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You searched for +publisher:"University of Southern California" +contributor:("Sha, Fei"). Showing records 1 – 17 of 17 total matches.

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University of Southern California

1. Lehmann, Kjong-Van. Understanding the characteristic of single nucleotide variants.

Degree: PhD, Computational Biology and Bioinformatics, 2013, University of Southern California

 This thesis explores a variety of characteristics towards their use in ranking single nucleotide polymorphisms. The thousand genomes project and many similar ongoing large-scale sequencing… (more)

Subjects/Keywords: functional variants; genomic annotation; snp

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APA (6th Edition):

Lehmann, K. (2013). Understanding the characteristic of single nucleotide variants. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/114943/rec/7670

Chicago Manual of Style (16th Edition):

Lehmann, Kjong-Van. “Understanding the characteristic of single nucleotide variants.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/114943/rec/7670.

MLA Handbook (7th Edition):

Lehmann, Kjong-Van. “Understanding the characteristic of single nucleotide variants.” 2013. Web. 12 Nov 2019.

Vancouver:

Lehmann K. Understanding the characteristic of single nucleotide variants. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/114943/rec/7670.

Council of Science Editors:

Lehmann K. Understanding the characteristic of single nucleotide variants. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/114943/rec/7670


University of Southern California

2. Tsiartas, Andreas. Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 This thesis focuses on developing a speech‐to‐speech (S2S) translation system that utilizes paralinguistic acoustic cues for achieving successful cross‐lingual interaction. To enable that goal, research… (more)

Subjects/Keywords: speech to speech translation; voice activity detection; bilingual data extraction

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APA (6th Edition):

Tsiartas, A. (2014). Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/414269/rec/2380

Chicago Manual of Style (16th Edition):

Tsiartas, Andreas. “Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/414269/rec/2380.

MLA Handbook (7th Edition):

Tsiartas, Andreas. “Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information.” 2014. Web. 12 Nov 2019.

Vancouver:

Tsiartas A. Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/414269/rec/2380.

Council of Science Editors:

Tsiartas A. Enhancing speech to speech translation through exploitation of bilingual resources and paralinguistic information. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/414269/rec/2380


University of Southern California

3. Audhkhasi, Kartik. A computational framework for diversity in ensembles of humans and machine systems.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 My Ph.D. thesis presents a computational framework for diversity in ensembles or collections of humans and machine systems used for signal and information processing. Machine… (more)

Subjects/Keywords: signal processing; information processing; machine learning; ensemble methods; diversity

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APA (6th Edition):

Audhkhasi, K. (2014). A computational framework for diversity in ensembles of humans and machine systems. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144

Chicago Manual of Style (16th Edition):

Audhkhasi, Kartik. “A computational framework for diversity in ensembles of humans and machine systems.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144.

MLA Handbook (7th Edition):

Audhkhasi, Kartik. “A computational framework for diversity in ensembles of humans and machine systems.” 2014. Web. 12 Nov 2019.

Vancouver:

Audhkhasi K. A computational framework for diversity in ensembles of humans and machine systems. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144.

Council of Science Editors:

Audhkhasi K. A computational framework for diversity in ensembles of humans and machine systems. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/445107/rec/144


University of Southern California

4. Gong, Boqing. Kernel methods for unsupervised domain adaptation.

Degree: PhD, Computer Science, 2015, University of Southern California

 In many applications (computer vision, natural language processing, speech recognition, etc.), the curse of domain mismatch arises when the test data (of a target domain)… (more)

Subjects/Keywords: kernel methods; domain adaptation; geodesic flow kernel; landmarks; rank of domains; latent domains; sequential determinantal point process

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APA (6th Edition):

Gong, B. (2015). Kernel methods for unsupervised domain adaptation. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3702

Chicago Manual of Style (16th Edition):

Gong, Boqing. “Kernel methods for unsupervised domain adaptation.” 2015. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3702.

MLA Handbook (7th Edition):

Gong, Boqing. “Kernel methods for unsupervised domain adaptation.” 2015. Web. 12 Nov 2019.

Vancouver:

Gong B. Kernel methods for unsupervised domain adaptation. [Internet] [Doctoral dissertation]. University of Southern California; 2015. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3702.

Council of Science Editors:

Gong B. Kernel methods for unsupervised domain adaptation. [Doctoral Dissertation]. University of Southern California; 2015. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/604358/rec/3702


University of Southern California

5. Thatte, Gautam. Applications of estimation and detection theory in decentralized networks.

Degree: PhD, Electrical Engineering, 2010, University of Southern California

 We explore three applications of decentralized networks in increasingly complex detection frameworks: unconstrained binary detection in computer networks, constrained binary detection in wireless sensor networks… (more)

Subjects/Keywords: optimization; resource allocation; anomaly detection; sensor networks; wireless body-area networks

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APA (6th Edition):

Thatte, G. (2010). Applications of estimation and detection theory in decentralized networks. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/390120/rec/863

Chicago Manual of Style (16th Edition):

Thatte, Gautam. “Applications of estimation and detection theory in decentralized networks.” 2010. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/390120/rec/863.

