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You searched for +publisher:"Texas State University – San Marcos" +contributor:("Lu, Yijuan"). Showing records 1 – 17 of 17 total matches.

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Texas State University – San Marcos

1. Li, Bo. Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries.

Degree: MS, Applied Mathematics, 2015, Texas State University – San Marcos

 Effectively and efficiently retrieving relevant 3D models (digital representation of objects in computer) for a 2D sketch query is important for various related appli- cations.… (more)

Subjects/Keywords: Sketch-based 3D model retrieval; Semantics; WordNet; Computer vision; Optical data processing; Image processing

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APA (6th Edition):

Li, B. (2015). Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5734

Chicago Manual of Style (16th Edition):

Li, Bo. “Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/5734.

MLA Handbook (7th Edition):

Li, Bo. “Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries.” 2015. Web. 18 Jan 2020.

Vancouver:

Li B. Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/5734.

Council of Science Editors:

Li B. Semantic Tree-based 3D Model Retrieval Using 2D Sketch Queries. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5734


Texas State University – San Marcos

2. Hinkle, Lee B. Determination of Emotional State through Physiological Measurement.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 The goal of this thesis is to develop and evaluate methods of emotional response classification using human physiological data. With the continued development of automated… (more)

Subjects/Keywords: Emotion Biosignal Machine Learning; Emotions; Psychophysiology

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APA (6th Edition):

Hinkle, L. B. (2016). Determination of Emotional State through Physiological Measurement. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6411

Chicago Manual of Style (16th Edition):

Hinkle, Lee B. “Determination of Emotional State through Physiological Measurement.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6411.

MLA Handbook (7th Edition):

Hinkle, Lee B. “Determination of Emotional State through Physiological Measurement.” 2016. Web. 18 Jan 2020.

Vancouver:

Hinkle LB. Determination of Emotional State through Physiological Measurement. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6411.

Council of Science Editors:

Hinkle LB. Determination of Emotional State through Physiological Measurement. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6411


Texas State University – San Marcos

3. Chen, Xinbo. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 In recent years, convolutional neural network (CNN) has been widely used to improve the training time and accuracy of image recognition applications. These CNNs are… (more)

Subjects/Keywords: Neural network; CNN; Energy efficiency

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APA (6th Edition):

Chen, X. (2016). Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6853

Chicago Manual of Style (16th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6853.

MLA Handbook (7th Edition):

Chen, Xinbo. “Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition.” 2016. Web. 18 Jan 2020.

Vancouver:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6853.

Council of Science Editors:

Chen X. Energy Efficiency Analysis and Optimization of Convolutional Neural Networks For Image Recognition. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6853


Texas State University – San Marcos

4. Ye, Yuxiang. Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 Human’s sketch understanding is an important and challenging research problem. It has many applications in human computer interaction, multimedia, and computer vision. Most previous methods… (more)

Subjects/Keywords: Deep learning; Convolutional neural network; Pattern recognition; Computer vision; Optical data processing; Computer graphics; Image processing – Digital techniques

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APA (6th Edition):

Ye, Y. (2016). Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6050

Chicago Manual of Style (16th Edition):

Ye, Yuxiang. “Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6050.

MLA Handbook (7th Edition):

Ye, Yuxiang. “Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval.” 2016. Web. 18 Jan 2020.

Vancouver:

Ye Y. Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6050.

Council of Science Editors:

Ye Y. Applying deep learning to scene sketch recognition and 3D sketch-based 3D model retrieval. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6050


Texas State University – San Marcos

5. Mahajan, Divya. Energy efficiency analysis and optimization of relational and NoSQL databases.

Degree: MS, Computer Science, 2016, Texas State University – San Marcos

 As big data becomes the norm of various industrial applications, the complexity of database workloads and database system design has increased significantly. To address these… (more)

Subjects/Keywords: Energy efficiency; Optimization; Relational databases; NoSQL; MongoDB; DVFS; Cassandra; MySQL

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APA (6th Edition):

Mahajan, D. (2016). Energy efficiency analysis and optimization of relational and NoSQL databases. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6963

Chicago Manual of Style (16th Edition):

Mahajan, Divya. “Energy efficiency analysis and optimization of relational and NoSQL databases.” 2016. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6963.

MLA Handbook (7th Edition):

Mahajan, Divya. “Energy efficiency analysis and optimization of relational and NoSQL databases.” 2016. Web. 18 Jan 2020.

Vancouver:

Mahajan D. Energy efficiency analysis and optimization of relational and NoSQL databases. [Internet] [Masters thesis]. Texas State University – San Marcos; 2016. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6963.

