subject:(spectral estimation)

1. Angelopoulos, Konstantinos. Προηγμένες μέθοδοι φασματικής εκτίμησης.

Degree: 2016, University of Peloponesse; Πανεπιστήμιο Πελοποννήσου

URL: http://hdl.handle.net/10442/hedi/40415

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The field of research that deals with the Spectral Estimation problem is fundamental and plays a pivotal role within the wider scientific area of signal… (more)

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The field of research that deals with the Spectral Estimation problem is fundamental and plays a pivotal role within the wider scientific area of signal processing. Even though this field has been the scope of considerable research activity for several decades, recently there has been increased interest from the scientific community regarding a specific category of Spectral Estimators, the so-called Adaptive Filter-Banks. This category of estimators falls within the class of Non Parametric methods for spectral estimation and comprises techniques that are known to produce high quality estimates. However, these methods are modeled by highly perplexed mathematical formulations which - unavoidably - require an extremely high computational load when implemented in a straightforward manner (brute force). This doctoral thesis attempts to contribute in this field of study, setting as its primary goal, the formulation of low complexity algorithms for a series of spectral estimation methods of the aforementioned category. The study focuses - mainly - on the Capon, APES and IAA methods (for MSC spectrum estimation) and forms a mathematical model for each one, which though mathematically equivalent to the brute force approach, its theoretical computational complexity is orders of magnitude lower. In addition, a novel MSC estimator is proposed, based on the SLIM method, while for the most recently introduced methods (IAA, SLIM), new estimation schemes are proposed in order to incorporate the processing of signals containing arbitrarily missing data samples (missing data case), as well as two-dimensional signals. For all the newly introduced techniques, efficient implementation algorithms are devised. Building upon the above implementations for the batch processing case, the focus of this study is then directed towards building spectral estimation techniques, that are able to process time-varying signals (time-adaptive algorithms). So, with the appropriate reformulation and adjustment of the IAA method, an estimator of MSC spectrum is built, suitable for time-varying signals (including the missing data case). The work on the time-varying signals is extended towards the area of the PSD spectrum estimation for the missing data case with concurrent implementation of a "recovery" mechanism for the missing samples. The proposed estimators operate by advancing the analysis window by one sample at a time or by blocks of samples. Moreover, the two dimensional time-varying signal hypothesis is also studied. Highly efficient algorithms are derived for all the time-varying (missing data) methods, that require far less computational effort compared to the fastest implementations of other high quality techniques. The performance of the aforementioned, proposed methods is illustrated via typical simulation experiments, while their theoretical complexity is calculated and depicted along with the complexity of the brute force implementations.

Ο τομέας της σχεδίασης και μελέτης τεχνικών εκτίμησης φάσματος συνιστά θεμελιώδες πεδίο έρευνας εντός…

Subjects/Keywords: Φασματική εκτίμηση; Spectral estimation

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

APA (6^th Edition):

Angelopoulos, K. (2016). Προηγμένες μέθοδοι φασματικής εκτίμησης. (Thesis). University of Peloponesse; Πανεπιστήμιο Πελοποννήσου. Retrieved from http://hdl.handle.net/10442/hedi/40415

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

Chicago Manual of Style (16^th Edition):

Angelopoulos, Konstantinos. “Προηγμένες μέθοδοι φασματικής εκτίμησης.” 2016. Thesis, University of Peloponesse; Πανεπιστήμιο Πελοποννήσου. Accessed March 07, 2021. http://hdl.handle.net/10442/hedi/40415.

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

MLA Handbook (7^th Edition):

Angelopoulos, Konstantinos. “Προηγμένες μέθοδοι φασματικής εκτίμησης.” 2016. Web. 07 Mar 2021.

Vancouver:

Angelopoulos K. Προηγμένες μέθοδοι φασματικής εκτίμησης. [Internet] [Thesis]. University of Peloponesse; Πανεπιστήμιο Πελοποννήσου; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10442/hedi/40415.

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

Council of Science Editors:

Angelopoulos K. Προηγμένες μέθοδοι φασματικής εκτίμησης. [Thesis]. University of Peloponesse; Πανεπιστήμιο Πελοποννήσου; 2016. Available from: http://hdl.handle.net/10442/hedi/40415

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

Cornell University

2. Zheng, Jingxian. Essays In Time Series Analysis In The Frequency Domain.

Degree: PhD, Economics, 2013, Cornell University

URL: http://hdl.handle.net/1813/34031

► In financial markets, economic relations can change abruptly as the result of rapid market reactions to exogenous shocks, or, alternatively, change gradually over a long… (more)

▼ In financial markets, economic relations can change abruptly as the result of rapid market reactions to exogenous shocks, or, alternatively, change gradually over a long time span incorporating various activities and responses from multiple market participants at different points in time. Studies on financial contagion concentrate on such changes in interdependence relations among economies, industries, or institutions. These changes in interdependence can be measured by the instabilities in the covariance structure of two asset returns, which consists of the contemporary covariance and all lag orders of the crossautocovariances. By Fourier Transform, a spectral density function contains equivalent information as covariance function. Therefore, any changes in the covariance structure can be capture by changes in the spectral density function. In the first chapter, Detection of Abrupt Structural Changes: A Spectral Approach, I propose a spectrum-based estimator to detect abrupt changes in the covariance structures. In this approach, detecting these abrupt changes is equivalent to locating the step discontinuities in the time-varying spectral densities and cross-spectral density. The estimator can then be implemented based on a comparison of the left and right limit spectra of the potential time spot. This method brings together and improves upon two strands of the literature on structural changes. Compared to the existing estimators in the structural break literature which mainly consider structural changes as discrete level shifts in an observation period, my method is more general in allowing occasional breaks to occur in a smooth change circumstance approximated by locally stationary processes, thus subsuming level shifts as a special case. My method also extends the literature that focuses on smooth changes approximated by local stationarity by relaxing the assumption of continuity and by introducing abrupt changes. I empirically apply the estimator to pairs of index returns in the subprime mortgage, stock, and bond markets during the 2007 subprime crisis and the 2008 global financial crisis. The empirical results show that during the crises, abrupt changes are apt to be but not necessarily triggered by specific shocking events. Moreover, most of the changes in the dependence structures of index returns are closely related to the changes in the marginal covariance structures of the returns. However, not all of the changes in marginal covariance structures lead to changes in the cross-covariance structures. The detection method is adopted in the second chapter, Post-Crisis Global Liquidity and Financial Spillover: From U.S. to Emerging Markets. This paper empirically investigates the linkages between U.S. markets and emerging markets to identify the global liquidity and financial spillover after the 2008 global financial crisis. A two-step method is adopted to capture dynamic patterns and structural changes in the linkages between the bond and stock markets in U.S. and BRICS. The results show that most abrupt… Advisors/Committee Members: Hong, Yongmiao (chair), Kiefer, Nicholas Maximillian (committee member), Karolyi, George Andrew (committee member).

Subjects/Keywords: structural change; generalized spectral estimation; liquidity spillover

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APA (6^th Edition):

Zheng, J. (2013). Essays In Time Series Analysis In The Frequency Domain. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/34031

Chicago Manual of Style (16^th Edition):

Zheng, Jingxian. “Essays In Time Series Analysis In The Frequency Domain.” 2013. Doctoral Dissertation, Cornell University. Accessed March 07, 2021. http://hdl.handle.net/1813/34031.

MLA Handbook (7^th Edition):

Zheng, Jingxian. “Essays In Time Series Analysis In The Frequency Domain.” 2013. Web. 07 Mar 2021.

Vancouver:

Zheng J. Essays In Time Series Analysis In The Frequency Domain. [Internet] [Doctoral dissertation]. Cornell University; 2013. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1813/34031.

Council of Science Editors:

Zheng J. Essays In Time Series Analysis In The Frequency Domain. [Doctoral Dissertation]. Cornell University; 2013. Available from: http://hdl.handle.net/1813/34031

Hong Kong University of Science and Technology

3. Liu, Xiaogang. Spectral characterization and spectral estimation of some graphs.

Degree: 2011, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-7091 ; https://doi.org/10.14711/thesis-b1129753 ; http://repository.ust.hk/ir/bitstream/1783.1-7091/1/th_redirect.html

► This thesis studies two subjects. One is the spectral characterization problem, the other is the spectral estimation probelm. For the former, we mainly investigate the… (more)

Subjects/Keywords: Graph theory ; Spectral theory (Mathematics) ; Estimation theory

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

APA (6^th Edition):

Liu, X. (2011). Spectral characterization and spectral estimation of some graphs. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7091 ; https://doi.org/10.14711/thesis-b1129753 ; http://repository.ust.hk/ir/bitstream/1783.1-7091/1/th_redirect.html

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

Chicago Manual of Style (16^th Edition):

Liu, Xiaogang. “Spectral characterization and spectral estimation of some graphs.” 2011. Thesis, Hong Kong University of Science and Technology. Accessed March 07, 2021. http://repository.ust.hk/ir/Record/1783.1-7091 ; https://doi.org/10.14711/thesis-b1129753 ; http://repository.ust.hk/ir/bitstream/1783.1-7091/1/th_redirect.html.

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

MLA Handbook (7^th Edition):

Liu, Xiaogang. “Spectral characterization and spectral estimation of some graphs.” 2011. Web. 07 Mar 2021.

Vancouver:

Liu X. Spectral characterization and spectral estimation of some graphs. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2011. [cited 2021 Mar 07]. Available from: http://repository.ust.hk/ir/Record/1783.1-7091 ; https://doi.org/10.14711/thesis-b1129753 ; http://repository.ust.hk/ir/bitstream/1783.1-7091/1/th_redirect.html.

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

Council of Science Editors:

Liu X. Spectral characterization and spectral estimation of some graphs. [Thesis]. Hong Kong University of Science and Technology; 2011. Available from: http://repository.ust.hk/ir/Record/1783.1-7091 ; https://doi.org/10.14711/thesis-b1129753 ; http://repository.ust.hk/ir/bitstream/1783.1-7091/1/th_redirect.html

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

4. Harouna Seybou, Aboubacar. Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control.

Degree: Docteur es, Traitemement du signal et des images, 2016, Poitiers

URL: http://www.theses.fr/2016POIT2282

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La couleur est un critère important dans de nombreux secteurs d'activité pour identifier, comparer ou encore contrôler la qualité de produits. Cette tâche est souvent… (more)

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La couleur est un critère important dans de nombreux secteurs d'activité pour identifier, comparer ou encore contrôler la qualité de produits. Cette tâche est souvent assumée par un opérateur humain qui effectue un contrôle visuel. Malheureusement la subjectivité de celui-ci rend ces contrôles peu fiables ou répétables. Pour contourner ces limitations, l'utilisation d'une caméra RGB permet d'acquérir et d'extraire des propriétés photométriques. Cette solution est facile à mettre en place et offre une rapidité de contrôle. Cependant, elle est sensible au phénomène de métamérisme. La mesure de réflectance spectrale est alors la solution la plus appropriée pour s'assurer de la conformité colorimétrique entre des échantillons et une référence. Ainsi dans l'imprimerie, des spectrophotomètres sont utilisés pour mesurer des patchs uniformes imprimés sur une bande latérale. Pour contrôler l'ensemble d'une surface imprimée, des caméras multi-spectrales sont utilisées pour estimer la réflectance de chaque pixel. Cependant, elles sont couteuses comparées aux caméras conventionnelles. Dans ces travaux de recherche, nous étudions l'utilisation d'une caméra RGB pour l'estimation de la réflectance dans le cadre de l'imprimerie. Nous proposons une description spectrale complète de la chaîne de reproduction pour réduire le nombre de mesures dans les phases d'apprentissage et pour compenser les limitations de l'acquisition. Notre première contribution concerne la prise en compte des limitations colorimétriques lors de la caractérisation spectrale d'une caméra. La deuxième contribution est l'exploitation du modèle spectrale de l'imprimante dans les méthodes d'estimation de réflectance.

