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Dept: Electrical Engineering

You searched for subject:(classification). Showing records 1 – 30 of 109 total matches.

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Texas A&M University

1. Timmaraju, Virisha. Automatic Classification of Microlensing Candidates.

Degree: MS, Electrical Engineering, 2018, Texas A&M University

 It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need… (more)

Subjects/Keywords: Microlensing; Machine Learning; Astronomy; Automatic Classification

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APA (6th Edition):

Timmaraju, V. (2018). Automatic Classification of Microlensing Candidates. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/172787

Chicago Manual of Style (16th Edition):

Timmaraju, Virisha. “Automatic Classification of Microlensing Candidates.” 2018. Masters Thesis, Texas A&M University. Accessed July 18, 2019. http://hdl.handle.net/1969.1/172787.

MLA Handbook (7th Edition):

Timmaraju, Virisha. “Automatic Classification of Microlensing Candidates.” 2018. Web. 18 Jul 2019.

Vancouver:

Timmaraju V. Automatic Classification of Microlensing Candidates. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/1969.1/172787.

Council of Science Editors:

Timmaraju V. Automatic Classification of Microlensing Candidates. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/172787


Penn State University

2. Pawar, Saurabh. Higher Order Modulation Recognition Using Approximate Entropy.

Degree: MS, Electrical Engineering, 2010, Penn State University

 Modulation recognition finds its application in today’s cognitive systems ranging from civilian to military installations. Existing modulation classification algorithms include classic likelihood approaches and feature… (more)

Subjects/Keywords: cognitive radio; modulation classification; approximate entropy

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APA (6th Edition):

Pawar, S. (2010). Higher Order Modulation Recognition Using Approximate Entropy. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11201

Chicago Manual of Style (16th Edition):

Pawar, Saurabh. “Higher Order Modulation Recognition Using Approximate Entropy.” 2010. Masters Thesis, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/11201.

MLA Handbook (7th Edition):

Pawar, Saurabh. “Higher Order Modulation Recognition Using Approximate Entropy.” 2010. Web. 18 Jul 2019.

Vancouver:

Pawar S. Higher Order Modulation Recognition Using Approximate Entropy. [Internet] [Masters thesis]. Penn State University; 2010. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/11201.

Council of Science Editors:

Pawar S. Higher Order Modulation Recognition Using Approximate Entropy. [Masters Thesis]. Penn State University; 2010. Available from: https://etda.libraries.psu.edu/catalog/11201


Penn State University

3. Bissinger, Brett. Minimum Hellinger Distance Classification of Underwater Acoustic Signals.

Degree: MS, Electrical Engineering, 2009, Penn State University

 Passive source classification in the underwater environment is a challenging problem in part because propagation through the space- and time-varying medium introduces variability and uncertainty… (more)

Subjects/Keywords: underwater acoustics; hellinger distance; minimum distance; classification

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APA (6th Edition):

Bissinger, B. (2009). Minimum Hellinger Distance Classification of Underwater Acoustic Signals. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/10271

Chicago Manual of Style (16th Edition):

Bissinger, Brett. “Minimum Hellinger Distance Classification of Underwater Acoustic Signals.” 2009. Masters Thesis, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/10271.

MLA Handbook (7th Edition):

Bissinger, Brett. “Minimum Hellinger Distance Classification of Underwater Acoustic Signals.” 2009. Web. 18 Jul 2019.

Vancouver:

Bissinger B. Minimum Hellinger Distance Classification of Underwater Acoustic Signals. [Internet] [Masters thesis]. Penn State University; 2009. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/10271.

Council of Science Editors:

Bissinger B. Minimum Hellinger Distance Classification of Underwater Acoustic Signals. [Masters Thesis]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/10271


Penn State University

4. Zhang, Yanxin. Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery.

Degree: PhD, Electrical Engineering, 2009, Penn State University

 The maximum entropy (ME) principle has been widely applied to specialized applications in statistical learning and pattern recognition. The concept of ME method is to… (more)

Subjects/Keywords: interaction discovery; maximum entropy; distributed classification; regression

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APA (6th Edition):

Zhang, Y. (2009). Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/9788

Chicago Manual of Style (16th Edition):

Zhang, Yanxin. “Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery.” 2009. Doctoral Dissertation, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/9788.

MLA Handbook (7th Edition):

Zhang, Yanxin. “Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery.” 2009. Web. 18 Jul 2019.

Vancouver:

Zhang Y. Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery. [Internet] [Doctoral dissertation]. Penn State University; 2009. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/9788.

