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You searched for subject:(Compressive sensing). Showing records 1 – 30 of 236 total matches.

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Texas A&M University

1. Apaydin, Meltem. Phase Retrieval of Sparse Signals from Magnitude Information.

Degree: 2014, Texas A&M University

 The ability to recover the phase information of a signal of interest from a measurement process plays an important role in many practical applications. When… (more)

Subjects/Keywords: phase retrieval; compressive sensing; sparsity

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APA (6th Edition):

Apaydin, M. (2014). Phase Retrieval of Sparse Signals from Magnitude Information. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153388

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

Chicago Manual of Style (16th Edition):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Thesis, Texas A&M University. Accessed May 26, 2019. http://hdl.handle.net/1969.1/153388.

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

MLA Handbook (7th Edition):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Web. 26 May 2019.

Vancouver:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 May 26]. Available from: http://hdl.handle.net/1969.1/153388.

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

Council of Science Editors:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153388

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


University of Toronto

2. Lee, Jefferson. Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods.

Degree: 2011, University of Toronto

The field of compressive sensing deals with the recovery of a sparse signal from a small set of measurements or linear projections of the signal.… (more)

Subjects/Keywords: Compressive Sensing; Belief Propagation; 0544

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

APA (6th Edition):

Lee, J. (2011). Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/31300

Chicago Manual of Style (16th Edition):

Lee, Jefferson. “Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods.” 2011. Masters Thesis, University of Toronto. Accessed May 26, 2019. http://hdl.handle.net/1807/31300.

MLA Handbook (7th Edition):

Lee, Jefferson. “Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods.” 2011. Web. 26 May 2019.

Vancouver:

Lee J. Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods. [Internet] [Masters thesis]. University of Toronto; 2011. [cited 2019 May 26]. Available from: http://hdl.handle.net/1807/31300.

Council of Science Editors:

Lee J. Stochastic Modelling of a Collection of Correlated Sparse Signals and its Recovery via Belief Propagation Methods. [Masters Thesis]. University of Toronto; 2011. Available from: http://hdl.handle.net/1807/31300

3. Elkhalil, Khalil. Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks.

Degree: 2015, King Abdullah University of Science and Technology

 User/relay selection is a simple technique that achieves spatial diversity in multiuser networks. However, for user/relay selection algorithms to make a selection decision, channel state… (more)

Subjects/Keywords: Feedback; multiuser networks; Compressive Sensing

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APA (6th Edition):

Elkhalil, K. (2015). Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/552522

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

Chicago Manual of Style (16th Edition):

Elkhalil, Khalil. “Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks.” 2015. Thesis, King Abdullah University of Science and Technology. Accessed May 26, 2019. http://hdl.handle.net/10754/552522.

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

MLA Handbook (7th Edition):

Elkhalil, Khalil. “Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks.” 2015. Web. 26 May 2019.

Vancouver:

Elkhalil K. Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/10754/552522.

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

Council of Science Editors:

Elkhalil K. Compressive Sensing for Feedback Reduction in Wireless Multiuser Networks. [Thesis]. King Abdullah University of Science and Technology; 2015. Available from: http://hdl.handle.net/10754/552522

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


King Abdullah University of Science and Technology

4. Swanson, Robin J. Sparse Representations of Hyperspectral Images.

Degree: 2015, King Abdullah University of Science and Technology

 Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas… (more)

Subjects/Keywords: hyperspectral; sparse; compressive; sensing

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APA (6th Edition):

Swanson, R. J. (2015). Sparse Representations of Hyperspectral Images. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/583304

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

Chicago Manual of Style (16th Edition):

Swanson, Robin J. “Sparse Representations of Hyperspectral Images.” 2015. Thesis, King Abdullah University of Science and Technology. Accessed May 26, 2019. http://hdl.handle.net/10754/583304.

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

MLA Handbook (7th Edition):

Swanson, Robin J. “Sparse Representations of Hyperspectral Images.” 2015. Web. 26 May 2019.

Vancouver:

Swanson RJ. Sparse Representations of Hyperspectral Images. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/10754/583304.

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

Council of Science Editors:

Swanson RJ. Sparse Representations of Hyperspectral Images. [Thesis]. King Abdullah University of Science and Technology; 2015. Available from: http://hdl.handle.net/10754/583304

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


University of Texas – Austin

5. -0782-4953. Robust network compressive sensing.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Networks are constantly generating an enormous amount of rich and diverse information. Such information creates exciting opportunities for network analytics and provides deep insights into… (more)

Subjects/Keywords: Compressive sensing; Big data

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APA (6th Edition):

-0782-4953. (2015). Robust network compressive sensing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/38181

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

Chicago Manual of Style (16th Edition):

-0782-4953. “Robust network compressive sensing.” 2015. Thesis, University of Texas – Austin. Accessed May 26, 2019. http://hdl.handle.net/2152/38181.

