

20. Higson, Edward John. Bayesian methods and machine learning in astrophysics.

Degree: PhD, 2019, University of Cambridge

URL: https://doi.org/10.17863/CAM.36974 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929

► This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters… (more)

▼ This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sampling calculation. Chapter 4 introduces diagnostic tests for detecting when software has not performed the nested sampling algorithm accurately, for example due to missing a mode in a multimodal posterior. The uncertainty estimates and diagnostics in Chapters 3 and 4 are implemented in the tt{nestcheck} software package, and both chapters describe an astronomical application of the techniques introduced. Chapter 5 describes dynamic nested sampling: a generalisation of the nested sampling algorithm which can produce large improvements in computational efficiency compared to standard nested sampling. We have implemented dynamic nested sampling in the tt{dyPolyChord} and tt{perfectns} software packages. Chapter 6 presents a principled Bayesian framework for signal reconstruction, in which the signal is modelled by basis functions whose number (and form, if required) is determined by the data themselves. This approach is based on a Bayesian interpretation of conventional sparse reconstruction and regularisation techniques, in which sparsity is imposed through priors via Bayesian model selection. We demonstrate our method for noisy 1- and 2-dimensional signals, including examples of processing astronomical images. The numerical implementation uses dynamic nested sampling, and uncertainties are calculated using the methods introduced in Chapters 3 and 4. Chapter 7 applies our Bayesian sparse reconstruction framework to artificial neural networks, where it allows the optimum network architecture to be determined by treating the number of nodes and hidden layers as parameters. We conclude by suggesting possible areas of future research in Chapter 8.

Subjects/Keywords: Machine Learning; Bayesian Inference; Nested sampling; Cosmology; Black Holes; Gravitational Waves; Neural Networks; Regression; Astrophysics; Sparsity; Parameter Estimation; Bayesian Evidence; Bayesian; Statistics; Bayesian Sparse Reconstruction; Computational Methods; Error Analysis; Dynamic Nested Sampling; nestcheck; perfectns; dyPolyChord; dynesty; Image Processing; Sparse Reconstruction; Planck; diagnostic tests; PolyChord; MultiNest; Hubble Space Telescope; Fitting; Nonparametric statistics

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APA (6^th Edition):

Higson, E. J. (2019). Bayesian methods and machine learning in astrophysics. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.36974 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929

Chicago Manual of Style (16^th Edition):

Higson, Edward John. “Bayesian methods and machine learning in astrophysics.” 2019. Doctoral Dissertation, University of Cambridge. Accessed January 22, 2021. https://doi.org/10.17863/CAM.36974 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929.

MLA Handbook (7^th Edition):

Higson, Edward John. “Bayesian methods and machine learning in astrophysics.” 2019. Web. 22 Jan 2021.

Vancouver:

Higson EJ. Bayesian methods and machine learning in astrophysics. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Jan 22]. Available from: https://doi.org/10.17863/CAM.36974 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929.

Council of Science Editors:

Higson EJ. Bayesian methods and machine learning in astrophysics. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://doi.org/10.17863/CAM.36974 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767929

Indian Institute of Science

21. Satyanarayana, J V. Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling.

Degree: PhD, Faculty of Engineering, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/3518

► Data acquisition from multiple analog channels is an important function in many embedded devices used in avionics, medical electronics, robotics and space applications. It is… (more)