Council of Science Editors:

Zhang Y. Maximum Entropy Modeling for Distributed Classification, Regression, and Interaction Discovery. [Doctoral Dissertation]. Penn State University; 2009. Available from: https://etda.libraries.psu.edu/catalog/9788


Florida International University

5. Jing, Xueyan. Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection.

Degree: PhD, Electrical Engineering, 2016, Florida International University

  Intrusion detection is the essential part of network security in combating against illegal network access or malicious cyberattacks. Due to the constantly evolving nature… (more)

Subjects/Keywords: intrusion detection; classification; fuzzy; Dempster-shafer theory

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APA (6th Edition):

Jing, X. (2016). Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection. (Doctoral Dissertation). Florida International University. Retrieved from http://digitalcommons.fiu.edu/etd/2436 ; 10.25148/etd.FIDC000288 ; FIDC000288

Chicago Manual of Style (16th Edition):

Jing, Xueyan. “Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection.” 2016. Doctoral Dissertation, Florida International University. Accessed July 18, 2019. http://digitalcommons.fiu.edu/etd/2436 ; 10.25148/etd.FIDC000288 ; FIDC000288.

MLA Handbook (7th Edition):

Jing, Xueyan. “Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection.” 2016. Web. 18 Jul 2019.

Vancouver:

Jing X. Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection. [Internet] [Doctoral dissertation]. Florida International University; 2016. [cited 2019 Jul 18]. Available from: http://digitalcommons.fiu.edu/etd/2436 ; 10.25148/etd.FIDC000288 ; FIDC000288.

Council of Science Editors:

Jing X. Innovative Two-Stage Fuzzy Classification for Unknown Intrusion Detection. [Doctoral Dissertation]. Florida International University; 2016. Available from: http://digitalcommons.fiu.edu/etd/2436 ; 10.25148/etd.FIDC000288 ; FIDC000288


Virginia Tech

6. Clark, William H. IV. Blind Comprehension of Waveforms through Statistical Observations.

Degree: MS, Electrical Engineering, 2015, Virginia Tech

 This paper proposes a cumulant based classification means to identify waveforms for a blind receiver in the presence of time varying channels, which is built… (more)

Subjects/Keywords: communications; modulation classification; cumulant; search engine platform

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APA (6th Edition):

Clark, W. H. I. (2015). Blind Comprehension of Waveforms through Statistical Observations. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52908

Chicago Manual of Style (16th Edition):

Clark, William H IV. “Blind Comprehension of Waveforms through Statistical Observations.” 2015. Masters Thesis, Virginia Tech. Accessed July 18, 2019. http://hdl.handle.net/10919/52908.

MLA Handbook (7th Edition):

Clark, William H IV. “Blind Comprehension of Waveforms through Statistical Observations.” 2015. Web. 18 Jul 2019.

Vancouver:

Clark WHI. Blind Comprehension of Waveforms through Statistical Observations. [Internet] [Masters thesis]. Virginia Tech; 2015. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/10919/52908.

Council of Science Editors:

Clark WHI. Blind Comprehension of Waveforms through Statistical Observations. [Masters Thesis]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52908


Texas A&M University

7. Timmaraju, Virisha. Automatic Classification of Microlensing Candidates.

Degree: MS, Electrical Engineering, 2018, Texas A&M University

 It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need… (more)

Subjects/Keywords: Microlensing; Machine Learning; Astronomy; Automatic Classification

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

APA (6th Edition):

Timmaraju, V. (2018). Automatic Classification of Microlensing Candidates. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/174097

Chicago Manual of Style (16th Edition):

Timmaraju, Virisha. “Automatic Classification of Microlensing Candidates.” 2018. Masters Thesis, Texas A&M University. Accessed July 18, 2019. http://hdl.handle.net/1969.1/174097.

MLA Handbook (7th Edition):

Timmaraju, Virisha. “Automatic Classification of Microlensing Candidates.” 2018. Web. 18 Jul 2019.

Vancouver:

Timmaraju V. Automatic Classification of Microlensing Candidates. [Internet] [Masters thesis]. Texas A&M University; 2018. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/1969.1/174097.