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

MLA Handbook (7th Edition):

-0782-4953. “Robust network compressive sensing.” 2015. Web. 26 May 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0782-4953. Robust network compressive sensing. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/2152/38181.

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

Council of Science Editors:

-0782-4953. Robust network compressive sensing. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/38181

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


Iowa State University

6. Rasberry, Darrin Thomas. On minimal support solutions of underdetermined systems of linear equations.

Degree: 2017, Iowa State University

 This paper explores the nature and application of minimal-support solutions of underdetermined systems of linear equations. First, methods for directly solving the problem are evaluated… (more)

Subjects/Keywords: Compressive; Minimal; Sensing; Support; Mathematics

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APA (6th Edition):

Rasberry, D. T. (2017). On minimal support solutions of underdetermined systems of linear equations. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/15405

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

Chicago Manual of Style (16th Edition):

Rasberry, Darrin Thomas. “On minimal support solutions of underdetermined systems of linear equations.” 2017. Thesis, Iowa State University. Accessed May 26, 2019. https://lib.dr.iastate.edu/etd/15405.

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

MLA Handbook (7th Edition):

Rasberry, Darrin Thomas. “On minimal support solutions of underdetermined systems of linear equations.” 2017. Web. 26 May 2019.

Vancouver:

Rasberry DT. On minimal support solutions of underdetermined systems of linear equations. [Internet] [Thesis]. Iowa State University; 2017. [cited 2019 May 26]. Available from: https://lib.dr.iastate.edu/etd/15405.

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

Council of Science Editors:

Rasberry DT. On minimal support solutions of underdetermined systems of linear equations. [Thesis]. Iowa State University; 2017. Available from: https://lib.dr.iastate.edu/etd/15405

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


University of New South Wales

7. Shen, Yiran. ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS.

Degree: Computer Science & Engineering, 2014, University of New South Wales

Compressive sensing is a mathematical theory concerning exact/approximate recovery ofsparse/compressible vectors using the minimum number of measurements called projections.Its theory covers topics such as l1… (more)

Subjects/Keywords: Compressive sensing; Embedded systems

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APA (6th Edition):

Shen, Y. (2014). ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53549 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12244/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Shen, Yiran. “ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS.” 2014. Doctoral Dissertation, University of New South Wales. Accessed May 26, 2019. http://handle.unsw.edu.au/1959.4/53549 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12244/SOURCE02?view=true.

MLA Handbook (7th Edition):

Shen, Yiran. “ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS.” 2014. Web. 26 May 2019.

Vancouver:

Shen Y. ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2019 May 26]. Available from: http://handle.unsw.edu.au/1959.4/53549 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12244/SOURCE02?view=true.

Council of Science Editors:

Shen Y. ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53549 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12244/SOURCE02?view=true


University of Arizona

8. Treeaporn, Vicha. Applications of Non-Traditional Measurements for Computational Imaging .

Degree: 2017, University of Arizona

 Imaging systems play an important role in many diverse applications. Requirements for these applications, however, can lead to complex or sub-optimal designs. Traditionally, imaging systems… (more)

Subjects/Keywords: Compressive Sensing; Computational Imaging

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APA (6th Edition):

Treeaporn, V. (2017). Applications of Non-Traditional Measurements for Computational Imaging . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/624569

Chicago Manual of Style (16th Edition):

Treeaporn, Vicha. “Applications of Non-Traditional Measurements for Computational Imaging .” 2017. Doctoral Dissertation, University of Arizona. Accessed May 26, 2019. http://hdl.handle.net/10150/624569.

MLA Handbook (7th Edition):

Treeaporn, Vicha. “Applications of Non-Traditional Measurements for Computational Imaging .” 2017. Web. 26 May 2019.

Vancouver:

Treeaporn V. Applications of Non-Traditional Measurements for Computational Imaging . [Internet] [Doctoral dissertation]. University of Arizona; 2017. [cited 2019 May 26]. Available from: http://hdl.handle.net/10150/624569.

Council of Science Editors:

Treeaporn V. Applications of Non-Traditional Measurements for Computational Imaging . [Doctoral Dissertation]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/624569


Rice University

9. El Smaili, Sami. Efficient Architectures for Wideband Receivers.

Degree: MS, Engineering, 2012, Rice University

 Reducing power consumption of radio receivers is becoming more critical with the advancement of biomedical portable and implantable devices due to the stringent power requirements… (more)

Subjects/Keywords: Wideband receiver; Compressive sensing

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APA (6th Edition):

El Smaili, S. (2012). Efficient Architectures for Wideband Receivers. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/77324

Chicago Manual of Style (16th Edition):

El Smaili, Sami. “Efficient Architectures for Wideband Receivers.” 2012. Masters Thesis, Rice University. Accessed May 26, 2019. http://hdl.handle.net/1911/77324.

MLA Handbook (7th Edition):

El Smaili, Sami. “Efficient Architectures for Wideband Receivers.” 2012. Web. 26 May 2019.