Color is a major criterion for many sectors to identify, to compare or simply to control the quality of products. This task is generally assumed by a human operator who performs a visual inspection. Unfortunately, this method is unreliable and not repeatable due to the subjectivity of the operator. To avoid these limitations, a RGB camera can be used to capture and extract the photometric properties. This method is simple to deploy and permits a high speed control. However, it's very sensitive to the metamerism effects. Therefore, the reflectance measurement is the more reliable solution to ensure the conformity between samples and a reference. Thus in printing industry, spectrophotometers are used to measure uniform color patches printed on a lateral band. For a control of the entire printed surface, multispectral cameras are used to estimate the reflectance of each pixel. However, they are very expensive compared to conventional cameras. In this thesis, we study the use of an RGB camera for the spectral reflectance estimation in the context of printing. We propose a complete spectral description of the reproduction chain to reduce the number of measurements in the training stages and to compensate for the acquisition limitations. Our first main contribution concerns the consideration of the colorimetric limitations in the spectral characterization…

Advisors/Committee Members: Khoudeir, Majdi (thesis director), Bringier, Benjamin (thesis director).

Subjects/Keywords: Reproduction d'image couleur; Modèle spectral d'imprimante; Caractérisation spectrale de caméra; Estimation de réflectance spectrale; Color image reproduction; Spectral printer model; Spectral camera characterization; Spectral reflectance estimation; 006.6

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

APA (6^th Edition):

Harouna Seybou, A. (2016). Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control. (Doctoral Dissertation). Poitiers. Retrieved from http://www.theses.fr/2016POIT2282

Chicago Manual of Style (16^th Edition):

Harouna Seybou, Aboubacar. “Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control.” 2016. Doctoral Dissertation, Poitiers. Accessed March 07, 2021. http://www.theses.fr/2016POIT2282.

MLA Handbook (7^th Edition):

Harouna Seybou, Aboubacar. “Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control.” 2016. Web. 07 Mar 2021.

Vancouver:

Harouna Seybou A. Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control. [Internet] [Doctoral dissertation]. Poitiers; 2016. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2016POIT2282.

Council of Science Editors:

Harouna Seybou A. Analyse d'images couleurs pour le contrôle qualité non destructif : Color images analysis for non-destructive quality control. [Doctoral Dissertation]. Poitiers; 2016. Available from: http://www.theses.fr/2016POIT2282

INP Toulouse

5. Altmann, Yoann. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.

Degree: Docteur es, Image, Information, Hypermedia, 2013, INP Toulouse

URL: http://www.theses.fr/2013INPT0084

► Le démélange spectral est un des sujets majeurs de l’analyse d’images hyperspectrales. Ce problème consiste à identifier les composants macroscopiques présents dans une image hyperspectrale… (more)

▼ Le démélange spectral est un des sujets majeurs de l’analyse d’images hyperspectrales. Ce problème consiste à identifier les composants macroscopiques présents dans une image hyperspectrale et à quantifier les proportions (ou abondances) de ces matériaux dans tous les pixels de l’image. La plupart des algorithmes de démélange suppose un modèle de mélange linéaire qui est souvent considéré comme une approximation au premier ordre du mélange réel. Cependant, le modèle linéaire peut ne pas être adapté pour certaines images associées par exemple à des scènes engendrant des trajets multiples (forêts, zones urbaines) et des modèles non-linéaires plus complexes doivent alors être utilisés pour analyser de telles images. Le but de cette thèse est d’étudier de nouveaux modèles de mélange non-linéaires et de proposer des algorithmes associés pour l’analyse d’images hyperspectrales. Dans un premier temps, un modèle paramétrique post-non-linéaire est étudié et des algorithmes d’estimation basés sur ce modèle sont proposés. Les connaissances a priori disponibles sur les signatures spectrales des composants purs, sur les abondances et les paramètres de la non-linéarité sont exploitées à l’aide d’une approche bayesienne. Le second modèle étudié dans cette thèse est basé sur l’approximation de la variété non-linéaire contenant les données observées à l’aide de processus gaussiens. L’algorithme de démélange associé permet d’estimer la relation non-linéaire entre les abondances des matériaux et les pixels observés sans introduire explicitement les signatures spectrales des composants dans le modèle de mélange. Ces signatures spectrales sont estimées dans un second temps par prédiction à base de processus gaussiens. La prise en compte d’effets non-linéaires dans les images hyperspectrales nécessite souvent des stratégies de démélange plus complexes que celles basées sur un modèle linéaire. Comme le modèle linéaire est souvent suffisant pour approcher la plupart des mélanges réels, il est intéressant de pouvoir détecter les pixels ou les régions de l’image où ce modèle linéaire est approprié. On pourra alors, après cette détection, appliquer les algorithmes de démélange non-linéaires aux pixels nécessitant réellement l’utilisation de modèles de mélange non-linéaires. La dernière partie de ce manuscrit se concentre sur l’étude de détecteurs de non-linéarités basés sur des modèles linéaires et non-linéaires pour l’analyse d’images hyperspectrales. Les méthodes de démélange non-linéaires proposées permettent d’améliorer la caractérisation des images hyperspectrales par rapport au méthodes basées sur un modèle linéaire. Cette amélioration se traduit en particulier par une meilleure erreur de reconstruction des données. De plus, ces méthodes permettent de meilleures estimations des signatures spectrales et des abondances quand les pixels résultent de mélanges non-linéaires. Les résultats de simulations effectuées sur des données synthétiques et réelles montrent l’intérêt d’utiliser des méthodes de détection de non-linéarités pour l’analyse… Advisors/Committee Members: Tourneret, Jean-Yves (thesis director), Dobigeon, Nicolas (thesis director).

Subjects/Keywords: Imagerie hyperspectrale; Démélange spectral; Estimation bayésienne; Modèles non-linéaires; Hyperspectral imagery; Spectral unmixing; Bayesian estimation; Nonlinear models

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

APA (6^th Edition):

Altmann, Y. (2013). Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2013INPT0084

Chicago Manual of Style (16^th Edition):

Altmann, Yoann. “Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.” 2013. Doctoral Dissertation, INP Toulouse. Accessed March 07, 2021. http://www.theses.fr/2013INPT0084.

MLA Handbook (7^th Edition):

Altmann, Yoann. “Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales.” 2013. Web. 07 Mar 2021.

Vancouver:

Altmann Y. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. [Internet] [Doctoral dissertation]. INP Toulouse; 2013. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2013INPT0084.

Council of Science Editors:

Altmann Y. Nonlinear unmixing of Hyperspectral images : Démélange non-linéaire d'images hyperspectrales. [Doctoral Dissertation]. INP Toulouse; 2013. Available from: http://www.theses.fr/2013INPT0084

6. Khan, Haris Ahmad. Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage.

Degree: Docteur es, Instrumentation et informatique de l'image, 2018, Bourgogne Franche-Comté; Norwegian university of science and technology (Trondheim, Norvège)

URL: http://www.theses.fr/2018UBFCK028

► En imagerie couleur, un système d’acquisition capture une scène avec une haute résolution spatiale mais une résolution spectrale limitée. L’imagerie hyperspectrale permet d’acquérir la scène… (more)

▼ En imagerie couleur, un système d’acquisition capture une scène avec une haute résolution spatiale mais une résolution spectrale limitée. L’imagerie hyperspectrale permet d’acquérir la scène avec une grande résolution spectrale. Un système d’acquisition hyperspectrale est un ensemble complexe et il est difficile de l’utiliser pour acquérir des données dans une situation où les conditions d’imageries ne sont pas contrôlées. De plus, ces systèmes sont chers et souvent encombrants ou difficiles à manipuler. À cause de ces problèmes, l’utilisation de l’imagerie hyperspectrale n’a pas encore été beaucoup utilisée en vision assistée par ordinateur, et la plupart des systèmes de vision utilise l’imagerie couleur.L’imagerie multispectrale propose une solution intermédiaire, elle permet de capturer une information moins résolue selon la dimension spectrale, comparée à l’hyperspectrale, tout en préservant la résolution spatiale. Ces systèmes sont moins encombrants et moins difficiles à maitriser grâce aux récentes avancées technologiques, et arrivent sur le marché en tant que produits commerciaux. On peut citer les matrices de filtres spectraux (spectral filter arrays) qui permettent l’acquisition en temps réel d’images multispectrales grâce à l’utilisation d’unecaméra de complexité similaire à une caméra couleur. Jusqu’ici, les informations capturées par ces systèmes étaient considérées de la même manière que les imageurs hyperspectraux en champ proche, c’est à dire que pour utiliser l’information au mieux, les conditions d’acquisitions devaient être connues et le système calibré, en particulier pour l’éclairage de la scène et la dynamique de la scène.Afin d’élargir l’utilisation de l’imagerie multispectrale pour la vision par ordinateur dans des conditions générales, je propose dans cette thèse de développer les méthodes calculatoires en imagerie couleur (computational color imaging) et de les adapter aux systèmes d’imagerie multispectraux. Une caractéristique très puissante de l’imagerie couleur est de proposer un rendu constant des couleurs de la surface d’un objet à travers différentes conditions d’acquisition via l’utilisation d’algorithmes et divers traitements de l’information.Dans cette thèse, j’étends la notion de constance des couleurs et de balance des blancs de l’imagerie couleur à l’imagerie multispectrale. J’introduis le terme de constance de l’information spectrale (multispectral constancy).Je propose la construction d’un ensemble d’outils permettant la représentation constante de l’information spectrale à travers le changement d’éclairage. La validité de ces outils est évaluée à travers la reconstruction de la réflectance spectrale des objets lorsque l’éclairage change. Nous avons également acquis de nouvelles images hyperspectrales et multispectrales mises à disposition de la communauté.Ces outils et données permettront de favoriser la généralisation de l’utilisation de l’imagerie multispectrale en champ proche dans les applications classiques utilisant traditionnellement l’imagerie couleur et de sortir ce… Advisors/Committee Members: Laligant, Olivier (thesis director), Thomas, Jean-Baptiste (thesis director), Hardeberg, Jon Yngve (thesis director).