▼ Data acquisition from multiple analog channels is an important function in many embedded devices used in avionics, medical electronics, robotics and space applications. It is desirable to engineer these systems to reduce their size, power consumption, heat dissipation and cost. The goal of this research is to explore designs that exploit a priori knowledge of the input signals in order to achieve these objectives. Sparsity is a commonly observed property in signals that facilitates sub-Nyquist sampling and reconstruction through compressed sensing, thereby reducing the number of A to D conversions. New architectures are proposed for the real-time, compressed acquisition of streaming signals. A. It is demonstrated that by sampling a collection of signals in a multiplexed fashion, it is possible to efficiently utilize all the available sampling cycles of the analogue-to-digital converters (ADCs), facilitating the acquisition of multiple signals using fewer ADCs. The proposed method is modified to accommodate more general signals, for which spectral leakage, due to the occurrence of non-integral number of cycles in the reconstruction window, violates the sparsity assumption. When the objective is to only detect the constituent frequencies in the signals, as against exact reconstruction, it can be achieved surprisingly well even in the presence of severe noise (SNR ~ 5 dB) and considerable undersampling. This has been applied to the detection of the carrier frequency in a noisy FM signal. Information redundancy due to inter-signal correlation gives scope for compressed acquisition of a set of signals that may not be individually sparse. A scheme has been proposed in which the correlation structure in a set of signals is progressively learnt within a small fraction of the duration of acquisition, because of which only a few ADCs are adequate for capturing the signals. Signals from the different channels of EEG possess significant correlation. Employing signals taken from the Physionet database, the correlation structure of nearby EEG electrodes was captured. Subsequent to this training phase, the learnt KLT matrix has been used to reconstruct signals of all the electrodes with reasonably good accuracy from the recordings of a subset of electrodes. Average error is below 10% between the original and reconstructed signals with respect to the power in delta, theta and alpha bands: and below 15% in the beta band. It was also possible to reconstruct all the channels in the 10-10 system of electrode placement with an average error less than 8% using recordings on the sparser 10-20 system. In another design, a set of signals are collectively sampled on a finer sampling grid using ADCs driven by phase-shifted clocks. Thus, each signal is sampled at an effective rate that is a multiple of the ADC sampling rate. So, it is possible to have a less steep transition between the pass band and the stop band, thereby reducing the order of the anti-aliasing filter from 30 to 8. This scheme has been applied to the acquisition of voltages… Advisors/Committee Members: Ramakrishnan, A G (advisor).

Subjects/Keywords: Analog-To-Digital Converters; Smart-Sampling Data Acquisition; Embedded Data Acquisition Systems; Sparse Signals; Embedded Systems; Compressed Sensing; Signal Processing; Multiplexed Compressed Sensing; Multiplexed Signal Acquisition; Data Acquisition; MOSAICS; Multiple Sparse Signals; Compact Embedded Designs; Correlated Signals; Multiplexed Optimal Signal Acquisition Involving Compressed Sensing; Electrical Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Satyanarayana, J. V. (2018). Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/3518

Chicago Manual of Style (16^th Edition):

Satyanarayana, J V. “Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 22, 2021. http://etd.iisc.ac.in/handle/2005/3518.

MLA Handbook (7^th Edition):

Satyanarayana, J V. “Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling.” 2018. Web. 22 Jan 2021.

Vancouver:

Satyanarayana JV. Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 22]. Available from: http://etd.iisc.ac.in/handle/2005/3518.

Council of Science Editors:

Satyanarayana JV. Efficient Design of Embedded Data Acquisition Systems Based on Smart Sampling. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/3518

Georgia Tech

22. Mena Arias, Dario Alberto. Characterization of matrix valued BMO by commutators and sparse domination of operators.

Degree: PhD, Mathematics, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/59857

► In the first part of this thesis, we characterize the space of matrix-valued, two-parameters BMO functions by using commutators with the Hilbert transform. The second… (more)

Subjects/Keywords: BMO; Commutators; Sparse operators; Sparse; Sparse forms

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APA (6^th Edition):

Mena Arias, D. A. (2018). Characterization of matrix valued BMO by commutators and sparse domination of operators. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59857

Chicago Manual of Style (16^th Edition):

Mena Arias, Dario Alberto. “Characterization of matrix valued BMO by commutators and sparse domination of operators.” 2018. Doctoral Dissertation, Georgia Tech. Accessed January 22, 2021. http://hdl.handle.net/1853/59857.