Vancouver:

El Smaili S. Efficient Architectures for Wideband Receivers. [Internet] [Masters thesis]. Rice University; 2012. [cited 2019 May 26]. Available from: http://hdl.handle.net/1911/77324.

Council of Science Editors:

El Smaili S. Efficient Architectures for Wideband Receivers. [Masters Thesis]. Rice University; 2012. Available from: http://hdl.handle.net/1911/77324


University of Victoria

10. Pant, Jeevan Kumar. Compressive sensing using lp optimization.

Degree: Dept. of Electrical and Computer Engineering, 2012, University of Victoria

 Three problems in compressive sensing, namely, recovery of sparse signals from noise-free measurements, recovery of sparse signals from noisy measurements, and recovery of so called… (more)

Subjects/Keywords: Compressive sensing; Lp Optimization; Sparse signal recovery; Nonconvex compressive sensing

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APA (6th Edition):

Pant, J. K. (2012). Compressive sensing using lp optimization. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/3921

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

Chicago Manual of Style (16th Edition):

Pant, Jeevan Kumar. “Compressive sensing using lp optimization.” 2012. Thesis, University of Victoria. Accessed May 26, 2019. http://hdl.handle.net/1828/3921.

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

MLA Handbook (7th Edition):

Pant, Jeevan Kumar. “Compressive sensing using lp optimization.” 2012. Web. 26 May 2019.

Vancouver:

Pant JK. Compressive sensing using lp optimization. [Internet] [Thesis]. University of Victoria; 2012. [cited 2019 May 26]. Available from: http://hdl.handle.net/1828/3921.

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

Council of Science Editors:

Pant JK. Compressive sensing using lp optimization. [Thesis]. University of Victoria; 2012. Available from: http://hdl.handle.net/1828/3921

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


Oklahoma State University

11. Nakarmi, Ukash. Compressive Spectrum Sensing for Cognitive Radio Networks.

Degree: School of Electrical & Computer Engineering, 2011, Oklahoma State University

 Spectrum sensing is the most important part in cognitive radios. Wideband spectrum sensing requires high speed and large data samples. It makes sampling process challenging… (more)

Subjects/Keywords: binary compressive sensing; cognitive radio; compressive sensing; resource allocation; spectrum sensing; wireless communication

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APA (6th Edition):

Nakarmi, U. (2011). Compressive Spectrum Sensing for Cognitive Radio Networks. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/10251

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

Chicago Manual of Style (16th Edition):

Nakarmi, Ukash. “Compressive Spectrum Sensing for Cognitive Radio Networks.” 2011. Thesis, Oklahoma State University. Accessed May 26, 2019. http://hdl.handle.net/11244/10251.

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

MLA Handbook (7th Edition):

Nakarmi, Ukash. “Compressive Spectrum Sensing for Cognitive Radio Networks.” 2011. Web. 26 May 2019.

Vancouver:

Nakarmi U. Compressive Spectrum Sensing for Cognitive Radio Networks. [Internet] [Thesis]. Oklahoma State University; 2011. [cited 2019 May 26]. Available from: http://hdl.handle.net/11244/10251.

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

Council of Science Editors:

Nakarmi U. Compressive Spectrum Sensing for Cognitive Radio Networks. [Thesis]. Oklahoma State University; 2011. Available from: http://hdl.handle.net/11244/10251

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


The Ohio State University

12. Elgayar, Saad M. From Theory to Practice: Randomly Sampled Arrays for Passive Radar.

Degree: PhD, Electrical and Computer Engineering, 2017, The Ohio State University

 Passive radar is a type of radar sensor that exploits non-cooperative radio frequency transmissions to detect, localize and track targets of interest. A plethora of… (more)

Subjects/Keywords: Electrical Engineering; Passive Radar, Compressive sensing, randomly subsampled arry, Spatial compressive sensing, matched filter compressive sensing

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APA (6th Edition):

Elgayar, S. M. (2017). From Theory to Practice: Randomly Sampled Arrays for Passive Radar. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023

Chicago Manual of Style (16th Edition):

Elgayar, Saad M. “From Theory to Practice: Randomly Sampled Arrays for Passive Radar.” 2017. Doctoral Dissertation, The Ohio State University. Accessed May 26, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023.

MLA Handbook (7th Edition):

Elgayar, Saad M. “From Theory to Practice: Randomly Sampled Arrays for Passive Radar.” 2017. Web. 26 May 2019.

Vancouver:

Elgayar SM. From Theory to Practice: Randomly Sampled Arrays for Passive Radar. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2019 May 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023.