Council of Science Editors:

Timmaraju V. Automatic Classification of Microlensing Candidates. [Masters Thesis]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/174097


Vanderbilt University

8. Nik Hashim, Nik Nur Wahidah. Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II).

Degree: PhD, Electrical Engineering, 2014, Vanderbilt University

 Patients who are diagnosed with depression without appropriate clinical recognition of their hidden suicidal tendencies are at elevated risk of making suicide attempts. An important… (more)

Subjects/Keywords: speech; suicide; regression; classification; depression; pauses

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APA (6th Edition):

Nik Hashim, N. N. W. (2014). Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II). (Doctoral Dissertation). Vanderbilt University. Retrieved from http://etd.library.vanderbilt.edu//available/etd-03192014-065659/ ;

Chicago Manual of Style (16th Edition):

Nik Hashim, Nik Nur Wahidah. “Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II).” 2014. Doctoral Dissertation, Vanderbilt University. Accessed July 18, 2019. http://etd.library.vanderbilt.edu//available/etd-03192014-065659/ ;.

MLA Handbook (7th Edition):

Nik Hashim, Nik Nur Wahidah. “Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II).” 2014. Web. 18 Jul 2019.

Vancouver:

Nik Hashim NNW. Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II). [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2019 Jul 18]. Available from: http://etd.library.vanderbilt.edu//available/etd-03192014-065659/ ;.

Council of Science Editors:

Nik Hashim NNW. Analysis of speech features as potential indicators for depression and high risk suicide and possible predictors for the Hamilton Depression rating (HAMD) and Beck Depression Inventory scale (BDI-II). [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://etd.library.vanderbilt.edu//available/etd-03192014-065659/ ;


Arizona State University

9. Muralidhar, Ashwini. Augmented Image Classification using Image Registration Techniques.

Degree: MS, Electrical Engineering, 2011, Arizona State University

 Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such… (more)

Subjects/Keywords: Electrical engineering; Computer science; Robotics; autonomous navigation; classification; registration; robotics; terrain classification

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APA (6th Edition):

Muralidhar, A. (2011). Augmented Image Classification using Image Registration Techniques. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/14376

Chicago Manual of Style (16th Edition):

Muralidhar, Ashwini. “Augmented Image Classification using Image Registration Techniques.” 2011. Masters Thesis, Arizona State University. Accessed July 18, 2019. http://repository.asu.edu/items/14376.

MLA Handbook (7th Edition):

Muralidhar, Ashwini. “Augmented Image Classification using Image Registration Techniques.” 2011. Web. 18 Jul 2019.

Vancouver:

Muralidhar A. Augmented Image Classification using Image Registration Techniques. [Internet] [Masters thesis]. Arizona State University; 2011. [cited 2019 Jul 18]. Available from: http://repository.asu.edu/items/14376.

Council of Science Editors:

Muralidhar A. Augmented Image Classification using Image Registration Techniques. [Masters Thesis]. Arizona State University; 2011. Available from: http://repository.asu.edu/items/14376


Wright State University

10. Like, Eric C. Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics.

Degree: MSEgr, Electrical Engineering, 2007, Wright State University

 This research proposes and evaluates a feature based modulation classification system designed to discriminate between AM, BFSK, OFDM, DS-CDMA, 4-ASK, 8-ASK, BPSK, QPSK, 8-PSK, 16-… (more)

Subjects/Keywords: Modulation Recognition; Cyclostationarity; Signal Classification; Spectral Correlation; Cognitive Radio

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APA (6th Edition):

Like, E. C. (2007). Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics. (Masters Thesis). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1197649202

Chicago Manual of Style (16th Edition):

Like, Eric C. “Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics.” 2007. Masters Thesis, Wright State University. Accessed July 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1197649202.

MLA Handbook (7th Edition):

Like, Eric C. “Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics.” 2007. Web. 18 Jul 2019.

Vancouver:

Like EC. Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics. [Internet] [Masters thesis]. Wright State University; 2007. [cited 2019 Jul 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1197649202.

Council of Science Editors:

Like EC. Non-Cooperative Modulation Recognition Via Exploitation of Cyclic Statistics. [Masters Thesis]. Wright State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1197649202


NSYSU

11. Tsai, Shian-Chi. A Mixed Approach for Multi-Label Document Classification.

Degree: Master, Electrical Engineering, 2010, NSYSU

 Unlike single-label document classification, where each document exactly belongs to a single category, when the document is classified into two or more categories, known as… (more)

Subjects/Keywords: relevance score; information retrieval; Multi-Label document classification; fuzzy similarity measure

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APA (6th Edition):

Tsai, S. (2010). A Mixed Approach for Multi-Label Document Classification. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810110-175700

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):

Tsai, Shian-Chi. “A Mixed Approach for Multi-Label Document Classification.” 2010. Thesis, NSYSU. Accessed July 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810110-175700.

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

MLA Handbook (7th Edition):

Tsai, Shian-Chi. “A Mixed Approach for Multi-Label Document Classification.” 2010. Web. 18 Jul 2019.