MLA Handbook (7^th Edition):

Mena Arias, Dario Alberto. “Characterization of matrix valued BMO by commutators and sparse domination of operators.” 2018. Web. 22 Jan 2021.

Vancouver:

Mena Arias DA. Characterization of matrix valued BMO by commutators and sparse domination of operators. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/1853/59857.

Council of Science Editors:

Mena Arias DA. Characterization of matrix valued BMO by commutators and sparse domination of operators. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59857

Delft University of Technology

23. Desmedt, S.G.L. (author). Dimension-adaptive sparse grid for industrial applications using Sobol variances.

Degree: 2015, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:09f5dfed-5185-401c-9bbd-065130fe2bda

►

The area of interest for this study is the field of uncertainty quantification in computational fluid dynamics. The goal is to contribute to a new… (more)

Subjects/Keywords: Sparse grid

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APA (6^th Edition):

Desmedt, S. G. L. (. (2015). Dimension-adaptive sparse grid for industrial applications using Sobol variances. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:09f5dfed-5185-401c-9bbd-065130fe2bda

Chicago Manual of Style (16^th Edition):

Desmedt, S G L (author). “Dimension-adaptive sparse grid for industrial applications using Sobol variances.” 2015. Masters Thesis, Delft University of Technology. Accessed January 22, 2021. http://resolver.tudelft.nl/uuid:09f5dfed-5185-401c-9bbd-065130fe2bda.

MLA Handbook (7^th Edition):

Desmedt, S G L (author). “Dimension-adaptive sparse grid for industrial applications using Sobol variances.” 2015. Web. 22 Jan 2021.

Vancouver:

Desmedt SGL(. Dimension-adaptive sparse grid for industrial applications using Sobol variances. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 22]. Available from: http://resolver.tudelft.nl/uuid:09f5dfed-5185-401c-9bbd-065130fe2bda.

Council of Science Editors:

Desmedt SGL(. Dimension-adaptive sparse grid for industrial applications using Sobol variances. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:09f5dfed-5185-401c-9bbd-065130fe2bda

University of Illinois – Urbana-Champaign

24. Ravishankar, Saiprasad. Adaptive sparse representations and their applications.

Degree: PhD, Electrical & Computer Engr, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/88322

► The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal processing, image processing, and… (more)

▼ The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal processing, image processing, and medical imaging. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, the data-driven learning of synthesis sparsifying dictionaries has become popular especially in applications such as denoising, inpainting, and compressed sensing. While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, the idea of learning sparsifying transforms has received no attention. In the first part of this thesis, we study the sparsifying transform model and its relationship to prior linear sparse models. Then, we propose novel problem formulations for learning square sparsifying transforms from data. The proposed algorithms for transform learning alternate between a sparse coding step and a transform update step, and are highly efficient. Specifically, as opposed to sparse coding in the synthesis or noisy analysis models which is NP-hard, the sparse coding step in transform learning can be performed exactly and cheaply by zeroing out all but a certain number of nonzero transform coefficients of largest magnitude. The transform update step is performed using iterative conjugate gradients. The proposed algorithms give rise to well-conditioned square sparsifying transforms in practice. We show the superiority of our approach over analytical sparsifying transforms such as the DCT for signal and image representation. We also show promising performance in signal denoising using the learned sparsifying transforms. The proposed approach is much faster than previous approaches involving learned synthesis, or analysis dictionaries. Next, we explore a specific structure for learned sparsifying transforms, that enables efficient implementations. Following up on the idea of learning square sparsifying transforms, we propose novel problem formulations for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learned, stored, and implemented efficiently. We show the superior promise of our learned doubly sparse transforms as compared to analytical sparsifying transforms such as the DCT or Wavelets for image representation. Adapted doubly sparse transforms also generalize better than the ‘unstructured’ (or non-sparse) transform. We show promising performance and speedups in image denoising using the learned doubly sparse transforms compared to approaches involving learned synthesis dictionaries such as the K-SVD algorithm. In the third part of this thesis, we further develop the alternating algorithms for learning unstructured (non-sparse) well-conditioned, or orthonormal square sparsifying transforms. While, in the first part of the thesis, we provided an iterative… Advisors/Committee Members: Bresler, Yoram (advisor), Bresler, Yoram (Committee Chair), Do, Minh N. (committee member), Sutton, Brad (committee member), Milenkovic, Olgica (committee member), Fessler, Jeffrey A (committee member).