MLA Handbook (7th Edition):

Thatte, Gautam. “Applications of estimation and detection theory in decentralized networks.” 2010. Web. 12 Nov 2019.

Vancouver:

Thatte G. Applications of estimation and detection theory in decentralized networks. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/390120/rec/863.

Council of Science Editors:

Thatte G. Applications of estimation and detection theory in decentralized networks. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/390120/rec/863


University of Southern California

6. Gong, Dian. Structure learning for manifolds and multivariate time series.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 This dissertation investigates a fundamental issue in machine learning and computer vision: unsupervised structure learning of high dimensional data from manifolds and multivariate time series,… (more)

Subjects/Keywords: machine learning; structure learning; multivariate time series; change point detection; temporal alignment; probabilistic tensor voting; manifold learning; kernel methods; nonparametric models; clustering; human motion analysis; human action recognition

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APA (6th Edition):

Gong, D. (2014). Structure learning for manifolds and multivariate time series. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/313397/rec/6126

Chicago Manual of Style (16th Edition):

Gong, Dian. “Structure learning for manifolds and multivariate time series.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/313397/rec/6126.

MLA Handbook (7th Edition):

Gong, Dian. “Structure learning for manifolds and multivariate time series.” 2014. Web. 12 Nov 2019.

Vancouver:

Gong D. Structure learning for manifolds and multivariate time series. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/313397/rec/6126.

Council of Science Editors:

Gong D. Structure learning for manifolds and multivariate time series. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/313397/rec/6126


University of Southern California

7. Ra, Moo-Ryong. Cloud-enabled mobile sensing systems.

Degree: PhD, Computer Science, 2013, University of Southern California

 Smart mobile devices have increasingly become the computing platform of choice. As distinct features, they have sensors and many useful applications based on the sensors… (more)

Subjects/Keywords: mobile cloud computing; mobile systems

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APA (6th Edition):

Ra, M. (2013). Cloud-enabled mobile sensing systems. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/303424/rec/1397

Chicago Manual of Style (16th Edition):

Ra, Moo-Ryong. “Cloud-enabled mobile sensing systems.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/303424/rec/1397.

MLA Handbook (7th Edition):

Ra, Moo-Ryong. “Cloud-enabled mobile sensing systems.” 2013. Web. 12 Nov 2019.

Vancouver:

Ra M. Cloud-enabled mobile sensing systems. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/303424/rec/1397.

Council of Science Editors:

Ra M. Cloud-enabled mobile sensing systems. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/303424/rec/1397


University of Southern California

8. Williams, Ryan K. Interaction and topology in distributed multi-agent coordination.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 Interconnected systems have become the recent focus of intense investigation, particularly in the context of autonomous coordination, yielding fundamental advantages in adaptability, scalability, and efficiency… (more)

Subjects/Keywords: distributed robot systems; dynamic networks; graph connectivity; graph rigidity; multi‐robot coordination; sensor networks

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APA (6th Edition):

Williams, R. K. (2014). Interaction and topology in distributed multi-agent coordination. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/432814/rec/3564

Chicago Manual of Style (16th Edition):

Williams, Ryan K. “Interaction and topology in distributed multi-agent coordination.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/432814/rec/3564.

MLA Handbook (7th Edition):

Williams, Ryan K. “Interaction and topology in distributed multi-agent coordination.” 2014. Web. 12 Nov 2019.

Vancouver:

Williams RK. Interaction and topology in distributed multi-agent coordination. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/432814/rec/3564.

Council of Science Editors:

Williams RK. Interaction and topology in distributed multi-agent coordination. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/432814/rec/3564


University of Southern California

9. Metallinou, Angeliki. Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare.

Degree: PhD, Electrical Engineering, 2013, University of Southern California

 Human expressive communication is characterized by the continuous flow of multimodal information, such as facial, vocal and bodily gestures, which may convey the participant's affect.… (more)

Subjects/Keywords: multimodal signal processing; speech processing; affective computing; machine learning; healthcare; autism spectrum disorders

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APA (6th Edition):

Metallinou, A. (2013). Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/318864/rec/4264

Chicago Manual of Style (16th Edition):

Metallinou, Angeliki. “Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/318864/rec/4264.

MLA Handbook (7th Edition):

Metallinou, Angeliki. “Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare.” 2013. Web. 12 Nov 2019.

Vancouver:

Metallinou A. Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/318864/rec/4264.

