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Georgia Tech
1.
Luo, Chenchi.
Non-uniform sampling: algorithms and architectures.
Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/45873 
► Modern signal processing applications emerging in telecommunication and instrumentation industries have placed an increasing demand for ADCs with higher speed and resolution. The most fundamental…
(more)
▼ Modern signal processing applications emerging in telecommunication and instrumentation industries have placed an increasing demand for ADCs with higher speed and resolution. The most fundamental challenge in such a progress lies at the heart of the classic signal processing: the Shannon-Nyquist sampling theorem which stated that when sampled uniformly, there is no way to increase the upper frequency in the signal spectrum and still unambiguously represent the signal except by raising the sampling rate. This thesis is dedicated to the exploration of the ways to break through the Shannon-Nyquist sampling rate by applying non-uniform sampling techniques.
Time interleaving is probably the most intuitive way to parallel the uniform sampling process in order to achieve a higher sampling rate. Unfortunately, the channel mismatches in the TIADC system make the system an instance of a recurrent non-uniform sampling system whose non-uniformities are detrimental to the performance of the system and need to be calibrated. Accordingly, this thesis proposed a flexible and efficient architecture to compensate for the channel mismatches in the TIADC system. As a key building block in the calibration architecture, the design of the Farrow structured adjustable fractional delay filter has been investigated in detail. A new modified Farrow structure is proposed to design the adjustable FD filters that are optimized for a given range of bandwidth and fractional delays. The application of the Farrow structure is not limited to the design of adjustable fractional delay filters. It can also be used to implement adjustable lowpass, highpass and bandpass filters as well as adjustable multirate filters. This thesis further extends the Farrow structure to the design of filters with adjustable polynomial phase responses.
Inspired by the theory of compressive sensing, another contribution of this thesis is to use randomization as a means to overcome the limit of the Nyquist rate. This thesis investigates the impact of random sampling intervals or jitters on the power spectrum of the sampled signal. It shows that the aliases of the original signal can be well shaped by choosing an appropriate probability distribution of the sampling intervals or jitters such that aliases can be viewed as a source of noise in the signal power spectrum. A new theoretical framework has been established to associate the probability mass function of the random sampling intervals or jitters with the aliasing shaping effect. Based on the theoretical framework, this thesis proposes three random sampling architectures, i.e., SAR ADC, ramp ADC and level crossing ADC, that can be easily implemented based on the corresponding standard ADC architectures. Detailed models and simulations are established to verify the effectiveness of the proposed architectures. A new reconstruction algorithm called the successive sine matching pursuit has also been proposed to recover a class of spectrally sparse signals from a sparse set of non-uniform samples onto a denser uniform time…
Advisors/Committee Members: McClellan, James (Committee Chair), Romberg, Justin (Committee Chair), Anderson, David (Committee Member), Davenport, Mark (Committee Member), Vempala, Santosh (Committee Member).
Subjects/Keywords: TIADC; Farrow structure; Compressive sensing; Sparsity; Analog-to-digital converters; Sampling (Statistics); Algorithms; Signal processing; Signal processing Digital techniques
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APA (6th Edition):
Luo, C. (2012). Non-uniform sampling: algorithms and architectures. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/45873
Chicago Manual of Style (16th Edition):
Luo, Chenchi. “Non-uniform sampling: algorithms and architectures.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/45873.
MLA Handbook (7th Edition):
Luo, Chenchi. “Non-uniform sampling: algorithms and architectures.” 2012. Web. 22 Sep 2019.
Vancouver:
Luo C. Non-uniform sampling: algorithms and architectures. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/45873.
Council of Science Editors:
Luo C. Non-uniform sampling: algorithms and architectures. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/45873
Georgia Tech
2.
Shih, Ping-Chang.
Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states.
Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech
URL: http://hdl.handle.net/1853/52320
► In this thesis, an innovative algorithm for improving the accuracy of variational space-time stereoscopic reconstruction of ocean surfaces is presented. The space-time reconstruction method, developed…
(more)
▼ In this thesis, an innovative algorithm for improving the accuracy of variational space-time stereoscopic reconstruction of ocean surfaces is presented. The space-time reconstruction method, developed based on stereo computer vision principles and variational optimization theory, takes videos captured by synchronized cameras as inputs and produces the shape and superficial pattern of an overlapped region of interest as outputs. These outputs are designed to be the minimizers of the variational optimization framework and are dependent on the estimation of the camera parameters. Therefore, from the perspective of computer vision, the proposed algorithm adjusts the estimation of camera parameters to lower the disagreement between the reconstruction and 2-D camera recordings. From a mathematical perspective, since the minimizers of the variational framework are determined by a set of partial differential equations (PDEs), the algorithm modifies the coefficients of the PDEs based on the current numerical
solutions to reduce the minimum of the optimization framework. Our algorithm increases the tolerance to the errors of camera parameters, so the joint operations of our algorithm and the variational reconstruction method can generate accurate space-time models even using videos captured by perturbed cameras as input. This breakthrough prompts the realization of ocean surface reconstruction using videos filmed by remotely-controlled helicopters in the future. A number of techniques, technical or theoretical, are explored to fulfill the development and implementation of the algorithm and relative computation issues. The effectiveness of the proposed algorithm is validated through the statistics applied to real ocean surface reconstructions of data collected from an offshore platform at the Crimean Peninsula, the Black Sea. Moreover, synthetic data generated using computer graphics are customized to simulate various situations that are not recorded in the Crimea dataset for the demonstration of the algorithm.
Advisors/Committee Members: Yezzi, Anthony (advisor), Fedele, Francesco (committee member), Vela, Patricio (committee member), Romberg, Justin (committee member), Dellaert, Frank (committee member), Tannenbaum, Allen (committee member).
Subjects/Keywords: Stereo computer vision; Camera calibration; 3-D reconstruction; Variational
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APA (6th Edition):
Shih, P. (2014). Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52320
Chicago Manual of Style (16th Edition):
Shih, Ping-Chang. “Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/52320.
MLA Handbook (7th Edition):
Shih, Ping-Chang. “Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states.” 2014. Web. 22 Sep 2019.
Vancouver:
Shih P. Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/52320.
Council of Science Editors:
Shih P. Joint variational camera calibration refinement and 4-D stereo reconstruction applied to oceanic sea states. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52320
Georgia Tech
3.
Xia, Dong.
Statistical inference for large matrices.
Degree: PhD, Mathematics, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/55632
► This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application…
(more)
▼ This thesis covers two topics on matrix analysis and estimation in machine learning and statistics. The first topic is about density matrix estimation with application in quantum state tomography. The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. We develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, in the case of Pauli measurements) with explicit dependence of the bounds on the rank and other complexity parameters. Such bounds are established for several statistically relevant distances, including quantum versions of Kullback-Leibler divergence (relative entropy distance) and of Hellinger distance (so called Bures distance), and Schatten p-norm distances. Sharp upper bounds and oracle inequalities for least squares estimator with von Neumann entropy penalization are obtained showing that minimax lower bounds are attained (up to logarithmic factors) for these distances.
Another topic is about the analysis of the spectral perturbations of matrices under Gaussian noise. Given a matrix contaminated with Gaussian noise, we develop sharp upper bounds on the perturbation of linear forms of singular vectors. In particular, sharp upper bounds are proved for the component-wise perturbation of singular vectors. These results can be applied on sub-matrices localization and spectral clustering algorithms.
Advisors/Committee Members: Koltchinskii, Vladimir (advisor), Lounici, Karim (committee member), Romberg, Justin (committee member), Tetali, Prasad (committee member), Song, Le (committee member).
Subjects/Keywords: Low rank; Matrix estimation; Singular vectors; Random perturbation
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APA (6th Edition):
Xia, D. (2016). Statistical inference for large matrices. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55632
Chicago Manual of Style (16th Edition):
Xia, Dong. “Statistical inference for large matrices.” 2016. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/55632.
MLA Handbook (7th Edition):
Xia, Dong. “Statistical inference for large matrices.” 2016. Web. 22 Sep 2019.
Vancouver:
Xia D. Statistical inference for large matrices. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/55632.
Council of Science Editors:
Xia D. Statistical inference for large matrices. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55632
Georgia Tech
4.
Zou, Jun.
Social computing for personalization and credible information mining using probabilistic graphical models.
