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You searched for +publisher:"University of Colorado" +contributor:("Shannon M. Hughes"). Showing records 1 – 5 of 5 total matches.

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University of Colorado

1. Tembarai Krishnamachari, Rajesh. A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback.

Degree: PhD, Electrical, Computer & Energy Engineering, 2011, University of Colorado

  We study the performance of multiple-antenna systems under finite-rate feedback of some function of the current channel realization from a channel-aware receiver to the… (more)

Subjects/Keywords: Finite-rate Feedback; Grassmann/Stiefel Manifold; Information Theory; MIMO; Riemannian Geometry; Wireless Communications; Electrical and Computer Engineering; Mathematics

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APA (6th Edition):

Tembarai Krishnamachari, R. (2011). A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/38

Chicago Manual of Style (16th Edition):

Tembarai Krishnamachari, Rajesh. “A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback.” 2011. Doctoral Dissertation, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/ecen_gradetds/38.

MLA Handbook (7th Edition):

Tembarai Krishnamachari, Rajesh. “A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback.” 2011. Web. 06 Mar 2021.

Vancouver:

Tembarai Krishnamachari R. A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback. [Internet] [Doctoral dissertation]. University of Colorado; 2011. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/ecen_gradetds/38.

Council of Science Editors:

Tembarai Krishnamachari R. A Geometric Framework for Analyzing the Performance of Multiple-Antenna Systems under Finite-Rate Feedback. [Doctoral Dissertation]. University of Colorado; 2011. Available from: https://scholar.colorado.edu/ecen_gradetds/38


University of Colorado

2. Ramirez Jr., Juan. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing.

Degree: MS, Electrical, Computer & Energy Engineering, 2012, University of Colorado

  Over the past several years, advances in sensor technology has lead to increases in the demand for computerized methods for analyzing seismological signals. Central… (more)

Subjects/Keywords: Machine Learning; Manifold-Valued Data; Seismology; Supervised Learning; Electrical and Computer Engineering

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APA (6th Edition):

Ramirez Jr., J. (2012). Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/44

Chicago Manual of Style (16th Edition):

Ramirez Jr., Juan. “Learning from Manifold-Valued Data: An Application to Seismic Signal Processing.” 2012. Masters Thesis, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/ecen_gradetds/44.

MLA Handbook (7th Edition):

Ramirez Jr., Juan. “Learning from Manifold-Valued Data: An Application to Seismic Signal Processing.” 2012. Web. 06 Mar 2021.

Vancouver:

Ramirez Jr. J. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. [Internet] [Masters thesis]. University of Colorado; 2012. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/ecen_gradetds/44.

Council of Science Editors:

Ramirez Jr. J. Learning from Manifold-Valued Data: An Application to Seismic Signal Processing. [Masters Thesis]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/ecen_gradetds/44


University of Colorado

3. Kambli, Ketan Pradeep. Manifold Learning for Organization of Text Documents.

Degree: MS, Electrical, Computer & Energy Engineering, 2011, University of Colorado

  The quantity of information in the world is soaring. We are living in an information age with abundant sources that generate information. While this… (more)

Subjects/Keywords: dimensionality reduction; earth mover's distance; manifold learning; text organization; textual data deluge; Artificial Intelligence and Robotics; Computer Sciences; Electrical and Computer Engineering

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APA (6th Edition):

Kambli, K. P. (2011). Manifold Learning for Organization of Text Documents. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/eeng_gradetds/18

Chicago Manual of Style (16th Edition):

Kambli, Ketan Pradeep. “Manifold Learning for Organization of Text Documents.” 2011. Masters Thesis, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/eeng_gradetds/18.

MLA Handbook (7th Edition):

Kambli, Ketan Pradeep. “Manifold Learning for Organization of Text Documents.” 2011. Web. 06 Mar 2021.

Vancouver:

Kambli KP. Manifold Learning for Organization of Text Documents. [Internet] [Masters thesis]. University of Colorado; 2011. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/eeng_gradetds/18.

Council of Science Editors:

Kambli KP. Manifold Learning for Organization of Text Documents. [Masters Thesis]. University of Colorado; 2011. Available from: https://scholar.colorado.edu/eeng_gradetds/18


University of Colorado

4. Qi, Hanchao. Low-Dimensional Signal Models in Compressive Sensing.

Degree: PhD, Electrical, Computer & Energy Engineering, 2013, University of Colorado

  In today's world, we often face an explosion of data that can be difficult to handle. Signal models help make this data tractable, and… (more)

Subjects/Keywords: algorithms; compressive sensing; nonlinear manifolds; high-dimensional signals; Electrical and Computer Engineering

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APA (6th Edition):

Qi, H. (2013). Low-Dimensional Signal Models in Compressive Sensing. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/68

Chicago Manual of Style (16th Edition):

Qi, Hanchao. “Low-Dimensional Signal Models in Compressive Sensing.” 2013. Doctoral Dissertation, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/ecen_gradetds/68.

MLA Handbook (7th Edition):

Qi, Hanchao. “Low-Dimensional Signal Models in Compressive Sensing.” 2013. Web. 06 Mar 2021.

Vancouver:

Qi H. Low-Dimensional Signal Models in Compressive Sensing. [Internet] [Doctoral dissertation]. University of Colorado; 2013. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/ecen_gradetds/68.

Council of Science Editors:

Qi H. Low-Dimensional Signal Models in Compressive Sensing. [Doctoral Dissertation]. University of Colorado; 2013. Available from: https://scholar.colorado.edu/ecen_gradetds/68


University of Colorado

5. Matviychuk, Yevgen. Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing.

Degree: PhD, Electrical, Computer & Energy Engineering, 2016, University of Colorado

  While the field of image processing has been around for some time, new applications across many diverse areas, such as medical imaging, remote sensing,… (more)

Subjects/Keywords: Image processing; Inverse problems; Kernel methods; Machine learning; Manifold models; Computer Sciences; Electrical and Computer Engineering

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

APA (6th Edition):

Matviychuk, Y. (2016). Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/ecen_gradetds/124

Chicago Manual of Style (16th Edition):

Matviychuk, Yevgen. “Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing.” 2016. Doctoral Dissertation, University of Colorado. Accessed March 06, 2021. https://scholar.colorado.edu/ecen_gradetds/124.

MLA Handbook (7th Edition):

Matviychuk, Yevgen. “Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing.” 2016. Web. 06 Mar 2021.

Vancouver:

Matviychuk Y. Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2021 Mar 06]. Available from: https://scholar.colorado.edu/ecen_gradetds/124.

Council of Science Editors:

Matviychuk Y. Learning and Mapping onto Manifolds with Applications to Patch-based Image Processing. [Doctoral Dissertation]. University of Colorado; 2016. Available from: https://scholar.colorado.edu/ecen_gradetds/124

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