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  Partner: UNT College of Engineering
GPS/GSM Based Tracking System for the Recovery of High Power Model Rockets

GPS/GSM Based Tracking System for the Recovery of High Power Model Rockets

Date: 2013
Creator: Bih, Michael; Gscheidle, Karl H.; Hardy, Debra; Kollipara, Naveen & Kulle, Gregory
Description: This report discusses research on GPS/GSM based tracking systems for the recovery of high power model rockets. This research is part of Research Experiences for Teachers (RET) in Sensor Education, a National Science Foundation (NSF) funded grant project.
Contributing Partner: UNT College of Engineering
GPS Tracking of High Power Model Rockets

GPS Tracking of High Power Model Rockets

Date: 2013
Creator: Gscheidle, Karl H.; Hardy, Debra; Kulle, Gregory; Bih, Michael; Acevedo, Miguel F. & Kollipara, Naveen
Description: Poster presented as part of the Research Experiences for Teachers (RET) in Sensor Education, a National Science Foundation (NSF) funded grant project. This poster discusses research on GPS/GSM based tracking systems for the recovery of high power model rockets.
Contributing Partner: UNT College of Engineering
Identifying Leaders in an Online Cancer Survivor Community

Identifying Leaders in an Online Cancer Survivor Community

Date: December 2011
Creator: Zhao, Kang; Qiu, Baojun; Caragea, Cornelia; Wu, Dinghao; Mitra, Prasenjit; Yen, John et al.
Description: Paper on identifying leaders in an online cancer survivor community.
Contributing Partner: UNT College of Engineering
Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Thread Specific Features Are Helpful For Identifying Subjectivity Orientation of Online Forum Threads

Date: October 2012
Creator: Biyani, Prakhar; Bhatia, Sumit; Caragea, Cornelia & Mitra, Prasenjit
Description: Paper discussing thread specific features for identifying subjectivity orientation of online forum threads.
Contributing Partner: UNT College of Engineering
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Date: April 29, 2009
Creator: Caragea, Cornelia; Sinapov, Jivko; Dobbs, Drena & Honavar, Vasant
Description: Article discussing models for increasing the reliability of computational methods for identifying functionally important sites from biomolecular sequences.
Contributing Partner: UNT College of Engineering
Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

Date: October 26, 2010
Creator: Caragea, Cornelia; Caragea, Doina; Silvescu, Adrian & Honavar, Vasant
Description: Article discussing the semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models.
Contributing Partner: UNT College of Engineering
Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

Date: May 10, 2012
Creator: Walia, Rasna R.; Caragea, Cornelia; Lewis, Benjamin A.; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser et al.
Description: Article presenting a review, comparison, and critical assessment of published approaches for predicting RNA-binding residues in proteins using non-redundant databases.
Contributing Partner: UNT College of Engineering
Abstraction Augmented Markov Models

Abstraction Augmented Markov Models

Date: December 2010
Creator: Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina & Honavar, Vasant
Description: Article discussing the abstraction augmented Markov models.
Contributing Partner: UNT College of Engineering
Protein sequence classification using feature hashing

Protein sequence classification using feature hashing

Date: June 21, 2012
Creator: Caragea, Cornelia; Silvescu, Adrian & Mitra, Prasenjit
Description: Article discussing protein sequence classification using feature hashing.
Contributing Partner: UNT College of Engineering
Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

Date: November 9, 2007
Creator: Caragea, Cornelia; Sinapov, Jivko; Silvescu, Adrian; Dobbs, Drena & Honavar, Vasant
Description: Article discussing the performance of different computational methods for prediction of glycosylation sites from amino acid sequences.
Contributing Partner: UNT College of Engineering