Council of Science Editors:

Mahajan D. Energy efficiency analysis and optimization of relational and NoSQL databases. [Masters Thesis]. Texas State University – San Marcos; 2016. Available from: https://digital.library.txstate.edu/handle/10877/6963


Texas State University – San Marcos

6. Sarangkar, Santosh R. PTUNE: A Tool for Online Autotuning with Optimizing Compilers.

Degree: MS, Computer Science, 2011, Texas State University – San Marcos

 Harnessing the full potential of complex architectures has been a perennial challenge for the high- performance computing community. Each new architecture has brought with it… (more)

Subjects/Keywords: Compiler Optimizations; Performance Tuning; Self-tuning; Autotuning; Compilers; Performance Improvement for Scientific Applications

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APA (6th Edition):

Sarangkar, S. R. (2011). PTUNE: A Tool for Online Autotuning with Optimizing Compilers. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/2466

Chicago Manual of Style (16th Edition):

Sarangkar, Santosh R. “PTUNE: A Tool for Online Autotuning with Optimizing Compilers.” 2011. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/2466.

MLA Handbook (7th Edition):

Sarangkar, Santosh R. “PTUNE: A Tool for Online Autotuning with Optimizing Compilers.” 2011. Web. 18 Jan 2020.

Vancouver:

Sarangkar SR. PTUNE: A Tool for Online Autotuning with Optimizing Compilers. [Internet] [Masters thesis]. Texas State University – San Marcos; 2011. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/2466.

Council of Science Editors:

Sarangkar SR. PTUNE: A Tool for Online Autotuning with Optimizing Compilers. [Masters Thesis]. Texas State University – San Marcos; 2011. Available from: https://digital.library.txstate.edu/handle/10877/2466


Texas State University – San Marcos

7. Bulgerin, Travis. 3D Sketch Recognition Using The Microsoft Kinect.

Degree: MS, Computer Science, 2014, Texas State University – San Marcos

 The concept of sketch-based recognition has recently been used to enhance object categorization and speed up image retrieval. However, in each of the previous studies,… (more)

Subjects/Keywords: Sketch; Recognition; Computer graphics; Optical data processing; Computer-aided design; Image processing; Optical pattern recognition; Pattern recognition systems; Human-computer interaction

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APA (6th Edition):

Bulgerin, T. (2014). 3D Sketch Recognition Using The Microsoft Kinect. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4988

Chicago Manual of Style (16th Edition):

Bulgerin, Travis. “3D Sketch Recognition Using The Microsoft Kinect.” 2014. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/4988.

MLA Handbook (7th Edition):

Bulgerin, Travis. “3D Sketch Recognition Using The Microsoft Kinect.” 2014. Web. 18 Jan 2020.

Vancouver:

Bulgerin T. 3D Sketch Recognition Using The Microsoft Kinect. [Internet] [Masters thesis]. Texas State University – San Marcos; 2014. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/4988.

Council of Science Editors:

Bulgerin T. 3D Sketch Recognition Using The Microsoft Kinect. [Masters Thesis]. Texas State University – San Marcos; 2014. Available from: https://digital.library.txstate.edu/handle/10877/4988


Texas State University – San Marcos

8. Coker, Dustin G. Object Recognition using Moments of the Signature Histogram.

Degree: MS, Computer Science, 2017, Texas State University – San Marcos

 The amount of digital information generated each day is increasing at a very high rate. Our ability to understand and make sense of such large… (more)

Subjects/Keywords: Object recognition; Moments; Digital signature; Image processing – Digital techniques

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APA (6th Edition):

Coker, D. G. (2017). Object Recognition using Moments of the Signature Histogram. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6572

Chicago Manual of Style (16th Edition):

Coker, Dustin G. “Object Recognition using Moments of the Signature Histogram.” 2017. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6572.

MLA Handbook (7th Edition):

Coker, Dustin G. “Object Recognition using Moments of the Signature Histogram.” 2017. Web. 18 Jan 2020.

Vancouver:

Coker DG. Object Recognition using Moments of the Signature Histogram. [Internet] [Masters thesis]. Texas State University – San Marcos; 2017. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6572.

Council of Science Editors:

Coker DG. Object Recognition using Moments of the Signature Histogram. [Masters Thesis]. Texas State University – San Marcos; 2017. Available from: https://digital.library.txstate.edu/handle/10877/6572


Texas State University – San Marcos

9. Ring, Patrick D. Movement Classification and Analysis from RGB – D Video Data.

Degree: MS, Computer Science, 2019, Texas State University – San Marcos

 The aim of this thesis is to develop and evaluate methods of human movement classification using motion tracking data captured using a RGB-D sensor. As… (more)

Subjects/Keywords: Kinect; Classification; Computer science; Image processing – Digital techniques; Image analysis

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APA (6th Edition):

Ring, P. D. (2019). Movement Classification and Analysis from RGB – D Video Data. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8325

Chicago Manual of Style (16th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/8325.