Council of Science Editors:

Elgayar SM. From Theory to Practice: Randomly Sampled Arrays for Passive Radar. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1503304471335023


University of New South Wales

13. Rana, Rajib Kumar. Addressing three wireless sensor network challenges using sparse approximation methods.

Degree: Computer Science & Engineering, 2011, University of New South Wales

 Wireless Sensor Networks (WSNs) offer a promising solution to monitor the physical world around us. Besides the traditional mote-based sensing, the Participatory WSN paradigm allows… (more)

Subjects/Keywords: Participatory sensing; Compressive sensing; Wireless sensor networks; Energy-aware sensing

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APA (6th Edition):

Rana, R. K. (2011). Addressing three wireless sensor network challenges using sparse approximation methods. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/50856 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9750/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Rana, Rajib Kumar. “Addressing three wireless sensor network challenges using sparse approximation methods.” 2011. Doctoral Dissertation, University of New South Wales. Accessed May 26, 2019. http://handle.unsw.edu.au/1959.4/50856 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9750/SOURCE02?view=true.

MLA Handbook (7th Edition):

Rana, Rajib Kumar. “Addressing three wireless sensor network challenges using sparse approximation methods.” 2011. Web. 26 May 2019.

Vancouver:

Rana RK. Addressing three wireless sensor network challenges using sparse approximation methods. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2019 May 26]. Available from: http://handle.unsw.edu.au/1959.4/50856 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9750/SOURCE02?view=true.

Council of Science Editors:

Rana RK. Addressing three wireless sensor network challenges using sparse approximation methods. [Doctoral Dissertation]. University of New South Wales; 2011. Available from: http://handle.unsw.edu.au/1959.4/50856 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:9750/SOURCE02?view=true


University of Delaware

14. Fu, Chen. Compressive polar spectral and polarization imaging .

Degree: 2018, University of Delaware

 Conventional digital imaging captures the desired image information directly on an imaging sensor. When high dimensional imaging capability is required such as spectral imaging and… (more)

Subjects/Keywords: Applied sciences; Compressive sensing; Polar coded aperture; Compressive spectropolarimetric imager

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APA (6th Edition):

Fu, C. (2018). Compressive polar spectral and polarization imaging . (Doctoral Dissertation). University of Delaware. Retrieved from http://udspace.udel.edu/handle/19716/23907

Chicago Manual of Style (16th Edition):

Fu, Chen. “Compressive polar spectral and polarization imaging .” 2018. Doctoral Dissertation, University of Delaware. Accessed May 26, 2019. http://udspace.udel.edu/handle/19716/23907.

MLA Handbook (7th Edition):

Fu, Chen. “Compressive polar spectral and polarization imaging .” 2018. Web. 26 May 2019.

Vancouver:

Fu C. Compressive polar spectral and polarization imaging . [Internet] [Doctoral dissertation]. University of Delaware; 2018. [cited 2019 May 26]. Available from: http://udspace.udel.edu/handle/19716/23907.

Council of Science Editors:

Fu C. Compressive polar spectral and polarization imaging . [Doctoral Dissertation]. University of Delaware; 2018. Available from: http://udspace.udel.edu/handle/19716/23907


Oklahoma State University

15. Mekisso, Betelhem Mateos. Unequal Compressive Imaging.

Degree: School of Electrical & Computer Engineering, 2011, Oklahoma State University

 Recently, novel compressive sensing (CS) techniques have been employed to concurrently perform compression and image sampling. Since an image has sparse representation in some proper… (more)

Subjects/Keywords: compressive sensing; image processing; region of interest; unequal compressive sampling

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APA (6th Edition):

Mekisso, B. M. (2011). Unequal Compressive Imaging. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/10243

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

Chicago Manual of Style (16th Edition):

Mekisso, Betelhem Mateos. “Unequal Compressive Imaging.” 2011. Thesis, Oklahoma State University. Accessed May 26, 2019. http://hdl.handle.net/11244/10243.

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

MLA Handbook (7th Edition):

Mekisso, Betelhem Mateos. “Unequal Compressive Imaging.” 2011. Web. 26 May 2019.

Vancouver:

Mekisso BM. Unequal Compressive Imaging. [Internet] [Thesis]. Oklahoma State University; 2011. [cited 2019 May 26]. Available from: http://hdl.handle.net/11244/10243.

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

Council of Science Editors:

Mekisso BM. Unequal Compressive Imaging. [Thesis]. Oklahoma State University; 2011. Available from: http://hdl.handle.net/11244/10243

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


Colorado School of Mines

16. Lim, Chia Wei. Compressive sampling of communication signals.

Degree: PhD, Electrical Engineering and Computer Science, 2015, Colorado School of Mines

 The theory of Compressive Sensing (CS) has recently enabled the efficient acquisition of signals which are sparse or compressible in some appropriate domain. The central… (more)

Subjects/Keywords: compressive sensing; modulation recognition; frequency hopping; compressive sampling

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APA (6th Edition):

Lim, C. W. (2015). Compressive sampling of communication signals. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/20182

Chicago Manual of Style (16th Edition):

Lim, Chia Wei. “Compressive sampling of communication signals.” 2015. Doctoral Dissertation, Colorado School of Mines. Accessed May 26, 2019. http://hdl.handle.net/11124/20182.