Degree: PhD, Electrical and Computer Engineering, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/55646
► In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including…
(more)
▼ In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including directed and undirected graphical models, to model a large number of observed and unobserved variables as well as various dependency relationships between variables, and develop efficient computation algorithms that exploit the graph structure to solve the problems. In recommender systems, we propose probabilistic graphical models for Collaborative Filtering (CF) algorithms in various problem settings, and solve them using Belief Propagation (BP) algorithms that allow scalable and distributed implementations. Firstly, user similarities are computed in factor graphs. Then unknown ratings are predicted in Pairwise Markov Random Fields (PMRFs). Further, when online social networks of users are provided, a Bayesian Network (BN) recommendation system is constructed based on user relations to improve recommendation for cold-start users or users do not have sufficient ratings. To preserve user privacy, a semi-distributed item-based CF system is developed, which employs semi-distributed BP for item similarity computation in factor graphs, without disclosing ratings to the server or other peer users. Finally, to protect CF recommender systems from shilling attacks, a factor graph is proposed to jointly detect colluding spammers, which significantly improves detection accuracy over classification algorithms based on a single user's rating patterns. In social media information mining, to detect false information and keep track of information credibility, we propose a generative probabilistic model to predict the credibility of events in Twitter-like social media using streaming tweets. The proposed algorithm predicts credibility much faster than existing offline algorithms and updates prediction online with newly observed tweets. Further, to identify suspicious users that perform malevolent activities such as spamming and phishing, we propose a probabilistic PMRF model for predicting the trustworthiness of social media users. The PMRF model improves prediction accuracy by taking into account user relationships compared to existing prediction algorithms for individual users.
Advisors/Committee Members: Fekri, Faramarz (advisor), Davenport, Mark A. (committee member), McLaughlin, Steven W. (committee member), Romberg, Justin (committee member), Mei, Yajun (committee member).
Subjects/Keywords: Social computing; Recommender systems; Collaborative filtering; Belief propagation; Probabilistic graphical models
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APA (6th Edition):
Zou, J. (2016). Social computing for personalization and credible information mining using probabilistic graphical models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55646
Chicago Manual of Style (16th Edition):
Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/55646.
MLA Handbook (7th Edition):
Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Web. 22 Sep 2019.
Vancouver:
Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/55646.
Council of Science Editors:
Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55646
Georgia Tech
5.
Lee, Jungwon.
Efficient image compression system using a CMOS transform imager.
Degree: PhD, Electrical and Computer Engineering, 2009, Georgia Tech
URL: http://hdl.handle.net/1853/31825
► This research focuses on the implementation of the efficient image compression system among the many potential applications of a transform imager system. The study includes…
(more)
▼ This research focuses on the implementation of the efficient image compression system among the many potential applications of a transform imager system. The study includes implementing the image
compression system using a transform imager, developing a novel image compression algorithm for the system, and improving the performance of the image compression system through efficient encoding and decoding algorithms for vector quantization.
A transform imaging system is implemented using a transform imager, and the baseline JPEG compression algorithm is implemented and tested to verify the functionality and performance of the
transform imager system. The computational reduction in digital processing is investigated from two perspectives, algorithmic and implementation. Algorithmically, a novel wavelet-based embedded image compression algorithm using dynamic index reordering vector quantization (DIRVQ) is proposed for the system. DIRVQ makes it possible for the proposed algorithm to achieve superior performance over the
embedded zero-tree wavelet (EZW) algorithm and the
successive approximation vector quantization (SAVQ) algorithm. However, because DIRVQ requires intensive computational complexity, additional focus is placed on the efficient implementation of DIRVQ, and highly efficient implementation is achieved without a compromise in performance.
Advisors/Committee Members: Anderson, David (Committee Chair), Dorsey, John (Committee Member), Hasler, Paul (Committee Member), Kang, Sung Ha (Committee Member), Romberg, Justin (Committee Member).
Subjects/Keywords: Index reordering; Wavelet; CMOS imager; Vector quantization; Image compression; Metal oxide semiconductors, Complementary; Image compression; Algorithms
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APA (6th Edition):
Lee, J. (2009). Efficient image compression system using a CMOS transform imager. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/31825
Chicago Manual of Style (16th Edition):
Lee, Jungwon. “Efficient image compression system using a CMOS transform imager.” 2009. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/31825.
MLA Handbook (7th Edition):
Lee, Jungwon. “Efficient image compression system using a CMOS transform imager.” 2009. Web. 22 Sep 2019.
Vancouver:
Lee J. Efficient image compression system using a CMOS transform imager. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/31825.
Council of Science Editors:
Lee J. Efficient image compression system using a CMOS transform imager. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/31825
Georgia Tech
6.
Ma, Chengyuan.
A detection-based pattern recognition framework and its applications.
Degree: PhD, Electrical and Computer Engineering, 2010, Georgia Tech
URL: http://hdl.handle.net/1853/33889
► The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video…
(more)
▼ The objective of this dissertation is to present a detection-based pattern recognition framework and demonstrate its applications in automatic speech recognition and broadcast news video story segmentation.
Inspired by the studies of modern cognitive psychology and real-world pattern recognition systems, a detection-based pattern recognition framework is proposed to provide an alternative solution for some complicated pattern recognition problems. The primitive features are first detected and the task-specific knowledge hierarchy is constructed level by level; then a variety of heterogeneous information sources are combined together and the high-level context is incorporated as additional information at certain stages.
A detection-based framework is a â divide-and-conquerâ design paradigm for pattern recognition problems, which will decompose a conceptually difficult problem into many elementary sub-problems that can be handled directly and reliably. Some information fusion strategies will be employed to integrate the evidence from a lower level to form the evidence at a higher level. Such a fusion procedure continues until reaching the top level. Generally, a detection-based framework has many advantages: (1) more flexibility in both detector design and fusion strategies, as these two parts
can be optimized separately; (2) parallel and distributed computational components in primitive feature detection. In such a component-based framework, any primitive component can be replaced by a new one while other components remain unchanged; (3) incremental information integration; (4) high level context information as additional information sources, which can be combined with bottom-up processing at any stage.
This dissertation presents the basic principles, criteria, and techniques for detector design and hypothesis verification based on the statistical detection and decision theory. In addition, evidence fusion strategies were investigated in this dissertation. Several novel detection algorithms and evidence fusion methods were proposed and their effectiveness was justified in automatic speech recognition and broadcast news video segmentation system. We believe such a detection-based framework can be employed
in more applications in the future.
Advisors/Committee Members: Lee, Chin-Hui (Committee Chair), Clements, Mark (Committee Member), Ghovanloo, Maysam (Committee Member), Romberg, Justin (Committee Member), Yuan, Ming (Committee Member).
Subjects/Keywords: Speech recognition; Detection-based; Evidence fusion; Pattern recognition systems; Automatic speech recognition; Digital video
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APA (6th Edition):
Ma, C. (2010). A detection-based pattern recognition framework and its applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33889
Chicago Manual of Style (16th Edition):
Ma, Chengyuan. “A detection-based pattern recognition framework and its applications.” 2010. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/33889.
MLA Handbook (7th Edition):
Ma, Chengyuan. “A detection-based pattern recognition framework and its applications.” 2010. Web. 22 Sep 2019.
Vancouver:
Ma C. A detection-based pattern recognition framework and its applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/33889.
Council of Science Editors:
Ma C. A detection-based pattern recognition framework and its applications. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/33889
Georgia Tech
7.
Ralli, Peter Sharp.
Curvature and isoperimetry in graphs.
Degree: PhD, Mathematics, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/59221
► This dissertation concerns isoperimetric and functional inequalities in discrete spaces. The majority of the work concerns discrete notions of curvature. There is also discussion of…
(more)
▼ This dissertation concerns isoperimetric and functional inequalities in discrete spaces. The majority of the work concerns discrete notions of curvature. There is also discussion of volume growth in graphs and of expansion in hypergraphs.
Advisors/Committee Members: Tetali, Prasad (advisor), Romberg, Justin (committee member), Trotter, William T. (committee member), Yu, Xingxing (committee member), Zhou, Haomin (committee member).
Subjects/Keywords: Combinatorics; Graph theory; Discrete geometry
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APA (6th Edition):
Ralli, P. S. (2017). Curvature and isoperimetry in graphs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59221
Chicago Manual of Style (16th Edition):
Ralli, Peter Sharp. “Curvature and isoperimetry in graphs.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59221.
MLA Handbook (7th Edition):
Ralli, Peter Sharp. “Curvature and isoperimetry in graphs.” 2017. Web. 22 Sep 2019.