Vancouver:

Tsai S. A Mixed Approach for Multi-Label Document Classification. [Internet] [Thesis]. NSYSU; 2010. [cited 2019 Jul 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810110-175700.

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

Council of Science Editors:

Tsai S. A Mixed Approach for Multi-Label Document Classification. [Thesis]. NSYSU; 2010. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810110-175700

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


NSYSU

12. Peng, Hung-Wen. A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems.

Degree: Master, Electrical Engineering, 2014, NSYSU

 We propose an algorithm for single label classification, multi-label classification, and regression estimation which incorporates a rotating similarity, weighted relevance, hybrid learning, and threshold checking.… (more)

Subjects/Keywords: weighted relevance; rotating cluster similarity; hybrid learning; regression estimation; classification

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APA (6th Edition):

Peng, H. (2014). A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-153159

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):

Peng, Hung-Wen. “A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems.” 2014. Thesis, NSYSU. Accessed July 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-153159.

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

MLA Handbook (7th Edition):

Peng, Hung-Wen. “A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems.” 2014. Web. 18 Jul 2019.

Vancouver:

Peng H. A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems. [Internet] [Thesis]. NSYSU; 2014. [cited 2019 Jul 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-153159.

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

Council of Science Editors:

Peng H. A Self-Constructing Rotating Similarity with Hybrid Learning Method for Classification and Regression Problems. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-1130114-153159

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


NSYSU

13. Chen, Hsing-Feng. Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement.

Degree: PhD, Electrical Engineering, 2010, NSYSU

 This thesis first discusses the ground plane effects of a test site on the antenna factors (AFs) of hybrid antenna (biconical log-periodic dipole array). Meanwhile,… (more)

Subjects/Keywords: normalized site attenuation; antenna factor; multiple signal classification

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APA (6th Edition):

Chen, H. (2010). Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement. (Doctoral Dissertation). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118110-211617

Chicago Manual of Style (16th Edition):

Chen, Hsing-Feng. “Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement.” 2010. Doctoral Dissertation, NSYSU. Accessed July 18, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118110-211617.

MLA Handbook (7th Edition):

Chen, Hsing-Feng. “Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement.” 2010. Web. 18 Jul 2019.

Vancouver:

Chen H. Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement. [Internet] [Doctoral dissertation]. NSYSU; 2010. [cited 2019 Jul 18]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118110-211617.

Council of Science Editors:

Chen H. Application of Hybrid Antennas in Normalized Site Attenuation Measurements and An Improved Method for Free-space Antenna Factor Measurement. [Doctoral Dissertation]. NSYSU; 2010. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0118110-211617


University of Dayton

14. Martell, Patrick Keith. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.

Degree: MS(M.S.), Electrical Engineering, 2017, University of Dayton

 Convolutional neural networks (CNNs) lack ample methods to improve performance without either adding more input data, modifying existing data, or changing network design. This work… (more)

Subjects/Keywords: Electrical Engineering; Convolutional Neural Network; Polynomial; CNN; Classification; MNIST

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APA (6th Edition):

Martell, P. K. (2017). Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038

Chicago Manual of Style (16th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Masters Thesis, University of Dayton. Accessed July 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

MLA Handbook (7th Edition):

Martell, Patrick Keith. “Hierarchical Auto-Associative Polynomial Convolutional Neural Networks.” 2017. Web. 18 Jul 2019.

Vancouver:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Internet] [Masters thesis]. University of Dayton; 2017. [cited 2019 Jul 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038.

Council of Science Editors:

Martell PK. Hierarchical Auto-Associative Polynomial Convolutional Neural Networks. [Masters Thesis]. University of Dayton; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1513164029518038


Wright State University

15. Pavy, Anne M. SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator.

Degree: PhD, Electrical Engineering, 2017, Wright State University

 In this dissertation, a novel algorithm, SV-Means, is developed motivated by the many functions needed to perform radar waveform classification in an evolving, contested environment.… (more)

Subjects/Keywords: Electrical Engineering; open set classification; one-class support vector machine

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APA (6th Edition):

Pavy, A. M. (2017). SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949

Chicago Manual of Style (16th Edition):

Pavy, Anne M. “SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator.” 2017. Doctoral Dissertation, Wright State University. Accessed July 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949.

MLA Handbook (7th Edition):

Pavy, Anne M. “SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator.” 2017. Web. 18 Jul 2019.