MLA Handbook (7th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Web. 18 Jan 2020.

Vancouver:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/8325.

Council of Science Editors:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/8325


Texas State University – San Marcos

10. Fang, Shaomin. Learning Image Saliency from Human Touch Behaviors.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 The concept of touch saliency has recently been introduced as a possible alternative for eye tracking in usability studies. This touch saliency study shows that… (more)

Subjects/Keywords: Visual Attention; Touch Saliency; Image Saliency; Touch Behaviors.

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APA (6th Edition):

Fang, S. (2013). Learning Image Saliency from Human Touch Behaviors. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/6271

Chicago Manual of Style (16th Edition):

Fang, Shaomin. “Learning Image Saliency from Human Touch Behaviors.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/6271.

MLA Handbook (7th Edition):

Fang, Shaomin. “Learning Image Saliency from Human Touch Behaviors.” 2013. Web. 18 Jan 2020.

Vancouver:

Fang S. Learning Image Saliency from Human Touch Behaviors. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/6271.

Council of Science Editors:

Fang S. Learning Image Saliency from Human Touch Behaviors. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/6271


Texas State University – San Marcos

11. Cleveland, Seth. MASFA: Mass-collaborative faceted search for online communities.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 Faceted search combines faceted navigation with direct keyword search, providing exploratory search capacities allowing progressive query refinement. It has become the de facto standard for… (more)

Subjects/Keywords: Faceted Search; Mass Collaboration; Direct Search; Exploratory Search; Search; Internet searching; Faceted classification

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APA (6th Edition):

Cleveland, S. (2013). MASFA: Mass-collaborative faceted search for online communities. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4853

Chicago Manual of Style (16th Edition):

Cleveland, Seth. “MASFA: Mass-collaborative faceted search for online communities.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/4853.

MLA Handbook (7th Edition):

Cleveland, Seth. “MASFA: Mass-collaborative faceted search for online communities.” 2013. Web. 18 Jan 2020.

Vancouver:

Cleveland S. MASFA: Mass-collaborative faceted search for online communities. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/4853.

Council of Science Editors:

Cleveland S. MASFA: Mass-collaborative faceted search for online communities. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4853


Texas State University – San Marcos

12. Martinez Torres, Jose Antonio. Investigating comparison-based evaluation for sparse data.

Degree: MS, Computer Science, 2014, Texas State University – San Marcos

 Evaluation is ubiquitous. Often we need to evaluate a set of target entities (movies, restaurants, products, courses, paper submissions) and obtain their true ratings (average… (more)

Subjects/Keywords: Ranking; Rank aggregation; Evaluation; Database management; Computational complexity

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APA (6th Edition):

Martinez Torres, J. A. (2014). Investigating comparison-based evaluation for sparse data. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5293

Chicago Manual of Style (16th Edition):

Martinez Torres, Jose Antonio. “Investigating comparison-based evaluation for sparse data.” 2014. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/5293.

MLA Handbook (7th Edition):

Martinez Torres, Jose Antonio. “Investigating comparison-based evaluation for sparse data.” 2014. Web. 18 Jan 2020.

Vancouver:

Martinez Torres JA. Investigating comparison-based evaluation for sparse data. [Internet] [Masters thesis]. Texas State University – San Marcos; 2014. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/5293.

Council of Science Editors:

Martinez Torres JA. Investigating comparison-based evaluation for sparse data. [Masters Thesis]. Texas State University – San Marcos; 2014. Available from: https://digital.library.txstate.edu/handle/10877/5293


Texas State University – San Marcos

13. Zemede, Binyam A. SPEAR: Search Personalization with Editable Profiles.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 Search personalization is an important technique for improving search performance. Existing approaches work in a black box, where users have no clue on how it… (more)

Subjects/Keywords: Information retrieval; Web search; Search personalization; Google; Facebook; Information technology; Data mining; Information retrieval; Human-computer interaction; User-centered system design

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APA (6th Edition):

Zemede, B. A. (2013). SPEAR: Search Personalization with Editable Profiles. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4881

Chicago Manual of Style (16th Edition):

Zemede, Binyam A. “SPEAR: Search Personalization with Editable Profiles.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/4881.

MLA Handbook (7th Edition):

Zemede, Binyam A. “SPEAR: Search Personalization with Editable Profiles.” 2013. Web. 18 Jan 2020.

Vancouver:

Zemede BA. SPEAR: Search Personalization with Editable Profiles. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/4881.

