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You searched for +publisher:"University of Illinois – Urbana-Champaign" +contributor:("Liang, Zhi-Pei"). Showing records 1 – 30 of 53 total matches.

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University of Illinois – Urbana-Champaign

1. Fu, Maojing. Dynamic speech imaging with low-rank approximation.

Degree: MS, 1200, 2012, University of Illinois – Urbana-Champaign

 Dynamic speech imaging is a powerful technique for real-time visualization of speech dynamics. As a promising modality for dynamic speech imaging, magnetic resonance imaging (MRI)… (more)

Subjects/Keywords: spatiotemporal modeling; partially separable functions; speech imaging; dynamic MRI; Magnetic resonance imaging (MRI)

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

APA (6th Edition):

Fu, M. (2012). Dynamic speech imaging with low-rank approximation. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32073

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):

Fu, Maojing. “Dynamic speech imaging with low-rank approximation.” 2012. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/32073.

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

MLA Handbook (7th Edition):

Fu, Maojing. “Dynamic speech imaging with low-rank approximation.” 2012. Web. 27 Oct 2020.

Vancouver:

Fu M. Dynamic speech imaging with low-rank approximation. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/32073.

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

Council of Science Editors:

Fu M. Dynamic speech imaging with low-rank approximation. [Thesis]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32073

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


University of Illinois – Urbana-Champaign

2. Li, Yudu. A subspace approach to spectral quantification for MR spectroscopic imaging.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 The problem of spectral quantification for magnetic resonance spectroscopic imaging (MRSI) is addressed in this thesis. We present a novel approach to solving this problem,… (more)

Subjects/Keywords: Magnetic resonance spectroscopic imaging (MRSI); Spectral estimation; Subspace; Spatiospectral constraints

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APA (6th Edition):

Li, Y. (2017). A subspace approach to spectral quantification for MR spectroscopic imaging. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99360

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):

Li, Yudu. “A subspace approach to spectral quantification for MR spectroscopic imaging.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/99360.

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

MLA Handbook (7th Edition):

Li, Yudu. “A subspace approach to spectral quantification for MR spectroscopic imaging.” 2017. Web. 27 Oct 2020.

Vancouver:

Li Y. A subspace approach to spectral quantification for MR spectroscopic imaging. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/99360.

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

Council of Science Editors:

Li Y. A subspace approach to spectral quantification for MR spectroscopic imaging. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99360

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


University of Illinois – Urbana-Champaign

3. Ning, Qiang. Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging.

Degree: MS, Electrical & Computer Engineering, 2015, University of Illinois – Urbana-Champaign

 Magnetic resonance spectroscopic imaging (MRSI) is a promising tool to acquire in vivo biochemical information, and spectral estimation (quantification) of MRSI data is an important… (more)

Subjects/Keywords: Magnetic resonance spectroscopic imaging (MRSI); spectral estimation; spatial regularization; sparsity constraint; Cramer-Rao Bound

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APA (6th Edition):

Ning, Q. (2015). Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/89025

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):

Ning, Qiang. “Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/89025.

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

MLA Handbook (7th Edition):

Ning, Qiang. “Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging.” 2015. Web. 27 Oct 2020.

Vancouver:

Ning Q. Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/89025.

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

Council of Science Editors:

Ning Q. Spectral estimation with spatio-spectral constraints for magnetic resonance spectroscopic imaging. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/89025

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


University of Illinois – Urbana-Champaign

4. Clifford, Bryan Alexander. Subspace estimation for subspace-based magnetic resonance spectroscopic imaging.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 Magnetic resonance spectroscopic imaging (MRSI) is a powerful technique that offers us the ability to non-invasively image chemical distributions within the human body. However, due… (more)

Subjects/Keywords: subspace model; subspace estimation; field inhomogeneity; magnetic resonance; MRI; MRSI; spectroscopy; spectroscopic imaging

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APA (6th Edition):

Clifford, B. A. (2016). Subspace estimation for subspace-based magnetic resonance spectroscopic imaging. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90903

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):

Clifford, Bryan Alexander. “Subspace estimation for subspace-based magnetic resonance spectroscopic imaging.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/90903.

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

MLA Handbook (7th Edition):

Clifford, Bryan Alexander. “Subspace estimation for subspace-based magnetic resonance spectroscopic imaging.” 2016. Web. 27 Oct 2020.