Subjects/Keywords: Multispectrales; Estimation illuminant; Spectral; Constance multispectrale; Illuminant invariant; Multispectral; Illuminant estimation; Spectral; Multispectral constancy; Illuminant invariant; 006.6

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

APA (6^th Edition):

Khan, H. A. (2018). Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage. (Doctoral Dissertation). Bourgogne Franche-Comté; Norwegian university of science and technology (Trondheim, Norvège). Retrieved from http://www.theses.fr/2018UBFCK028

Chicago Manual of Style (16^th Edition):

Khan, Haris Ahmad. “Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage.” 2018. Doctoral Dissertation, Bourgogne Franche-Comté; Norwegian university of science and technology (Trondheim, Norvège). Accessed March 07, 2021. http://www.theses.fr/2018UBFCK028.

MLA Handbook (7^th Edition):

Khan, Haris Ahmad. “Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage.” 2018. Web. 07 Mar 2021.

Vancouver:

Khan HA. Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage. [Internet] [Doctoral dissertation]. Bourgogne Franche-Comté; Norwegian university of science and technology (Trondheim, Norvège); 2018. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2018UBFCK028.

Council of Science Editors:

Khan HA. Multispectral constancy for illuminant invariant representation of multispectral images : Constance multispectrale pour l'obtention de représentations d'images multispectrales invariantes en fonction de l'éclairage. [Doctoral Dissertation]. Bourgogne Franche-Comté; Norwegian university of science and technology (Trondheim, Norvège); 2018. Available from: http://www.theses.fr/2018UBFCK028

Duke University

7. Krishnamurthy, Kalyani. Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds .

Degree: 2011, Duke University

URL: http://hdl.handle.net/10161/3945

► This dissertation presents methods and associated performance bounds for spectral image processing tasks such as reconstruction and target detection, which are useful in a… (more)

▼ This dissertation presents methods and associated performance bounds for spectral image processing tasks such as reconstruction and target detection, which are useful in a variety of applications such as astronomical imaging, biomedical imaging and remote sensing. The key idea behind our spectral image processing methods is the fact that important information in a spectral image can often be captured by low-dimensional manifolds embedded in high-dimensional spectral data. Based on this key idea, our work focuses on the reconstruction of spectral images from photon-limited, and distorted observations. This dissertation presents a partition-based, maximum penalized likelihood method that recovers spectral images from noisy observations and enjoys several useful properties; namely, it (a) adapts to spatial and spectral smoothness of the underlying spectral image, (b) is computationally efficient, (c) is near-minimax optimal over an anisotropic Holder-Besov function class, and (d) can be extended to inverse problem frameworks. There are many applications where accurate localization of desired targets in a spectral image is more crucial than a complete reconstruction. Our work draws its inspiration from classical detection theory and compressed sensing to develop computationally efficient methods to detect targets from few projection measurements of each spectrum in the spectral image. Assuming the availability of a spectral dictionary of possible targets, the methods discussed in this work detect targets that either come from the spectral dictionary or otherwise. The theoretical performance bounds offer insight on the performance of our detectors as a function of the number of measurements, signal-to-noise ratio, background contamination and properties of the spectral dictionary. A related problem is that of level set estimation where the goal is to detect the regions in an image where the underlying intensity function exceeds a threshold. This dissertation studies the problem of accurately extracting the level set of a function from indirect projection measurements without reconstructing the underlying function. Our partition-based set estimation method extracts the level set of proxy observations constructed from such projection measurements. The theoretical analysis presented in this work illustrates how the projection matrix, proxy construction and signal strength of the underlying function affect the estimation performance. Advisors/Committee Members: Willett, Rebecca M (advisor).

Subjects/Keywords: Electrical Engineering; Compressed sesning; Level set estimation; Performance bounds; Poisson intensity estimation; Spectral imaging; Spectral target detection

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APA (6^th Edition):

Krishnamurthy, K. (2011). Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/3945

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

Chicago Manual of Style (16^th Edition):

Krishnamurthy, Kalyani. “Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds .” 2011. Thesis, Duke University. Accessed March 07, 2021. http://hdl.handle.net/10161/3945.

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

MLA Handbook (7^th Edition):

Krishnamurthy, Kalyani. “Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds .” 2011. Web. 07 Mar 2021.

Vancouver:

Krishnamurthy K. Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds . [Internet] [Thesis]. Duke University; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10161/3945.

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

Council of Science Editors:

Krishnamurthy K. Spectral Image Processing Theory and Methods: Reconstruction, Target Detection, and Fundamental Performance Bounds . [Thesis]. Duke University; 2011. Available from: http://hdl.handle.net/10161/3945

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

Wright State University

8. Puladas, Charan. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.

Degree: MSEE, Electrical Engineering, 2016, Wright State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356

► The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop… (more)

▼ The rich spectral information captured by hyperspectral sensors has given rise to a number of remote sensing applications, ranging from vegetative assessment and crop health monitoring, to military surveillance and combatant identification. However, due to limited spatial resolution, multiple ground materials generally contribute, i.e. mix, to form the spectrum recorded for a single pixel. The unmixing problem considers the inverse problem of determining the underlying material spectra, called endmembers, from sensor measurements. While classical unmixing approaches were deterministic in nature and did not attempt to identify in-scene materials, recent methods use labeled training data to generate statistical models of endmember variabilities and perform statistical unmixing for simultaneous material identification and abundance estimation. However, the computational complexity of statistical unmixing with endmember variability is Ο(N³), cubic in the number N of sensed spectral bands. This large computational demand is at odds with continuous technological improvements that are dramatically increasing the spectral resolution of remote spectroscopy methods. In particular, current sensor technology is transitioning from the hyperspectral realm (hundreds of spectral bands) to the ultraspectral realm (thousands of spectral bands) and eclipsing the ability to perform statistical unmixing. In this thesis we develop a computationally tractable statistical unmixing method. The proposed method uses Markov chains to model endmember variability and the spectral correlation properties present within endmembers. We use a probabilistic graphical model over multiple Markov chains to capture the mixing effects of the spectral sensor and employ sum-product message passing to develop an accelerated statistical unmixing algorithm. The computational complexity, Ο(NM³), of the proposed algorithm is only linear in the number of bands and depends on the number of endmembers M in a cubic fashion. As M is generally small and fixed (in the 10s), the accelerated algorithm represents a dramatic speed-up over existing methods. Examples demonstrate comparable error rates with two orders of magnitude reduction in computation time compared to existing statistical unmixing methods. Advisors/Committee Members: Ash, Joshua N. (Advisor).

Subjects/Keywords: Electrical Engineering; Remote Sensing; Computer Science; Hyperspectral Imaging; Spectral Unmixing; Spectral Estimation; Endmember Variablity; Spectral Correlation; Signal Processing; Machine Learning

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

APA (6^th Edition):

Puladas, C. (2016). Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. (Masters Thesis). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356

Chicago Manual of Style (16^th Edition):

Puladas, Charan. “Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.” 2016. Masters Thesis, Wright State University. Accessed March 07, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356.

MLA Handbook (7^th Edition):

Puladas, Charan. “Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm.” 2016. Web. 07 Mar 2021.

Vancouver:

Puladas C. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. [Internet] [Masters thesis]. Wright State University; 2016. [cited 2021 Mar 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356.

Council of Science Editors:

Puladas C. Accelerated Hyperspectral Unmixing with Endmember Variability via the Sum-Product Algorithm. [Masters Thesis]. Wright State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1464214356

Cornell University

9. Ghods, Ramina. Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning.

Degree: PhD, Electrical and Computer Engineering, 2019, Cornell University

URL: http://hdl.handle.net/1813/70015

► Nonlinearities play a critical role in a large number of signal and information processing applications, including the areas of machine learning, imaging, signal processing, and… (more)

Subjects/Keywords: data detection; hyperparameter initialization; nonlinear estimation; nonparametric estimation; phase retrieval; spectral initialization

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APA (6^th Edition):

Ghods, R. (2019). Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/70015

Chicago Manual of Style (16^th Edition):

Ghods, Ramina. “Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning.” 2019. Doctoral Dissertation, Cornell University. Accessed March 07, 2021. http://hdl.handle.net/1813/70015.

MLA Handbook (7^th Edition):

Ghods, Ramina. “Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning.” 2019. Web. 07 Mar 2021.

Vancouver:

Ghods R. Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning. [Internet] [Doctoral dissertation]. Cornell University; 2019. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1813/70015.

Council of Science Editors:

Ghods R. Nonlinear Estimation with Applications to Wireless Communications, Imaging, and Machine Learning. [Doctoral Dissertation]. Cornell University; 2019. Available from: http://hdl.handle.net/1813/70015

University of New Orleans

10. Tsiappoutas, Kyriakos Michael. Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music.

Degree: PhD, Physics, 2011, University of New Orleans

URL: https://scholarworks.uno.edu/td/1358

► Digitized acoustical signals of Byzantine music performed by Iakovos Nafpliotis are used to extract the fundamental frequency of each note of the diatonic scale.… (more)

▼ Digitized acoustical signals of Byzantine music performed by Iakovos Nafpliotis are used to extract the fundamental frequency of each note of the diatonic scale. These empirical results are then contrasted to the theoretical suggestions and previous empirical findings. Several parametric and non-parametric spectral parameter estimation methods are implemented. These include: (1) Phase vocoder method, (2) McAulay-Quatieri method, (3) Levinson-Durbin algorithm,(4) YIN, (5) Quinn & Fernandes Estimator, (6) Pisarenko Frequency Estimator, (7) MUltiple SIgnal Characterization (MUSIC) algorithm, (8) Periodogram method, (9) Quinn & Fernandes Filtered Periodogram, (10) Rife & Vincent Estimator, and (11) the Fourier transform. Algorithm performance was very precise. The psychophysical aspect of human pitch discrimination is explored. The results of eight (8) psychoacoustical experiments were used to determine the aural just noticeable difference (jnd) in pitch and deduce patterns utilized to customize acceptable performable pitch deviation to the application at hand. These customizations [Acceptable Performance Difference (a new measure of frequency differential acceptability), Perceptual Confidence Intervals (a new concept of confidence intervals based on psychophysical experiment rather than statistics of performance data), and one based purely on music-theoretical asymphony] are proposed, discussed, and used in interpretation of results. The results suggest that Nafpliotis' intervals are closer to just intonation than Byzantine theory (with minor exceptions), something not generally found in Thrasivoulos Stanitsas' data. Nafpliotis' perfect fifth is identical to the just intonation, even though he overstretches his octaveby fifteen (15)cents. His perfect fourth is also more just, as opposed to Stanitsas' fourth which is directionally opposite. Stanitsas' tendency to exaggerate the major third interval A4-F4 is still seen in Nafpliotis, but curbed. This is the only noteworthy departure from just intonation, with Nafpliotis being exactly Chrysanthian (the most exaggerated theoretical suggestion of all) and Stanitsas overstretching it even more than Nafpliotis and Chrysanth. Nafpliotis ascends in the second tetrachord more robustly diatonically than Stanitsas. The results are reported and interpreted within the framework of Acceptable Performance Differences. Advisors/Committee Members: George E. Ioup, Juliette W. Ioup, Huimin Chen.

