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Dept: Electrical and Computer Engineering

You searched for subject:(info eu repo classification ddc 330). Showing records 1 – 30 of 133 total matches.

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Virginia Tech

1. Ramkumar, Barathram. Automatic Modulation Classication and Blind Equalization for Cognitive Radios.

Degree: PhD, Electrical and Computer Engineering, 2011, Virginia Tech

 Cognitive Radio (CR) is an emerging wireless communications technology that addresses the inefficiency of current radio spectrum usage. CR also supports the evolution of existing… (more)

Subjects/Keywords: signal classification; blind equalization

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APA (6th Edition):

Ramkumar, B. (2011). Automatic Modulation Classication and Blind Equalization for Cognitive Radios. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28666

Chicago Manual of Style (16th Edition):

Ramkumar, Barathram. “Automatic Modulation Classication and Blind Equalization for Cognitive Radios.” 2011. Doctoral Dissertation, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/28666.

MLA Handbook (7th Edition):

Ramkumar, Barathram. “Automatic Modulation Classication and Blind Equalization for Cognitive Radios.” 2011. Web. 20 Oct 2019.

Vancouver:

Ramkumar B. Automatic Modulation Classication and Blind Equalization for Cognitive Radios. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/28666.

Council of Science Editors:

Ramkumar B. Automatic Modulation Classication and Blind Equalization for Cognitive Radios. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/28666


Mississippi State University

2. Kalluri, Hemanth Reddy. FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION.

Degree: MS, Electrical and Computer Engineering, 2009, Mississippi State University

 Developments in sensor technology have made high resolution hyperspectral remote sensing data available to the remote sensing analyst for ground cover classification and target recognition… (more)

Subjects/Keywords: classification; derivatives; Hyperspectral; Adaptive

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APA (6th Edition):

Kalluri, H. R. (2009). FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-11062009-124333/ ;

Chicago Manual of Style (16th Edition):

Kalluri, Hemanth Reddy. “FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION.” 2009. Masters Thesis, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11062009-124333/ ;.

MLA Handbook (7th Edition):

Kalluri, Hemanth Reddy. “FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION.” 2009. Web. 20 Oct 2019.

Vancouver:

Kalluri HR. FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION. [Internet] [Masters thesis]. Mississippi State University; 2009. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-11062009-124333/ ;.

Council of Science Editors:

Kalluri HR. FUSION OF SPECTRAL REFLECTANCE AND DERIVATIVE INFORMATION FOR ROBUST HYPERSPECTRAL LAND COVER CLASSIFICATION. [Masters Thesis]. Mississippi State University; 2009. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-11062009-124333/ ;


Mississippi State University

3. Turlapaty, Anish Chand. APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS.

Degree: PhD, Electrical and Computer Engineering, 2010, Mississippi State University

  Data assimilation of satellite-based observations of hydrological variables with full numerical physics models can be used to downscale these observations from coarse to high… (more)

Subjects/Keywords: classification; consistency analysis; machine learning

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APA (6th Edition):

Turlapaty, A. C. (2010). APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03282010-141624/ ;

Chicago Manual of Style (16th Edition):

Turlapaty, Anish Chand. “APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS.” 2010. Doctoral Dissertation, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-03282010-141624/ ;.

MLA Handbook (7th Edition):

Turlapaty, Anish Chand. “APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS.” 2010. Web. 20 Oct 2019.

Vancouver:

Turlapaty AC. APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS. [Internet] [Doctoral dissertation]. Mississippi State University; 2010. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03282010-141624/ ;.

Council of Science Editors:

Turlapaty AC. APPLICATION OF PATTERN RECOGNITION AND ADAPTIVE DSP METHODS FOR SPATIO-TEMPORAL ANALYSIS OF SATELLITE BASED HYDROLOGICAL DATASETS. [Doctoral Dissertation]. Mississippi State University; 2010. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03282010-141624/ ;


University of Florida

4. Cao, Zheng. Information Theoretic Classification of Marine Animal Imagery.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Florida

 To analyze marine animal behavior, seasonal distribution and abundance, digital imagery can be acquired by Lidar or optical camera. The Unobtrusive Multistatic Serial LiDAR Imager… (more)

Subjects/Keywords: classification  – itl  – lidar  – multiview  – shapematching

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APA (6th Edition):

Cao, Z. (2017). Information Theoretic Classification of Marine Animal Imagery. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051002

Chicago Manual of Style (16th Edition):

Cao, Zheng. “Information Theoretic Classification of Marine Animal Imagery.” 2017. Doctoral Dissertation, University of Florida. Accessed October 20, 2019. http://ufdc.ufl.edu/UFE0051002.