Vancouver:

Clifford BA. Subspace estimation for subspace-based magnetic resonance spectroscopic imaging. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/90903.

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

Council of Science Editors:

Clifford BA. Subspace estimation for subspace-based magnetic resonance spectroscopic imaging. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90903

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


University of Illinois – Urbana-Champaign

5. Silkaitis, Michael. A subspace method for reconstruction of time-series fMRI images from sparse data.

Degree: MS, Electrical & Computer Engr, 2019, University of Illinois – Urbana-Champaign

 Functional magnetic resonance imaging (fMRI) is a powerful imaging modality commonly used to study brain functions. It utilizes the difference in the oxygen content of… (more)

Subjects/Keywords: MRI; fMRI; SPICE

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APA (6th Edition):

Silkaitis, M. (2019). A subspace method for reconstruction of time-series fMRI images from sparse data. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104896

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):

Silkaitis, Michael. “A subspace method for reconstruction of time-series fMRI images from sparse data.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/104896.

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

MLA Handbook (7th Edition):

Silkaitis, Michael. “A subspace method for reconstruction of time-series fMRI images from sparse data.” 2019. Web. 27 Oct 2020.

Vancouver:

Silkaitis M. A subspace method for reconstruction of time-series fMRI images from sparse data. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/104896.

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

Council of Science Editors:

Silkaitis M. A subspace method for reconstruction of time-series fMRI images from sparse data. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104896

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


University of Illinois – Urbana-Champaign

6. Lam, Fan. Motion compensation from limited data for reference-constrained image reconstruction.

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

 When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order… (more)

Subjects/Keywords: Reference-constrained image reconstruction; Motion compensation; Generalized series model; Sparse image; Compressed Sensing; Variable projection; Affine transformation; Free-form deformation; Cramer-Rao bound

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APA (6th Edition):

Lam, F. (2011). Motion compensation from limited data for reference-constrained image reconstruction. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/24111

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):

Lam, Fan. “Motion compensation from limited data for reference-constrained image reconstruction.” 2011. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/24111.

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

MLA Handbook (7th Edition):

Lam, Fan. “Motion compensation from limited data for reference-constrained image reconstruction.” 2011. Web. 27 Oct 2020.

Vancouver:

Lam F. Motion compensation from limited data for reference-constrained image reconstruction. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2011. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/24111.

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

Council of Science Editors:

Lam F. Motion compensation from limited data for reference-constrained image reconstruction. [Thesis]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/24111

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

7. Perkins, Kevin. A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils.

Degree: MS, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 Magnetic resonance spectroscopic imaging (MRSI) enables in-vivo analysis of the spatial distribution of chemicals within the human body. Through MRSI, one can infer the concentration… (more)

Subjects/Keywords: Magnetic Resonance Spectroscopic Imaging; MRSI; Parallel Imaging; SENSE; Deep Learning; DnCNN

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APA (6th Edition):

Perkins, K. (2018). A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/100976

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):

Perkins, Kevin. “A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/100976.

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

MLA Handbook (7th Edition):

Perkins, Kevin. “A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils.” 2018. Web. 27 Oct 2020.

Vancouver:

Perkins K. A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/100976.

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

Council of Science Editors:

Perkins K. A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/100976

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

8. Liu, Ding. Rank constrained denoising in magnetic resonance imaging.

Degree: MS, 1200, 2015, University of Illinois – Urbana-Champaign

 Noise is an important issue in magnetic resonance imaging (MRI), since the signal-to-noise ratio (SNR) is a major limiting factor for imaging speed and the… (more)

Subjects/Keywords: Image Denoising; Magnetic Resonance Imaging (MRI)

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APA (6th Edition):

Liu, D. (2015). Rank constrained denoising in magnetic resonance imaging. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72922

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):

Liu, Ding. “Rank constrained denoising in magnetic resonance imaging.” 2015. Thesis, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/72922.

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

MLA Handbook (7th Edition):

Liu, Ding. “Rank constrained denoising in magnetic resonance imaging.” 2015. Web. 27 Oct 2020.

Vancouver:

Liu D. Rank constrained denoising in magnetic resonance imaging. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/72922.