Council of Science Editors:

Metallinou A. Multimodality, context and continuous dynamics for recognition and analysis of emotional states, and applications in healthcare. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/318864/rec/4264


University of Southern California

10. Das, Abhimanyu. Subset selection algorithms for prediction.

Degree: PhD, Computer Science, 2011, University of Southern California

 In this dissertation, we study the subset selection problem for prediction. It deals with choosing the “best” or “most informative” k-subset from a large set… (more)

Subjects/Keywords: approximation algorithms; machine learning; regression; feature selection; sparse approximation; compressed sensing; submodularity

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APA (6th Edition):

Das, A. (2011). Subset selection algorithms for prediction. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6188

Chicago Manual of Style (16th Edition):

Das, Abhimanyu. “Subset selection algorithms for prediction.” 2011. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6188.

MLA Handbook (7th Edition):

Das, Abhimanyu. “Subset selection algorithms for prediction.” 2011. Web. 12 Nov 2019.

Vancouver:

Das A. Subset selection algorithms for prediction. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6188.

Council of Science Editors:

Das A. Subset selection algorithms for prediction. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6188


University of Southern California

11. Mower, Emily K. Emotions in engineering: methods for the interpretation of ambiguous emotional content.

Degree: PhD, Electrical Engineering, 2010, University of Southern California

 Emotion has intrigued researchers for generations. This fascination has permeated the engineering community, motivating the development of affective computational models for the classification of affective… (more)

Subjects/Keywords: emotion; perception; emotion profiles; emotion classification; emotion representation; audio-visual emotion; audio-visual emotion perception; multimodal emotion expression; facial emotion expression; McGurk effect; hidden Markov model; agglomerative hierarchical clustering; expressive animation; multimodality; affective computing

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APA (6th Edition):

Mower, E. K. (2010). Emotions in engineering: methods for the interpretation of ambiguous emotional content. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2313

Chicago Manual of Style (16th Edition):

Mower, Emily K. “Emotions in engineering: methods for the interpretation of ambiguous emotional content.” 2010. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2313.

MLA Handbook (7th Edition):

Mower, Emily K. “Emotions in engineering: methods for the interpretation of ambiguous emotional content.” 2010. Web. 12 Nov 2019.

Vancouver:

Mower EK. Emotions in engineering: methods for the interpretation of ambiguous emotional content. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2313.

Council of Science Editors:

Mower EK. Emotions in engineering: methods for the interpretation of ambiguous emotional content. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/419710/rec/2313


University of Southern California

12. Vathsangam, Harshvardhan. Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors.

Degree: PhD, Computer Science, 2014, University of Southern California

 Healthcare is undergoing a paradigm shift from the episodic, expert-driven, curative approaches of the past towards a self-empowered, preventative model for the future. Central to… (more)

Subjects/Keywords: accelerometer; machine learning; gyroscope; mobile phone; energy expenditure; statistical; regression; Gaussian process regression; Bayesian; Fourier transform; walking; physical activity

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APA (6th Edition):

Vathsangam, H. (2014). Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/308578/rec/5785

Chicago Manual of Style (16th Edition):

Vathsangam, Harshvardhan. “Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/308578/rec/5785.

MLA Handbook (7th Edition):

Vathsangam, Harshvardhan. “Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors.” 2014. Web. 12 Nov 2019.

Vancouver:

Vathsangam H. Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/308578/rec/5785.

Council of Science Editors:

Vathsangam H. Sense and sensibility: statistical techniques for human energy expenditure estimation using kinematic sensors. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/308578/rec/5785


University of Southern California

13. Ting, Jo-Anne Su Yin. Bayesian methods for autonomous learning systems.

Degree: PhD, Computer Science, 2009, University of Southern California

 We propose a set of Bayesian methods that help us toward the goal of autonomous learning systems. Systems that can react autonomously, with minimal human… (more)

Subjects/Keywords: autonomous learning systems; Bayesian statistics; function approximation; machine learning; real-time learning; regression

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APA (6th Edition):

Ting, J. S. Y. (2009). Bayesian methods for autonomous learning systems. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/151821/rec/1042

Chicago Manual of Style (16th Edition):

Ting, Jo-Anne Su Yin. “Bayesian methods for autonomous learning systems.” 2009. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/151821/rec/1042.

MLA Handbook (7th Edition):

Ting, Jo-Anne Su Yin. “Bayesian methods for autonomous learning systems.” 2009. Web. 12 Nov 2019.

Vancouver:

Ting JSY. Bayesian methods for autonomous learning systems. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/151821/rec/1042.