MLA Handbook (7th Edition):

Cao, Zheng. “Information Theoretic Classification of Marine Animal Imagery.” 2017. Web. 20 Oct 2019.

Vancouver:

Cao Z. Information Theoretic Classification of Marine Animal Imagery. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Oct 20]. Available from: http://ufdc.ufl.edu/UFE0051002.

Council of Science Editors:

Cao Z. Information Theoretic Classification of Marine Animal Imagery. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051002


Colorado State University

5. Hall, John Joseph. Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries.

Degree: MS(M.S.), Electrical and Computer Engineering, 2016, Colorado State University

 This work is concerned with the development of a system for the discrimination of military munitions and unexploded ordnances (UXO) from non- UXO's, man-made objects,… (more)

Subjects/Keywords: Sonar; Underwater; Classification; UXO; Sparse

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APA (6th Edition):

Hall, J. J. (2016). Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries. (Masters Thesis). Colorado State University. Retrieved from http://hdl.handle.net/10217/176755

Chicago Manual of Style (16th Edition):

Hall, John Joseph. “Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries.” 2016. Masters Thesis, Colorado State University. Accessed October 20, 2019. http://hdl.handle.net/10217/176755.

MLA Handbook (7th Edition):

Hall, John Joseph. “Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries.” 2016. Web. 20 Oct 2019.

Vancouver:

Hall JJ. Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries. [Internet] [Masters thesis]. Colorado State University; 2016. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10217/176755.

Council of Science Editors:

Hall JJ. Underwater UXO classification using matched subspace classifier with synthetic sparse dictionaries. [Masters Thesis]. Colorado State University; 2016. Available from: http://hdl.handle.net/10217/176755


Georgia Tech

6. Rizwan, Muhammad. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the ASR for a particular speaker or a group of speakers.… (more)

Subjects/Keywords: Speaker adaptation; Adaptive phoneme classification; Deep neural networks; Accent classification

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APA (6th Edition):

Rizwan, M. (2017). Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60171

Chicago Manual of Style (16th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Doctoral Dissertation, Georgia Tech. Accessed October 20, 2019. http://hdl.handle.net/1853/60171.

MLA Handbook (7th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Web. 20 Oct 2019.

Vancouver:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/1853/60171.

Council of Science Editors:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60171


The Ohio State University

7. Cammenga, Zachary Andrew. High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification.

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

 This work advances the use of radar for target classification. High range resolution (HRR) and micro-Doppler signal analysis both provide radar operators the ability to… (more)

Subjects/Keywords: Electrical Engineering; Radar HRR Classification Doppler

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APA (6th Edition):

Cammenga, Z. A. (2017). High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1483438572645656

Chicago Manual of Style (16th Edition):

Cammenga, Zachary Andrew. “High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification.” 2017. Doctoral Dissertation, The Ohio State University. Accessed October 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1483438572645656.

MLA Handbook (7th Edition):

Cammenga, Zachary Andrew. “High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification.” 2017. Web. 20 Oct 2019.

Vancouver:

Cammenga ZA. High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2019 Oct 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1483438572645656.

Council of Science Editors:

Cammenga ZA. High Range Resolution Micro-Doppler Radar Theory and Its Application to Human Gait Classification. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1483438572645656


University of Florida

8. Liu, Fujun. Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Florida

Subjects/Keywords: classification; detection; segmentation

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APA (6th Edition):

Liu, F. (2017). Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0051554

Chicago Manual of Style (16th Edition):

Liu, Fujun. “Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems.” 2017. Doctoral Dissertation, University of Florida. Accessed October 20, 2019. http://ufdc.ufl.edu/UFE0051554.

MLA Handbook (7th Edition):

Liu, Fujun. “Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems.” 2017. Web. 20 Oct 2019.