Subjects/Keywords: statistical spectral estimation, fundamental frequency estimation, statistical signal processing, Fourier transform, autocorrelation, autoregression; Engineering Physics

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APA (6^th Edition):

Tsiappoutas, K. M. (2011). Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music. (Doctoral Dissertation). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/1358

Chicago Manual of Style (16^th Edition):

Tsiappoutas, Kyriakos Michael. “Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music.” 2011. Doctoral Dissertation, University of New Orleans. Accessed March 07, 2021. https://scholarworks.uno.edu/td/1358.

MLA Handbook (7^th Edition):

Tsiappoutas, Kyriakos Michael. “Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music.” 2011. Web. 07 Mar 2021.

Vancouver:

Tsiappoutas KM. Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music. [Internet] [Doctoral dissertation]. University of New Orleans; 2011. [cited 2021 Mar 07]. Available from: https://scholarworks.uno.edu/td/1358.

Council of Science Editors:

Tsiappoutas KM. Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music. [Doctoral Dissertation]. University of New Orleans; 2011. Available from: https://scholarworks.uno.edu/td/1358

11. Tamburello, Philip Michael. Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise.

Degree: PhD, Electrical Engineering, 2016, Virginia Tech

URL: http://hdl.handle.net/10919/64785

► The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard… (more)

▼ The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard method used to estimate this function given a finite number of observations. Non-Gaussian, impulsive observation noise following probability density functions with thick tails, which often occurs in practice, can bias this estimator, rendering classical time series analysis methods ineffective. This work examines the robustness of two estimators of correlation based on memoryless nonlinear functions of observations, the Phase-Phase Correlator (PPC) and the Median- of-Ratios Estimator (MRE), which are applicable to complex-valued Gaussian random pro- cesses. These estimators are very fast and easy to implement in current processors. We show that these estimators are robust from a bias perspective when complex-valued Gaussian pro- cesses are contaminated with impulsive noise at the expense of statistical efficiency at the assumed Gaussian distribution. Additionally, iterative versions of these estimators named the IMRE and IPPC are developed, realizing an improved bias performance over their non- iterative counterparts and the well-known robust Schweppe-type Generalized M-estimator utilizing a Huber cost function (SHGM). An impulsive noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals to identity and replace corrupted observations. It does not have the same initialization issues as the robust filter cleaner. Robust spectral estimation methods are developed using these new estimators and impulsive noise suppression techniques. Results are obtained for synthetic complex-valued Guassian processes and real-world digital television signals collected using a software defined radio. Advisors/Committee Members: Mili, Lamine M. (committeechair), Clancy, Thomas Charles (committee member), Reed, Jeffrey H. (committee member), Triantis, Konstantinos P. (committee member), Beex, Aloysius A. (committee member).

Subjects/Keywords: Robust Estimation; Spectral Estimation; Filtering

…115 6.3 Robust Spectral Estimation Using Multiple-Basis Signal Representation to Remove… …Burg method parameteric spectral estimation using purely Gaussian data… …5.4 Burg method spectral estimation results in the presence of outliers. . . . . . 106 5.5… …method spectral estimation example, with purely Gaussian data and no contamination… …114 6.2 Multi-taper method spectral estimation with contaminated data. . . . . . . . 116…

6 more images…

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APA (6^th Edition):

Tamburello, P. M. (2016). Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64785

Chicago Manual of Style (16^th Edition):

Tamburello, Philip Michael. “Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise.” 2016. Doctoral Dissertation, Virginia Tech. Accessed March 07, 2021. http://hdl.handle.net/10919/64785.

MLA Handbook (7^th Edition):

Tamburello, Philip Michael. “Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise.” 2016. Web. 07 Mar 2021.

Vancouver:

Tamburello PM. Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10919/64785.

Council of Science Editors:

Tamburello PM. Iterative Memoryless Non-linear Estimators of Correlation for Complex-Valued Gaussian Processes that Exhibit Robustness to Impulsive Noise. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/64785

12. Διαμαντής, Κωνσταντίνος. Super resolution techniques for the analysis of ultrasound signals.

Degree: 2011, University of Patras

URL: http://hdl.handle.net/10889/4865

►

In ultrasound contrast imaging, the discrimination between acoustic echoes from tissue and contrast microbubbles would have as a result the increase of the Contrast-to-Tissue-Ratio, improving… (more)

▼

In ultrasound contrast imaging, the discrimination between acoustic echoes from tissue and contrast microbubbles would have as a result the increase of the Contrast-to-Tissue-Ratio, improving therefore the quality of the imaging. The main idea is to differentiate the responses from those two kinds of signals based on their spectral content. The most important features of those sinusoidal signals are that they are very short in duration and than they are very likely to have many closely spaced frequency components. So, in order to achieve this target a novel Bayesian parametric spectral estimation technique has been originally designed by Yan Yan (PhD University of Edinburgh), that is supposed to have greater resolving capabilities than commonly used spectral estimation methods. The new technique uses a reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm so as to identify the frequency components of a signal and it is called parametric because it assumes a model and then the problem of spectral estimation is reduced to that of estimating the parameters of the model. This new method has been initially tested with synthetic signals created in Matlab, so as to define on which parameters it depends and to extract mathematical equations that describe these dependences. And although some coarse comparisons with other techniques showed that the capabilities of this method were great, there was plenty room for improvements. Corrections in the Matlab code of this method, analysis of the code’s output in various ways so as to find which is superior, and the proposal of a new simpler model are just some of the changes that have evidently improved the method’s function. But the most important one is the completion of the amplitude estimation that was left unfinished in the past, as a complete spectral analysis implies both frequency and amplitude estimation. Now, signal reconstruction is possible and also, direct comparisons of the method’s resulting spectrum with the one of the Discrete Fourier Transform or of any other nonparametric (DFT-based) or parametric method can be made. The new version of the code has been applied apart from synthetic signals, to the real ones providing indeed information that was undisclosed in the past concerning the spectral content of those signals. However, further research is required, in order to take advantage of this information and in order to determine the exact performance and limitations of this method that remains still in experimental level.

Στην απεικόνιση με υπέρηχους όταν χρησιμοποιείται μέσο αντίθεσης, ο διαχωρισμός ανάμεσα στην ακουστική ηχώ που προέρχεται από τον ιστό και σε αυτή που προέρχεται από τo μέσο αντίθεσης όπως οι μικροφυσαλίδες, θα μπορούσε να έχει σαν αποτέλεσμα τη βελτίωση της ποιότητας της εικόνας. Η βασική ιδέα είναι να διαφοροποιηθούν οι αποκρίσεις από τα δύο διαφορετικά είδη σημάτων υπερηχοτομογραφίας με βάση το φασματικό τους περιεχόμενο. Τα κυριότερα χαρακτηριστικά αυτών των ημιτονοειδών σημάτων είναι ότι είναι πολύ μικρά σε χρονική διάρκεια και ότι…

Advisors/Committee Members: Παλληκαράκης, Νικόλαος, Diamantis, Konstantinos, Κουτσούρης, Δημήτρης, Τσαγγάρης, Σωκράτης.

Subjects/Keywords: Spectral estimation; Microbubbles; Ultrasounds; 616.075 43; Συχνοτική ανάλυση; Μικροφυσαλίδες; Υπέρηχοι

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

APA (6^th Edition):

Διαμαντής, �. (2011). Super resolution techniques for the analysis of ultrasound signals. (Masters Thesis). University of Patras. Retrieved from http://hdl.handle.net/10889/4865

Chicago Manual of Style (16^th Edition):

Διαμαντής, Κωνσταντίνος. “Super resolution techniques for the analysis of ultrasound signals.” 2011. Masters Thesis, University of Patras. Accessed March 07, 2021. http://hdl.handle.net/10889/4865.

MLA Handbook (7^th Edition):

Διαμαντής, Κωνσταντίνος. “Super resolution techniques for the analysis of ultrasound signals.” 2011. Web. 07 Mar 2021.

Vancouver:

Διαμαντής �. Super resolution techniques for the analysis of ultrasound signals. [Internet] [Masters thesis]. University of Patras; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10889/4865.

Council of Science Editors:

Διαμαντής �. Super resolution techniques for the analysis of ultrasound signals. [Masters Thesis]. University of Patras; 2011. Available from: http://hdl.handle.net/10889/4865

George Mason University

13. Schwarzwalder, Joseph James. Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming .

Degree: 2011, George Mason University

URL: http://hdl.handle.net/1920/6345

► The covariance matrix for a sensor array observing a stationary space-time process is determined by the individual sensor element locations, the directional response and noise… (more)

▼ The covariance matrix for a sensor array observing a stationary space-time process is determined by the individual sensor element locations, the directional response and noise of those elements, and the spatial spectrum of the process. Under this model the covariance matrix has a particular structure that can be exploited, improving adaptive beamformer performance both in terms of the number of snapshots required for good performance and robustness against correlated signal and interference environments. These performance improvements are particularly bene¯cial for large aperture arrays with large numbers of sensor elements that are operating in non-stationary and multi-path environments. No closed form solution exists for estimating structured covariance for the general problem of an unknown number of signals in non-white noise. We look to exploit the naturally intuitive interpretation of the process in the azimuth-elevation or frequency-wavenumber domains to address the problem. This dissertation develops a covariance from spatial spectrum (CSS) method by ¯rst estimating the spectrum of the process, and then applying standard spectral to covariance transforms. The initial characterization in the transform domain, either direction of arrival or wavenumber, provides a natural reinforcement of the underlying space-time process model. Additionally, spectral estimation techniques take advantage of the number of spatial samples, in particular for arrays with many elements, in a manner simple snapshot averaging cannot. While ad-hoc, such a structured covariance technique can provide near optimal performance for passive signal detection or recovery with very few snapshots. The ¯rst objective of this work is to understand the performance of minimum vari- ance distortionless response adaptive beamforming when covariance is estimated from the spatial spectrum. Positive de¯niteness of the covariance matrix and estimation bias are investigated. Performance predictions are developed for the case of a uniform line array and classical power spectral estimation techniques. This analysis highlights the need to explicitly deal with mixed spectra that arise in environments containing both point source and spatially-spread signals. Thomson's multi-taper spectral estimation neatly combines both the convenience of the non-parametric spectral estimation algorithms and the required harmonic analysis to handle such mixed spectra. Adaptive beamformer performance is as- sessed for various interference and noise environments against existing snapshot de¯cient algorithms. Extensions to support arbitrary array geometry are considered. A correlated signal and interference environment cannot be modeled as a stationary space-time process. A second objective of this work is to investigate how constraining the covariance to a stationary space-time process model mitigates signal cancellation due to correlation between the signal and interference. Reduction in correlation, and the resultant covariance bias are… Advisors/Committee Members: Wage, Kathleen E (advisor).

Subjects/Keywords: Structured Covariance; Adaptive Beamforming; Array Processing; Multitaper Spectral Estimation

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APA (6^th Edition):

Schwarzwalder, J. J. (2011). Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/6345

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

Chicago Manual of Style (16^th Edition):

Schwarzwalder, Joseph James. “Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming .” 2011. Thesis, George Mason University. Accessed March 07, 2021. http://hdl.handle.net/1920/6345.

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

MLA Handbook (7^th Edition):

Schwarzwalder, Joseph James. “Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming .” 2011. Web. 07 Mar 2021.

Vancouver:

Schwarzwalder JJ. Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming . [Internet] [Thesis]. George Mason University; 2011. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1920/6345.