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

Council of Science Editors:

Liu D. Rank constrained denoising in magnetic resonance imaging. [Thesis]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72922

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


University of Illinois – Urbana-Champaign

9. Christodoulou, Anthony Glenn. A subspace approach to accelerated cardiovascular magnetic resonance imaging.

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

 Magnetic resonance imaging (MRI) is a uniquely flexible tool for imaging the heart, as it has the potential to perform a significant number of structural… (more)

Subjects/Keywords: Magnetic resonance imaging; cardiovascular imaging; cardiac imaging

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

APA (6th Edition):

Christodoulou, A. G. (2015). A subspace approach to accelerated cardiovascular magnetic resonance imaging. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78384

Chicago Manual of Style (16th Edition):

Christodoulou, Anthony Glenn. “A subspace approach to accelerated cardiovascular magnetic resonance imaging.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/78384.

MLA Handbook (7th Edition):

Christodoulou, Anthony Glenn. “A subspace approach to accelerated cardiovascular magnetic resonance imaging.” 2015. Web. 27 Oct 2020.

Vancouver:

Christodoulou AG. A subspace approach to accelerated cardiovascular magnetic resonance imaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/78384.

Council of Science Editors:

Christodoulou AG. A subspace approach to accelerated cardiovascular magnetic resonance imaging. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78384


University of Illinois – Urbana-Champaign

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

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

 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)

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

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

APA (6th 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 (16th Edition):

Lam, Fan. “A subspace approach to high-resolution magnetic resonance spectroscopic imaging.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/78611.

MLA Handbook (7th Edition):

Lam, Fan. “A subspace approach to high-resolution magnetic resonance spectroscopic imaging.” 2015. Web. 27 Oct 2020.

Vancouver:

Lam F. A subspace approach to high-resolution magnetic resonance spectroscopic imaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. 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


University of Illinois – Urbana-Champaign

11. Ngo, Giang-Chau. Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging.

Degree: PhD, Bioengineering, 2018, University of Illinois – Urbana-Champaign

 While magnetic susceptibility is an essential contrast mechanism in magnetic resonance imaging (MRI), it also causes significant disruptions to the imaging process. Strong differences in… (more)

Subjects/Keywords: Magnetic Resonance Imaging; Magnetic susceptibility; Magnetic field inhomogeneity; Model-based imaging reconstruction; Functional MRI; R2* estimation; Quantitative susceptibility mapping

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

APA (6th Edition):

Ngo, G. (2018). Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101258

Chicago Manual of Style (16th Edition):

Ngo, Giang-Chau. “Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/101258.

MLA Handbook (7th Edition):

Ngo, Giang-Chau. “Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging.” 2018. Web. 27 Oct 2020.

Vancouver:

Ngo G. Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/101258.

Council of Science Editors:

Ngo G. Model-based reconstruction for correcting magnetic susceptibility-induced artifacts in magnetic resonance imaging. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101258


University of Illinois – Urbana-Champaign

12. Yu, Jiahui. Towards efficient, on-demand and automated deep learning.

Degree: PhD, Electrical & Computer Engr, 2020, University of Illinois – Urbana-Champaign

 In the past decade, deep learning has achieved great breakthroughs on tasks of computer vision, speech, language, control and many others. The advanced and dedicated… (more)

Subjects/Keywords: efficient; on-demand; automated; deep learning; automl

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APA (6th Edition):

Yu, J. (2020). Towards efficient, on-demand and automated deep learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/107845

Chicago Manual of Style (16th Edition):

Yu, Jiahui. “Towards efficient, on-demand and automated deep learning.” 2020. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/107845.

MLA Handbook (7th Edition):

Yu, Jiahui. “Towards efficient, on-demand and automated deep learning.” 2020. Web. 27 Oct 2020.

Vancouver:

Yu J. Towards efficient, on-demand and automated deep learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2020. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/107845.

Council of Science Editors:

Yu J. Towards efficient, on-demand and automated deep learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/107845


University of Illinois – Urbana-Champaign

13. Van, Anh T. High resolution 3D diffusion tensor imaging for delineating neuronal architectures.

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

 Diffusion tensor imaging (DTI) has long been an important tool for early diagnosis and monitoring of neuronal diseases as well as for understanding the connectivity… (more)

Subjects/Keywords: 3D Diffusion Tensor Imaging; Motion-Induced Phase Errors; Reduced-Field-of-View Imaging; Submillimeter Resolution; Parallel Imaging; Magnectic Susceptibility; Pons; Hippocampus

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APA (6th Edition):

Van, A. T. (2011). High resolution 3D diffusion tensor imaging for delineating neuronal architectures. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18355

Chicago Manual of Style (16th Edition):

Van, Anh T. “High resolution 3D diffusion tensor imaging for delineating neuronal architectures.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/18355.