Vancouver:

Liu F. Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2019 Oct 20]. Available from: http://ufdc.ufl.edu/UFE0051554.

Council of Science Editors:

Liu F. Robust Pathology Image Processing Methods and Applications In Computer Aided Diagnosis and Prognosis Systems. [Doctoral Dissertation]. University of Florida; 2017. Available from: http://ufdc.ufl.edu/UFE0051554


Mississippi State University

9. Mitzev, Ivan. Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application.

Degree: PhD, Electrical and Computer Engineering, 2018, Mississippi State University

  Time series are very common in presenting collected data such as economic indicators, natural phenomenon, control engineering data, among others. In the last decade,… (more)

Subjects/Keywords: machine learning; time series classification; shapelets

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APA (6th Edition):

Mitzev, I. (2018). Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03152018-144453/ ;

Chicago Manual of Style (16th Edition):

Mitzev, Ivan. “Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application.” 2018. Doctoral Dissertation, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-03152018-144453/ ;.

MLA Handbook (7th Edition):

Mitzev, Ivan. “Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application.” 2018. Web. 20 Oct 2019.

Vancouver:

Mitzev I. Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application. [Internet] [Doctoral dissertation]. Mississippi State University; 2018. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03152018-144453/ ;.

Council of Science Editors:

Mitzev I. Concatenated decision paths classification for time series shapelets A new approach for one dimensional data classification and its application. [Doctoral Dissertation]. Mississippi State University; 2018. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03152018-144453/ ;


Virginia Tech

10. Chavali, Venkata Gautham. Signal Detection and Modulation Classification in Non-Gaussian Noise Environments.

Degree: PhD, Electrical and Computer Engineering, 2012, Virginia Tech

 Signal detection and modulation classification are becoming increasingly important in a variety of wireless communication systems such as those involving spectrum management and electronic warfare… (more)

Subjects/Keywords: Modulation classification; Non-Gaussian noise; Signal detection

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APA (6th Edition):

Chavali, V. G. (2012). Signal Detection and Modulation Classification in Non-Gaussian Noise Environments. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28387

Chicago Manual of Style (16th Edition):

Chavali, Venkata Gautham. “Signal Detection and Modulation Classification in Non-Gaussian Noise Environments.” 2012. Doctoral Dissertation, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/28387.

MLA Handbook (7th Edition):

Chavali, Venkata Gautham. “Signal Detection and Modulation Classification in Non-Gaussian Noise Environments.” 2012. Web. 20 Oct 2019.

Vancouver:

Chavali VG. Signal Detection and Modulation Classification in Non-Gaussian Noise Environments. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/28387.

Council of Science Editors:

Chavali VG. Signal Detection and Modulation Classification in Non-Gaussian Noise Environments. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28387


Virginia Tech

11. Blake, Madison Thomas. An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments.

Degree: MS, Electrical and Computer Engineering, 2014, Virginia Tech

 In this thesis, an activity classification algorithm is developed to support a human ambulatory monitoring system. This algorithm, to be deployed on an e-textile garment,… (more)

Subjects/Keywords: Activity Classification; Wearable Computing; User-indepdendence

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APA (6th Edition):

Blake, M. T. (2014). An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/25876

Chicago Manual of Style (16th Edition):

Blake, Madison Thomas. “An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments.” 2014. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/25876.

MLA Handbook (7th Edition):

Blake, Madison Thomas. “An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments.” 2014. Web. 20 Oct 2019.

Vancouver:

Blake MT. An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/25876.

Council of Science Editors:

Blake MT. An Ambulatory Monitoring Algorithm to Unify Diverse E-Textile Garments. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/25876


Virginia Tech

12. Nagabushan, Naresh. Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications.

Degree: MS, Electrical and Computer Engineering, 2019, Virginia Tech

 Brain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible… (more)

Subjects/Keywords: Brain-Computer-Interface; iEEG; EEG; Classification

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APA (6th Edition):

Nagabushan, N. (2019). Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90183

Chicago Manual of Style (16th Edition):

Nagabushan, Naresh. “Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications.” 2019. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/90183.

MLA Handbook (7th Edition):

Nagabushan, Naresh. “Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications.” 2019. Web. 20 Oct 2019.

Vancouver:

Nagabushan N. Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/90183.