Subjects/Keywords: Inverse problems; Computer vision; Classification; Structured overcomplete transform learning; Union of transforms; Overcomplete transform learning; Structured transforms; Convex formulation; Real-time applications; Big data; Online learning; Adaptive Sampling; Image reconstruction; Block Coordinate descent; Blind compressed sensing; Convergence guarantees; Efficient updates; Closed-form solutions; Machine learning; Nonconvex optimization; Alternating minimization; Doubly sparse transform learning; Square transform learning; Adaptive sparse models; Denoising; dictionary learning; Magnetic resonance imaging; Compressed sensing; Sparse representations; Sparsifying transform learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ravishankar, S. (2014). Adaptive sparse representations and their applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88322

Chicago Manual of Style (16^th Edition):

Ravishankar, Saiprasad. “Adaptive sparse representations and their applications.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 22, 2021. http://hdl.handle.net/2142/88322.

MLA Handbook (7^th Edition):

Ravishankar, Saiprasad. “Adaptive sparse representations and their applications.” 2014. Web. 22 Jan 2021.

Vancouver:

Ravishankar S. Adaptive sparse representations and their applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2142/88322.

Council of Science Editors:

Ravishankar S. Adaptive sparse representations and their applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/88322

25. Li, Yi. Sublinear Time Algorithms for the Sparse Recovery Problem.

Degree: PhD, Computer Science & Engineering, 2013, University of Michigan

URL: http://hdl.handle.net/2027.42/102438

► In the sparse recovery problem, we have a signal x in R^N that is sparse; i.e., it consists of k significant entries (heavy hitters) while… (more)

Subjects/Keywords: Sublinear-time Algorithms; Sparse Recovery Problem; Off-the-Grid Fourier Sampling; Computer Science; Engineering

…LIST OF ALGORITHMS Algorithm 1.1 General framework of the sparse recovery problem… …ABSTRACT Sublinear Time Algorithms for the Sparse Recovery Problem by Yi Li Chair: Martin… …Strauss In the sparse recovery problem, we have a signal x ∈ RN that is sparse; i.e., it… …expressed in the same mathematical formulation, called the sparse recovery problem. This problem… …compressive sensing group at Rice University. In the sparse recovery problem, we have a signal x of…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, Y. (2013). Sublinear Time Algorithms for the Sparse Recovery Problem. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/102438

Chicago Manual of Style (16^th Edition):

Li, Yi. “Sublinear Time Algorithms for the Sparse Recovery Problem.” 2013. Doctoral Dissertation, University of Michigan. Accessed January 22, 2021. http://hdl.handle.net/2027.42/102438.

MLA Handbook (7^th Edition):

Li, Yi. “Sublinear Time Algorithms for the Sparse Recovery Problem.” 2013. Web. 22 Jan 2021.

Vancouver:

Li Y. Sublinear Time Algorithms for the Sparse Recovery Problem. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2027.42/102438.