MLA Handbook (7th Edition):

Lim, Chia Wei. “Compressive sampling of communication signals.” 2015. Web. 26 May 2019.

Vancouver:

Lim CW. Compressive sampling of communication signals. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/11124/20182.

Council of Science Editors:

Lim CW. Compressive sampling of communication signals. [Doctoral Dissertation]. Colorado School of Mines; 2015. Available from: http://hdl.handle.net/11124/20182


Rice University

17. Chen, Jianbo. Exploiting compressive matrices for dynamic infrared object tracking.

Degree: MS, Engineering, 2016, Rice University

 Recent development on compressive sensing (CS) presents a great potential for this technique to be used in broader applications from hyper-spectroscopy microscopy to homeland security.… (more)

Subjects/Keywords: compressive sensing; compressive video; object tracking; anomaly detection

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APA (6th Edition):

Chen, J. (2016). Exploiting compressive matrices for dynamic infrared object tracking. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/96527

Chicago Manual of Style (16th Edition):

Chen, Jianbo. “Exploiting compressive matrices for dynamic infrared object tracking.” 2016. Masters Thesis, Rice University. Accessed May 26, 2019. http://hdl.handle.net/1911/96527.

MLA Handbook (7th Edition):

Chen, Jianbo. “Exploiting compressive matrices for dynamic infrared object tracking.” 2016. Web. 26 May 2019.

Vancouver:

Chen J. Exploiting compressive matrices for dynamic infrared object tracking. [Internet] [Masters thesis]. Rice University; 2016. [cited 2019 May 26]. Available from: http://hdl.handle.net/1911/96527.

Council of Science Editors:

Chen J. Exploiting compressive matrices for dynamic infrared object tracking. [Masters Thesis]. Rice University; 2016. Available from: http://hdl.handle.net/1911/96527


Rice University

18. Li, Yun. Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging.

Degree: PhD, Engineering, 2015, Rice University

Compressive imaging is a technology that uses multiplexed measurements and the sparsity of many natural images to efficiently capture and reconstruct images. The compressive single… (more)

Subjects/Keywords: Compressive Sensing; Single Pixel Camera; Anomaly Detection; Compressive Classification

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APA (6th Edition):

Li, Y. (2015). Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/88091

Chicago Manual of Style (16th Edition):

Li, Yun. “Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging.” 2015. Doctoral Dissertation, Rice University. Accessed May 26, 2019. http://hdl.handle.net/1911/88091.

MLA Handbook (7th Edition):

Li, Yun. “Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging.” 2015. Web. 26 May 2019.

Vancouver:

Li Y. Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging. [Internet] [Doctoral dissertation]. Rice University; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/1911/88091.

Council of Science Editors:

Li Y. Imaging and Visual Classification by Knowledge-Enhanced Compressive Imaging. [Doctoral Dissertation]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88091


University of Rochester

19. Lum, Daniel Jacob; Howell, John C. (1971 - ). Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking.

Degree: PhD, 2018, University of Rochester

 High-dimensional systems are desired for their ability to transfer large amounts of information. This dissertation focuses on the characterization and usage of high-dimensional optical systems… (more)

Subjects/Keywords: Compressive FMCW LiDAR; Compressive sensing; Entanglement characterization; Quantum data locking

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APA (6th Edition):

Lum, Daniel Jacob; Howell, J. C. (. -. ). (2018). Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/33851

Chicago Manual of Style (16th Edition):

Lum, Daniel Jacob; Howell, John C (1971 - ). “Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking.” 2018. Doctoral Dissertation, University of Rochester. Accessed May 26, 2019. http://hdl.handle.net/1802/33851.

MLA Handbook (7th Edition):

Lum, Daniel Jacob; Howell, John C (1971 - ). “Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking.” 2018. Web. 26 May 2019.

Vancouver:

Lum, Daniel Jacob; Howell JC(-). Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking. [Internet] [Doctoral dissertation]. University of Rochester; 2018. [cited 2019 May 26]. Available from: http://hdl.handle.net/1802/33851.

Council of Science Editors:

Lum, Daniel Jacob; Howell JC(-). Characterizing high-dimensional optical systems with applications in compressive sensing and quantum data locking. [Doctoral Dissertation]. University of Rochester; 2018. Available from: http://hdl.handle.net/1802/33851


University of Ontario Institute of Technology

20. Takeva-Velkova, Viliyana. Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).

Degree: 2010, University of Ontario Institute of Technology

 Magnetic Resonance Imaging (MRI) is an essential instrument in clinical diag- nosis; however, it is burdened by a slow data acquisition process due to physical… (more)

Subjects/Keywords: Compressive sensing; Sparse MRI; Convex optimization

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APA (6th Edition):

Takeva-Velkova, V. (2010). Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/104

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

Chicago Manual of Style (16th Edition):

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Thesis, University of Ontario Institute of Technology. Accessed May 26, 2019. http://hdl.handle.net/10155/104.