MLA Handbook (7th Edition):

Van, Anh T. “High resolution 3D diffusion tensor imaging for delineating neuronal architectures.” 2011. Web. 27 Oct 2020.

Vancouver:

Van AT. High resolution 3D diffusion tensor imaging for delineating neuronal architectures. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/18355.

Council of Science Editors:

Van AT. High resolution 3D diffusion tensor imaging for delineating neuronal architectures. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18355


University of Illinois – Urbana-Champaign

14. Tan, Jiawei. The development of MRI-compatible SPECT system.

Degree: PhD, 0139, 2012, University of Illinois – Urbana-Champaign

 In recent years, combined MRI and nuclear imaging techniques such as PET and SPECT have received great attentions. MRI provides high-resolution anatomical and functional information… (more)

Subjects/Keywords: MRI-compatible SPECT system; small-pixel CdTe detectors; charges collection; clinical 3 T MR Scanner; Magnetic resonance imaging (MRI)

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APA (6th Edition):

Tan, J. (2012). The development of MRI-compatible SPECT system. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32011

Chicago Manual of Style (16th Edition):

Tan, Jiawei. “The development of MRI-compatible SPECT system.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/32011.

MLA Handbook (7th Edition):

Tan, Jiawei. “The development of MRI-compatible SPECT system.” 2012. Web. 27 Oct 2020.

Vancouver:

Tan J. The development of MRI-compatible SPECT system. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/32011.

Council of Science Editors:

Tan J. The development of MRI-compatible SPECT system. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32011


University of Illinois – Urbana-Champaign

15. Wang, Jiangping. Learning sparse features and metric in signal and image processing.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 This dissertation studies two aspects of feature learning: representation learning and metric in feature space, from a machine learning perspective. Feature learning is a fundamental… (more)

Subjects/Keywords: Feature learning; Sparse coding; Metric learning; Dictionary learning; Spectral analysis; Image classification; Wildlife monitoring; Shape matching

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APA (6th Edition):

Wang, J. (2017). Learning sparse features and metric in signal and image processing. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98199

Chicago Manual of Style (16th Edition):

Wang, Jiangping. “Learning sparse features and metric in signal and image processing.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/98199.

MLA Handbook (7th Edition):

Wang, Jiangping. “Learning sparse features and metric in signal and image processing.” 2017. Web. 27 Oct 2020.

Vancouver:

Wang J. Learning sparse features and metric in signal and image processing. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/98199.

Council of Science Editors:

Wang J. Learning sparse features and metric in signal and image processing. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98199


University of Illinois – Urbana-Champaign

16. Wen, Bihan. Adaptive nonlocal and structured sparse signal modeling and applications.

Degree: PhD, Electrical & Computer Engr, 2018, University of Illinois – Urbana-Champaign

 Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. Many applications such as… (more)

Subjects/Keywords: machine learning; image restoration; transform learning; computational imaging; denoising; inpainting; magnetic resonance imaging; video denoising; online learning; overcomplete model; rotation invariant; medical imaging; sparse coding; sparse representation; low-rank model; joint optimization

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APA (6th Edition):

Wen, B. (2018). Adaptive nonlocal and structured sparse signal modeling and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102464

Chicago Manual of Style (16th Edition):

Wen, Bihan. “Adaptive nonlocal and structured sparse signal modeling and applications.” 2018. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/102464.

MLA Handbook (7th Edition):

Wen, Bihan. “Adaptive nonlocal and structured sparse signal modeling and applications.” 2018. Web. 27 Oct 2020.

Vancouver:

Wen B. Adaptive nonlocal and structured sparse signal modeling and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2018. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/102464.

Council of Science Editors:

Wen B. Adaptive nonlocal and structured sparse signal modeling and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102464


University of Illinois – Urbana-Champaign

17. Shi, Honghui. Deep learning in sequential data analysis.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Deep learning has achieved great success in recent years in computer vision and its related areas. For core computer vision tasks such as image classification,… (more)

Subjects/Keywords: Deep learning; Sequential data analysis; Visual recognition; Video object detection; Video object tracking

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APA (6th Edition):

Shi, H. (2017). Deep learning in sequential data analysis. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99513

Chicago Manual of Style (16th Edition):

Shi, Honghui. “Deep learning in sequential data analysis.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/99513.