Vancouver:
Ralli PS. Curvature and isoperimetry in graphs. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59221.
Council of Science Editors:
Ralli PS. Curvature and isoperimetry in graphs. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59221
Georgia Tech
8.
Gupta, Udit.
Towards development of no-reference objective measures for perceptual evaluation of singing voice separation.
Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/59749
► Singing Voice Separation (SVS) uses audio source separation methods to isolate the vocal component from the background accompaniment in a song mix. A key challenge…
(more)
▼ Singing Voice Separation (SVS) uses audio source separation methods to isolate the vocal component from the background accompaniment in a song mix. A key challenge currently associated with evaluation of SVS is a lack of objective measures which correlate consistently with subjective evaluation. Additionally, the current state-of-the-art evaluation measures require the use of unmixed vocal and instrumental tracks which are often not available. The research presented in this thesis is an attempt to address these challenges by introducing two new objective measures for perceptually relevant evaluation of SVS. The Vocal Isolation Score (VIS) is designed to assess the quality of isolation produced by various SVS algorithms when separating the vocals from the accompaniment. Similarly, the Vocal Intelligibility Preservation Score (VIPS) evaluates the preservation of intelligibility in the separated vocals. Other than an improvement upon the state-of-the-art, both VIS and VIPS have the additional advantage that they do not require references in the form of unmixed vocal or instrumental tracks to perform objective evaluation, unlike the currently popular objective measures used for evaluating audio source separation.
Advisors/Committee Members: Moore, Elliot (advisor), Lerch, Alexander G. (advisor), Clements, Mark (committee member), Bhatti, Pamela (committee member), Romberg, Justin (committee member), Hsu, Timothy (committee member).
Subjects/Keywords: Source separation; Music information retrieval; Perceptual evaluation
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APA ·
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APA (6th Edition):
Gupta, U. (2017). Towards development of no-reference objective measures for perceptual evaluation of singing voice separation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59749
Chicago Manual of Style (16th Edition):
Gupta, Udit. “Towards development of no-reference objective measures for perceptual evaluation of singing voice separation.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59749.
MLA Handbook (7th Edition):
Gupta, Udit. “Towards development of no-reference objective measures for perceptual evaluation of singing voice separation.” 2017. Web. 22 Sep 2019.
Vancouver:
Gupta U. Towards development of no-reference objective measures for perceptual evaluation of singing voice separation. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59749.
Council of Science Editors:
Gupta U. Towards development of no-reference objective measures for perceptual evaluation of singing voice separation. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59749
Georgia Tech
9.
Wells, Joshua W.
Content-adaptive cross-layer optimized video processing using real-time feature feedback.
Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/59751
► The objective of this research is to design a low-power video processing system capable of minimizing power consumption through graceful reduction of the quality of…
(more)
▼ The objective of this research is to design a low-power video processing system capable of minimizing power consumption through graceful reduction of the quality of the processed signal. Methods for post-processing signal analysis are proposed for determining the presence and quality of features essential to the high-level application goals. Two applications, video encoding and object tracking, are selected for study based on their high demand on mobile platforms. Methods for scaling the complexity of the entire system are proposed by simultaneously scaling down the input signal and algorithms to focus computational effort on information salient to the application. A cross-layer control system is proposed for determining the optimal complexity scaling and dynamic voltage and frequency scaling. The control system will be charged with providing dynamic voltage and frequency scaling control information for accurate prediction of imminent throughput requirements, allowing for minimal power consumption while narrowly achieving the high-level application goals.
Advisors/Committee Members: Chatterjee, Abhijit (advisor), Romberg, Justin (committee member), Raychowdhury, Arijit (committee member), AlRegib, Ghassan (committee member), Hays, James (committee member).
Subjects/Keywords: Video processing; Video encoding; Object tracking; Low power; Error tolerant; Content adaptive
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APA (6th Edition):
Wells, J. W. (2017). Content-adaptive cross-layer optimized video processing using real-time feature feedback. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59751
Chicago Manual of Style (16th Edition):
Wells, Joshua W. “Content-adaptive cross-layer optimized video processing using real-time feature feedback.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59751.
MLA Handbook (7th Edition):
Wells, Joshua W. “Content-adaptive cross-layer optimized video processing using real-time feature feedback.” 2017. Web. 22 Sep 2019.
Vancouver:
Wells JW. Content-adaptive cross-layer optimized video processing using real-time feature feedback. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59751.
Council of Science Editors:
Wells JW. Content-adaptive cross-layer optimized video processing using real-time feature feedback. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59751
Georgia Tech
10.
Fair, Kaitlin Lindsay.
A biologically plausible sparse approximation solver on neuromorphic hardware.
Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/59782
► We develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the sparse…
(more)
▼ We develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the sparse approximation of a signal, offering the largest LCA dictionaries implemented on neuromorphic hardware to date with perfect precision. We observe low-power consumption in the operation of the LCA on the TrueNorth chip. We also explain methods to map other sparsity-based probabilistic inference problems onto the hardware using our design methodology. We describe the optimal way to achieve high-precision calculations by encoding and decoding signals within time windows. We discuss in detail functional processing units for use on the hardware that offer non-linear thresholds, increased vector-matrix multiplication precision, and the ability to accurately implement a recurrent network on the TrueNorth chip. Our design methodology offers the foundation for low-power embedded systems signal processing applications using the TrueNorth chip.
Advisors/Committee Members: Anderson, David (advisor), Romberg, Justin (committee member), Rozell, Christopher (committee member), Davenport, Mark (committee member), Andreou, Andreas (committee member).
Subjects/Keywords: Neuromorphic; Bio-inspired; TrueNorth; Sparsity; Sparse approximation
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APA (6th Edition):
Fair, K. L. (2017). A biologically plausible sparse approximation solver on neuromorphic hardware. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59782
Chicago Manual of Style (16th Edition):
Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59782.
MLA Handbook (7th Edition):
Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Web. 22 Sep 2019.
Vancouver:
Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59782.
Council of Science Editors:
Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59782
Georgia Tech
11.
Nichols, Brendan.
Exploiting ambient noise for coherent processing of mobile vector sensor arrays.
Degree: PhD, Mechanical Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/59897
► A network of mobile sensors, such as vector sensors mounted to drifting floats, can be used as an array for locating acoustic sources in an…
(more)
▼ A network of mobile sensors, such as vector sensors mounted to drifting floats, can be used as an array for locating acoustic sources in an ocean environment. Accurate localization using coherent processing on such an array dictates the locations of sensor elements must be well-known. In many cases, a mobile, submerged array cannot meet this requirement, however the presence of ambient acoustic noise provides an opportunity to correct sensor location errors. It has been previously shown that ambient noise correlations across separated, fixed hydrophones can provide the separation distance between them (K. G. Sabra et al., 2005, IEEE J. Ocean Engineering, Vol. 30). A time-domain framework for this method is presented for the case of vector sensors in isotropic ambient noise to quantify their gain relative to traditional hydrophone correlations. Furthermore, a novel method is presented for identifying hidden ambient noise correlation peaks when the separation distance is changing, and its accuracy is found to match that of GPS. Lastly, a novel weighted coherent processing algorithm is presented and its performance compared to traditional methods, finding increased localization precision even in the presence of severe noise. This method is applied to locating a source, and succeeds using both GPS and ambient-noise-corrected sensor locations. All experimental data used in these studies were collected from a novel vector sensor array, and details of its design and deployment are presented as well.
Advisors/Committee Members: Sabra, Karim (advisor), Trivett, David (committee member), Meaud, Julien (committee member), Arvanitis, Costas (committee member), Romberg, Justin (committee member).
Subjects/Keywords: Ambient noise; Coherent processing; Beamforming; Vector sensor; Array signal processing; Stochastic search; Experimental data; Noise correlation
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APA (6th Edition):
Nichols, B. (2018). Exploiting ambient noise for coherent processing of mobile vector sensor arrays. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59897
Chicago Manual of Style (16th Edition):
Nichols, Brendan. “Exploiting ambient noise for coherent processing of mobile vector sensor arrays.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59897.
MLA Handbook (7th Edition):
Nichols, Brendan. “Exploiting ambient noise for coherent processing of mobile vector sensor arrays.” 2018. Web. 22 Sep 2019.
Vancouver:
Nichols B. Exploiting ambient noise for coherent processing of mobile vector sensor arrays. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59897.
Council of Science Editors:
Nichols B. Exploiting ambient noise for coherent processing of mobile vector sensor arrays. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59897
Georgia Tech
12.