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

Council of Science Editors:

Schwarzwalder JJ. Structured Covariance Estimation From Spatial Spectra For Adaptive Beamforming . [Thesis]. George Mason University; 2011. Available from: http://hdl.handle.net/1920/6345

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

University of North Carolina – Greensboro

14. Chen, Wei. Spectral estimation for random processes with stationary increments.

Degree: 2018, University of North Carolina – Greensboro

URL: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=23130

► In studying a stationary random process on ℝ, the covariance function is commonly used to characterize the second-order spatial dependency. Through the inversion of Fourier… (more)

Subjects/Keywords: Spectral theory (Mathematics); Estimation theory; Stochastic processes; Stationary processes

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APA (6^th Edition):

Chen, W. (2018). Spectral estimation for random processes with stationary increments. (Doctoral Dissertation). University of North Carolina – Greensboro. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=23130

Chicago Manual of Style (16^th Edition):

Chen, Wei. “Spectral estimation for random processes with stationary increments.” 2018. Doctoral Dissertation, University of North Carolina – Greensboro. Accessed March 07, 2021. http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=23130.

MLA Handbook (7^th Edition):

Chen, Wei. “Spectral estimation for random processes with stationary increments.” 2018. Web. 07 Mar 2021.

Vancouver:

Chen W. Spectral estimation for random processes with stationary increments. [Internet] [Doctoral dissertation]. University of North Carolina – Greensboro; 2018. [cited 2021 Mar 07]. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=23130.

Council of Science Editors:

Chen W. Spectral estimation for random processes with stationary increments. [Doctoral Dissertation]. University of North Carolina – Greensboro; 2018. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=23130

University of Illinois – Urbana-Champaign

15. Li, Yudu. A subspace approach to spectral quantification for MR spectroscopic imaging.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99360

► The problem of spectral quantification for magnetic resonance spectroscopic imaging (MRSI) is addressed in this thesis. We present a novel approach to solving this problem,… (more)

Subjects/Keywords: Magnetic resonance spectroscopic imaging (MRSI); Spectral estimation; Subspace; Spatiospectral constraints

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APA (6^th Edition):

Li, Y. (2017). A subspace approach to spectral quantification for MR spectroscopic imaging. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99360

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

Chicago Manual of Style (16^th Edition):

Li, Yudu. “A subspace approach to spectral quantification for MR spectroscopic imaging.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed March 07, 2021. http://hdl.handle.net/2142/99360.

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

MLA Handbook (7^th Edition):

Li, Yudu. “A subspace approach to spectral quantification for MR spectroscopic imaging.” 2017. Web. 07 Mar 2021.

Vancouver:

Li Y. A subspace approach to spectral quantification for MR spectroscopic imaging. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/2142/99360.

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

Council of Science Editors:

Li Y. A subspace approach to spectral quantification for MR spectroscopic imaging. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99360

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

Université de Sherbrooke

16. Bosco, Julien. Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre.

Degree: 2018, Université de Sherbrooke

URL: http://hdl.handle.net/11143/13571

► Ce projet de recherche propose une technique de rehaussement de la parole basée sur le domaine spectral et le domaine des modulations du spectre. Dans… (more)

Subjects/Keywords: Domaine spectral; Rehaussement de la parole; Estimation de bruit; Domaine modulatoire

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APA (6^th Edition):

Bosco, J. (2018). Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre. (Masters Thesis). Université de Sherbrooke. Retrieved from http://hdl.handle.net/11143/13571

Chicago Manual of Style (16^th Edition):

Bosco, Julien. “Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre.” 2018. Masters Thesis, Université de Sherbrooke. Accessed March 07, 2021. http://hdl.handle.net/11143/13571.

MLA Handbook (7^th Edition):

Bosco, Julien. “Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre.” 2018. Web. 07 Mar 2021.

Vancouver:

Bosco J. Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre. [Internet] [Masters thesis]. Université de Sherbrooke; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11143/13571.

Council of Science Editors:

Bosco J. Rehaussement de la parole combinant le domaine spectral et le domaine des modulations du spectre. [Masters Thesis]. Université de Sherbrooke; 2018. Available from: http://hdl.handle.net/11143/13571

University of Georgia

17. Praissman, Jeremy Lawrence. Finding structure in multivariate time series.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/24470

► The scope of scientific data collection in modern projects such as the human genome project has made it effectively impossible for careful by-hand analyses of… (more)

Subjects/Keywords: Generalized Canonical Correlation Analysis; Multitaper Spectral Estimation; Singular Value Decomposition

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APA (6^th Edition):

Praissman, J. L. (2014). Finding structure in multivariate time series. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/24470

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

Chicago Manual of Style (16^th Edition):

Praissman, Jeremy Lawrence. “Finding structure in multivariate time series.” 2014. Thesis, University of Georgia. Accessed March 07, 2021. http://hdl.handle.net/10724/24470.

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

MLA Handbook (7^th Edition):

Praissman, Jeremy Lawrence. “Finding structure in multivariate time series.” 2014. Web. 07 Mar 2021.

Vancouver:

Praissman JL. Finding structure in multivariate time series. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10724/24470.

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

Council of Science Editors:

Praissman JL. Finding structure in multivariate time series. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/24470

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

18. Chen, Wei. Spectral estimation for random processes with stationary increments.

Degree: 2018, NC Docks

URL: http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf

► In studying a stationary random process on R, the covariance function is commonlyused to characterize the second-order spatial dependency. Through the inversionof Fourier transformation, its… (more)

Subjects/Keywords: Spectral theory (Mathematics); Estimation theory; Stochastic processes; Stationary processes

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APA (6^th Edition):

Chen, W. (2018). Spectral estimation for random processes with stationary increments. (Thesis). NC Docks. Retrieved from http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf

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

Chicago Manual of Style (16^th Edition):

Chen, Wei. “Spectral estimation for random processes with stationary increments.” 2018. Thesis, NC Docks. Accessed March 07, 2021. http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf.

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

MLA Handbook (7^th Edition):

Chen, Wei. “Spectral estimation for random processes with stationary increments.” 2018. Web. 07 Mar 2021.

Vancouver:

Chen W. Spectral estimation for random processes with stationary increments. [Internet] [Thesis]. NC Docks; 2018. [cited 2021 Mar 07]. Available from: http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf.

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

Council of Science Editors:

Chen W. Spectral estimation for random processes with stationary increments. [Thesis]. NC Docks; 2018. Available from: http://libres.uncg.edu/ir/uncg/f/Chen_uncg_0154D_12424.pdf

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

University of Lund

19. Brynolfsson, Johan. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.

Degree: 2019, University of Lund

URL: https://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; https://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf

► This thesis deals with estimation and classification problems of non-stationary processes in a few special cases.In paper A and paper D we make strong assumptions… (more)

▼ This thesis deals with estimation and classification problems of non-stationary processes in a few special cases.In paper A and paper D we make strong assumptions about the observed signal, where a specific model is assumed and the parameters of the model are estimated.In Paper B, Paper C, and Paper E more general assumptions about the structure of the observed processes are made, and the methods in these papers may be applied to a wider range of parameter estimation and classification scenarios.All papers handle non-stationary signals where the spectral power distribution may change with respect to time. Here, we are interested in finding time-frequency representations (TFR) of the signal which can depict how the frequencies and corresponding amplitudes change.In Paper A, we consider the estimation of the shape parameter detailing time- and frequency translated Gaussian bell functions.The algorithm is based on the scaled reassigned spectrogram, where the spectrogram is calculated using a unit norm Gaussian window.The spectrogram is then reassigned using a large set of candidate scaling factors.For the correct scaling factor, with regards to the shape parameter, the reassigned spectrogram of a Gaussian function will be perfectly localized into one single point.In Paper B, we expand on the concept in Paper A, and allow it to be applied to any twice differentiable transient function in any dimension.Given that the matched window function is used when calculating the spectrogram, we prove that all energy is reassigned to one single point in the time-frequency domain if scaled reassignment is applied.Given a parametric model of an observed signal, one may tune the parameter(s) to minimize the entropy of the matched reassigned spectrogram.We also present a classification scheme, where one may apply multiple different parametric models and evaluate which one of the models that best fit the data. In Paper C, we consider the problem of estimating the spectral content of signals where the spectrum is assumed to have a smooth structure.By dividing the spectral representation into a coarse grid and assuming that the spectrum within each segment may be well approximated as linear, a smooth version of the Fourier transform is derived.Using this, we minimize the least squares norm of the difference between the sample covariance matrix of an observed signal and any covariance matrix belonging to a piece-wise linear spectrum.Additionally, we allow for adding constraints that make the solution obey common assumptions of spectral representations.We apply the algorithm to stationary signals in one and two dimensions, as well as to one-dimensional non-stationary processes. In Paper D we consider the problem of estimating the parameters of a multi-component chirp signal, where a harmonic structure may be imposed.The algorithm is based on a group sparsity with sparse groups framework where a large dictionary of candidate parameters is constructed.An optimization scheme is formulated such as to find harmonic groups of chirps that also…

Subjects/Keywords: Signal Processing; Time-Frequency Estimation; Parameter Estimation; Reassignment method; Non-Stationary Processes; Smooth spectral estimation; Neural Networks

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APA (6^th Edition):

Brynolfsson, J. (2019). Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; https://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf

Chicago Manual of Style (16^th Edition):

Brynolfsson, Johan. “Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.” 2019. Doctoral Dissertation, University of Lund. Accessed March 07, 2021. https://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; https://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf.

MLA Handbook (7^th Edition):

Brynolfsson, Johan. “Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis.” 2019. Web. 07 Mar 2021.

Vancouver:

Brynolfsson J. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. [Internet] [Doctoral dissertation]. University of Lund; 2019. [cited 2021 Mar 07]. Available from: https://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; https://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf.