MLA Handbook (7th Edition):

Shi, Honghui. “Deep learning in sequential data analysis.” 2017. Web. 27 Oct 2020.

Vancouver:

Shi H. Deep learning in sequential data analysis. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/99513.

Council of Science Editors:

Shi H. Deep learning in sequential data analysis. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99513


University of Illinois – Urbana-Champaign

18. Xu, Ning. Image and video object selection.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Image and video object selection present fundamental research problems in the computer vision field and have many practical applications. They are important technologies in image… (more)

Subjects/Keywords: Object selection; Computer vision; Deep learning; Image segmentation; Video segmentation

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APA (6th Edition):

Xu, N. (2017). Image and video object selection. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99515

Chicago Manual of Style (16th Edition):

Xu, Ning. “Image and video object selection.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/99515.

MLA Handbook (7th Edition):

Xu, Ning. “Image and video object selection.” 2017. Web. 27 Oct 2020.

Vancouver:

Xu N. Image and video object selection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/99515.

Council of Science Editors:

Xu N. Image and video object selection. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99515


University of Illinois – Urbana-Champaign

19. Li, Zhen. Generative and discriminative models for person verification and efficient search.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 This dissertation studies the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or… (more)

Subjects/Keywords: Person Verification; Efficient Person Search

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APA (6th Edition):

Li, Z. (2013). Generative and discriminative models for person verification and efficient search. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44478

Chicago Manual of Style (16th Edition):

Li, Zhen. “Generative and discriminative models for person verification and efficient search.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/44478.

MLA Handbook (7th Edition):

Li, Zhen. “Generative and discriminative models for person verification and efficient search.” 2013. Web. 27 Oct 2020.

Vancouver:

Li Z. Generative and discriminative models for person verification and efficient search. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/44478.

Council of Science Editors:

Li Z. Generative and discriminative models for person verification and efficient search. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44478


University of Illinois – Urbana-Champaign

20. Wu, Xiao-Long. Tiger: tiled iterative genome assembler and approximate multi-genome aligner.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 Sequence assembly and alignments are two important stepping stones for comparative genomics. With the fast adoption of the next-generation sequencing (NGS) technologies and the coming… (more)

Subjects/Keywords: De novo genome assembly; next-generation sequencing; third-generation sequencing; iterative genome assembler; read partitioning; Multiple sequence alignment; multiple genome alignment

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APA (6th Edition):

Wu, X. (2013). Tiger: tiled iterative genome assembler and approximate multi-genome aligner. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/45618

Chicago Manual of Style (16th Edition):

Wu, Xiao-Long. “Tiger: tiled iterative genome assembler and approximate multi-genome aligner.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/45618.

MLA Handbook (7th Edition):

Wu, Xiao-Long. “Tiger: tiled iterative genome assembler and approximate multi-genome aligner.” 2013. Web. 27 Oct 2020.

Vancouver:

Wu X. Tiger: tiled iterative genome assembler and approximate multi-genome aligner. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/45618.

Council of Science Editors:

Wu X. Tiger: tiled iterative genome assembler and approximate multi-genome aligner. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/45618


University of Illinois – Urbana-Champaign

21. Tsai, Min-Hsuan. On recommendations in heterogeneous social media networks.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 In this dissertation, we study the problem of social media recommendations with a heavy emphasis on exploiting social, content and contextual information. The problem of… (more)

Subjects/Keywords: heterogeneous network; recommendations

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APA (6th Edition):

Tsai, M. (2014). On recommendations in heterogeneous social media networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46830

Chicago Manual of Style (16th Edition):

Tsai, Min-Hsuan. “On recommendations in heterogeneous social media networks.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/46830.

MLA Handbook (7th Edition):

Tsai, Min-Hsuan. “On recommendations in heterogeneous social media networks.” 2014. Web. 27 Oct 2020.

Vancouver:

Tsai M. On recommendations in heterogeneous social media networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/46830.

Council of Science Editors:

Tsai M. On recommendations in heterogeneous social media networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46830


University of Illinois – Urbana-Champaign

22. Qi, Guo-Jun. Information trust, inference and transfer in social and information networks.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 In this thesis, our overarching goal is to aggregate crowdsourced information that is collected from computing systems based on social networks and represented in information… (more)

Subjects/Keywords: information trust; information inference; information transfer; information networks; social networks

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APA (6th Edition):

Qi, G. (2014). Information trust, inference and transfer in social and information networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46854

Chicago Manual of Style (16th Edition):

Qi, Guo-Jun. “Information trust, inference and transfer in social and information networks.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/46854.