Council of Science Editors:

Ting JSY. Bayesian methods for autonomous learning systems. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/151821/rec/1042


University of Southern California

14. Qi, Feng. Computational transcranial magnetic stimulation (TMS).

Degree: PhD, Neuroscience, 2010, University of Southern California

 Transcranial Magnetic Stimulation (TMS) is a noninvasive brain stimulation technique that is increasingly used in clinical neuroscience research. Since the invention of TMS in 1985,… (more)

Subjects/Keywords: transcranial magnetic stimulation; TMS; neuroscience; brain stimulation; motor cortex; machine learning; computation

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APA (6th Edition):

Qi, F. (2010). Computational transcranial magnetic stimulation (TMS). (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/332750/rec/1551

Chicago Manual of Style (16th Edition):

Qi, Feng. “Computational transcranial magnetic stimulation (TMS).” 2010. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/332750/rec/1551.

MLA Handbook (7th Edition):

Qi, Feng. “Computational transcranial magnetic stimulation (TMS).” 2010. Web. 12 Nov 2019.

Vancouver:

Qi F. Computational transcranial magnetic stimulation (TMS). [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/332750/rec/1551.

Council of Science Editors:

Qi F. Computational transcranial magnetic stimulation (TMS). [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/332750/rec/1551


University of Southern California

15. Tan, Qun Feng. Novel variations of sparse representation techniques with applications.

Degree: PhD, Electrical Engineering, 2013, University of Southern California

 This thesis proposes novel variations of Sparse Representation techniques and shows successful applications to a variety of fields such as Automatic Speech Recognition (ASR) denoising,… (more)

Subjects/Keywords: algorithms; convex optimization; machine learning; pattern recognition; sparse representation; speech processing

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APA (6th Edition):

Tan, Q. F. (2013). Novel variations of sparse representation techniques with applications. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/241019/rec/4469

Chicago Manual of Style (16th Edition):

Tan, Qun Feng. “Novel variations of sparse representation techniques with applications.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/241019/rec/4469.

MLA Handbook (7th Edition):

Tan, Qun Feng. “Novel variations of sparse representation techniques with applications.” 2013. Web. 12 Nov 2019.

Vancouver:

Tan QF. Novel variations of sparse representation techniques with applications. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/241019/rec/4469.

Council of Science Editors:

Tan QF. Novel variations of sparse representation techniques with applications. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/241019/rec/4469


University of Southern California

16. Song, Qiang. Whole genome bisulfite sequencing: analytical methods and biological insights.

Degree: PhD, Computational Biology and Bioinformatics, 2013, University of Southern California

 Whole genome bisulfite sequencing is a powerful technique for profiling methylation patterns, which provides measurement of methylation levels of individual cytosine sites across the genome.… (more)

Subjects/Keywords: differentiation; epigenetics; hidden Markov model; methylation; sequencing; statistics

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

APA (6th Edition):

Song, Q. (2013). Whole genome bisulfite sequencing: analytical methods and biological insights. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/229360/rec/7925

Chicago Manual of Style (16th Edition):

Song, Qiang. “Whole genome bisulfite sequencing: analytical methods and biological insights.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/229360/rec/7925.

MLA Handbook (7th Edition):

Song, Qiang. “Whole genome bisulfite sequencing: analytical methods and biological insights.” 2013. Web. 12 Nov 2019.

Vancouver:

Song Q. Whole genome bisulfite sequencing: analytical methods and biological insights. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/229360/rec/7925.

Council of Science Editors:

Song Q. Whole genome bisulfite sequencing: analytical methods and biological insights. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/229360/rec/7925


University of Southern California

17. Li, Ming. Representation, classification and information fusion for robust and efficient multimodal human states recognition.

Degree: PhD, Electrical Engineering, 2013, University of Southern California

 The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered… (more)

Subjects/Keywords: human state characterization; speaker verification; language identification; multimodal biometrics; emotion recognition; simplified supervised i-vector; sparse representation; physical activity recognition; ECG processing; speech production; articulation; vocal tract morphology

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

APA (6th Edition):

Li, M. (2013). Representation, classification and information fusion for robust and efficient multimodal human states recognition. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/317515/rec/5533

Chicago Manual of Style (16th Edition):

Li, Ming. “Representation, classification and information fusion for robust and efficient multimodal human states recognition.” 2013. Doctoral Dissertation, University of Southern California. Accessed November 12, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/317515/rec/5533.

MLA Handbook (7th Edition):

Li, Ming. “Representation, classification and information fusion for robust and efficient multimodal human states recognition.” 2013. Web. 12 Nov 2019.

Vancouver:

Li M. Representation, classification and information fusion for robust and efficient multimodal human states recognition. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2019 Nov 12]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/317515/rec/5533.

Council of Science Editors:

Li M. Representation, classification and information fusion for robust and efficient multimodal human states recognition. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/317515/rec/5533

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