Council of Science Editors:

Brynolfsson J. Estimation and Classification of Non-Stationary Processes : Applications in Time-Frequency Analysis. [Doctoral Dissertation]. University of Lund; 2019. Available from: https://lup.lub.lu.se/record/b961aec1-d348-4a7a-84a4-83b4b15647da ; https://portal.research.lu.se/ws/files/64669735/kappa_brynolfsson.pdf

University of Florida

20. Ojowu, Ode, Jr. Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications.

Degree: PhD, Electrical and Computer Engineering, 2013, University of Florida

URL: https://ufdc.ufl.edu/UFE0046086

► Spectral analysis of signals, or the problem of spectral estimation revolves around estimating the distribution of power over frequency of a random signal. It has… (more)

▼ Spectral analysis of signals, or the problem of spectral estimation revolves around estimating the distribution of power over frequency of a random signal. It has useful applications in various fields of study (including Speech analysis, Medicine, RADAR and SONAR) due to the fact that the frequency content of an observed signal can provide very useful information in these fields. A well known method for estimating the spectral content of a signal is the Periodogram (developed by Arthur Schuster), which is a data-independent method of estimation. This method is based on computing the Fourier transform of the signal which can be computed efficiently using the Fast Fourier Transform (FFT) algorithm. This method however, is limited by relatively poor resolution and high side-lobes, which can lead to degradation in retrieval of the desired information present within the signal. Data-dependent (adaptive) techniques both non-parametric and parametric can offer superior performance over data-independent methods like the periodogram at a cost of increased computational complexity. These data-adaptive approaches however, can lead to improved spectral resolution and lower side-lobes, which can reveal more information about the signal under study. These advantages have led to increased interest in data-adaptive approaches to the problem of spectral estimation. This dissertation proposal revolves around applying robust adaptive techniques to real-world problems to achieve superior performance. In Chapter 2, adaptive techniques are used in the problem of frequency estimation (harmonic retrieval) in the presence of strong interference. The focus is on the problem of digital audio forensics, where the goal is to extract the embedded network frequency from a digital recording and compare it to a known database for digital audio verification. In the presence of significant interference, extracting the network frequency using the standard method (Periodogram) is difficult due to poor resolution and high-sidelobes. We therefore use a robust adaptive algorithm (Iterative Adaptive Approach) to improve the spectral resolution and suppress side-lobes hence effectively separating the network frequency from interference. A frequency tracking method based on dynamic programming is used in addition to this data-adaptive method to extract the Network frequency accurately and hence provide more reliability for the verification process compared to the current standard. In Chapter 3, we once again apply an adaptive technique for harmonic retrieval. The goal here is to effectively suppress Radio Frequency Interference (RFI) picked up by an Ultra-wideband (UWB) RADAR (currently being built by the Army Research Lab (ARL) for landmine detection) which samples its returned signals using an equivalent sampling scheme. This equivalent sampling scheme makes RFI suppression difficult (due to under-sampling (aliasing)). The current method for RFI suppression for this UWB RADAR is simply averaging multiple realizations of the measured data. We model the… Advisors/Committee Members: LI,JIAN (committee chair), ZMUDA,HENRY (committee member), FAN,ZHONGHUI HUGH (committee member).

Subjects/Keywords: Algorithms; Estimate reliability; Imaging; Photographs; Radar; Signal processing; Signals; Sine waves; Spectral resolution; Spices; estimation – remote – sensing – spectral

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APA (6^th Edition):

Ojowu, Ode, J. (2013). Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0046086

Chicago Manual of Style (16^th Edition):

Ojowu, Ode, Jr. “Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications.” 2013. Doctoral Dissertation, University of Florida. Accessed March 07, 2021. https://ufdc.ufl.edu/UFE0046086.

MLA Handbook (7^th Edition):

Ojowu, Ode, Jr. “Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications.” 2013. Web. 07 Mar 2021.

Vancouver:

Ojowu, Ode J. Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2021 Mar 07]. Available from: https://ufdc.ufl.edu/UFE0046086.

Council of Science Editors:

Ojowu, Ode J. Data-Adaptive Spectral Estimation Algorithms and Their Sensing Applications. [Doctoral Dissertation]. University of Florida; 2013. Available from: https://ufdc.ufl.edu/UFE0046086

University of Tennessee – Knoxville

21. Bull, Nora Dianne. An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation.

Degree: 2016, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_graddiss/3897

► Instrumentation in a nuclear power plant is critical in monitoring the stability and safety levels of a reactor. Temperature is a key measurement performed on… (more)

▼ Instrumentation in a nuclear power plant is critical in monitoring the stability and safety levels of a reactor. Temperature is a key measurement performed on the core of a reactor to control the power output and sustain a safe thermal margin. If there is a dramatic change in temperature, failure is likely to follow if action is not taken to cool the system. Traditionally, to measure the temperature of a reactor, several resistance temperature detectors are placed in predefined locations on the system. Resistance temperature detectors (RTD) are typically platinum coiled wire wrapped around a ceramic cylinder and encased in a metal sheath. Due to the harsh environment of nuclear reactors, the RTDs degrade and their resistance measurements drift over time. This drift in resistance can be misunderstood as a drift in reactor temperature. In the past, the RTDs would be serviced every few years either through calibration or replacement. To service the RTDs the reactor is shut down and a person is sent into a dangerous environment. Utilizing Johnson Noise Thermometry (JNT) will reduce the occurrences of service needed for RTDs and provide a high- accuracy temperature measurement. JNT is a first order fundamental expression of temperature invulnerable to drift in the RTD’s physical condition. The signal processing behind JNT is presented in the following document. Spectral Estimation methods are utilized in order to remove electromagnetic interference (EMI) from the JNT measurement. These methods are unique to this dissertation. The EMI estimation method is modeled and simulation results are presented. The modeling of the EMI estimation involves locating EMI, analysis of EMI effects, and removal without bias. Finally, results from numerical and experimental verification are presented. The research presented here is important to furthering the future of the nuclear industry for several reasons. With this technology applied to existing systems reactor shut-down time can be decreased, technicians limit their exposure to dangerous radiation zones, and financial support for lengthy shut-downs is saved. The instrumentation community will benefit through the innovation of signal processing for very small signal versus noise interference.

Subjects/Keywords: spectral estimation; probability density function; power spectral density; electromagnetic interference; EMI removal; Johnson Noise thermometry; Signal Processing

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APA (6^th Edition):

Bull, N. D. (2016). An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/3897

Chicago Manual of Style (16^th Edition):

Bull, Nora Dianne. “An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation.” 2016. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed March 07, 2021. https://trace.tennessee.edu/utk_graddiss/3897.

MLA Handbook (7^th Edition):

Bull, Nora Dianne. “An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation.” 2016. Web. 07 Mar 2021.

Vancouver:

Bull ND. An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2016. [cited 2021 Mar 07]. Available from: https://trace.tennessee.edu/utk_graddiss/3897.

Council of Science Editors:

Bull ND. An Innovative Approach to Johnson Noise Thermometry by Means of Spectral Estimation. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2016. Available from: https://trace.tennessee.edu/utk_graddiss/3897

Macquarie University

22. Grant, Andrew. Parametric methods for time series discrimination.

Degree: 2018, Macquarie University

URL: http://hdl.handle.net/1959.14/1272839

►

Empirical thesis.

Bibliography: pages 195-199.

1. Introduction – 2. Background – 3. Autoregressive spectral discrimination – 4. ARMA spectral discrimination – 5. Comparing multivariate time… (more)

▼

Empirical thesis.

Bibliography: pages 195-199.

1. Introduction – 2. Background – 3. Autoregressive spectral discrimination – 4. ARMA spectral discrimination – 5. Comparing multivariate time series – 6. The estimation of frequency in the multichannel sinusoidal model - 7. Discriminating between time series with periodic components – 8. Conclusion – References.

In this thesis we consider the problem of determining whether two or more independent time series have been generated by the same underlying stochastic process, or by the same mechanism.There is an extensive literature on comparing time series from univariate stationary processes on the basis of their second order properties, that is, their dependence structures over time. These existing methods are nonparametric and are based on comparing periodograms or sample autocovariances. They are generally limited by requiring equal sample sizes and Gaussian assumptions. We introduce a parametric approach which involves fitting parametric models to the time series and comparing model parameters. The parametric approach avoids the limitations of the nonparametric and simulations are used to show that it results in a more powerful test. We also show how to extend the parametric approach to compare time series from multivariate stationary processes.

A further extension is to compare time series which are from stochastic processes which contain periodic components. Such time series are typically modelled using mixed models which are made up of a deterministic periodic component and a stationary stochastic component. We develop tests for whether two or more time series have been generated by processes with periodicities at the same fixed frequencies and stationary components with the same second order properties. In order to extend the procedures to the multivariate case we first develop novel methods for frequency estimation in the multivariate mixed model.

1 online resource (xii, 199 pages) graphs, tables

Advisors/Committee Members: Macquarie University. Department of Mathematics and Statistics.

Subjects/Keywords: Time-series analysis – Mathematical models; autoregression; discriminant analysis; multivariant stationary process; spectral comparison; multichannel frequency estimation; spectral density

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APA (6^th Edition):

Grant, A. (2018). Parametric methods for time series discrimination. (Doctoral Dissertation). Macquarie University. Retrieved from http://hdl.handle.net/1959.14/1272839

Chicago Manual of Style (16^th Edition):

Grant, Andrew. “Parametric methods for time series discrimination.” 2018. Doctoral Dissertation, Macquarie University. Accessed March 07, 2021. http://hdl.handle.net/1959.14/1272839.

MLA Handbook (7^th Edition):

Grant, Andrew. “Parametric methods for time series discrimination.” 2018. Web. 07 Mar 2021.

Vancouver:

Grant A. Parametric methods for time series discrimination. [Internet] [Doctoral dissertation]. Macquarie University; 2018. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1959.14/1272839.

Council of Science Editors:

Grant A. Parametric methods for time series discrimination. [Doctoral Dissertation]. Macquarie University; 2018. Available from: http://hdl.handle.net/1959.14/1272839

23. Kouakou, Kouadio Simplice. Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields.

Degree: Docteur es, Mathématiques appliquées. Statistiques, 2012, Rennes 2; Université de Cocody (Abidjan, Côte d'Ivoire)

URL: http://www.theses.fr/2012REN20019

►

Dans ce travail nous nous intéressons à l'estimation de la densité spectrale par la méthode du noyau pour des processus à temps continu et des… (more)

▼

Dans ce travail nous nous intéressons à l'estimation de la densité spectrale par la méthode du noyau pour des processus à temps continu et des champs aléatoires observés selon des schémas d'échantillonnage (ou plan d'expériences) discrets aléatoires. Deux types d'échantillonnage aléatoire sont ici considérés : schémas aléatoires dilatés, et schémas aléatoires poissonniens. Aucune condition de gaussiannité n'est imposée aux processus et champs étudiés, les hypothèses concerneront leurs cumulants.En premier nous examinons un échantillonnage aléatoire dilaté utilisé par Hall et Patil (1994) et plus récemment par Matsuda et Yajima (2009) pour l'estimation de la densité spectrale d'un champ gaussien. Nous établissons la convergence en moyenne quadratique dans un cadre plus large, ainsi que la vitesse de convergence de l'estimateur.Ensuite nous appliquons l'échantillonnage aléatoire poissonnien dans deux situations différentes : estimation spectrale d'un processus soumis à un changement de temps aléatoire (variation d'horloge ou gigue), et estimation spectrale d'un champ aléatoire sur R2. Le problème de l'estimation de la densité spectrale d'un processus soumis à un changement de temps est résolu par projection sur la base des vecteurs propres d'opérateurs intégraux définis à partir de la fonction caractéristique de l'accroissement du changement de temps aléatoire. Nous établissons la convergence en moyenne quadratique et le normalité asymptotique de deux estimateurs construits l'un à partir d'une observation continue, et l'autre à partir d'un échantillonnage poissonnien du processus résultant du changement de temps.La dernière partie de ce travail est consacrée au cas d'un champ aléatoire sur R2 observé selon un schéma basé sur deux processus de Poissons indépendants, un pour chaque axe de R2. Les résultats de convergence sont illustrés par des simulations

In this work, we are dealing in the kernel estimation of the spectral density for a continuous time process or random eld observed along random discrete sampling schemes. Here we consider two kind of sampling schemes : random dilated sampling schemes, and Poissonian sampling schemes. There is no gaussian condition for the process or the random eld, the hypotheses apply to their cumulants.First, we consider a dilated sampling scheme introduced by Hall and Patil (1994) and used more recently by Matsuda and Yajima (2009) for the estimation of the spectral density of a Gaussian random eld.We establish the quadratic mean convergence in our more general context, as well as the rate of convergence of the estimator.Next we apply the Poissonian sampling scheme to two different frameworks : to the spectral estimation for a process disturbed by a random clock change (or time jitter), and to the spectral estimation of a random field on R2.The problem of the estimatin of the spectral density of a process disturbed by a clock change is solved with projection on the basis of eigen-vectors of kernel integral operators defined from the characteristic function of the increment of the…

Advisors/Committee Members: Dehay, Dominique (thesis director), Monsan, Vincent (thesis director).