Council of Science Editors:

Zemede BA. SPEAR: Search Personalization with Editable Profiles. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4881


Texas State University – San Marcos

14. Phillips, Clark Raymond. Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records.

Degree: MS, Computer Science, 2015, Texas State University – San Marcos

 When collecting person records for census, identifying individuals accurately is paramount. Over time, people change their phone numbers, their addresses, even their names. Without a… (more)

Subjects/Keywords: Decision trees; Machine learning; Alternating decision tree; Computer science – Mathematics; Combinatorial analysis

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APA (6th Edition):

Phillips, C. R. (2015). Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5576

Chicago Manual of Style (16th Edition):

Phillips, Clark Raymond. “Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/5576.

MLA Handbook (7th Edition):

Phillips, Clark Raymond. “Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records.” 2015. Web. 18 Jan 2020.

Vancouver:

Phillips CR. Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/5576.

Council of Science Editors:

Phillips CR. Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5576


Texas State University – San Marcos

15. Hanz, Trevor R. An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems.

Degree: MS, Computer Science, 2013, Texas State University – San Marcos

 Mobile Cyber-Physical Systems (CPSs) widely vary in the underlying hardware technologies and capabilities of their mobile devices. This makes the problem of developing portable high-level… (more)

Subjects/Keywords: Mobile Cyber-Physical System; Automation; Mobile Device; Automatic control; Cybernetics; Systems engineering

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APA (6th Edition):

Hanz, T. R. (2013). An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/4600

Chicago Manual of Style (16th Edition):

Hanz, Trevor R. “An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems.” 2013. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/4600.

MLA Handbook (7th Edition):

Hanz, Trevor R. “An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems.” 2013. Web. 18 Jan 2020.

Vancouver:

Hanz TR. An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems. [Internet] [Masters thesis]. Texas State University – San Marcos; 2013. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/4600.

Council of Science Editors:

Hanz TR. An Abstraction Layer for Controlling Heterogeneous Mobile Cyber-Physical Systems. [Masters Thesis]. Texas State University – San Marcos; 2013. Available from: https://digital.library.txstate.edu/handle/10877/4600


Texas State University – San Marcos

16. Anastasiu, Dragos. Personalized and Collaborative Clustering of Search Results.

Degree: MS, Computer Science, 2011, Texas State University – San Marcos

 Organizing and presenting search results plays a critical role in the utility of search engines. Due to the unprecedented scale of the Web and diversity… (more)

Subjects/Keywords: Document clustering; Database searching; Wikis; Computer interfaces

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APA (6th Edition):

Anastasiu, D. (2011). Personalized and Collaborative Clustering of Search Results. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8892

Chicago Manual of Style (16th Edition):

Anastasiu, Dragos. “Personalized and Collaborative Clustering of Search Results.” 2011. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/8892.

MLA Handbook (7th Edition):

Anastasiu, Dragos. “Personalized and Collaborative Clustering of Search Results.” 2011. Web. 18 Jan 2020.

Vancouver:

Anastasiu D. Personalized and Collaborative Clustering of Search Results. [Internet] [Masters thesis]. Texas State University – San Marcos; 2011. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/8892.

Council of Science Editors:

Anastasiu D. Personalized and Collaborative Clustering of Search Results. [Masters Thesis]. Texas State University – San Marcos; 2011. Available from: https://digital.library.txstate.edu/handle/10877/8892

17. Holland, Corey D. Development, Evaluation, and Analysis of Complex Eye Movement Biometrics.

Degree: MS, Computer Science, 2015, Texas State University – San Marcos

 Eye movements present a novel and unique solution to the challenges faced by modern biometrics. Consisting of both physical and neurological components, and due to… (more)

Subjects/Keywords: Biometrics; Eye Tracking; Biometric identification; Human-machine systems; Biomedical engineering; Eye movements

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

APA (6th Edition):

Holland, C. D. (2015). Development, Evaluation, and Analysis of Complex Eye Movement Biometrics. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/5515

Chicago Manual of Style (16th Edition):

Holland, Corey D. “Development, Evaluation, and Analysis of Complex Eye Movement Biometrics.” 2015. Masters Thesis, Texas State University – San Marcos. Accessed January 18, 2020. https://digital.library.txstate.edu/handle/10877/5515.

MLA Handbook (7th Edition):

Holland, Corey D. “Development, Evaluation, and Analysis of Complex Eye Movement Biometrics.” 2015. Web. 18 Jan 2020.

Vancouver:

Holland CD. Development, Evaluation, and Analysis of Complex Eye Movement Biometrics. [Internet] [Masters thesis]. Texas State University – San Marcos; 2015. [cited 2020 Jan 18]. Available from: https://digital.library.txstate.edu/handle/10877/5515.

Council of Science Editors:

Holland CD. Development, Evaluation, and Analysis of Complex Eye Movement Biometrics. [Masters Thesis]. Texas State University – San Marcos; 2015. Available from: https://digital.library.txstate.edu/handle/10877/5515

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