MLA Handbook (7th Edition):

Qi, Guo-Jun. “Information trust, inference and transfer in social and information networks.” 2014. Web. 27 Oct 2020.

Vancouver:

Qi G. Information trust, inference and transfer in social and information networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/46854.

Council of Science Editors:

Qi G. Information trust, inference and transfer in social and information networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46854


University of Illinois – Urbana-Champaign

23. Lin, Kai-Hsiang. Saliency in audio and visual signals.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 This dissertation studies saliency and its applications in audio and visual signals. For each portion of the signal, its saliency means the likelihood of attracting… (more)

Subjects/Keywords: Saliency Detection; License Plate Detection; Foreground Detection; Audio Visualization

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APA (6th Edition):

Lin, K. (2015). Saliency in audio and visual signals. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/73072

Chicago Manual of Style (16th Edition):

Lin, Kai-Hsiang. “Saliency in audio and visual signals.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/73072.

MLA Handbook (7th Edition):

Lin, Kai-Hsiang. “Saliency in audio and visual signals.” 2015. Web. 27 Oct 2020.

Vancouver:

Lin K. Saliency in audio and visual signals. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/73072.

Council of Science Editors:

Lin K. Saliency in audio and visual signals. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/73072


University of Illinois – Urbana-Champaign

24. Vo, Loan. Predicting learning success from patterns of pre-training magnetic resonance images.

Degree: PhD, Electrical and Computer Engineering, 2012, University of Illinois – Urbana-Champaign

 Performance in most complex cognitive and psychomotor tasks improves with training, yet the extent of improvement varies among individuals. Is it possible to forecast the… (more)

Subjects/Keywords: nonheme iron; T2*; time-averaged T2*; Magnetic resonance imaging (MRI); learning; striatum; caudate nucleus; putamen; nucleus accumbens

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APA (6th Edition):

Vo, L. (2012). Predicting learning success from patterns of pre-training magnetic resonance images. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/31976

Chicago Manual of Style (16th Edition):

Vo, Loan. “Predicting learning success from patterns of pre-training magnetic resonance images.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/31976.

MLA Handbook (7th Edition):

Vo, Loan. “Predicting learning success from patterns of pre-training magnetic resonance images.” 2012. Web. 27 Oct 2020.

Vancouver:

Vo L. Predicting learning success from patterns of pre-training magnetic resonance images. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/31976.

Council of Science Editors:

Vo L. Predicting learning success from patterns of pre-training magnetic resonance images. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/31976


University of Illinois – Urbana-Champaign

25. Liu, Xianming. Feedback convolutional neural network in applications of computer vision.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network… (more)

Subjects/Keywords: Convolutional Neural Network; Feedback; Computer Vision

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APA (6th Edition):

Liu, X. (2016). Feedback convolutional neural network in applications of computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95471

Chicago Manual of Style (16th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/95471.

MLA Handbook (7th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Web. 27 Oct 2020.

Vancouver:

Liu X. Feedback convolutional neural network in applications of computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/95471.

Council of Science Editors:

Liu X. Feedback convolutional neural network in applications of computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95471


University of Illinois – Urbana-Champaign

26. Wang, Zhangyang. Task-specific and interpretable feature learning.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 Deep learning models have had tremendous impacts in recent years, while a question has been raised by many: Is deep learning just a triumph of… (more)

Subjects/Keywords: deep learning; sparse representation

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APA (6th Edition):

Wang, Z. (2016). Task-specific and interpretable feature learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95560

Chicago Manual of Style (16th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/95560.

MLA Handbook (7th Edition):

Wang, Zhangyang. “Task-specific and interpretable feature learning.” 2016. Web. 27 Oct 2020.

Vancouver:

Wang Z. Task-specific and interpretable feature learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/95560.

Council of Science Editors:

Wang Z. Task-specific and interpretable feature learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95560


University of Illinois – Urbana-Champaign

27. Khorrami, Pooya Rezvani. How deep learning can help emotion recognition.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 As technological systems become more and more advanced, the need for including the human during the interaction process has become more apparent. One simple way… (more)

Subjects/Keywords: Emotion recognition; Deep learning; Machine learning; Computer vision; Facial expression recognition; Affective computing; Deep neural networks

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APA (6th Edition):

Khorrami, P. R. (2017). How deep learning can help emotion recognition. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97284

Chicago Manual of Style (16th Edition):

Khorrami, Pooya Rezvani. “How deep learning can help emotion recognition.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/97284.

MLA Handbook (7th Edition):

Khorrami, Pooya Rezvani. “How deep learning can help emotion recognition.” 2017. Web. 27 Oct 2020.

Vancouver:

Khorrami PR. How deep learning can help emotion recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/97284.

Council of Science Editors:

Khorrami PR. How deep learning can help emotion recognition. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97284


University of Illinois – Urbana-Champaign

28. Chidester, Benjamin Wilson. Histopathological image analysis with connections to genomics.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 The fields of imaging and genomics in cancer research have been mostly studied independently, but recently available datasets have made investigation into the synergy of… (more)

Subjects/Keywords: Histopathological image processing; Genomics; Computational biology; Computational pathology; Signal processing; Image processing

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APA (6th Edition):

Chidester, B. W. (2017). Histopathological image analysis with connections to genomics. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97764

Chicago Manual of Style (16th Edition):

Chidester, Benjamin Wilson. “Histopathological image analysis with connections to genomics.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/97764.

MLA Handbook (7th Edition):

Chidester, Benjamin Wilson. “Histopathological image analysis with connections to genomics.” 2017. Web. 27 Oct 2020.

Vancouver:

Chidester BW. Histopathological image analysis with connections to genomics. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/97764.

Council of Science Editors:

Chidester BW. Histopathological image analysis with connections to genomics. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97764

29. Ma, Chao. Design of multidimensional radiofrequency pulses in magnetic resonance imaging.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 Multidimensional radiofrequency (RF) pulses have wide applications in magnetic resonance imaging and spectroscopy experiments. These applications include reduced field-of-view (FOV) imaging of region-of-interest (ROI), B1… (more)

Subjects/Keywords: magnetic resonance imaging; radiofrequency pulses; multidimensional radiofrequency pulses; Shinnar-Le Roux radiofrequency pulse design; parallel excitation; spoke trajectory; nonlinear gradient

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APA (6th Edition):

Ma, C. (2013). Design of multidimensional radiofrequency pulses in magnetic resonance imaging. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/45448

Chicago Manual of Style (16th Edition):

Ma, Chao. “Design of multidimensional radiofrequency pulses in magnetic resonance imaging.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/45448.

MLA Handbook (7th Edition):

Ma, Chao. “Design of multidimensional radiofrequency pulses in magnetic resonance imaging.” 2013. Web. 27 Oct 2020.

Vancouver:

Ma C. Design of multidimensional radiofrequency pulses in magnetic resonance imaging. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/45448.

Council of Science Editors:

Ma C. Design of multidimensional radiofrequency pulses in magnetic resonance imaging. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/45448

30. Zhao, Bo. Fast MRI with sparse sampling: models, algorithms, and applications.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 Conventional magnetic resonance imaging (MRI) methods are based on the Shannon-Nyquist sampling theorem. The number of required Nyquist samples grows exponentially with respect to the… (more)

Subjects/Keywords: Constrained Imaging; Dynamic Imaging; Magnetic Resonance Imaging; Sparsity; Low-Rank Matrices; Parameter Estimation; Quantitative Magnetic Resonance Imaging; Medical Imaging Model-Based Magnetic Resonance Imaging

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APA (6th Edition):

Zhao, B. (2015). Fast MRI with sparse sampling: models, algorithms, and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72950

Chicago Manual of Style (16th Edition):

Zhao, Bo. “Fast MRI with sparse sampling: models, algorithms, and applications.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 27, 2020. http://hdl.handle.net/2142/72950.

MLA Handbook (7th Edition):

Zhao, Bo. “Fast MRI with sparse sampling: models, algorithms, and applications.” 2015. Web. 27 Oct 2020.

Vancouver:

Zhao B. Fast MRI with sparse sampling: models, algorithms, and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2020 Oct 27]. Available from: http://hdl.handle.net/2142/72950.

Council of Science Editors:

Zhao B. Fast MRI with sparse sampling: models, algorithms, and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72950

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