Council of Science Editors:

Li Y. Sublinear Time Algorithms for the Sparse Recovery Problem. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/102438

26. Merlet, Sylvain. Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI.

Degree: Docteur es, Automatique, traitement du signal et des images, 2013, Nice

URL: http://www.theses.fr/2013NICE4061

►

Cette thèse est consacrée à l'élaboration de nouvelles méthodes d'acquisition et de traitement de données en IRM de diffusion (IRMd) afin de caractériser la diffusion… (more)

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Cette thèse est consacrée à l'élaboration de nouvelles méthodes d'acquisition et de traitement de données en IRM de diffusion (IRMd) afin de caractériser la diffusion des molécules d'eau dans les fibres de matière blanche à l'échelle d'un voxel. Plus particulièrement, nous travaillons sur un moyen de reconstruction précis de l'Ensemble Average Propagator (EAP), qui représente la fonction de probabilité de diffusion des molécules d'eau. Plusieurs modèles de diffusion tels que le tenseur de diffusion ou la fonction de distribution d'orientation sont très utilisés dans la communauté de l'IRMd afin de quantifier la diffusion des molécules d'eau dans le cerveau. Ces modèles sont des représentations partielles de l'EAP et ont été développés en raison du petit nombre de mesures nécessaires à leurs estimations. Cependant, il est important de pouvoir reconstruire précisément l'EAP afin d'acquérir une meilleure compréhension des mécanismes du cerveau et d'améliorer le diagnostique des troubles neurologiques. Une estimation correcte de l'EAP nécessite l'acquisition de nombreuses images de diffusion sensibilisées à des orientations différentes dans le q-space. Ceci rend son estimation trop longue pour être utilisée dans la plupart des scanners cliniques. Dans cette thèse, nous utilisons des techniques de reconstruction parcimonieuses et en particulier la technique connue sous le nom de Compressive Sensing (CS) afin d’accélérer le calcul de l'EAP. Les multiples aspects de la théorie du CS et de son application à l'IRMd sont présentés dans cette thèse.

This thesis is dedicated to the development of new acquisition and processing methods in diffusion MRI (dMRI) to characterize the diffusion of water molecules in white matter fiber bundles at the scale of a voxel. In particular, we focus our attention on the accurate recovery of the Ensemble Average Propagator (EAP), which represents the full 3D displacement of water molecule diffusion. Diffusion models such that the Diffusion Tensor or the Orientation Distribution Function (ODF) are largely used in the dMRI community in order to quantify water molecule diffusion. These models are partial EAP representations and have been developed due to the small number of measurement required for their estimations. It is thus of utmost importance to be able to accurately compute the EAP and order to acquire a better understanding of the brain mechanisms and to improve the diagnosis of neurological disorders. Estimating the full 3D EAP requires the acquisition of many diffusion images sensitized todifferent orientations in the q-space, which render the estimation of the EAP impossible in most of the clinical dMRI scanner. A surge of interest has been seen in order to decrease this time for acquisition. Some works focus on the development of new and efficient acquisition sequences. In this thesis, we use sparse coding techniques, and in particular Compressive Sensing (CS) to accelerate the computation of the EAP. Multiple aspects of the CS theory and its application to dMRI are presented in this…

Advisors/Committee Members: Deriche, Rachid (thesis director).

Subjects/Keywords: IRM de diffusion; Acquisition compressée; Reconstruction parcimonieuse; Apprentissage de dictionnaire; Propagateur de diffusion; Diffusion MRI; Compressive sensing; Sparse coding; Dictionary learning; Q-space sampling; Q-ball imaging; Ensemble average propagator; Diffusion spectrum imaging

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Merlet, S. (2013). Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI. (Doctoral Dissertation). Nice. Retrieved from http://www.theses.fr/2013NICE4061

Chicago Manual of Style (16^th Edition):

Merlet, Sylvain. “Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI.” 2013. Doctoral Dissertation, Nice. Accessed January 22, 2021. http://www.theses.fr/2013NICE4061.

MLA Handbook (7^th Edition):

Merlet, Sylvain. “Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI.” 2013. Web. 22 Jan 2021.

Vancouver:

Merlet S. Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI. [Internet] [Doctoral dissertation]. Nice; 2013. [cited 2021 Jan 22]. Available from: http://www.theses.fr/2013NICE4061.