Vancouver:

Pavy AM. SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator. [Internet] [Doctoral dissertation]. Wright State University; 2017. [cited 2019 Jul 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949.

Council of Science Editors:

Pavy AM. SV-Means: A Fast One-Class Support Vector Machine-Based Level Set Estimator. [Doctoral Dissertation]. Wright State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1516047120200949


University of Dayton

16. Varney, Nina M. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 Light Detection and Ranging, (LiDAR) presents a series of unique challenges, the foremost of these being object identification. Because of the ease of aerial collection… (more)

Subjects/Keywords: Electrical Engineering; LiDAR; classification; segmentation; aerial; SELF; NORM

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APA (6th Edition):

Varney, N. M. (2015). LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790

Chicago Manual of Style (16th Edition):

Varney, Nina M. “LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.” 2015. Masters Thesis, University of Dayton. Accessed July 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790.

MLA Handbook (7th Edition):

Varney, Nina M. “LiDAR Data Analysis for Automatic Region Segmentation and Object Classification.” 2015. Web. 18 Jul 2019.

Vancouver:

Varney NM. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2019 Jul 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790.

Council of Science Editors:

Varney NM. LiDAR Data Analysis for Automatic Region Segmentation and Object Classification. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1446747790


Penn State University

17. Weiss, Daniel Ross. An Automated Detection Scheme For.

Degree: MS, Electrical Engineering, 2014, Penn State University

 The field of protocol analysis arose from the increasing need for network security. Analysis and identification of the protocols of traffic on a network allowed… (more)

Subjects/Keywords: Protocol Analysis; Wireless Protocols; Wireless Protocol Classification; Automated Protocol Detection

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APA (6th Edition):

Weiss, D. R. (2014). An Automated Detection Scheme For. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/23703

Chicago Manual of Style (16th Edition):

Weiss, Daniel Ross. “An Automated Detection Scheme For.” 2014. Masters Thesis, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/23703.

MLA Handbook (7th Edition):

Weiss, Daniel Ross. “An Automated Detection Scheme For.” 2014. Web. 18 Jul 2019.

Vancouver:

Weiss DR. An Automated Detection Scheme For. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/23703.

Council of Science Editors:

Weiss DR. An Automated Detection Scheme For. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/23703


Penn State University

18. Raghuram, Jayaram. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.

Degree: PhD, Electrical Engineering, 2014, Penn State University

 This dissertation makes contributions towards the following three closely related, important problems in machine learning: {\em 1. Semi-supervised classification, 2. Semi-supervised learning with instance-level constraints,… (more)

Subjects/Keywords: semisupervised classification; semisupervised constraint based learning; classifier domain adaptation; machine learning

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APA (6th Edition):

Raghuram, J. (2014). Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/23319

Chicago Manual of Style (16th Edition):

Raghuram, Jayaram. “Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.” 2014. Doctoral Dissertation, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/23319.

MLA Handbook (7th Edition):

Raghuram, Jayaram. “Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints.” 2014. Web. 18 Jul 2019.

Vancouver:

Raghuram J. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. [Internet] [Doctoral dissertation]. Penn State University; 2014. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/23319.

Council of Science Editors:

Raghuram J. Improved generative modeling approaches for semi-supervised and domain adaptive classifier learning from labels and constraints. [Doctoral Dissertation]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/23319


Penn State University

19. Bufler, Travis Dale. Radar Signature Analysis of Indoor Clutter and.

Degree: PhD, Electrical Engineering, 2016, Penn State University

 Research in through-the-wall radar (TTWR) is a recent area of focus and as such many challenges have arisen related to target detection, location, and tracking.… (more)

Subjects/Keywords: Radar; SVM; FDTD; RCS; Indoor Clutter; Human Scattering; Target Classification

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APA (6th Edition):

Bufler, T. D. (2016). Radar Signature Analysis of Indoor Clutter and. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/28796

Chicago Manual of Style (16th Edition):

Bufler, Travis Dale. “Radar Signature Analysis of Indoor Clutter and.” 2016. Doctoral Dissertation, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/28796.

MLA Handbook (7th Edition):

Bufler, Travis Dale. “Radar Signature Analysis of Indoor Clutter and.” 2016. Web. 18 Jul 2019.

Vancouver:

Bufler TD. Radar Signature Analysis of Indoor Clutter and. [Internet] [Doctoral dissertation]. Penn State University; 2016. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/28796.