Tian, Ning.
Multichannel blind deconvolution in underwater acoustic channels.
Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/59906
► This thesis developed new techniques for solving the multichannel blind deconvolution problem and implemented these techniques in acoustic waveguide multiple environment. We developed a systematic…
(more)
▼ This thesis developed new techniques for solving the multichannel blind deconvolution problem and implemented these techniques in acoustic waveguide multiple environment. We developed a systematic way to build an efficient and accurate channel models incorporating a priori information about the expected Channel Impulse Responses' (CIRs) arrival-time structure. Based on the linear and bilinear channel models in the underwater acoustic applications, we solved the problem using two approaches. In the first approach, we formulated the problem as solving a system of bilinear equations, which in turn can be recast as recovering a low-rank matrix from a set of linear observations. In the second approach, we formed a cross-correlation matrix from the channel outputs and solved the problem by minimizing a quadratic function over a non-convex set. We demonstrated the efficiency and robustness of both multichannel blind deconvolution methods on realistic acoustic channels in ocean waveguides and experimentally validated the methods using at-sea data. In the end, we investigated methods to learn the subspace of CIRs directly from multiple snapshots in the context of solving multichannel blind deconvolution.
Advisors/Committee Members: Romberg, Justin (advisor), Sabra, Karim (committee member), McClellan, James (committee member), Davenport, Mark (committee member), Rozell, Christopher (committee member).
Subjects/Keywords: Multichannel blind deconvolution; Low-rank recovery; Cross-convolution methods; Non-convex optimizations
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APA (6th Edition):
Tian, N. (2018). Multichannel blind deconvolution in underwater acoustic channels. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59906
Chicago Manual of Style (16th Edition):
Tian, Ning. “Multichannel blind deconvolution in underwater acoustic channels.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59906.
MLA Handbook (7th Edition):
Tian, Ning. “Multichannel blind deconvolution in underwater acoustic channels.” 2018. Web. 22 Sep 2019.
Vancouver:
Tian N. Multichannel blind deconvolution in underwater acoustic channels. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59906.
Council of Science Editors:
Tian N. Multichannel blind deconvolution in underwater acoustic channels. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59906
Georgia Tech
13.
Whitaker, Bradley M.
Modifying sparse coding to model imbalanced datasets.
Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech
URL: http://hdl.handle.net/1853/59919
► The objective of this research is to explore the use of sparse coding as a tool for unsupervised feature learning to more effectively model imbalanced…
(more)
▼ The objective of this research is to explore the use of sparse coding as a tool for unsupervised feature learning to more effectively model imbalanced datasets. Traditional sparse coding dictionaries are learned by minimizing the average approximation error between a vector and its sparse decomposition. As such, these dictionaries may overlook important features that occur infrequently in the data. Without these features, it may be difficult to accurately classify between classes if one or more classes are not well-represented in the training data. To overcome this problem, this work explores novel modifications to the sparse coding dictionary learning framework that encourage dictionaries to learn anomalous features. Sparse coding also inherently assumes that a vector can be represented as a sparse linear combination of a feature set. This work addresses the ability of sparse coding to learn a representative dictionary when the underlying data has a nonlinear sparse structure. Finally, this work illustrates one benefit of improved signal modeling by utilizing sparse coding in three imbalanced classification tasks.
Advisors/Committee Members: Anderson, David V. (advisor), Rozell, Christopher J. (committee member), Romberg, Justin K. (committee member), Li, Wing (committee member), Clifford, Gari D. (committee member).
Subjects/Keywords: Sparse coding; Imbalanced data; Machine learning
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APA (6th Edition):
Whitaker, B. M. (2018). Modifying sparse coding to model imbalanced datasets. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59919
Chicago Manual of Style (16th Edition):
Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/59919.
MLA Handbook (7th Edition):
Whitaker, Bradley M. “Modifying sparse coding to model imbalanced datasets.” 2018. Web. 22 Sep 2019.
Vancouver:
Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/59919.
Council of Science Editors:
Whitaker BM. Modifying sparse coding to model imbalanced datasets. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59919
Georgia Tech
14.
Zafar, Munzir.
Whole Body Control of Wheeled Inverted Pendulum Humanoids.
Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61739
► A framework for controlling a Wheeled Inverted Pendulum (WIP) Humanoid to perform useful interactions with the environment, while dynamically balancing itself on two wheels, was…
(more)
▼ A framework for controlling a Wheeled Inverted Pendulum (WIP) Humanoid to perform useful interactions with the environment, while dynamically balancing itself on two wheels, was proposed. As humanoid platforms are characterized by several degrees of freedom, they have the ability to perform several tasks simultaneously, while obeying constraints on their motion and control. This problem is referred as Whole-Body Control in the wider humanoid literature. We develop a framework for whole-body control of WIP humanoids that can be applied directly on the physical robot, which means that it can be made robust to modeling errors. The proposed approach is hierarchical with a low level controller responsible for controlling the manipulator/body and a high-level controller that defines center of mass targets for the low-level controller to control zero dynamics of the system driving the wheels. The low-level controller plans for shorter horizons while considering more complete dynamics of the system, while the high-level controller plans for longer horizon based on an approximate model of the robot for computational efficiency.
Advisors/Committee Members: Hutchinson, Seth (advisor), Theodorou, Evangelos A (committee member), Boots, Byron E (committee member), Christensen, Henrik I (committee member), Romberg, Justin (committee member).
Subjects/Keywords: Whole Body Control; Wheeled Inverted Pendulum; Humanoids; Hierarchical; Optimization; Operational Space; Model Predictive Control
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APA (6th Edition):
Zafar, M. (2019). Whole Body Control of Wheeled Inverted Pendulum Humanoids. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61739
Chicago Manual of Style (16th Edition):
Zafar, Munzir. “Whole Body Control of Wheeled Inverted Pendulum Humanoids.” 2019. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/61739.
MLA Handbook (7th Edition):
Zafar, Munzir. “Whole Body Control of Wheeled Inverted Pendulum Humanoids.” 2019. Web. 22 Sep 2019.
Vancouver:
Zafar M. Whole Body Control of Wheeled Inverted Pendulum Humanoids. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/61739.
Council of Science Editors:
Zafar M. Whole Body Control of Wheeled Inverted Pendulum Humanoids. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61739
15.
Somoye, Idris Olansile.
GPU accelerated adaptive compressed sensing.
Degree: MS, Electrical and Computer Engineering, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/56379
► There are presently image sensors based around compressed sensing that apply the fundamental theory to video acquisition; however, these imagers require specialized hardware modules that…
(more)
▼ There are presently image sensors based around compressed sensing that apply the fundamental theory to video acquisition; however, these imagers require specialized hardware modules that are not widely available and therefore are not currently practical for video sensing. To deliver a practical image sensor that applies compressive sensing, I propose an imaging system based on a GPU and an off-the-shelf conventional image sensor that takes advantage of parallel computations for efficient transforming of data to the compressed sensing domain. This imaging system, by taking advantage of GPU processing along with straightforward communication methods between the host and the GPU, easily accommodates algorithms that rapidly change the sensing basis, making compressed sensing more applicable despite the general lack of hardware. Measurement results show that the GPU based compressive sensing imaging system delivers a viable and practical imager that is able to quickly compress images, providing a real-time video encoder for low power systems.
Advisors/Committee Members: Chatterjee, Abhijit (advisor), Raychowdhury, Arijit (committee member), Romberg, Justin (committee member).
Subjects/Keywords: GPU; Compressed sensing; GPGPU; Predictive video encoding
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APA (6th Edition):
Somoye, I. O. (2016). GPU accelerated adaptive compressed sensing. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56379
Chicago Manual of Style (16th Edition):
Somoye, Idris Olansile. “GPU accelerated adaptive compressed sensing.” 2016. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/56379.
MLA Handbook (7th Edition):
Somoye, Idris Olansile. “GPU accelerated adaptive compressed sensing.” 2016. Web. 22 Sep 2019.
Vancouver:
Somoye IO. GPU accelerated adaptive compressed sensing. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/56379.
Council of Science Editors:
Somoye IO. GPU accelerated adaptive compressed sensing. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56379
16.
Wong, Lok S.
Optimal partitions for the fast multipole method.