Council of Science Editors:

Nagabushan N. Analyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applications. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90183


Mississippi State University

13. Sumarsono, Alex. Low rank and sparse representation for hyperspectral imagery analysis.

Degree: PhD, Electrical and Computer Engineering, 2015, Mississippi State University

  This dissertation develops new techniques employing the Low-rank and Sparse Representation approaches to improve the performance of state-of-the-art algorithms in hyperspectral image analysis. The… (more)

Subjects/Keywords: estimation of the number of signal sources; supervised classification; unsupervised classification; target detection; anomaly detection; LRR; LRSR

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APA (6th Edition):

Sumarsono, A. (2015). Low rank and sparse representation for hyperspectral imagery analysis. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;

Chicago Manual of Style (16th Edition):

Sumarsono, Alex. “Low rank and sparse representation for hyperspectral imagery analysis.” 2015. Doctoral Dissertation, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;.

MLA Handbook (7th Edition):

Sumarsono, Alex. “Low rank and sparse representation for hyperspectral imagery analysis.” 2015. Web. 20 Oct 2019.

Vancouver:

Sumarsono A. Low rank and sparse representation for hyperspectral imagery analysis. [Internet] [Doctoral dissertation]. Mississippi State University; 2015. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;.

Council of Science Editors:

Sumarsono A. Low rank and sparse representation for hyperspectral imagery analysis. [Doctoral Dissertation]. Mississippi State University; 2015. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;


The Ohio State University

14. Ramakrishnan, Naveen. Distributed Learning Algorithms for Sensor Networks.

Degree: PhD, Electrical and Computer Engineering, 2010, The Ohio State University

  Wireless sensor networks have received significant attention in the last decade owing to their widespread use not only in monitoring the physical world but… (more)

Subjects/Keywords: Electrical Engineering; Sensor Networks; Machine Learning; Gossip; Distributed algorithms; Classification

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APA (6th Edition):

Ramakrishnan, N. (2010). Distributed Learning Algorithms for Sensor Networks. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632

Chicago Manual of Style (16th Edition):

Ramakrishnan, Naveen. “Distributed Learning Algorithms for Sensor Networks.” 2010. Doctoral Dissertation, The Ohio State University. Accessed October 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632.

MLA Handbook (7th Edition):

Ramakrishnan, Naveen. “Distributed Learning Algorithms for Sensor Networks.” 2010. Web. 20 Oct 2019.

Vancouver:

Ramakrishnan N. Distributed Learning Algorithms for Sensor Networks. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2019 Oct 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632.

Council of Science Editors:

Ramakrishnan N. Distributed Learning Algorithms for Sensor Networks. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1284991632


The Ohio State University

15. Doo, Seung Ho. Analysis, Modeling & Exploitation of Variability in Radar Images.

Degree: PhD, Electrical and Computer Engineering, 2016, The Ohio State University

 This dissertation explores the variability in radar measurements that arises due to small changes in target aspect angle, proposes a target modeling approach with augmented… (more)

Subjects/Keywords: Electrical Engineering; Radar, Variability, Target Classification, Feature Extraction, Image Processing

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APA (6th Edition):

Doo, S. H. (2016). Analysis, Modeling & Exploitation of Variability in Radar Images. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996

Chicago Manual of Style (16th Edition):

Doo, Seung Ho. “Analysis, Modeling & Exploitation of Variability in Radar Images.” 2016. Doctoral Dissertation, The Ohio State University. Accessed October 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996.

MLA Handbook (7th Edition):

Doo, Seung Ho. “Analysis, Modeling & Exploitation of Variability in Radar Images.” 2016. Web. 20 Oct 2019.

Vancouver:

Doo SH. Analysis, Modeling & Exploitation of Variability in Radar Images. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2019 Oct 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996.

Council of Science Editors:

Doo SH. Analysis, Modeling & Exploitation of Variability in Radar Images. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996


Virginia Tech

16. Marballie, Gladstone Washington. Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform.

Degree: MS, Electrical and Computer Engineering, 2010, Virginia Tech

 The Universal Classifier Synchronizer (UCS) is a Cognitive Radio system/sensor that can detect, classify, and extract the relevant parameters from a received signal to establish… (more)

Subjects/Keywords: FPGA; Signal Classification; Symbol Timing; DSP; SDR; Cognitive Radio

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APA (6th Edition):

Marballie, G. W. (2010). Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/35409

Chicago Manual of Style (16th Edition):

Marballie, Gladstone Washington. “Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform.” 2010. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/35409.