Council of Science Editors:

Bufler TD. Radar Signature Analysis of Indoor Clutter and. [Doctoral Dissertation]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/28796


Penn State University

20. Rao, Abhishek Krishna. Comparison of Machine Learning techniques for painting classification.

Degree: MS, Electrical Engineering, 2015, Penn State University

 The aim of this thesis is to classify paintings style using machine learning techniques. The data set consists of paintings from WikiArt.org website and the… (more)

Subjects/Keywords: Machine Learning; Deep Learning; Art; Image classification; Classifiers

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APA (6th Edition):

Rao, A. K. (2015). Comparison of Machine Learning techniques for painting classification. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26405

Chicago Manual of Style (16th Edition):

Rao, Abhishek Krishna. “Comparison of Machine Learning techniques for painting classification.” 2015. Masters Thesis, Penn State University. Accessed July 18, 2019. https://etda.libraries.psu.edu/catalog/26405.

MLA Handbook (7th Edition):

Rao, Abhishek Krishna. “Comparison of Machine Learning techniques for painting classification.” 2015. Web. 18 Jul 2019.

Vancouver:

Rao AK. Comparison of Machine Learning techniques for painting classification. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2019 Jul 18]. Available from: https://etda.libraries.psu.edu/catalog/26405.

Council of Science Editors:

Rao AK. Comparison of Machine Learning techniques for painting classification. [Masters Thesis]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26405


UCLA

21. Wang, Yan Wang. Scalable Networked Human Daily Activity Profiling.

Degree: Electrical Engineering, 2016, UCLA

 Human activity analysis is becoming increasingly important to enable preventative, diagnostic and rehabilitative measures in health and wellness applications. Wearable sensors are now taking a… (more)

Subjects/Keywords: Electrical engineering; activity classification; indoor navigation; motion tracking; wearable sensors

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APA (6th Edition):

Wang, Y. W. (2016). Scalable Networked Human Daily Activity Profiling. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/9mx4t62s

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):

Wang, Yan Wang. “Scalable Networked Human Daily Activity Profiling.” 2016. Thesis, UCLA. Accessed July 18, 2019. http://www.escholarship.org/uc/item/9mx4t62s.

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

MLA Handbook (7th Edition):

Wang, Yan Wang. “Scalable Networked Human Daily Activity Profiling.” 2016. Web. 18 Jul 2019.

Vancouver:

Wang YW. Scalable Networked Human Daily Activity Profiling. [Internet] [Thesis]. UCLA; 2016. [cited 2019 Jul 18]. Available from: http://www.escholarship.org/uc/item/9mx4t62s.

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

Council of Science Editors:

Wang YW. Scalable Networked Human Daily Activity Profiling. [Thesis]. UCLA; 2016. Available from: http://www.escholarship.org/uc/item/9mx4t62s

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


University of Southern California

22. Liu, Kuan-Hsien. Facial age grouping and estimation via ensemble learning.

Degree: PhD, Electrical Engineering, 2016, University of Southern California

 Age estimation has been attracted lots of attention last decade. This dissertation includes six chapters. In Chapter 1, we give an introduction on this dissertation,… (more)

Subjects/Keywords: age estimation; age grouping; classification; feature selection; fusion; machine learning; regression

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APA (6th Edition):

Liu, K. (2016). Facial age grouping and estimation via ensemble learning. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/444922/rec/2704

Chicago Manual of Style (16th Edition):

Liu, Kuan-Hsien. “Facial age grouping and estimation via ensemble learning.” 2016. Doctoral Dissertation, University of Southern California. Accessed July 18, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/444922/rec/2704.

MLA Handbook (7th Edition):

Liu, Kuan-Hsien. “Facial age grouping and estimation via ensemble learning.” 2016. Web. 18 Jul 2019.

Vancouver:

Liu K. Facial age grouping and estimation via ensemble learning. [Internet] [Doctoral dissertation]. University of Southern California; 2016. [cited 2019 Jul 18]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/444922/rec/2704.

Council of Science Editors:

Liu K. Facial age grouping and estimation via ensemble learning. [Doctoral Dissertation]. University of Southern California; 2016. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/444922/rec/2704


Rochester Institute of Technology

23. Graydon, Tucker B. Novel Detection and Analysis using Deep Variational Autoencoders.

Degree: MS, Electrical Engineering, 2018, Rochester Institute of Technology

  This paper presents a Novel Identification System which uses generative modeling techniques and Gaussian Mixture Models (GMMs) to identify the main process variables involved… (more)

Subjects/Keywords: Fault detection; Feature extraction; Machine learning; One class classification; Statistical modeling

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APA (6th Edition):

Graydon, T. B. (2018). Novel Detection and Analysis using Deep Variational Autoencoders. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9897

Chicago Manual of Style (16th Edition):

Graydon, Tucker B. “Novel Detection and Analysis using Deep Variational Autoencoders.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed July 18, 2019. https://scholarworks.rit.edu/theses/9897.