Subjects/Keywords: Densité spectrale; Périodogramme; Estimation non-paramétrique; Échantillonage poissonien; Spectral density function; Periodogram; Nonparametric estimation; Poissonian sampling scheme; 519.5

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APA (6^th Edition):

Kouakou, K. S. (2012). Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields. (Doctoral Dissertation). Rennes 2; Université de Cocody (Abidjan, Côte d'Ivoire). Retrieved from http://www.theses.fr/2012REN20019

Chicago Manual of Style (16^th Edition):

Kouakou, Kouadio Simplice. “Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields.” 2012. Doctoral Dissertation, Rennes 2; Université de Cocody (Abidjan, Côte d'Ivoire). Accessed March 07, 2021. http://www.theses.fr/2012REN20019.

MLA Handbook (7^th Edition):

Kouakou, Kouadio Simplice. “Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields.” 2012. Web. 07 Mar 2021.

Vancouver:

Kouakou KS. Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields. [Internet] [Doctoral dissertation]. Rennes 2; Université de Cocody (Abidjan, Côte d'Ivoire); 2012. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2012REN20019.

Council of Science Editors:

Kouakou KS. Echantillonnage aléatoire et estimation spectrale de processus et de champs stationnaires : Random sampling and spectral estimation of stationary processes and fields. [Doctoral Dissertation]. Rennes 2; Université de Cocody (Abidjan, Côte d'Ivoire); 2012. Available from: http://www.theses.fr/2012REN20019

24. Bun, Joël. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.

Degree: Docteur es, Physique, 2016, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2016SACLS245

►

De nos jours, il est de plus en plus fréquent de travailler sur des bases de données de très grandes tailles dans plein de domaines… (more)

▼

De nos jours, il est de plus en plus fréquent de travailler sur des bases de données de très grandes tailles dans plein de domaines différents. Cela ouvre la voie à de nouvelles possibilités d'exploitation ou d'exploration de l'information, et de nombreuses technologies numériques ont été créées récemment dans cette optique. D'un point de vue théorique, ce problème nous contraint à revoir notre manière d'analyser et de comprendre les données enregistrées. En effet, dans cet univers communément appelé « Big Data », un bon nombre de méthodes traditionnelles d'inférence statistique multivariée deviennent inadaptées. Le but de cette thèse est donc de mieux comprendre ce phénomène, appelé fléau (ou malédiction) de la dimension, et ensuite de proposer différents outils statistiques exploitant explicitement la dimension du problème et permettant d'extraire des informations fiables des données. Pour cela, nous nous intéresserons beaucoup aux vecteurs propres de matrices symétriques. Nous verrons qu’il est possible d’extraire de l'information présentant un certain degré d’universalité. En particulier, cela nous permettra de construire des estimateurs optimaux, observables, et cohérents avec le régime de grande dimension.

Nowadays, it is easy to get a lot ofquantitative or qualitative data in a lot ofdifferent fields. This access to new databrought new challenges about data processingand there are now many different numericaltools to exploit very large database. In atheoretical standpoint, this framework appealsfor new or refined results to deal with thisamount of data. Indeed, it appears that mostresults of classical multivariate statisticsbecome inaccurate in this era of “Big Data”.The aim of this thesis is twofold: the first one isto understand theoretically this so-called curseof dimensionality that describes phenomenawhich arise in high-dimensional space.Then, we shall see how we can use these toolsto extract signals that are consistent with thedimension of the problem. We shall study thestatistics of the eigenvalues and especially theeigenvectors of large symmetrical matrices. Wewill highlight that we can extract someuniversal properties of these eigenvectors andthat will help us to construct estimators that areoptimal, observable and consistent with thehigh dimensional framework.

Advisors/Committee Members: Majumdar, Satya (thesis director).

Subjects/Keywords: Matrices aléatoires; Statistiques en grande dimension; Estimation; Décomposition Spectrale; Random matrices; High dimensional statistics; Estimation; Spectral decomposition

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APA (6^th Edition):

Bun, J. (2016). Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLS245

Chicago Manual of Style (16^th Edition):

Bun, Joël. “Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed March 07, 2021. http://www.theses.fr/2016SACLS245.

MLA Handbook (7^th Edition):

Bun, Joël. “Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics.” 2016. Web. 07 Mar 2021.

Vancouver:

Bun J. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2016SACLS245.

Council of Science Editors:

Bun J. Application de la théorie des matrices aléatoires pour les statistiques en grande dimension : Application of Random Matrix Theory to High Dimensional Statistics. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLS245

University of Lund

25. Elvander, Filip. Modeling and Sampling of Spectrally Structured Signals.

Degree: 2020, University of Lund

URL: https://lup.lub.lu.se/record/ca35ac5b-ad2e-4e44-b01f-9232dd74664f ; https://portal.research.lu.se/ws/files/81904925/FilipElvanderPhD_full.pdf

► This thesis consists of five papers concerned with the modeling of stochastic signals, as well as deterministic signals in stochastic noise, exhibiting different kinds of… (more)

▼ This thesis consists of five papers concerned with the modeling of stochastic signals, as well as deterministic signals in stochastic noise, exhibiting different kinds of structure. This structure is manifested as the existence of finite-dimensional parameterizations, and/or in the geometry of the signals' spectral representations. The two first papers of the thesis, Papers A and B, consider the modeling of differences, or distances, between stochastic processes based on their second-order statistics, i.e., covariances. By relating the covariance structure of a stochastic process to spectral representations, it is proposed to quantify the dissimilarity between two processes in terms of the cost associated with morphing one spectral representation to the other, with the cost of morphing being defined in terms of the solutions to optimal mass transport problems. The proposed framework allows for modeling smooth changes in the frequency characteristics of stochastic processes, which is shown to yield interpretable and physically sensible predictions when used in applications of temporal and spatial spectral estimation. Also presented are efficient computational tools, allowing for the framework to be used in high-dimensional problems.Paper C considers the modeling of so-called inharmonic signals, i.e., signals that are almost, but not quite, harmonic. Such signals appear in many fields of signal processing, not least in audio. Inharmonicity may be interpreted as a deviation from a parametric structure, as well as from a particular spectral structure. Based on these views, as well as on a third, stochastic interpretation, Paper C proposes three different definitions of the concept of fundamental frequency for inharmonic signals, and studies the estimation theoretical implications of utilizing either of these definitions. Paper D then considers deliberate deviations from a parametric signal structure arising in spectroscopy applications. With the motivation of decreasing the computational complexity of parameter estimation, the paper studies the implications of estimating the parameters of the signal in a sequential fashion, starting out with a simplified model that is then refined step by step.Lastly, Paper E studies how parametric descriptions of signals can be leveraged as to design optimal, in an estimation theoretical sense, schemes for sampling or collecting measurements from the signal. By means of a convex program, samples are selected as to minimize bounds on estimator variance, allowing for efficiently measuring parametric signals, even when the parametrization is only partially known.

Subjects/Keywords: Probability Theory and Statistics; Signal Processing; spectral estimation; parameter estimation; optimal mass transport; covariance interpolation; misspecified models; inharmonicity

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APA (6^th Edition):

Elvander, F. (2020). Modeling and Sampling of Spectrally Structured Signals. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/ca35ac5b-ad2e-4e44-b01f-9232dd74664f ; https://portal.research.lu.se/ws/files/81904925/FilipElvanderPhD_full.pdf

Chicago Manual of Style (16^th Edition):

Elvander, Filip. “Modeling and Sampling of Spectrally Structured Signals.” 2020. Doctoral Dissertation, University of Lund. Accessed March 07, 2021. https://lup.lub.lu.se/record/ca35ac5b-ad2e-4e44-b01f-9232dd74664f ; https://portal.research.lu.se/ws/files/81904925/FilipElvanderPhD_full.pdf.

MLA Handbook (7^th Edition):

Elvander, Filip. “Modeling and Sampling of Spectrally Structured Signals.” 2020. Web. 07 Mar 2021.

Vancouver:

Elvander F. Modeling and Sampling of Spectrally Structured Signals. [Internet] [Doctoral dissertation]. University of Lund; 2020. [cited 2021 Mar 07]. Available from: https://lup.lub.lu.se/record/ca35ac5b-ad2e-4e44-b01f-9232dd74664f ; https://portal.research.lu.se/ws/files/81904925/FilipElvanderPhD_full.pdf.

Council of Science Editors:

Elvander F. Modeling and Sampling of Spectrally Structured Signals. [Doctoral Dissertation]. University of Lund; 2020. Available from: https://lup.lub.lu.se/record/ca35ac5b-ad2e-4e44-b01f-9232dd74664f ; https://portal.research.lu.se/ws/files/81904925/FilipElvanderPhD_full.pdf

26. Zebadúa, Augusto. Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints.

Degree: Docteur es, Signal image parole telecoms, 2017, Université Grenoble Alpes (ComUE)

URL: http://www.theses.fr/2017GREAT085

►

La surveillance de phénomènes physiques à l’aide d’un réseau de capteurs (autonomes mais communicants) est fortement contrainte en consommation énergétique, principalement pour la transmission de… (more)

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La surveillance de phénomènes physiques à l’aide d’un réseau de capteurs (autonomes mais communicants) est fortement contrainte en consommation énergétique, principalement pour la transmission de données. Dans ce cadre, cette thèse propose des méthodes de traitement du signal permettant de réduire les communications sans compromettre la précision des calculs ultérieurs. La complexité de ces méthodes est réduite, de façon à ne consommer que peu d’énergie supplémentaire. Deux éléments servent à leur synthèse : la compression dès l’acquisition (Acquisition compressive) et la quantification grossière (sur 1 bit). D’abord, on étudie le corrélateur compressé, un estimateur qui permet d’évaluer les fonctions de corrélation, temps de retard et densités spectrales en exploitant directement des signaux compressés. Ses performances sont comparées au corrélateur usuel. Si le signal à traiter possède un support spectral étroit, l’estimateur proposé s’avère sensiblement meilleur que l’usuel. Ensuite, inspirés par les corrélateurs à forte quantification des années 50 et 60, deux nouveaux corrélateurs sont étudiés : le compressé sur 1 bit et le compressé hybride, qui peuvent également surpasser les performances de leurs contreparties non-compressées. Finalement, on montre la pertinence de ces méthodes pour les applications envisagées à travers l’exploitation de données réelles.