Degree: MS, Electrical and Computer Engineering, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/56360
► The fast multipole method is an algorithm first developed to approximately solve the N-body problem in linear time. Part of the FMM involves recursively partitioning…
(more)
▼ The fast multipole method is an algorithm first developed to approximately solve the N-body problem in linear time. Part of the FMM involves recursively partitioning a region of source points into cells. Insight from studying lattices and covering problems leads to new, more efficient partitions for the FMM. New partitions are designed to reduce near-field and far-field calculations. Results from simulations show significant computation time reduction with little to no additional error in many cases.
Advisors/Committee Members: Barnes, Christopher (advisor), Romberg, Justin (committee member), Lanterman, Aaron (committee member).
Subjects/Keywords: Fast multipole method; Partition
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APA (6th Edition):
Wong, L. S. (2016). Optimal partitions for the fast multipole method. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56360
Chicago Manual of Style (16th Edition):
Wong, Lok S. “Optimal partitions for the fast multipole method.” 2016. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/56360.
MLA Handbook (7th Edition):
Wong, Lok S. “Optimal partitions for the fast multipole method.” 2016. Web. 22 Sep 2019.
Vancouver:
Wong LS. Optimal partitions for the fast multipole method. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/56360.
Council of Science Editors:
Wong LS. Optimal partitions for the fast multipole method. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56360
17.
Kagie, Matthew Joseph.
Time-of-arrival estimation for saturated optical transients using censored probabilistic models.
Degree: MS, Electrical and Computer Engineering, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/56365
► The objective of the proposed research is to estimate the time-of-arrival of a transient optical signal subjected to a particular type of nonlinear distortion. The…
(more)
▼ The objective of the proposed research is to estimate the time-of-arrival of a transient optical signal subjected to a particular type of nonlinear distortion. The limited dynamic range of optical sensors can result in nonlinear distortion when measuring extreme transient events, such as lightning. To deal with saturated signals, we employ censored probabilistic models to develop maximum-likelihood procedures for estimating the time-of-arrival of lightning strikes, along with associated nuisance parameters. The received signal is modeled as a realization of a Poisson point process characterized by parametric models of a lightning strike's time-varying intensity. The models are extracted from the FORTÉ lighting database via machine learning techniques. Using Monte Carlo simulations, we compare the variances of different algorithms as a function of signal magnitude and saturation threshold. We also compare these variances to analytical performance bounds such as the Cramér-Rao lower bound.
Advisors/Committee Members: Lanterman, Aaron (advisor), Romberg, Justin (committee member), Citrin, David (committee member).
Subjects/Keywords: Expectation maximization algorithms; Poisson models; Cramér-Rao bounds; Lightning; Machine learning
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APA (6th Edition):
Kagie, M. J. (2016). Time-of-arrival estimation for saturated optical transients using censored probabilistic models. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56365
Chicago Manual of Style (16th Edition):
Kagie, Matthew Joseph. “Time-of-arrival estimation for saturated optical transients using censored probabilistic models.” 2016. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/56365.
MLA Handbook (7th Edition):
Kagie, Matthew Joseph. “Time-of-arrival estimation for saturated optical transients using censored probabilistic models.” 2016. Web. 22 Sep 2019.
Vancouver:
Kagie MJ. Time-of-arrival estimation for saturated optical transients using censored probabilistic models. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/56365.
Council of Science Editors:
Kagie MJ. Time-of-arrival estimation for saturated optical transients using censored probabilistic models. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56365
18.
Tan, Edward S.
Hyper-wideband OFDM system.
Degree: MS, Electrical and Computer Engineering, 2016, Georgia Tech
URL: http://hdl.handle.net/1853/55056
► Hyper-wideband communications represent the next frontier in spread spectrum RF systems with an excess of 10 GHz instantaneous bandwidth. In this thesis, an end-to-end physical…
(more)
▼ Hyper-wideband communications represent the next frontier in spread spectrum RF systems with an excess of 10 GHz instantaneous bandwidth. In this thesis, an end-to-end physical layer link is implemented featuring 16k-OFDM with a 4 GHz-wide channel centered at 9 GHz. No a priori channel state information is assumed; channel information is derived from the preamble and comb pilot structure. Due to the unique expansive spectral properties, the channel estimator is primarily composed of least squares channel estimates combined with a robust support vector statistical learning approach using autonomously selected parameters. The system’s performance is demonstrated through indoor wireless experiments, including line-of-sight and near-line-of-sight links. Moreover, it is shown that the support vector approach performs superior to linear and cubic spline inter/extrapolation of the least squares channel estimates.
Advisors/Committee Members: Ralph, Stephen E. (advisor), Barry, John R. (advisor), Romberg, Justin K. (advisor).
Subjects/Keywords: OFDM; Support vector regression; Hyper-wideband
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APA (6th Edition):
Tan, E. S. (2016). Hyper-wideband OFDM system. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55056
Chicago Manual of Style (16th Edition):
Tan, Edward S. “Hyper-wideband OFDM system.” 2016. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/55056.
MLA Handbook (7th Edition):
Tan, Edward S. “Hyper-wideband OFDM system.” 2016. Web. 22 Sep 2019.
Vancouver:
Tan ES. Hyper-wideband OFDM system. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/55056.
Council of Science Editors:
Tan ES. Hyper-wideband OFDM system. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55056
19.
Lani, Shane W.
Passive acoustic imaging and monitoring using ambient noise.
Degree: MS, Mechanical Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/50136
► An approximate of the Green's function can be obtained by taking the cross-correlation of ambient noise that has been simultaneously recorded on separate sensors. This…
(more)
▼ An approximate of the Green's function can be obtained by taking the cross-correlation of ambient noise that has been simultaneously recorded on separate sensors. This method is applied for two experiments, which illustrate the advantages and challenges of this technique. The first experiment is in the ultrasonic regime [5-30] MHz and uses capacitive micromachined ultrasonic transducer arrays to image the near field and compares the passive imaging to the conventional pulse-echo imaging. Both the array and target are immersed in a fluid with the sensors recording the fluid's random thermal-mechanical motion as the ambient noise. The second experiment is a passive ocean monitoring experiment, which uses spatiotemporal filtering to rapidly extract coherent arrivals between two vertical line arrays. In this case the ambient noise in the frequency band [250 1500] Hz is dominated by non-stationary shipping noise. For imaging purposes, the cross-correlation needs to extract the Green's function so that the imaging can be done correctly. While for monitoring purposes, the important feature is the change in arrivals, which corresponds to the environment changing. Results of both experiments are presented along with the advantages of this passive method over the more accepted active methods.
Advisors/Committee Members: Sabra, Karim (advisor), Degertekin, F. Levent (committee member), Romberg, Justin (committee member).
Subjects/Keywords: Ultrasound imaging; Noise imaging; Ocean monitoring; Noise; Cross-correlation; Noise monitoring; Ambient sounds; Green's functions
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APA (6th Edition):
Lani, S. W. (2012). Passive acoustic imaging and monitoring using ambient noise. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50136
Chicago Manual of Style (16th Edition):
Lani, Shane W. “Passive acoustic imaging and monitoring using ambient noise.” 2012. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/50136.
MLA Handbook (7th Edition):
Lani, Shane W. “Passive acoustic imaging and monitoring using ambient noise.” 2012. Web. 22 Sep 2019.
Vancouver:
Lani SW. Passive acoustic imaging and monitoring using ambient noise. [Internet] [Masters thesis]. Georgia Tech; 2012. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/50136.
Council of Science Editors:
Lani SW. Passive acoustic imaging and monitoring using ambient noise. [Masters Thesis]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/50136
20.
Colón, Guillermo J.
Avian musing feature space analysis.
Degree: MS, Electrical and Computer Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/44754
► The purpose of this study was to analyze the possibility of utilizing known signal processing and machine learning algorithms to correlate environmental data to chicken…
(more)
▼ The purpose of this study was to analyze the possibility of utilizing known
signal processing and machine learning algorithms to correlate environmental
data to chicken vocalizations. The specific musing to be analyzed consist of
not just one chicken's vocalizations but of a whole collective, it therefore
becomes a chatter problem. There have been similar attempts to create such a
correlation in the past but with singled out birds instead of a multitude. This
study was performed on broiler chickens (birds used in meat production).
One of the reasons why this correlation is useful is for the purpose of an
automated control system. Utilizing the chickens own vocalization to determine
the temperature, the humidity, the levels of ammonia among other environmental
factors, reduces, and might even remove, the need for sophisticated sensors.
Another factor that this study wanted to correlate was stress in the chickens
to their vocalization. This has great implications in animal welfare, to
guarantee that the animals are being properly take care off. Also, it has been
shown that the meat of non-stressed chickens is of much better quality than the
opposite.
The audio was filtered and certain features were extracted to predict stress.
The features considered were loudness, spectral centroid, spectral sparsity,
temporal sparsity, transient index, temporal average, temporal standard
deviation, temporal skewness, and temporal kurtosis.
In the end, out of all the features analyzed it was shown that the kurtosis
and loudness proved to be the best features for identifying stressed birds in
audio.
Advisors/Committee Members: Anderson, David (Committee Chair), Romberg, Justin (Committee Member), Vela, Patricio (Committee Member).
Subjects/Keywords: Broiler; Chicken; Feature; Audio; Segmentation; Signal processing Digital techniques; Machine learning; Sound production by animals; Chickens Vocalization
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APA (6th Edition):
Colón, G. J. (2012). Avian musing feature space analysis. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44754
Chicago Manual of Style (16th Edition):
Colón, Guillermo J. “Avian musing feature space analysis.” 2012. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/44754.
MLA Handbook (7th Edition):
Colón, Guillermo J. “Avian musing feature space analysis.” 2012. Web. 22 Sep 2019.
Vancouver:
Colón GJ. Avian musing feature space analysis. [Internet] [Masters thesis]. Georgia Tech; 2012. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/44754.
Council of Science Editors:
Colón GJ. Avian musing feature space analysis. [Masters Thesis]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44754
21.
Shaban, Fahad.
Application of L1 reconstruction of sparse signals to ambiguity resolution in radar.
Degree: MS, Electrical and Computer Engineering, 2013, Georgia Tech
URL: http://hdl.handle.net/1853/47637
► The objective of the proposed research is to develop a new algorithm for range and Doppler ambiguity resolution in radar detection data using L1 minimization…
(more)
▼ The objective of the proposed research is to develop a new algorithm for range and Doppler ambiguity resolution in radar detection data using L1 minimization methods for sparse signals and to investigate the properties of such techniques. This novel approach to ambiguity resolution makes use of the sparse measurement structure of the post-detection data in multiple pulse repetition frequency radars and the resulting equivalence of the computationally intractable L0 minimization and the surrogate L1 minimization methods. The ambiguity resolution problem is cast as a linear system of equations which is then solved for the unique sparse solution in the absence of errors. It is shown that the new technique successfully resolves range and Doppler ambiguities and the recovery is exact in the ideal case of no errors in the system. The behavior of the technique is then investigated in the presence of real world data errors encountered in radar measurement and detection process. Examples of such errors include blind zone effects, collisions, false alarms and missed detections. It is shown that the mathematical model consisting of a linear system of equations developed for the ideal case can be adjusted to account for data errors. Empirical results show that the L1 minimization approach also works well in the presence of errors with minor extensions to the algorithm. Several examples are presented to demonstrate the successful implementation of the new technique for range and Doppler ambiguity resolution in pulse Doppler radars.
Advisors/Committee Members: Richards, Mark (Committee Chair), Lanterman, Aaron (Committee Member), Romberg, Justin (Committee Member).
Subjects/Keywords: Ambiguity resolution; Pulse Doppler radars; Sparse reconstruction; Multiple PRFs; L1 minimization; Radar; Signal detection; Sparse matrices
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APA (6th Edition):
Shaban, F. (2013). Application of L1 reconstruction of sparse signals to ambiguity resolution in radar. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47637
Chicago Manual of Style (16th Edition):
Shaban, Fahad. “Application of L1 reconstruction of sparse signals to ambiguity resolution in radar.” 2013. Masters Thesis, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/47637.
MLA Handbook (7th Edition):
Shaban, Fahad. “Application of L1 reconstruction of sparse signals to ambiguity resolution in radar.” 2013. Web. 22 Sep 2019.
Vancouver:
Shaban F. Application of L1 reconstruction of sparse signals to ambiguity resolution in radar. [Internet] [Masters thesis]. Georgia Tech; 2013. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/47637.
Council of Science Editors:
Shaban F. Application of L1 reconstruction of sparse signals to ambiguity resolution in radar. [Masters Thesis]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47637
Georgia Tech
22.
Bales, Michael Ryan.
Illumination compensation in video surveillance analysis.
Degree: PhD, Electrical and Computer Engineering, 2011, Georgia Tech
URL: http://hdl.handle.net/1853/39535
► Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses…
(more)
▼ Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model – called BigBackground – is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.
Advisors/Committee Members: Wills, Scott (Committee Chair), Wills, Linda (Committee Co-Chair), Bader, David (Committee Member), Howard, Ayanna (Committee Member), Kim, Jongman (Committee Member), Romberg, Justin (Committee Member).
Subjects/Keywords: Tracking; Color; Computer vision; Background model; BigBackground; Illumination change; Video surveillance; Lighting; Video recording Lighting
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APA (6th Edition):
Bales, M. R. (2011). Illumination compensation in video surveillance analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39535
Chicago Manual of Style (16th Edition):
Bales, Michael Ryan. “Illumination compensation in video surveillance analysis.” 2011. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/39535.
MLA Handbook (7th Edition):
Bales, Michael Ryan. “Illumination compensation in video surveillance analysis.” 2011. Web. 22 Sep 2019.
Vancouver:
Bales MR. Illumination compensation in video surveillance analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/39535.
Council of Science Editors:
Bales MR. Illumination compensation in video surveillance analysis. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/39535
Georgia Tech
23.
Zachariah, Nishant.
Prospective and retrospective retooling of magnetic resonance imaging.
Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech
URL: http://hdl.handle.net/1853/60111
► At its heart, signal processing can be broken down into two broad categories based on a single core principle: attaining a signal based objective (higher…
(more)
▼ At its heart, signal processing can be broken down into two broad categories based on a single core principle: attaining a signal based objective (higher resolution, reduction in noise interference, signal based statistical inference, signal separation / characteristic manipulation etc.). The goal then of signal processing is to achieve this objective either once the data has been acquired (herein referred to as retrospective reconstruction) or by designing the system to achieve the desired objective (herein referred to as prospective reconstruction). In this work, we consider retrospective and prospective medical image reconstruction with special attention to magnetic resonance imaging. Convex relaxations of sparse priors have given birth to strident improvements in the way signals are recovered from under-determined systems. In the retrospective vein of image reconstruction, we seek to extend the benefits afforded by sparse regularization by invoking non-convex sparse priors for inverse problems..We develop a novel algorithmic solution, both in its design and computational efficiency, for analysis based non-convex sparse priors in tight frames. Theoretically, we show that our algorithm is guaranteed to converge to a local minimum based on the non-convex objective function being examined. Numerically, this class of non-convex regularized linear inverse problems has a range of practical applications: under-determined signal recovery, single image super-resolution and image denoising. In each of these applications, we demonstrate that our non-convex formulation can outperform both convex and non-convex state of the art counterparts. To truly achieve a desired objective, both the data acquisition methodology and reconstruction pipeline must be jointly designed. Speed of imaging is of great concern in magnetic resonance imaging (MRI). In MR systems, faster imaging translates to a range of benefits from increased temporal / spatial resolution to reduced motion artifacts. In the prospective approach, we develop a novel MR data acquisition and reconstruction framework to accelerate MR imaging beyond what is currently commercially available. This is done by leveraging phase encoding gradients during the data acquisition process thereby affording better control over the spectral distribution of the underlying encoding operator. In doing so, we recover signals at higher acceleration factors than the current state of the art method. Additionally, at state of the art achievable acceleration factors, we demonstrate that our method can recover the underlying signal with greater fidelity. We demonstrate the viability of our method through proof of concept 1D simulations and 3D phantom data acquired on a 3T human scanner. Finally, our reconstruction methodology forms a generalized framework seamless transition between 1D and 3D MR image reconstruction.
Advisors/Committee Members: Romberg, Justin K. (advisor), Hu, Xiaoping P. (committee member), Rozell, Christopher J. (committee member), Oshinski, John (committee member), Butera, Robert (committee member), Keilholz, Shella (committee member).
Subjects/Keywords: Harmonic analysis; Signal processing; Magnetic resonance imaging; Non convex regularization; Acceleration imaging; Inverse problems
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APA (6th Edition):
Zachariah, N. (2017). Prospective and retrospective retooling of magnetic resonance imaging. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60111
Chicago Manual of Style (16th Edition):
Zachariah, Nishant. “Prospective and retrospective retooling of magnetic resonance imaging.” 2017. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/60111.
MLA Handbook (7th Edition):
Zachariah, Nishant. “Prospective and retrospective retooling of magnetic resonance imaging.” 2017. Web. 22 Sep 2019.
Vancouver:
Zachariah N. Prospective and retrospective retooling of magnetic resonance imaging. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/60111.
Council of Science Editors:
Zachariah N. Prospective and retrospective retooling of magnetic resonance imaging. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60111
24.
Moon, Thomas.
Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms.
Degree: PhD, Electrical and Computer Engineering, 2015, Georgia Tech
URL: http://hdl.handle.net/1853/54326
► The objective of the proposed research is to develop a framework for the signal reconstruction algorithm with sub-Nyquist sampling rate and the low-cost hardware design…
(more)
▼ The objective of the proposed research is to develop a framework for the signal reconstruction algorithm with sub-Nyquist sampling rate and the low-cost hardware design in system level. A further objective of the proposed research is to monitor the device-under-test (DUT) and to adapt its behaviors. The key contribution of this research is that the high-speed signal acquisition is done by direct subsampling. As the signal is directly sampled without any front-end radio-frequency (RF) components such as mixers or filters, the cost of hardware is reduced. Furthermore, the distortion and the nonlinearity from the RF components can be avoided. The first proposed work is wideband signal reconstruction by dual-rate time-interleaved subsampling hardware and Multi-coset signal reconstruction. Using the combination of the dual-rate hardware and the multi-coset algorithm, the number of sampling channel is significantly reduced compared to the conventional multi-coset works. The second proposed work is jitter tracking by accurate period estimation with incoherent subsampling. In this work, the long-term jitter in PRBS is tracked without hardware synchronization and clock-data-recovery (CDR) circuits. The third proposed work is eye-monitoring and time-domain-reflectometry (TDR) by monobit receiver signal reconstruction. Using a monobit receiver based on incoherent subsampling and time-variant threshold signal, high resolution of reconstructed signal in both amplitude and time is achieved. Compared to a multibit-receiver, the scalability of the test-system is significantly increased.
Advisors/Committee Members: Chatterjee, Abhijit (advisor), Keezer, David (committee member), Romberg, Justin (committee member), Swaminathan, Madhavan (committee member), Kim, Seong-Hee (committee member).
Subjects/Keywords: High-speed signal characterization; Sub-Nyquist rate reconstruction; Direct subsampling; Low-cost test
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APA (6th Edition):
Moon, T. (2015). Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54326
Chicago Manual of Style (16th Edition):
Moon, Thomas. “Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms.” 2015. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/54326.
MLA Handbook (7th Edition):
Moon, Thomas. “Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms.” 2015. Web. 22 Sep 2019.
Vancouver:
Moon T. Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/54326.
Council of Science Editors:
Moon T. Testing and characterization of high-speed signals using incoherent undersampling driven signal reconstruction algorithms. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54326
25.
Lin, Chung-Ching.
Detecting and tracking moving objects from a moving platform.
Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/49014
► Detecting and tracking moving objects are important topics in computer vision research. Classical methods perform well in applications of steady cameras. However, these techniques are…
(more)
▼ Detecting and tracking moving objects are important topics in computer vision research. Classical methods perform well in applications of steady cameras. However, these techniques are not suitable for the applications of moving cameras because the unconstrained nature of realistic environments and sudden camera movement makes cues to object positions rather fickle. A major difficulty is that every pixel moves and new background keeps showing up when a handheld or car-mounted camera moves. In this dissertation, a novel estimation method of camera motion parameters will be discussed first. Based on the estimated camera motion parameters, two detection algorithms are developed using Bayes' rule and belief propagation. Next, an MCMC-based feature-guided particle filtering method is presented to track detected moving objects. In addition, two detection algorithms without using camera motion parameters will be further discussed. These two approaches require no pre-defined class or model to be trained in advance. The experiment results will demonstrate robust detecting and tracking performance in object sizes and positions.
Advisors/Committee Members: Wolf, Marilyn (advisor), Yezzi, Anthony (committee member), Kim, Hyesoon (committee member), Romberg, Justin (committee member), Yalamanchili, Sudhakar (committee member).
Subjects/Keywords: Object detection; Object tracking; Bayesian decision rule; Belief propagation; Particle filter; Computer vision; Image processing; Algorithms; Pattern recognition systems
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APA (6th Edition):
Lin, C. (2012). Detecting and tracking moving objects from a moving platform. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49014
Chicago Manual of Style (16th Edition):
Lin, Chung-Ching. “Detecting and tracking moving objects from a moving platform.” 2012. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/49014.
MLA Handbook (7th Edition):
Lin, Chung-Ching. “Detecting and tracking moving objects from a moving platform.” 2012. Web. 22 Sep 2019.
Vancouver:
Lin C. Detecting and tracking moving objects from a moving platform. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/49014.
Council of Science Editors:
Lin C. Detecting and tracking moving objects from a moving platform. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/49014
26.
Mantzel, William.
Parametric estimation of randomly compressed functions.
Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech
URL: http://hdl.handle.net/1853/49053
► Within the last decade, a new type of signal acquisition has emerged called Compressive Sensing that has proven especially useful in providing a recoverable representation…
(more)
▼ Within the last decade, a new type of signal acquisition has emerged called Compressive Sensing that has proven especially useful in providing a recoverable representation of sparse signals. This thesis presents similar results for Compressive Parametric Estimation. Here, signals known to lie on some unknown parameterized subspace may be recovered via randomized compressive measurements, provided the number of compressive measurements is a small factor above the product of the parametric dimension with the subspace dimension with an additional logarithmic term. In addition to potential applications that simplify the acquisition hardware, there is also the potential to reduce the computational burden in other applications, and we explore one such application in depth in this thesis.
Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. We introduce a technique that mitigates this computational workload by ``compressing'' these computations. Drawing on key concepts from the recently developed field of compressed sensing, we show how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then, the source can be located by performing a number of ``short'' correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented which demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP, but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.
This thesis also introduces a round-robin approach for multi-source localization based on Matched-Field Processing. Each new source location is estimated from the ambiguity function after nulling from the data vector the current source location estimates using a robust projection matrix. This projection matrix effectively minimizes mean-square energy near current source location estimates subject to a rank constraint that prevents excessive interference with sources outside of these neighborhoods. Numerical simulations are presented for multiple sources transmitting through a generic Pekeris ocean waveguide that illustrate the performance of the proposed approach which compares favorably against other previously published approaches. Furthermore, the efficacy with which randomized back-propagations may also be incorporated for computational advantage (as in the case of compressive parametric estimation) is also presented.
Advisors/Committee Members: Romberg, Justin (advisor), McClellan, Jim (committee member), Davenport, Mark (committee member), Sabra, Karim (committee member), Verriest, Erik (committee member).
Subjects/Keywords: Compressed sensing; Acoustic localization; Matched-field processing; Parametric estimation; Signal processing; Differential equations, Partial; Differential equations; Signal processing Digital techniques; Signal processing Digital techniques Mathematics
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APA (6th Edition):
Mantzel, W. (2013). Parametric estimation of randomly compressed functions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49053
Chicago Manual of Style (16th Edition):
Mantzel, William. “Parametric estimation of randomly compressed functions.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/49053.
MLA Handbook (7th Edition):
Mantzel, William. “Parametric estimation of randomly compressed functions.” 2013. Web. 22 Sep 2019.
Vancouver:
Mantzel W. Parametric estimation of randomly compressed functions. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/49053.
Council of Science Editors:
Mantzel W. Parametric estimation of randomly compressed functions. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49053
27.
Robucci, Ryan Wayne.
Development of a computational image sensor with applications
in integrated sensing and processing.
Degree: PhD, Electrical and Computer Engineering, 2009, Georgia Tech
URL: http://hdl.handle.net/1853/33943
► The objective of this research was to build a reprogrammable computational imager utilizing on-chip analog computations for the purpose of studying the capabilities of integrated…
(more)
▼ The objective of this research was to build a reprogrammable computational imager utilizing on-chip analog computations for the purpose of studying the capabilities of integrated sensing and processing. Unlike conventional imaging systems, which acquire image data and
perform calculations on it, this system tightly integrates the computation
and sensing into one process. This allows the exploration of intelligent
and efficient sensory and processing. The IC architecture and circuit designs
have focused on wide dynamic range signals. The fundamental computation
performed is a separable two-dimensional transform. This allows various
operations, including block transformations and separable convolutions. The operations
are reprogramable and utilize analog memory and processing along with
digital control. The random access to both the image plane and the
computational operations allows for intraframe transform variations creating a hardware foundation for dynamic sampling and computation.