MLA Handbook (7th Edition):

Graydon, Tucker B. “Novel Detection and Analysis using Deep Variational Autoencoders.” 2018. Web. 18 Jul 2019.

Vancouver:

Graydon TB. Novel Detection and Analysis using Deep Variational Autoencoders. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2019 Jul 18]. Available from: https://scholarworks.rit.edu/theses/9897.

Council of Science Editors:

Graydon TB. Novel Detection and Analysis using Deep Variational Autoencoders. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9897


Virginia Tech

24. Hauser, Steven Charles. Real-World Considerations for Deep Learning in Spectrum Sensing.

Degree: MS, Electrical Engineering, 2018, Virginia Tech

 Recently, automatic modulation classification techniques using deep neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based… (more)

Subjects/Keywords: Machine Learning; Spectrum Sensing; Neural Networks; Automatic Modulation Classification; Communication Systems

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APA (6th Edition):

Hauser, S. C. (2018). Real-World Considerations for Deep Learning in Spectrum Sensing. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83560

Chicago Manual of Style (16th Edition):

Hauser, Steven Charles. “Real-World Considerations for Deep Learning in Spectrum Sensing.” 2018. Masters Thesis, Virginia Tech. Accessed July 18, 2019. http://hdl.handle.net/10919/83560.

MLA Handbook (7th Edition):

Hauser, Steven Charles. “Real-World Considerations for Deep Learning in Spectrum Sensing.” 2018. Web. 18 Jul 2019.

Vancouver:

Hauser SC. Real-World Considerations for Deep Learning in Spectrum Sensing. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/10919/83560.

Council of Science Editors:

Hauser SC. Real-World Considerations for Deep Learning in Spectrum Sensing. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83560


Oklahoma State University

25. Li, Bo. Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach.

Degree: Electrical Engineering, 2014, Oklahoma State University

 The American Burying Beetle (ABB) (Nicrophorus americanus) is a critically endangered insect whose distribution is limited to several states at the periphery of its historical… (more)

Subjects/Keywords: computer vision; insect classification; pattern recognition; shape decomposition

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APA (6th Edition):

Li, B. (2014). Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/14965

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, Bo. “Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach.” 2014. Thesis, Oklahoma State University. Accessed July 18, 2019. http://hdl.handle.net/11244/14965.

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

MLA Handbook (7th Edition):

Li, Bo. “Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach.” 2014. Web. 18 Jul 2019.

Vancouver:

Li B. Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach. [Internet] [Thesis]. Oklahoma State University; 2014. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/11244/14965.

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

Council of Science Editors:

Li B. Shape-based Insect Classification: a Hybrid Region-based and Contour-based Approach. [Thesis]. Oklahoma State University; 2014. Available from: http://hdl.handle.net/11244/14965

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


University of Minnesota

26. Roy Chowdhury, Sohini. Automated segmentation and pathology detection in ophthalmic images.

Degree: PhD, Electrical Engineering, 2014, University of Minnesota

 Computer-aided medical diagnostic system design is an emerging inter-disciplinary technology that assists medical practitioners for providing quick and accurate diagnosis and prognosis of pathology. Since… (more)

Subjects/Keywords: Classification; Detection; Features; Machine-learning; Segmentation; Electrical engineering

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APA (6th Edition):

Roy Chowdhury, S. (2014). Automated segmentation and pathology detection in ophthalmic images. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/167433

Chicago Manual of Style (16th Edition):

Roy Chowdhury, Sohini. “Automated segmentation and pathology detection in ophthalmic images.” 2014. Doctoral Dissertation, University of Minnesota. Accessed July 18, 2019. http://hdl.handle.net/11299/167433.

MLA Handbook (7th Edition):

Roy Chowdhury, Sohini. “Automated segmentation and pathology detection in ophthalmic images.” 2014. Web. 18 Jul 2019.

Vancouver:

Roy Chowdhury S. Automated segmentation and pathology detection in ophthalmic images. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Jul 18]. Available from: http://hdl.handle.net/11299/167433.