Council of Science Editors:

Merlet S. Acquisition compressée en IRM de diffusion : Compressive sensing in diffusion MRI. [Doctoral Dissertation]. Nice; 2013. Available from: http://www.theses.fr/2013NICE4061

Brno University of Technology

27. Berky, Martin. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/180568

► This diploma thesis deals with separation of moving objects from static unchanging background in video sequence. Thesis contains description of common method of separation and… (more)

Subjects/Keywords: Řídká reprezentace signálů; komprimované snímání; medianova metoda; Matlab; video sekvence; separace pozadí; pohyblivé objekty; zpracování obrazu; Sparse signal reprezentation; compressive sampling; median method; Matlab; video sequence; background substraction; moving objects; image processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Berky, M. (2019). Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180568

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Berky, Martin. “Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/180568.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Berky, Martin. “Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.” 2019. Web. 22 Jan 2021.

Vancouver:

Berky M. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/180568.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Berky M. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180568

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Missouri University of Science and Technology

28. Zhang, Ling. Sparse emission source microscopy for rapid emission source imaging.

Degree: M.S. in Electrical Engineering, Electrical Engineering, Missouri University of Science and Technology

URL: https://scholarsmine.mst.edu/masters_theses/7729

► "In Paper I, Sparse Emission Source Microscopy (ESM) methodology will be introduced and discussed for the localization of major EMI radiation sources in complex… (more)

Subjects/Keywords: EMI Coupling Paths; EMI Mitigation; Emission Source Microscopy; Optical Transceiver Modules; Source Localization; Sparse Sampling; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhang, L. (n.d.). Sparse emission source microscopy for rapid emission source imaging. (Masters Thesis). Missouri University of Science and Technology. Retrieved from https://scholarsmine.mst.edu/masters_theses/7729

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Chicago Manual of Style (16^th Edition):

Zhang, Ling. “Sparse emission source microscopy for rapid emission source imaging.” Masters Thesis, Missouri University of Science and Technology. Accessed January 22, 2021. https://scholarsmine.mst.edu/masters_theses/7729.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

MLA Handbook (7^th Edition):

Zhang, Ling. “Sparse emission source microscopy for rapid emission source imaging.” Web. 22 Jan 2021.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Zhang L. Sparse emission source microscopy for rapid emission source imaging. [Internet] [Masters thesis]. Missouri University of Science and Technology; [cited 2021 Jan 22]. Available from: https://scholarsmine.mst.edu/masters_theses/7729.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Council of Science Editors:

Zhang L. Sparse emission source microscopy for rapid emission source imaging. [Masters Thesis]. Missouri University of Science and Technology; Available from: https://scholarsmine.mst.edu/masters_theses/7729

Note: this citation may be lacking information needed for this citation format:
No year of publication.

29. Haldar, Justin P. Constrained imaging: denoising and sparse sampling.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/24286

► Magnetic resonance imaging (MRI) is a powerful tool for studying the anatomy, physiology, and metabolism of biological systems. Despite the fact that MRI was introduced… (more)

