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You searched for +publisher:"Georgia Tech" +contributor:("James H. McClellan"). Showing records 1 – 10 of 10 total matches.

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1. Lee, Yeongseon. Bayesian 3D multiple people tracking using multiple indoor cameras and microphones.

Degree: PhD, Electrical and Computer Engineering, 2009, Georgia Tech

 This thesis represents Bayesian joint audio-visual tracking for the 3D locations of multiple people and a current speaker in a real conference environment. To achieve… (more)

Subjects/Keywords: Object tracking; Particle filter; Data association; Sensor fusion; Visual feature detection; TDOA detection; Multiple target tracking; Automatic tracking; Sensor networks; Multisensor data fusion; Context-aware computing; Human face recognition (Computer science); Motion perception (Vision) Computer networks; Acoustic localization

…Georgia Institute of Technology Professor James H. McClellan School of Electrical and Computer… …time I have been in Georgia Tech for PhD research not only means studying a particular area… …analysis. The Aware House [19] project at Georgia Tech, for example, focuses on… 

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APA (6th Edition):

Lee, Y. (2009). Bayesian 3D multiple people tracking using multiple indoor cameras and microphones. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/29668

Chicago Manual of Style (16th Edition):

Lee, Yeongseon. “Bayesian 3D multiple people tracking using multiple indoor cameras and microphones.” 2009. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/29668.

MLA Handbook (7th Edition):

Lee, Yeongseon. “Bayesian 3D multiple people tracking using multiple indoor cameras and microphones.” 2009. Web. 22 Oct 2020.

Vancouver:

Lee Y. Bayesian 3D multiple people tracking using multiple indoor cameras and microphones. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/29668.

Council of Science Editors:

Lee Y. Bayesian 3D multiple people tracking using multiple indoor cameras and microphones. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/29668


Georgia Tech

2. Cevher, Volkan. A Bayesian Framework for Target Tracking using Acoustic and Image Measurements.

Degree: PhD, Electrical and Computer Engineering, 2005, Georgia Tech

 Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features… (more)

Subjects/Keywords: Target tracking; Monte Carlo method; Acoustic calibration; Particle filters; State-space models; Image tracking; Acoustic tracking

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APA (6th Edition):

Cevher, V. (2005). A Bayesian Framework for Target Tracking using Acoustic and Image Measurements. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/6824

Chicago Manual of Style (16th Edition):

Cevher, Volkan. “A Bayesian Framework for Target Tracking using Acoustic and Image Measurements.” 2005. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/6824.

MLA Handbook (7th Edition):

Cevher, Volkan. “A Bayesian Framework for Target Tracking using Acoustic and Image Measurements.” 2005. Web. 22 Oct 2020.

Vancouver:

Cevher V. A Bayesian Framework for Target Tracking using Acoustic and Image Measurements. [Internet] [Doctoral dissertation]. Georgia Tech; 2005. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/6824.

Council of Science Editors:

Cevher V. A Bayesian Framework for Target Tracking using Acoustic and Image Measurements. [Doctoral Dissertation]. Georgia Tech; 2005. Available from: http://hdl.handle.net/1853/6824


Georgia Tech

3. Yaman, Sibel. A multi-objective programming perspective to statistical learning problems.

Degree: PhD, Electrical and Computer Engineering, 2008, Georgia Tech

 It has been increasingly recognized that realistic problems often involve a tradeoff among many conflicting objectives. Traditional methods aim at satisfying multiple objectives by combining… (more)

Subjects/Keywords: Pattern recognition; Statistical language modeling; Automatic speech recognition; Multiobjective optimization; Speech processing systems; Electronic data processing; Interpolation; Approximation theory

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APA (6th Edition):

Yaman, S. (2008). A multi-objective programming perspective to statistical learning problems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/26470

Chicago Manual of Style (16th Edition):

Yaman, Sibel. “A multi-objective programming perspective to statistical learning problems.” 2008. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/26470.

MLA Handbook (7th Edition):

Yaman, Sibel. “A multi-objective programming perspective to statistical learning problems.” 2008. Web. 22 Oct 2020.