MLA Handbook (7th Edition):

Marballie, Gladstone Washington. “Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform.” 2010. Web. 20 Oct 2019.

Vancouver:

Marballie GW. Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/35409.

Council of Science Editors:

Marballie GW. Symbol Timing and Coarse Classification of Phase Modulated Signals on a Standalone SDR Platform. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/35409


Virginia Tech

17. Bond, Zachary. Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm.

Degree: MS, Electrical and Computer Engineering, 2006, Virginia Tech

 This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be… (more)

Subjects/Keywords: clustering; music; rhythm; timbre; classification

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APA (6th Edition):

Bond, Z. (2006). Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/36469

Chicago Manual of Style (16th Edition):

Bond, Zachary. “Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm.” 2006. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/36469.

MLA Handbook (7th Edition):

Bond, Zachary. “Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm.” 2006. Web. 20 Oct 2019.

Vancouver:

Bond Z. Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm. [Internet] [Masters thesis]. Virginia Tech; 2006. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/36469.

Council of Science Editors:

Bond Z. Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm. [Masters Thesis]. Virginia Tech; 2006. Available from: http://hdl.handle.net/10919/36469


Virginia Tech

18. Rebholz, Matthew John. Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties.

Degree: MS, Electrical and Computer Engineering, 2013, Virginia Tech

 This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access… (more)

Subjects/Keywords: Dynamic Spectrum Access; Cognitive Radios; Naïve Bayesian Classification; Matlab Simulation

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APA (6th Edition):

Rebholz, M. J. (2013). Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23244

Chicago Manual of Style (16th Edition):

Rebholz, Matthew John. “Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties.” 2013. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/23244.

MLA Handbook (7th Edition):

Rebholz, Matthew John. “Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties.” 2013. Web. 20 Oct 2019.

Vancouver:

Rebholz MJ. Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/23244.

Council of Science Editors:

Rebholz MJ. Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23244


Virginia Tech

19. Headley, William C. Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments.

Degree: PhD, Electrical and Computer Engineering, 2015, Virginia Tech

 The task of spectrum sensing, defined here to consist of signal detection, signal parameter estimation, and signal identification, is a critically important task in a… (more)

Subjects/Keywords: spectrum sensing; signal parameter estimation; modulation classification; collaborative sensing

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APA (6th Edition):

Headley, W. C. (2015). Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52915

Chicago Manual of Style (16th Edition):

Headley, William C. “Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments.” 2015. Doctoral Dissertation, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/52915.

MLA Handbook (7th Edition):

Headley, William C. “Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments.” 2015. Web. 20 Oct 2019.

Vancouver:

Headley WC. Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/52915.

Council of Science Editors:

Headley WC. Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/52915


Virginia Tech

20. Steiner, Michael Paul. Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time.

Degree: MS, Electrical and Computer Engineering, 2011, Virginia Tech

 This paper describes the implementation of a scanning signal detector and automatic modulation classification system. The classification technique is a completely blind method, with no… (more)

Subjects/Keywords: implementation; blind classification; cumulant; modulation classificaiton; real-time; spectrum sensing

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APA (6th Edition):

Steiner, M. P. (2011). Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32161

Chicago Manual of Style (16th Edition):

Steiner, Michael Paul. “Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time.” 2011. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/32161.

MLA Handbook (7th Edition):

Steiner, Michael Paul. “Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time.” 2011. Web. 20 Oct 2019.

Vancouver:

Steiner MP. Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/32161.

Council of Science Editors:

Steiner MP. Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/32161


Virginia Tech

21. Malady, Amy Colleen. Cyclostationarity Feature-Based Detection and Classification.

Degree: MS, Electrical and Computer Engineering, 2011, Virginia Tech

 Cyclostationarity feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection… (more)

Subjects/Keywords: robust estimation; continuous phase modulation; cyclostationarity; detection; automatic modulation classification

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APA (6th Edition):

Malady, A. C. (2011). Cyclostationarity Feature-Based Detection and Classification. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32280

Chicago Manual of Style (16th Edition):

Malady, Amy Colleen. “Cyclostationarity Feature-Based Detection and Classification.” 2011. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/32280.

MLA Handbook (7th Edition):

Malady, Amy Colleen. “Cyclostationarity Feature-Based Detection and Classification.” 2011. Web. 20 Oct 2019.

Vancouver:

Malady AC. Cyclostationarity Feature-Based Detection and Classification. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/32280.

Council of Science Editors:

Malady AC. Cyclostationarity Feature-Based Detection and Classification. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/32280


Mississippi State University

22. Ganapathiraju, Aravind. Support Vector Machines for Speech Recognition.

Degree: PhD, Electrical and Computer Engineering, 2002, Mississippi State University

 Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic… (more)

Subjects/Keywords: classification; kernel methods; acoustic modeling

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APA (6th Edition):

Ganapathiraju, A. (2002). Support Vector Machines for Speech Recognition. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;

Chicago Manual of Style (16th Edition):

Ganapathiraju, Aravind. “Support Vector Machines for Speech Recognition.” 2002. Doctoral Dissertation, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;.

MLA Handbook (7th Edition):

Ganapathiraju, Aravind. “Support Vector Machines for Speech Recognition.” 2002. Web. 20 Oct 2019.

Vancouver:

Ganapathiraju A. Support Vector Machines for Speech Recognition. [Internet] [Doctoral dissertation]. Mississippi State University; 2002. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;.

Council of Science Editors:

Ganapathiraju A. Support Vector Machines for Speech Recognition. [Doctoral Dissertation]. Mississippi State University; 2002. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/ ;


Mississippi State University

23. Chanda, Naveen Kumar. ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS.

Degree: MS, Electrical and Computer Engineering, 2011, Mississippi State University

  Uninterrupted power supply is an important requirement for electric ship since it has to confront frequent travel and hostilities. However, the occurrence of faults… (more)

Subjects/Keywords: fault classification; artificial neural networks; fault location; MVDC shipboard power systems

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APA (6th Edition):

Chanda, N. K. (2011). ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022011-230909/ ;

Chicago Manual of Style (16th Edition):

Chanda, Naveen Kumar. “ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS.” 2011. Masters Thesis, Mississippi State University. Accessed October 20, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022011-230909/ ;.

MLA Handbook (7th Edition):

Chanda, Naveen Kumar. “ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS.” 2011. Web. 20 Oct 2019.

Vancouver:

Chanda NK. ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS. [Internet] [Masters thesis]. Mississippi State University; 2011. [cited 2019 Oct 20]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022011-230909/ ;.

Council of Science Editors:

Chanda NK. ANN-BASED FAULT CLASSIFICATION AND LOCATION ON MVDC CABLES OF SHIPBOARD POWER SYSTEMS. [Masters Thesis]. Mississippi State University; 2011. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022011-230909/ ;


Portland State University

24. Nguyen, Phu Duy. Physics Based Approach for Seafloor Classification.

Degree: MS(M.S.) in Electrical and Computer Engineering, Electrical and Computer Engineering, 2017, Portland State University

  The seafloor properties are of high importance for many applications such as marine biology, oil and gas exploration, laying cables, dredging operations and off-shore… (more)

Subjects/Keywords: Ocean bottom  – Classification  – Remote sensing; Underwater acoustics; Electrical and Computer Engineering

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APA (6th Edition):

Nguyen, P. D. (2017). Physics Based Approach for Seafloor Classification. (Masters Thesis). Portland State University. Retrieved from https://pdxscholar.library.pdx.edu/open_access_etds/4060

Chicago Manual of Style (16th Edition):

Nguyen, Phu Duy. “Physics Based Approach for Seafloor Classification.” 2017. Masters Thesis, Portland State University. Accessed October 20, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/4060.

MLA Handbook (7th Edition):

Nguyen, Phu Duy. “Physics Based Approach for Seafloor Classification.” 2017. Web. 20 Oct 2019.

Vancouver:

Nguyen PD. Physics Based Approach for Seafloor Classification. [Internet] [Masters thesis]. Portland State University; 2017. [cited 2019 Oct 20]. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/4060.

Council of Science Editors:

Nguyen PD. Physics Based Approach for Seafloor Classification. [Masters Thesis]. Portland State University; 2017. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/4060


University of New Mexico

25. Jacoby, Abigail R. Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation.

Degree: Electrical and Computer Engineering, 2017, University of New Mexico

Classification of human activity in raw video presents a challenging problem that remains unsolved, and is of great interest for large datasets. Though there… (more)

Subjects/Keywords: human activity classification; context-based methods; Electrical and Computer Engineering

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APA (6th Edition):

Jacoby, A. R. (2017). Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation. (Masters Thesis). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/ece_etds/404

Chicago Manual of Style (16th Edition):

Jacoby, Abigail R. “Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation.” 2017. Masters Thesis, University of New Mexico. Accessed October 20, 2019. https://digitalrepository.unm.edu/ece_etds/404.

MLA Handbook (7th Edition):

Jacoby, Abigail R. “Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation.” 2017. Web. 20 Oct 2019.

Vancouver:

Jacoby AR. Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation. [Internet] [Masters thesis]. University of New Mexico; 2017. [cited 2019 Oct 20]. Available from: https://digitalrepository.unm.edu/ece_etds/404.

Council of Science Editors:

Jacoby AR. Context-Sensitive Human Activity Classification in Video Utilizing Object Recognition and Motion Estimation. [Masters Thesis]. University of New Mexico; 2017. Available from: https://digitalrepository.unm.edu/ece_etds/404


University of New Mexico

26. Espinoza Sanchez, Marco Antonio. Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis.

Degree: Electrical and Computer Engineering, 2015, University of New Mexico

 This thesis presents the utilization of a portable medical device intended to help in the diagnosis of the Anterior Cruciate Ligament(ACL) knee injury. The prototype… (more)

Subjects/Keywords: ACL; Pivot Shift; accelerometer; knee; injury; KNN; classification

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APA (6th Edition):

Espinoza Sanchez, M. A. (2015). Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/27925

Chicago Manual of Style (16th Edition):

Espinoza Sanchez, Marco Antonio. “Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis.” 2015. Masters Thesis, University of New Mexico. Accessed October 20, 2019. http://hdl.handle.net/1928/27925.

MLA Handbook (7th Edition):

Espinoza Sanchez, Marco Antonio. “Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis.” 2015. Web. 20 Oct 2019.

Vancouver:

Espinoza Sanchez MA. Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis. [Internet] [Masters thesis]. University of New Mexico; 2015. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/1928/27925.

Council of Science Editors:

Espinoza Sanchez MA. Design and Implementation of a Pivot Shift Prototype for Quantitative Analysis. [Masters Thesis]. University of New Mexico; 2015. Available from: http://hdl.handle.net/1928/27925

27. Montazeripouragha, Amanallah. Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea.

Degree: Electrical and Computer Engineering, 2012, University of Manitoba

 Obstructive Sleep Apnea (OSA) is a common respiratory disorder during sleep. Apnea is cessation of airflow to the lungs, which lasts for at least 10… (more)

Subjects/Keywords: Biomedical; Sleep Apnea; Classification

…4.1.3 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.1.4… …Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Discussion… …30 SENSITIVITY, SPECIFICITY AND CLASSIFICATION ACCURACY FOR THE LDA CLASSIFIER USING MPUNI… …VKUNI AND MKSNI AS THE CLASSIFICATION FEATURES… …35 4.3 SENSITIVITY, SPECIFICITY AND CLASSIFICATION ACCURACY FOR THE LDA CLASSIFIER USING… 

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APA (6th Edition):

Montazeripouragha, A. (2012). Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/5194

Chicago Manual of Style (16th Edition):

Montazeripouragha, Amanallah. “Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea.” 2012. Masters Thesis, University of Manitoba. Accessed October 20, 2019. http://hdl.handle.net/1993/5194.

MLA Handbook (7th Edition):

Montazeripouragha, Amanallah. “Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea.” 2012. Web. 20 Oct 2019.

Vancouver:

Montazeripouragha A. Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea. [Internet] [Masters thesis]. University of Manitoba; 2012. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/1993/5194.

Council of Science Editors:

Montazeripouragha A. Acoustical analysis of respiratory sounds for detection of obstructive sleep apnea. [Masters Thesis]. University of Manitoba; 2012. Available from: http://hdl.handle.net/1993/5194


New Jersey Institute of Technology

28. Wu, Keyuan. A fuzzy logic-based text classification method for social media.

Degree: MSin Electrical Engineering - (M.S.), Electrical and Computer Engineering, 2017, New Jersey Institute of Technology

Social media offer abundant information for studying people’s behaviors, emotions and opinions during the evolution of various rare events such as natural disasters. It is useful to analyze the correlation between social media and human-affected Advisors/Committee Members: MengChu Zhou, Ali Abdi, Hesuan Hu.

Subjects/Keywords: Social media; Text classification; Fuzzy logic; Electrical and Electronics

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APA (6th Edition):

Wu, K. (2017). A fuzzy logic-based text classification method for social media. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/31

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):

Wu, Keyuan. “A fuzzy logic-based text classification method for social media.” 2017. Thesis, New Jersey Institute of Technology. Accessed October 20, 2019. https://digitalcommons.njit.edu/theses/31.

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

MLA Handbook (7th Edition):

Wu, Keyuan. “A fuzzy logic-based text classification method for social media.” 2017. Web. 20 Oct 2019.

Vancouver:

Wu K. A fuzzy logic-based text classification method for social media. [Internet] [Thesis]. New Jersey Institute of Technology; 2017. [cited 2019 Oct 20]. Available from: https://digitalcommons.njit.edu/theses/31.

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

Council of Science Editors:

Wu K. A fuzzy logic-based text classification method for social media. [Thesis]. New Jersey Institute of Technology; 2017. Available from: https://digitalcommons.njit.edu/theses/31

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


Queens University

29. Ramezani-Kebrya, Ali. Likelihood-Based Modulation Classification for Multiple-Antenna Receivers .

Degree: Electrical and Computer Engineering, 2012, Queens University

 Prior to signal demodulation, blind recognition of the modulation scheme of the received signal is an important task for intelligent radios in various commercial and… (more)

Subjects/Keywords: Modulation Classification; Estimation Theory; Cramer Rao Lower Bound

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APA (6th Edition):

Ramezani-Kebrya, A. (2012). Likelihood-Based Modulation Classification for Multiple-Antenna Receivers . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/7491

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):

Ramezani-Kebrya, Ali. “Likelihood-Based Modulation Classification for Multiple-Antenna Receivers .” 2012. Thesis, Queens University. Accessed October 20, 2019. http://hdl.handle.net/1974/7491.

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

MLA Handbook (7th Edition):

Ramezani-Kebrya, Ali. “Likelihood-Based Modulation Classification for Multiple-Antenna Receivers .” 2012. Web. 20 Oct 2019.

Vancouver:

Ramezani-Kebrya A. Likelihood-Based Modulation Classification for Multiple-Antenna Receivers . [Internet] [Thesis]. Queens University; 2012. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/1974/7491.

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

Council of Science Editors:

Ramezani-Kebrya A. Likelihood-Based Modulation Classification for Multiple-Antenna Receivers . [Thesis]. Queens University; 2012. Available from: http://hdl.handle.net/1974/7491

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


Virginia Tech

30. Headley, William Christopher. Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels.

Degree: MS, Electrical and Computer Engineering, 2009, Virginia Tech

 One of the fundamental hurdles in realizing new spectrum sharing allocation policies is that of reliable spectrum sensing. In this thesis, three research thrusts are… (more)

Subjects/Keywords: asynchronous classification and estimation; spectrum sensing; SNR estimation; distributed detection; modulation classification

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APA (6th Edition):

Headley, W. C. (2009). Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32519

Chicago Manual of Style (16th Edition):

Headley, William Christopher. “Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels.” 2009. Masters Thesis, Virginia Tech. Accessed October 20, 2019. http://hdl.handle.net/10919/32519.

MLA Handbook (7th Edition):

Headley, William Christopher. “Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels.” 2009. Web. 20 Oct 2019.

Vancouver:

Headley WC. Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels. [Internet] [Masters thesis]. Virginia Tech; 2009. [cited 2019 Oct 20]. Available from: http://hdl.handle.net/10919/32519.

Council of Science Editors:

Headley WC. Classification and Parameter Estimation of Asynchronously Received PSK/QAM Modulated Signals in Flat-Fading Channels. [Masters Thesis]. Virginia Tech; 2009. Available from: http://hdl.handle.net/10919/32519

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