▼ Magnetic resonance imaging (MRI) is a powerful tool for studying the anatomy, physiology, and metabolism of biological systems. Despite the fact that MRI was introduced decades ago and has already revolutionized medical imaging, current applications are still far from utilizing the full potential of the MR signal. Traditional MRI data acquisition and image reconstruction methods are based on simple Fourier inversion, leading to undesirable trade-offs between image resolution, signal-to-noise ratio (SNR), and data acquisition time. Classical approaches to addressing these trade-offs have relied on improved imaging hardware and more efficient pulse sequences. In contrast, our work addresses the limitations of MR using relatively less-explored signal processing approaches, which have recently become practical because of increasing computational capabilities. This dissertation concerns the use of constrained imaging models to guide the design of both data acquisition and image reconstruction, leading to improved imaging performance in the context of both noise-limited and resolution-limited scenarios. To address noise limitations for high-resolution imaging, we introduce a quasi-Bayesian edge-preserving smoothness prior for modeling correlated image sequences. The prior models the correlated edge structures that are observed in the image sequence, and is used within a penalized maximum likelihood framework to reduce image noise while preserving high-resolution anatomical structure. In contrast to many constrained imaging methods, we demonstrate that the proposed method is relatively simple to analyze and is robust to model inaccuracy when reconstruction parameters are chosen appropriately. Resolution and SNR analysis shows that the proposed formulations lead to substantial improvements in SNR with only a moderate decrease in spatial resolution. An examination of resolution and SNR trade-offs is presented, which serves as a guide for the optimal design of data acquisition and image reconstruction procedures in this context. To address limited spatial resolution in high-SNR scenarios, we design specialized data acquisition and image reconstruction procedures to enable image reconstruction from sparsely-sampled data. Specifically, we leverage prior information that the image has sparse or low-rank structure to significantly reduce sampling requirements in two different contexts. In the first context, we assume that the image is sparse in a known transform domain, and develop a novel non-Fourier data acquisition scheme to enable high-quality reconstruction from undersampled data. The second context is specific to spatiotemporal imaging, and it is assumed that the temporal evolution of the spatiotemporal image is highly correlated at different spatial positions. This correlation leads to the formulation of a novel low-rank matrix recovery problem, which we demonstrate can be solved efficiently and effectively using special algorithms. Applications of the proposed techniques are illustrated with simulated… Advisors/Committee Members: Liang, Zhi-Pei (advisor), Liang, Zhi-Pei (Committee Chair), Bresler, Yoram (committee member), Carney, Paul S. (committee member), Sutton, Bradley P. (committee member).

Subjects/Keywords: Magnetic Resonance Imaging; Constrained Reconstruction; Denoising; Sparse Sampling; Regularization; Compressed Sensing; Sparsity; Low Rank

…5.2.1 Rank-Constrained Matrix Recovery with the PS Model . 5.2.2 Sampling Considerations and… …5.5.1 Specialized Sampling Versus Random Sampling . . . . 5.5.2 Selection of L… …acquisition in conventional MRI is typically modeled as sampling in the spatial Fourier domain (… …x7D;M m=1 is the set of k-space sampling locations, {dm }M m=1 is the set of… …Image resolution. MR image resolution is a function of the Fourier-domain sampling pattern…

10 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Haldar, J. P. (2011). Constrained imaging: denoising and sparse sampling. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24286

Chicago Manual of Style (16^th Edition):

Haldar, Justin P. “Constrained imaging: denoising and sparse sampling.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed January 22, 2021. http://hdl.handle.net/2142/24286.

MLA Handbook (7^th Edition):

Haldar, Justin P. “Constrained imaging: denoising and sparse sampling.” 2011. Web. 22 Jan 2021.

Vancouver:

Haldar JP. Constrained imaging: denoising and sparse sampling. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/2142/24286.

Council of Science Editors:

Haldar JP. Constrained imaging: denoising and sparse sampling. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24286

Brno University of Technology

30. Berky, Martin. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/177522

► This diploma thesis deals with separation of moving objects from static unchanging background in video sequence. In this thesis are described common method of separation… (more)

Subjects/Keywords: Řídká reprezentace signálů; komprimované snímání; medianova metoda; Matlab; video sekvence; separace pozadí; pohyblivé objekty; zpracování obrazu; Sparse signal reprezentation; compressive sampling; median method; Matlab; video sequence; background substraction; moving objects; image processing

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Berky, M. (2019). Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/177522

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Berky, Martin. “Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.” 2019. Thesis, Brno University of Technology. Accessed January 22, 2021. http://hdl.handle.net/11012/177522.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Berky, Martin. “Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence.” 2019. Web. 22 Jan 2021.

Vancouver:

Berky M. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 22]. Available from: http://hdl.handle.net/11012/177522.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Berky M. Vytvoření bezchybné fotografie z narušené videosekvence: Clean photo out of corrupted videosequence. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/177522

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations