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You searched for subject:(Sparse sensor array). Showing records 1 – 2 of 2 total matches.

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1. Chen, Xin. Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage.

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

A load-enhanced methodology has been proposed to enable the in situ detection, localization, and characterization of damage in metallic plate-like structures using Lamb waves. A baseline-free load-differential method using the delay-and-sum imaging algorithm is proposed for defect detection and localization. The term “load-differential” refers to the comparison of recorded ultrasonic signals at various levels of stress. Defect characterization is achieved by incorporating expected scattering information of guided waves interacting with defects into the minimum variance imaging algorithm, and a method for estimating such scattering patterns from the measurements of a sparse transducer array is developed. The estimation method includes signal preprocessing, extracting initial scattering values from baseline subtraction results, and obtaining the complete scattering matrix by applying radial basis function interpolation. The factors that cause estimation errors, such as the shape parameter used to form the basis function and the filling distance used in the interpolation, are discussed. The estimated scattering patterns from sparse array measurements agree reasonably well with laser wavefield data and are further used in the load-enhanced method. The results from fatigue tests show that the load-enhanced method is capable of detecting cracks, providing reasonable estimates of their localizations and orientations, and discriminating them from drilled holes, disbonds, and fastener tightness variations. Advisors/Committee Members: Michaels, Jennifer E. (advisor), Michaels, Thomas E. (committee member), Lanterman, Aaron D. (committee member), Durgin, Gregory D. (committee member), Zhang, Ying (committee member), Ruzzene, Massimo (committee member).

Subjects/Keywords: Lamb waves; Scattering; Sparse sensor array; Load-enhanced method; Crack detection; Crack localization; Crack characterization; Complex components; Laser vibrometry

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APA (6th Edition):

Chen, X. (2015). Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54859

Chicago Manual of Style (16th Edition):

Chen, Xin. “Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage.” 2015. Doctoral Dissertation, Georgia Tech. Accessed October 19, 2019. http://hdl.handle.net/1853/54859.

MLA Handbook (7th Edition):

Chen, Xin. “Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage.” 2015. Web. 19 Oct 2019.

Vancouver:

Chen X. Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Oct 19]. Available from: http://hdl.handle.net/1853/54859.

Council of Science Editors:

Chen X. Load-enhanced lamb wave methods for the in situ detection, localization and characterization of damage. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54859


Pontifical Catholic University of Rio de Janeiro

2. YUNEISY ESTHELA GARCIA GUZMAN. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.

Degree: 2018, Pontifical Catholic University of Rio de Janeiro

[pt] A estimação de direção de chegada (DoA) é uma importante área de processamento de arranjos de sensores que é encontrada em uma ampla gama de aplicações de engenharia. Este fato, juntamente com o desenvolvimento da área de Compressed Sensing (CS) nos últimos anos, são a principal motivação desta dissertação. Nesta dissertação, é apresentada uma formulação do problema de estimação de direção de chegada como um problema de representação esparsa da sinal e vários algoritmos de recuperação esparsa são derivados e investigados para resolver o problema atual. Os algoritmos propostos são baseados na incorporação da informação prévia sobre o sinal esparso no processo de estimativa. Na primeira parte, nos concentramos no desenvolvimento de dois algoritmos Bayesianos , que se baseiam principalmente no algoritmo iterative hard thresholding (IHT). Devido ao desempenho inferior dos algoritmos convencionais de estimação de chegada em cenários com fontes correlacionadas, nós prestamos atenção especial ao desempenho dos algoritmos propostos nesta condição. Na segunda parte, o problema de otimização baseados na minimização da norma l1 é apresentado e um algoritmo bayesiano é proposto para resolver o problema chamado basis pursuit denoising (BPDN). Os resultados da simulação mostram que os estimadores Bayesianos superam os estimadores não Bayesianos e que a incorporação do conhecimento prévio da distribuição do sinal melhorou substancialmente o desempenho dos algoritmos.

[en] Direction of arrival (DoA) estimation is a key area of sensor array processing which is encountered in a broad range of important engineering applications. This fact together with the development of the Compressed Sensing (CS) area in the last years are the principal motivation of this thesis. In this dissertation, a formulation of the source localization problem as a sparse signal representation problem is presented and several sparse recovery algorithms are derived and investigated for solving the current problem. The proposed algorithms are based on the incorporation of the prior information about the sparse signal in the estimation process. In the first part, we focus on the development of two Bayesian greedy algorithms which are principally based on the iterative hard thresholding (IHT) algorithm. Due to the inferior performance of the conventional DoA estimation algorithm in scenarios with correlated sources, we pay special attention to the performance of the proposed algorithms under this condition. In the second part, the optimization problem using a l1 penalty is introduced and a Bayesian algorithm for solving the basis pursuit denoising problem is presented. Simulation results shows that Bayesian estimators which take into account the prior knowledge of the signal distribution outperform and improve substantially the performance of the non-Bayesian estimators.

Advisors/Committee Members: RODRIGO CAIADO DE LAMARE.

Subjects/Keywords: [pt] ESTIMACAO DE DIRECAO DE CHEGADA - DOA; [en] DIRECTION-OF-ARRIVAL ESTIMATION - DOA; [pt] PROCESSAMENTO DE ARRANJOS DE SINAIS; [en] SENSOR ARRAY SIGNAL PROCESSING; [pt] COMPRESSED SENSING - CS; [en] COMPRESSED SENSING - CS; [pt] RECUPERACAO ESPARSA; [en] SPARSE RECOVERY; [pt] ITERATIVE HARD THRESHOLDING - IHT - ALGORITHM; [en] ITERATIVE HARD THRESHOLDING - IHT - ALGORITHM

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

APA (6th Edition):

GUZMAN, Y. E. G. (2018). [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608

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

Chicago Manual of Style (16th Edition):

GUZMAN, YUNEISY ESTHELA GARCIA. “[en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.” 2018. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed October 19, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608.

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

MLA Handbook (7th Edition):

GUZMAN, YUNEISY ESTHELA GARCIA. “[en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.” 2018. Web. 19 Oct 2019.

Vancouver:

GUZMAN YEG. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. [cited 2019 Oct 19]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608.

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

Council of Science Editors:

GUZMAN YEG. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608

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

.