One can also capture scenes with non-uniform resolution. Advantages, including utilization of feedback from processing to sensing and extensions of the technology including support
for wavelets and larger transforms are also explored.
Advisors/Committee Members: Hasler, Paul (Committee Chair), Anderson, David (Committee Member), Ghovanloo, Maysam (Committee Member), Romberg, Justin (Committee Member), Smith, Mark (Committee Member).
Subjects/Keywords: Computational CMOS Image Sensor; Imaging systems; Image processing; Detectors; Integrated circuits
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APA (6th Edition):
Robucci, R. W. (2009). Development of a computational image sensor with applications
in integrated sensing and processing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/33943
Chicago Manual of Style (16th Edition):
Robucci, Ryan Wayne. “Development of a computational image sensor with applications
in integrated sensing and processing.” 2009. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/33943.
MLA Handbook (7th Edition):
Robucci, Ryan Wayne. “Development of a computational image sensor with applications
in integrated sensing and processing.” 2009. Web. 22 Sep 2019.
Vancouver:
Robucci RW. Development of a computational image sensor with applications
in integrated sensing and processing. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/33943.
Council of Science Editors:
Robucci RW. Development of a computational image sensor with applications
in integrated sensing and processing. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/33943
28.
Dai, Rui.
Correlation-based communication in wireless multimedia sensor networks.
Degree: PhD, Electrical and Computer Engineering, 2011, Georgia Tech
URL: http://hdl.handle.net/1853/42736
► Wireless multimedia sensor networks (WMSNs) are networks of interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment.…
(more)
▼ Wireless multimedia sensor networks (WMSNs) are networks of interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the observations of camera sensors with overlapped coverage areas, which introduces substantial data redundancy in the network. In this dissertation, efficient communication schemes are designed for WMSNs by leveraging the correlation of visual information observed by camera sensors. First, a spatial correlation model is developed to estimate the correlation of visual information and the joint entropy of multiple correlated camera sensors. The compression performance of correlated visual information is then studied. An entropy-based divergence measure is proposed to predict the compression efficiency of performing joint coding on the images from correlated cameras. Based on the predicted compression efficiency, a clustered coding technique is proposed that maximizes the overall compression gain of the visual information gathered in WMSNs. The correlation of visual information is then utilized to design a network scheduling scheme to maximize the lifetime of WMSNs. Furthermore, as many WMSN applications require QoS support, a correlation-aware QoS routing algorithm is introduced that can efficiently deliver visual information under QoS constraints. Evaluation results show that, by utilizing the correlation of visual information in the communication process, the energy efficiency and networking performance of WMSNs could be improved significantly.
Advisors/Committee Members: Akyildiz, Ian (Committee Chair), Ammar, Mostafa (Committee Member), Ji, Chuanyi (Committee Member), Li, Ye (Committee Member), Romberg, Justin (Committee Member).
Subjects/Keywords: Communication protocols; Wireless sensor networks; Spatial correlation; Source coding; QoS; Multimedia; Wireless communication systems; Source code (Computer science); Computer science; Multimedia communications; Multimedia systems
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APA (6th Edition):
Dai, R. (2011). Correlation-based communication in wireless multimedia sensor networks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/42736
Chicago Manual of Style (16th Edition):
Dai, Rui. “Correlation-based communication in wireless multimedia sensor networks.” 2011. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/42736.
MLA Handbook (7th Edition):
Dai, Rui. “Correlation-based communication in wireless multimedia sensor networks.” 2011. Web. 22 Sep 2019.
Vancouver:
Dai R. Correlation-based communication in wireless multimedia sensor networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/42736.
Council of Science Editors:
Dai R. Correlation-based communication in wireless multimedia sensor networks. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/42736
29.
Sconyers, Christopher.
Particle filter-based architecture for video target tracking and geo-location using multiple UAVs.
Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech
URL: http://hdl.handle.net/1853/47734
► Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a…
(more)
▼ Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.
Advisors/Committee Members: Vachtsevanos, George (Committee Chair), Johnson, Eric (Committee Member), Michaels, Thomas (Committee Member), Romberg, Justin (Committee Member), Yezzi, Anthony (Committee Member).
Subjects/Keywords: Rotorcraft; Localization; Monte Carlo; Particle filter; Multi-modal; State space; Observer; Gimbal; Markov; Drone aircraft; Target acquisition
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APA (6th Edition):
Sconyers, C. (2013). Particle filter-based architecture for video target tracking and geo-location using multiple UAVs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47734
Chicago Manual of Style (16th Edition):
Sconyers, Christopher. “Particle filter-based architecture for video target tracking and geo-location using multiple UAVs.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/47734.
MLA Handbook (7th Edition):
Sconyers, Christopher. “Particle filter-based architecture for video target tracking and geo-location using multiple UAVs.” 2013. Web. 22 Sep 2019.
Vancouver:
Sconyers C. Particle filter-based architecture for video target tracking and geo-location using multiple UAVs. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/47734.
Council of Science Editors:
Sconyers C. Particle filter-based architecture for video target tracking and geo-location using multiple UAVs. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47734
30.
Asif, Muhammad Salman.
Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.
Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech
URL: http://hdl.handle.net/1853/49106
► This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal,…
(more)
▼ This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal, streaming measurements, or an adaptive signal transform. Compressive sensing theory has shown that under certain conditions, a sparse signal can be recovered from a small number of linear, incoherent measurements. The recovery algorithms, however, for the most part are static: they focus on finding the solution for a fixed set of measurements, assuming a fixed (sparse) structure of the signal.
In this thesis, we present a suite of sparse recovery algorithms that cater to various dynamical settings. The main contributions of this research can be classified into the following two categories: 1) Efficient algorithms for fast updating of L1-norm minimization problems in dynamical settings. 2) Efficient modeling of the signal dynamics to improve the reconstruction quality; in particular, we use inter-frame motion in videos to improve their reconstruction from compressed measurements.
Dynamic L1 updating: We present homotopy-based algorithms for quickly updating the solution for various L1 problems whenever the system changes slightly. Our objective is to avoid solving an L1-norm minimization program from scratch; instead, we use information from an already solved L1 problem to quickly update the solution for a modified system. Our proposed updating schemes can incorporate time-varying signals, streaming measurements, iterative reweighting, and data-adaptive transforms. Classical signal processing methods, such as recursive least squares and the Kalman filters provide solutions for similar problems in the least squares framework, where each solution update requires a simple low-rank update. We use homotopy continuation for updating L1 problems, which requires a series of rank-one updates along the so-called homotopy path.
Dynamic models in video: We present a compressive-sensing based framework for the recovery of a video sequence from incomplete, non-adaptive measurements. We use a linear dynamical system to describe the measurements and the temporal variations of the video sequence, where adjacent images are related to each other via inter-frame motion. Our goal is to recover a quality video sequence from the available set of compressed measurements, for which we exploit the spatial structure using sparse representations of individual images in a spatial transform and the temporal structure, exhibited by dependencies among neighboring images, using inter-frame motion. We discuss two problems in this work: low-complexity video compression and accelerated dynamic MRI. Even though the processes for recording compressed measurements are quite different in these two problems, the procedure for reconstructing the videos is very similar.
Advisors/Committee Members: Romberg, Justin (advisor), McClellan, James (committee member), Davenport, Mark (committee member), Vela, Patricio (committee member), Kang, Sung Ha (committee member).
Subjects/Keywords: L1 norm minimization; Homotopy; Dynamic MRI; Kalman filter; Streaming technology (Telecommunications); Streaming video; Signal processing; Computer vision; Signal processing Digital techniques Mathematics
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APA (6th Edition):
Asif, M. S. (2013). Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49106
Chicago Manual of Style (16th Edition):
Asif, Muhammad Salman. “Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.” 2013. Doctoral Dissertation, Georgia Tech. Accessed September 22, 2019.
http://hdl.handle.net/1853/49106.
MLA Handbook (7th Edition):
Asif, Muhammad Salman. “Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video.” 2013. Web. 22 Sep 2019.
Vancouver:
Asif MS. Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Sep 22].
Available from: http://hdl.handle.net/1853/49106.
Council of Science Editors:
Asif MS. Dynamic compressive sensing: sparse recovery algorithms for streaming signals and video. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49106
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