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

MLA Handbook (7th Edition):

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Web. 26 May 2019.

Vancouver:

Takeva-Velkova V. Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). [Internet] [Thesis]. University of Ontario Institute of Technology; 2010. [cited 2019 May 26]. Available from: http://hdl.handle.net/10155/104.

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

Council of Science Editors:

Takeva-Velkova V. Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). [Thesis]. University of Ontario Institute of Technology; 2010. Available from: http://hdl.handle.net/10155/104

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


University of Victoria

21. Teixeira, Flavio C.A. Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions.

Degree: Department of Electrical and Computer Engineering, 2014, University of Victoria

 Recent research has shown that compressible signals can be recovered from a very limited number of measurements by minimizing nonconvex functions that closely resemble the… (more)

Subjects/Keywords: Signal Processing; Numerical Optimization; Compressive Sensing

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APA (6th Edition):

Teixeira, F. C. A. (2014). Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/5823

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

Chicago Manual of Style (16th Edition):

Teixeira, Flavio C A. “Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions.” 2014. Thesis, University of Victoria. Accessed May 26, 2019. http://hdl.handle.net/1828/5823.

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

MLA Handbook (7th Edition):

Teixeira, Flavio C A. “Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions.” 2014. Web. 26 May 2019.

Vancouver:

Teixeira FCA. Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions. [Internet] [Thesis]. University of Victoria; 2014. [cited 2019 May 26]. Available from: http://hdl.handle.net/1828/5823.

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

Council of Science Editors:

Teixeira FCA. Signal-Recovery Methods for Compressive Sensing Using Nonconvex Sparsity-Promoting Functions. [Thesis]. University of Victoria; 2014. Available from: http://hdl.handle.net/1828/5823

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


Delft University of Technology

22. Lamelas Polo, Y. Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:.

Degree: Electrical Engineering, Mathematics and Computer Science, Telecommunications, 2008, Delft University of Technology

 It has been widely recognized that utilization of radio spectrum by licensed wireless systems, e.g., TV broadcasting, aeronautical telemetry, is quite low. In particular, at… (more)

Subjects/Keywords: cognitive radio; spectrum sensing; compressive sampling

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APA (6th Edition):

Lamelas Polo, Y. (2008). Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2f16fbcc-1a61-4144-bcd2-72effdfc2b9d

Chicago Manual of Style (16th Edition):

Lamelas Polo, Y. “Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:.” 2008. Masters Thesis, Delft University of Technology. Accessed May 26, 2019. http://resolver.tudelft.nl/uuid:2f16fbcc-1a61-4144-bcd2-72effdfc2b9d.

MLA Handbook (7th Edition):

Lamelas Polo, Y. “Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:.” 2008. Web. 26 May 2019.

Vancouver:

Lamelas Polo Y. Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:. [Internet] [Masters thesis]. Delft University of Technology; 2008. [cited 2019 May 26]. Available from: http://resolver.tudelft.nl/uuid:2f16fbcc-1a61-4144-bcd2-72effdfc2b9d.

Council of Science Editors:

Lamelas Polo Y. Compressive Wideband Spectrum Sensing for Cognitive Radio Applications:. [Masters Thesis]. Delft University of Technology; 2008. Available from: http://resolver.tudelft.nl/uuid:2f16fbcc-1a61-4144-bcd2-72effdfc2b9d


Delft University of Technology

23. Gishkori, S.S. Compressive Sampling for PPM and FSK Modulated Signals:.

Degree: 2009, Delft University of Technology

 Efficiency of the Analog to Digital Converters (ADCs) has always been an issue of concern, especially, when it comes to sampling wide band signals which… (more)

Subjects/Keywords: Compressive Sampling; PPM; FSK; UWB; Compressed Sensing

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APA (6th Edition):

Gishkori, S. S. (2009). Compressive Sampling for PPM and FSK Modulated Signals:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9

Chicago Manual of Style (16th Edition):

Gishkori, S S. “Compressive Sampling for PPM and FSK Modulated Signals:.” 2009. Masters Thesis, Delft University of Technology. Accessed May 26, 2019. http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9.

MLA Handbook (7th Edition):

Gishkori, S S. “Compressive Sampling for PPM and FSK Modulated Signals:.” 2009. Web. 26 May 2019.

Vancouver:

Gishkori SS. Compressive Sampling for PPM and FSK Modulated Signals:. [Internet] [Masters thesis]. Delft University of Technology; 2009. [cited 2019 May 26]. Available from: http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9.