Vancouver:

Yaman S. A multi-objective programming perspective to statistical learning problems. [Internet] [Doctoral dissertation]. Georgia Tech; 2008. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/26470.

Council of Science Editors:

Yaman S. A multi-objective programming perspective to statistical learning problems. [Doctoral Dissertation]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/26470


Georgia Tech

4. Ozgur, Soner. Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical… (more)

Subjects/Keywords: Iterative receiver; Turbo receiver; Blind receiver; Monte Carlo method; Wireless communication systems; Monte Carlo method; Line receivers (Integrated circuits)

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APA (6th Edition):

Ozgur, S. (2006). Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/10518

Chicago Manual of Style (16th Edition):

Ozgur, Soner. “Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers.” 2006. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/10518.

MLA Handbook (7th Edition):

Ozgur, Soner. “Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers.” 2006. Web. 22 Oct 2020.

Vancouver:

Ozgur S. Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/10518.

Council of Science Editors:

Ozgur S. Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/10518


Georgia Tech

5. Le, Qiang. Resource management for wireless networks of bearings-only sensors.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 The thesis focuses on resource management or sensor allocation when we use bearings-only measurements to track targets in an unattended ground sensor (UGS) network. Intelligent… (more)

Subjects/Keywords: Resource management; Kalman filters; Wireless networks

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APA (6th Edition):

Le, Q. (2006). Resource management for wireless networks of bearings-only sensors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/10548

Chicago Manual of Style (16th Edition):

Le, Qiang. “Resource management for wireless networks of bearings-only sensors.” 2006. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/10548.

MLA Handbook (7th Edition):

Le, Qiang. “Resource management for wireless networks of bearings-only sensors.” 2006. Web. 22 Oct 2020.

Vancouver:

Le Q. Resource management for wireless networks of bearings-only sensors. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/10548.

Council of Science Editors:

Le Q. Resource management for wireless networks of bearings-only sensors. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/10548


Georgia Tech

6. Rosen, Gail L. Signal processing for biologically-inspired gradient source localization and DNA sequence analysis.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified… (more)

Subjects/Keywords: DNA analysis; Ficks second law; Hebbian learning; Biased random walk; Sensor cross-correlation; Delay-and-Sum beamforming; Turbulent plumes; Electronic nose; Tandem repeats; Gradient sensing; Bacterial chemotaxis navigation; Chemotaxis; Sensor networks; Signal processing; Biologically-inspired computing; Chemotaxis; Nervous system Degeneration; Nucleotide sequence

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APA (6th Edition):

Rosen, G. L. (2006). Signal processing for biologically-inspired gradient source localization and DNA sequence analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/11628

Chicago Manual of Style (16th Edition):

Rosen, Gail L. “Signal processing for biologically-inspired gradient source localization and DNA sequence analysis.” 2006. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/11628.

MLA Handbook (7th Edition):

Rosen, Gail L. “Signal processing for biologically-inspired gradient source localization and DNA sequence analysis.” 2006. Web. 22 Oct 2020.

Vancouver:

Rosen GL. Signal processing for biologically-inspired gradient source localization and DNA sequence analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/11628.

Council of Science Editors:

Rosen GL. Signal processing for biologically-inspired gradient source localization and DNA sequence analysis. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/11628


Georgia Tech

7. Rahnavard, Nazanin. Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting.

Degree: PhD, Electrical and Computer Engineering, 2007, Georgia Tech

 This thesis investigates both theoretical and practical aspects of the design and analysis of modern error-control coding schemes, namely low-density parity-check (LDPC) codes and rateless… (more)

Subjects/Keywords: Efficient broadcasting; Wireless sensor networks; Unequal error protection; Multicast; Density evolution; Data dissemination; Rateless codes; Low-density parity-check codes; Modern error-control coding; Wireless ad-hoc networks; LT codes; Raptor codes; Ad hoc networks (Computer networks); Computer networks; Error-correcting codes (Information theory); Coding theory

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APA (6th Edition):

Rahnavard, N. (2007). Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/26673

Chicago Manual of Style (16th Edition):

Rahnavard, Nazanin. “Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting.” 2007. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/26673.

MLA Handbook (7th Edition):

Rahnavard, Nazanin. “Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting.” 2007. Web. 22 Oct 2020.

Vancouver:

Rahnavard N. Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting. [Internet] [Doctoral dissertation]. Georgia Tech; 2007. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/26673.

Council of Science Editors:

Rahnavard N. Coding for wireless ad-hoc and sensor networks: unequal error protection and efficient data broadcasting. [Doctoral Dissertation]. Georgia Tech; 2007. Available from: http://hdl.handle.net/1853/26673


Georgia Tech

8. Rohling, Gregory Allen. Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives.

Degree: PhD, Electrical and Computer Engineering, 2004, Georgia Tech

 This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective… (more)

Subjects/Keywords: Multiple objective evolutionary algorithms; Missile warning receiver; Multiple objective evolutionary algorithms; Genetic programming; Optimization; Pareto optimal; Multiple criteria decision making Computer simulation; Pattern recognition systems; Mathematical optimization

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APA (6th Edition):

Rohling, G. A. (2004). Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/4835

Chicago Manual of Style (16th Edition):

Rohling, Gregory Allen. “Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives.” 2004. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/4835.

MLA Handbook (7th Edition):

Rohling, Gregory Allen. “Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives.” 2004. Web. 22 Oct 2020.

Vancouver:

Rohling GA. Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives. [Internet] [Doctoral dissertation]. Georgia Tech; 2004. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/4835.

Council of Science Editors:

Rohling GA. Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives. [Doctoral Dissertation]. Georgia Tech; 2004. Available from: http://hdl.handle.net/1853/4835


Georgia Tech

9. Gonzalez Rosiles, Jose Gerardo. Image and Texture Analysis using Biorthogonal Angular Filter Banks.

Degree: PhD, Electrical and Computer Engineering, 2004, Georgia Tech

 In this thesis we develop algorithms for the processing of textures and images using a ladder-based biorthogonal directional filter bank (DFB). This work is based… (more)

Subjects/Keywords: Directional filter banks; Image analysis; Image processing; Texture analysis; Image compression; SAR image processing; Texture synthesis; Segmentation; Classification

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APA (6th Edition):

Gonzalez Rosiles, J. G. (2004). Image and Texture Analysis using Biorthogonal Angular Filter Banks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/5042

Chicago Manual of Style (16th Edition):

Gonzalez Rosiles, Jose Gerardo. “Image and Texture Analysis using Biorthogonal Angular Filter Banks.” 2004. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/5042.

MLA Handbook (7th Edition):

Gonzalez Rosiles, Jose Gerardo. “Image and Texture Analysis using Biorthogonal Angular Filter Banks.” 2004. Web. 22 Oct 2020.

Vancouver:

Gonzalez Rosiles JG. Image and Texture Analysis using Biorthogonal Angular Filter Banks. [Internet] [Doctoral dissertation]. Georgia Tech; 2004. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/5042.

Council of Science Editors:

Gonzalez Rosiles JG. Image and Texture Analysis using Biorthogonal Angular Filter Banks. [Doctoral Dissertation]. Georgia Tech; 2004. Available from: http://hdl.handle.net/1853/5042


Georgia Tech

10. Alam, Mubashir. Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 The use of seismic waves to detect subsurface targets such as landmines is a very promising technology compared to existing methods like Ground Penetrating Radar… (more)

Subjects/Keywords: Confirmation; Localization; Spectrum anaylysis; Array processing; Land mines; Seismic; Maneuvering

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APA (6th Edition):

Alam, M. (2006). Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/11458

Chicago Manual of Style (16th Edition):

Alam, Mubashir. “Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors.” 2006. Doctoral Dissertation, Georgia Tech. Accessed October 22, 2020. http://hdl.handle.net/1853/11458.

MLA Handbook (7th Edition):

Alam, Mubashir. “Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors.” 2006. Web. 22 Oct 2020.

Vancouver:

Alam M. Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2020 Oct 22]. Available from: http://hdl.handle.net/1853/11458.

Council of Science Editors:

Alam M. Localization of Subsurface Targets using Optimal Maneuvers of Seismic Sensors. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/11458

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