Council of Science Editors:

Roy Chowdhury S. Automated segmentation and pathology detection in ophthalmic images. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/167433


Rochester Institute of Technology

27. Bosen, Adam Kevin. Modification of an asynchronous dexterous hand movement decoder for hardware implementation.

Degree: Electrical Engineering, 2010, Rochester Institute of Technology

 One of the goals of modern prosthetics research is to provide natural, neurologically driven control of a prosthetic device, preferably in a portable format. Previously,… (more)

Subjects/Keywords: Artifical neural network; Brain-computer interfacing; FPGA; Movement classification

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APA (6th Edition):

Bosen, A. K. (2010). Modification of an asynchronous dexterous hand movement decoder for hardware implementation. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/5610

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):

Bosen, Adam Kevin. “Modification of an asynchronous dexterous hand movement decoder for hardware implementation.” 2010. Thesis, Rochester Institute of Technology. Accessed July 18, 2019. https://scholarworks.rit.edu/theses/5610.

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

MLA Handbook (7th Edition):

Bosen, Adam Kevin. “Modification of an asynchronous dexterous hand movement decoder for hardware implementation.” 2010. Web. 18 Jul 2019.

Vancouver:

Bosen AK. Modification of an asynchronous dexterous hand movement decoder for hardware implementation. [Internet] [Thesis]. Rochester Institute of Technology; 2010. [cited 2019 Jul 18]. Available from: https://scholarworks.rit.edu/theses/5610.

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

Council of Science Editors:

Bosen AK. Modification of an asynchronous dexterous hand movement decoder for hardware implementation. [Thesis]. Rochester Institute of Technology; 2010. Available from: https://scholarworks.rit.edu/theses/5610

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


North Carolina State University

28. Patel, Parita. Classification and Modeling of Internet Applications.

Degree: MS, Electrical Engineering, 2004, North Carolina State University

 The classification of Internet traffic is an active research topic due to its applicability in the areas like differentiated services and network security. The introduction… (more)

Subjects/Keywords: modeling; clustering; classification; network traffic

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APA (6th Edition):

Patel, P. (2004). Classification and Modeling of Internet Applications. (Thesis). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/1009

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):

Patel, Parita. “Classification and Modeling of Internet Applications.” 2004. Thesis, North Carolina State University. Accessed July 18, 2019. http://www.lib.ncsu.edu/resolver/1840.16/1009.

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

MLA Handbook (7th Edition):

Patel, Parita. “Classification and Modeling of Internet Applications.” 2004. Web. 18 Jul 2019.

Vancouver:

Patel P. Classification and Modeling of Internet Applications. [Internet] [Thesis]. North Carolina State University; 2004. [cited 2019 Jul 18]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/1009.

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

Council of Science Editors:

Patel P. Classification and Modeling of Internet Applications. [Thesis]. North Carolina State University; 2004. Available from: http://www.lib.ncsu.edu/resolver/1840.16/1009

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


North Carolina State University

29. Aouada, Djamila. Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes.

Degree: PhD, Electrical Engineering, 2009, North Carolina State University

 The increasing size and complexity of data often invokes the extraction of information from their reduced representations while preserving their inherent structure. In this thesis,… (more)

Subjects/Keywords: Manifold theory; 3D shape comparison; Object classification; Differential geometry

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APA (6th Edition):

Aouada, D. (2009). Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4492

Chicago Manual of Style (16th Edition):

Aouada, Djamila. “Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes.” 2009. Doctoral Dissertation, North Carolina State University. Accessed July 18, 2019. http://www.lib.ncsu.edu/resolver/1840.16/4492.

MLA Handbook (7th Edition):

Aouada, Djamila. “Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes.” 2009. Web. 18 Jul 2019.

Vancouver:

Aouada D. Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2019 Jul 18]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4492.

Council of Science Editors:

Aouada D. Geometric, Statistical, and Topological Modeling of Intrinsic Data Manifolds: Application to 3D Shapes. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4492


University of Dayton

30. Uppala, Roshni. Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 The memristor is a novel nano-scale device discovered in 2008. Memristors are basically nonvolatile variable resistors. Various breakthroughs of memristive devices have shown the potential… (more)

Subjects/Keywords: Computer Engineering; Electrical Engineering; Engineering; memristor; parallel simulation; neuromorphic computing; Xyce; approximate memristor model; offline training of memristor; pattern classification; face image classification; neural networks

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APA (6th Edition):

Uppala, R. (2015). Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073

Chicago Manual of Style (16th Edition):

Uppala, Roshni. “Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications.” 2015. Masters Thesis, University of Dayton. Accessed July 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

MLA Handbook (7th Edition):

Uppala, Roshni. “Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications.” 2015. Web. 18 Jul 2019.

Vancouver:

Uppala R. Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2019 Jul 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

Council of Science Editors:

Uppala R. Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073

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