Council of Science Editors:

Gishkori SS. Compressive Sampling for PPM and FSK Modulated Signals:. [Masters Thesis]. Delft University of Technology; 2009. Available from: http://resolver.tudelft.nl/uuid:badbddb8-9f61-48a5-ac50-fc3e0bd719e9


Colorado School of Mines

24. Eftekhari, Armin. Model-based signal recovery : a geometric perspective.

Degree: PhD, Electrical Engineering and Computer Sciences, 2016, Colorado School of Mines

 Model-based signal processing is concerned with measuring, understanding, and communicating data under the assumption that the (potentially high-dimensional) data in hand has in fact few… (more)

Subjects/Keywords: Compressive sensing; Manifold models; Signal processing

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APA (6th Edition):

Eftekhari, A. (2016). Model-based signal recovery : a geometric perspective. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/166679

Chicago Manual of Style (16th Edition):

Eftekhari, Armin. “Model-based signal recovery : a geometric perspective.” 2016. Doctoral Dissertation, Colorado School of Mines. Accessed May 26, 2019. http://hdl.handle.net/11124/166679.

MLA Handbook (7th Edition):

Eftekhari, Armin. “Model-based signal recovery : a geometric perspective.” 2016. Web. 26 May 2019.

Vancouver:

Eftekhari A. Model-based signal recovery : a geometric perspective. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2016. [cited 2019 May 26]. Available from: http://hdl.handle.net/11124/166679.

Council of Science Editors:

Eftekhari A. Model-based signal recovery : a geometric perspective. [Doctoral Dissertation]. Colorado School of Mines; 2016. Available from: http://hdl.handle.net/11124/166679


University of Houston

25. -1801-637X. High-Performance Sparse Fourier Transform on Parallel Architectures.

Degree: Computer Science, Department of, 2016, University of Houston

 Fast Fourier Transform (FFT) is one of the most important numerical algorithms widely used in numerous scientific and engineering computations. With the emergence of big… (more)

Subjects/Keywords: Sparse FFT; Parallel Computing; Compressive Sensing; GPU

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APA (6th Edition):

-1801-637X. (2016). High-Performance Sparse Fourier Transform on Parallel Architectures. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3269

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

Chicago Manual of Style (16th Edition):

-1801-637X. “High-Performance Sparse Fourier Transform on Parallel Architectures.” 2016. Thesis, University of Houston. Accessed May 26, 2019. http://hdl.handle.net/10657/3269.

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

MLA Handbook (7th Edition):

-1801-637X. “High-Performance Sparse Fourier Transform on Parallel Architectures.” 2016. Web. 26 May 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1801-637X. High-Performance Sparse Fourier Transform on Parallel Architectures. [Internet] [Thesis]. University of Houston; 2016. [cited 2019 May 26]. Available from: http://hdl.handle.net/10657/3269.

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

Council of Science Editors:

-1801-637X. High-Performance Sparse Fourier Transform on Parallel Architectures. [Thesis]. University of Houston; 2016. Available from: http://hdl.handle.net/10657/3269

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


University of Arizona

26. Kerviche, Ronan. Scalable Computational Optical Imaging System Designs .

Degree: 2017, University of Arizona

 Computational imaging and sensing leverages the joint-design of optics, detectors and processing to overcome the performance bottlenecks inherent to the traditional imaging paradigm. This novel… (more)

Subjects/Keywords: compressive; computational; design; imaging; scalability; sensing

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APA (6th Edition):

Kerviche, R. (2017). Scalable Computational Optical Imaging System Designs . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/624555

Chicago Manual of Style (16th Edition):

Kerviche, Ronan. “Scalable Computational Optical Imaging System Designs .” 2017. Doctoral Dissertation, University of Arizona. Accessed May 26, 2019. http://hdl.handle.net/10150/624555.

MLA Handbook (7th Edition):

Kerviche, Ronan. “Scalable Computational Optical Imaging System Designs .” 2017. Web. 26 May 2019.

Vancouver:

Kerviche R. Scalable Computational Optical Imaging System Designs . [Internet] [Doctoral dissertation]. University of Arizona; 2017. [cited 2019 May 26]. Available from: http://hdl.handle.net/10150/624555.

Council of Science Editors:

Kerviche R. Scalable Computational Optical Imaging System Designs . [Doctoral Dissertation]. University of Arizona; 2017. Available from: http://hdl.handle.net/10150/624555


Rochester Institute of Technology

27. Busuioceanu, Maria. Analysis of compressive sensing for hyperspectral remote sensing applications.

Degree: Chester F. Carlson Center for Imaging Science (COS), 2013, Rochester Institute of Technology

Compressive Sensing (CS) systems capture data with fewer measurements than traditional sensors assuming that imagery is redundant and compressible in the spectral and spatial dimensions.… (more)

Subjects/Keywords: Atmospheric compensation; Compressive sensing; Hyperspectral imaging

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APA (6th Edition):

Busuioceanu, M. (2013). Analysis of compressive sensing for hyperspectral remote sensing applications. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/2915

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

Chicago Manual of Style (16th Edition):

Busuioceanu, Maria. “Analysis of compressive sensing for hyperspectral remote sensing applications.” 2013. Thesis, Rochester Institute of Technology. Accessed May 26, 2019. https://scholarworks.rit.edu/theses/2915.

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

MLA Handbook (7th Edition):

Busuioceanu, Maria. “Analysis of compressive sensing for hyperspectral remote sensing applications.” 2013. Web. 26 May 2019.

Vancouver:

Busuioceanu M. Analysis of compressive sensing for hyperspectral remote sensing applications. [Internet] [Thesis]. Rochester Institute of Technology; 2013. [cited 2019 May 26]. Available from: https://scholarworks.rit.edu/theses/2915.

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

Council of Science Editors:

Busuioceanu M. Analysis of compressive sensing for hyperspectral remote sensing applications. [Thesis]. Rochester Institute of Technology; 2013. Available from: https://scholarworks.rit.edu/theses/2915

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


Rice University

28. Wilhelm, Keith. Classification Techniques for Undersampled Electromyography and Electrocardiography.

Degree: MS, Engineering, 2012, Rice University

 Electrophysiological signals including electrocardiography (ECG) and electromyography (EMG) are widely used in clinical environments for monitoring of patients and for diagnosis of conditions including cardiac… (more)

Subjects/Keywords: Electromyography; Electrocardiography; Support vector machine; Compressive sensing

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APA (6th Edition):

Wilhelm, K. (2012). Classification Techniques for Undersampled Electromyography and Electrocardiography. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/77325

Chicago Manual of Style (16th Edition):

Wilhelm, Keith. “Classification Techniques for Undersampled Electromyography and Electrocardiography.” 2012. Masters Thesis, Rice University. Accessed May 26, 2019. http://hdl.handle.net/1911/77325.

MLA Handbook (7th Edition):

Wilhelm, Keith. “Classification Techniques for Undersampled Electromyography and Electrocardiography.” 2012. Web. 26 May 2019.

Vancouver:

Wilhelm K. Classification Techniques for Undersampled Electromyography and Electrocardiography. [Internet] [Masters thesis]. Rice University; 2012. [cited 2019 May 26]. Available from: http://hdl.handle.net/1911/77325.

Council of Science Editors:

Wilhelm K. Classification Techniques for Undersampled Electromyography and Electrocardiography. [Masters Thesis]. Rice University; 2012. Available from: http://hdl.handle.net/1911/77325


Rice University

29. Valiollahzadeh, Majid. Compressive Sensing in Positron Emission Tomography (PET) Imaging.

Degree: PhD, Engineering, 2015, Rice University

 Positron emission tomography (PET) is a nuclear medicine functional imaging modality, applicable to several clinical problems, but especially in detecting the metabolic activity (as in… (more)

Subjects/Keywords: Compressive sensing; PET imaging; signal processing

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APA (6th Edition):

Valiollahzadeh, M. (2015). Compressive Sensing in Positron Emission Tomography (PET) Imaging. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/88183

Chicago Manual of Style (16th Edition):

Valiollahzadeh, Majid. “Compressive Sensing in Positron Emission Tomography (PET) Imaging.” 2015. Doctoral Dissertation, Rice University. Accessed May 26, 2019. http://hdl.handle.net/1911/88183.

MLA Handbook (7th Edition):

Valiollahzadeh, Majid. “Compressive Sensing in Positron Emission Tomography (PET) Imaging.” 2015. Web. 26 May 2019.

Vancouver:

Valiollahzadeh M. Compressive Sensing in Positron Emission Tomography (PET) Imaging. [Internet] [Doctoral dissertation]. Rice University; 2015. [cited 2019 May 26]. Available from: http://hdl.handle.net/1911/88183.

Council of Science Editors:

Valiollahzadeh M. Compressive Sensing in Positron Emission Tomography (PET) Imaging. [Doctoral Dissertation]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88183


University of Rochester

30. Howland, Gregory A. (1985 - ). Compressive sensing for quantum imaging.

Degree: PhD, 2014, University of Rochester

 This thesis describes the application of compressive sensing to several challenging problems in quantum imaging with practical and fundamental implications. Compressive sensing is a measurement… (more)

Subjects/Keywords: Compressive sensing; Lidar; Quantum entanglement; Quantum optics

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APA (6th Edition):

Howland, G. A. (. -. ). (2014). Compressive sensing for quantum imaging. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28861

Chicago Manual of Style (16th Edition):

Howland, Gregory A (1985 - ). “Compressive sensing for quantum imaging.” 2014. Doctoral Dissertation, University of Rochester. Accessed May 26, 2019. http://hdl.handle.net/1802/28861.

MLA Handbook (7th Edition):

Howland, Gregory A (1985 - ). “Compressive sensing for quantum imaging.” 2014. Web. 26 May 2019.

Vancouver:

Howland GA(-). Compressive sensing for quantum imaging. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2019 May 26]. Available from: http://hdl.handle.net/1802/28861.

Council of Science Editors:

Howland GA(-). Compressive sensing for quantum imaging. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28861

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