Monitoring physical phenomena by using a network of sensors (autonomous but interconnected) is highly constrained in energy consumption, mainly for data transmission. In this context, this thesis proposes signal processing tools to reduce communications without compromising computational accuracy in subsequent calculations. The complexity of these methods is reduced, so as to consume only little additional energy. Our two building blocks are compression during signal acquisition (Compressive Sensing) and CoarseQuantization (1 bit). We first study the Compressed Correlator, an estimator which allows for evaluating correlation functions, time-delay, and spectral densities directly from compressed signals. Its performance is compared with the usual correlator. As we show, if the signal of interest has limited frequency content, the proposed estimator significantly outperforms theconventional correlator. Then, inspired by the coarse quantization correlators from the 50s and 60s, two new correlators are studied: The 1-bit Compressed and the Hybrid Compressed, which can also outperform their uncompressed counterparts. Finally, we show the applicability of these methods in the context of interest through the exploitation of real data.

Advisors/Committee Members: Amblard, Pierre-Olivier (thesis director).

Subjects/Keywords: Acquisition compresive; Projections Aléatoires; Quantification; Estimation; Correlation; Analyse Spectrale; Compressive sensing; Random Projections; One bit quantization; Estimation; Correlation; Spectral Analysis; 620

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

APA (6^th Edition):

Zebadúa, A. (2017). Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2017GREAT085

Chicago Manual of Style (16^th Edition):

Zebadúa, Augusto. “Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints.” 2017. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed March 07, 2021. http://www.theses.fr/2017GREAT085.

MLA Handbook (7^th Edition):

Zebadúa, Augusto. “Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints.” 2017. Web. 07 Mar 2021.

Vancouver:

Zebadúa A. Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2017. [cited 2021 Mar 07]. Available from: http://www.theses.fr/2017GREAT085.

Council of Science Editors:

Zebadúa A. Traitement du signal dans le domaine compressé et quantification sur un bit : deux outils pour les contextes sous contraintes de communication : Compressed-domain signal processing and one-bit quantization : two tools for contexts undercommunication constraints. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2017. Available from: http://www.theses.fr/2017GREAT085

27. Smith-Boughner, Lindsay. Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding.

Degree: Earth Sciences, 2016, University of California – San Diego

URL: http://www.escholarship.org/uc/item/7hn0993t

► Many Earth systems cannot be studied directly. One cannot measure the velocities of convecting fluid in the Earth’s core but can measure the magnetic field… (more)

▼ Many Earth systems cannot be studied directly. One cannot measure the velocities of convecting fluid in the Earth’s core but can measure the magnetic field generated by these motions on the surface. Examining how the magnetic field changes over long periods of time, using power spectral density estimation provides insight into the dynamics driving the system. The changes in the magnetic field can also be used to study Earth properties - variations in magnetic fields outside of Earth like the ring-current induce currents to flow in the Earth, generating magnetic fields. Estimating the transfer function between the external changes and the induced response characterizes the electromagnetic response of the Earth. From this response inferences can be made about the electrical conductivity of the Earth.However, these types of time series, and many others have long breaks in the record with no samples available and limit the analysis. Standard methods require interpolation or section averaging, with associated problems of introducing bias or reducing the frequency resolution.Extending the methods of Fodor and Stark (2000)- who adapt a set of orthogonal multi-tapers to compensate for breaks in sampling- an algorithm and software package for applying these techniques is developed. Methods of empirically estimating the average transfer function of a set of tapers and confidence intervals are also tested. These methods are extended for cross-spectral, coherence and transfer function estimation in the presence of noise.With these methods, new analysis of a highly interrupted ocean sediment corefrom the Oligocene (Hartl et al., 1993) reveals a quasi-periodic signal in the calibrated paleointensity time series at 2.5 cpMy. The power in the magnetic field during this period appears to be dominated by reversal rate processes with less overall power than the early Oligocene. Previous analysis of the early Oligocene by Constable et al. (1998) detected a signal near 8 cpMy. These results suggest that a strong magnetic field inhibits reversals and has more variability in shorter term field changes. Using over 9 years of data from the CHAMP low-Earth orbiting magnetic satellite and the techniques developed here, more robust estimates of the electromagnetic response of the Earth can be made. The tapers adapted for gaps provide flexibility to study the effects of local time, storm conditions on Earth’s 1-D electromagnetic response as well as providing robust estimates of the C-response at longer periods than previous satellite studies.

Subjects/Keywords: Geophysics; Applied mathematics; geomagnetism; missing data; spectral estimation; time series; transfer function

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APA (6^th Edition):

Smith-Boughner, L. (2016). Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/7hn0993t

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

Chicago Manual of Style (16^th Edition):

Smith-Boughner, Lindsay. “Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding.” 2016. Thesis, University of California – San Diego. Accessed March 07, 2021. http://www.escholarship.org/uc/item/7hn0993t.

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

MLA Handbook (7^th Edition):

Smith-Boughner, Lindsay. “Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding.” 2016. Web. 07 Mar 2021.

Vancouver:

Smith-Boughner L. Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/7hn0993t.

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

Council of Science Editors:

Smith-Boughner L. Spectral Estimation Techniques for time series with Long Gaps: Applications to Paleomagnetism and Geomagnetic Depth Sounding. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/7hn0993t

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

University of California – San Diego

28. Zhu, Tingyi. Kernel Methods in Nonparametric Functional Time Series.

Degree: Mathematics, 2017, University of California – San Diego

URL: http://www.escholarship.org/uc/item/43w2x9qr

► Functional data objects are usually collected sequentially over time exhibiting forms of dependence. Such data structure is known as functional time series. While there is… (more)

Subjects/Keywords: Statistics; bootstrap; flat-top kernel; functional time series; kernel estimation; nonparametric autoregression; spectral analysis

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

APA (6^th Edition):

Zhu, T. (2017). Kernel Methods in Nonparametric Functional Time Series. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/43w2x9qr

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

Chicago Manual of Style (16^th Edition):

Zhu, Tingyi. “Kernel Methods in Nonparametric Functional Time Series.” 2017. Thesis, University of California – San Diego. Accessed March 07, 2021. http://www.escholarship.org/uc/item/43w2x9qr.

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

MLA Handbook (7^th Edition):

Zhu, Tingyi. “Kernel Methods in Nonparametric Functional Time Series.” 2017. Web. 07 Mar 2021.

Vancouver:

Zhu T. Kernel Methods in Nonparametric Functional Time Series. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2021 Mar 07]. Available from: http://www.escholarship.org/uc/item/43w2x9qr.

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

Council of Science Editors:

Zhu T. Kernel Methods in Nonparametric Functional Time Series. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/43w2x9qr

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

Rochester Institute of Technology

29. Gewali, Utsav B. Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images.

Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2019, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/10284

► The major challenges in hyperspectral (HS) imaging and data analysis are expensive sensors, high dimensionality of the signal, limited ground truth, and spectral variability.… (more)

▼ The major challenges in hyperspectral (HS) imaging and data analysis are expensive sensors, high dimensionality of the signal, limited ground truth, and spectral variability. This dissertation develops and analyzes machine learning based methods to address these problems. In the first part, we examine one of the most important HS data analysis tasks-vegetation parameter estimation. We present two Gaussian processes based approaches for improving the accuracy of vegetation parameter retrieval when ground truth is limited and/or spectral variability is high. The first is the adoption of covariance functions based on well-established metrics, such as, spectral angle and spectral correlation, which are known to be better measures of similarity for spectral data. The second is the joint modeling of related vegetation parameters by multitask Gaussian processes so that the prediction accuracy of the vegetation parameter of interest can be improved with the aid of related vegetation parameters for which a larger set of ground truth is available. The efficacy of the proposed methods is demonstrated by comparing them against state-of-the art approaches on three real-world HS datasets and one synthetic dataset. In the second part, we demonstrate how Bayesian optimization can be applied to jointly tune the different components of hyperspectral data analysis frameworks for better performance. Experimental validation on the spatial-spectral classification framework consisting of a classifier and a Markov random field is provided. In the third part, we investigate whether high dimensional HS spectra can be reconstructed from low dimensional multispectral (MS) signals, that can be obtained from much cheaper, lower spectral resolution sensors. A novel end-to-end convolutional residual neural network architecture is proposed that can simultaneously optimize both the MS bands and the transformation to reconstruct HS spectra from MS signals by analyzing a large quantity of HS data. The learned band can be implemented in sensor hardware and the learned transformation can be incorporated in the data processing pipeline to build a low-cost hyperspectral data collection system. Using a diverse set of real-world datasets, we show how the proposed approach of optimizing MS bands along with the transformation rather than just optimizing the transformation with fixed bands, as proposed by previous studies, can drastically increase the reconstruction accuracy. Additionally, we also investigate the prospects of using reconstructed HS spectra for land cover classification. Advisors/Committee Members: Eli Saber.

Subjects/Keywords: Deep learning; Gaussian processes; Hyperspectral imaging; Machine learning; Spectral super-resolution; Vegetation parameter estimation

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

APA (6^th Edition):

Gewali, U. B. (2019). Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10284

Chicago Manual of Style (16^th Edition):

Gewali, Utsav B. “Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images.” 2019. Doctoral Dissertation, Rochester Institute of Technology. Accessed March 07, 2021. https://scholarworks.rit.edu/theses/10284.

MLA Handbook (7^th Edition):

Gewali, Utsav B. “Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images.” 2019. Web. 07 Mar 2021.

Vancouver:

Gewali UB. Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2019. [cited 2021 Mar 07]. Available from: https://scholarworks.rit.edu/theses/10284.

Council of Science Editors:

Gewali UB. Machine Learning for Robust Understanding of Scene Materials in Hyperspectral Images. [Doctoral Dissertation]. Rochester Institute of Technology; 2019. Available from: https://scholarworks.rit.edu/theses/10284

30. Deb, Abhishek. Spectral Estimation for Graph Signals Using Reed-Solomon Decoding.

Degree: MS, Electrical Engineering, 2017, Texas A&M University

URL: http://hdl.handle.net/1969.1/166067

► Spectral estimation, coding theory and compressed sensing are three important sub-fields of signal processing and information theory. Although these fields developed fairly independently, several important… (more)

Subjects/Keywords: Graph Signal Processing; Reed-Solomon decoding; Compressed Sensing; Spectral Estimation; Multiple Access Communication

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APA (6^th Edition):

Deb, A. (2017). Spectral Estimation for Graph Signals Using Reed-Solomon Decoding. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/166067

Chicago Manual of Style (16^th Edition):

Deb, Abhishek. “Spectral Estimation for Graph Signals Using Reed-Solomon Decoding.” 2017. Masters Thesis, Texas A&M University. Accessed March 07, 2021. http://hdl.handle.net/1969.1/166067.

MLA Handbook (7^th Edition):

Deb, Abhishek. “Spectral Estimation for Graph Signals Using Reed-Solomon Decoding.” 2017. Web. 07 Mar 2021.

Vancouver:

Deb A. Spectral Estimation for Graph Signals Using Reed-Solomon Decoding. [Internet] [Masters thesis]. Texas A&M University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/1969.1/166067.

Council of Science Editors:

Deb A. Spectral Estimation for Graph Signals Using Reed-Solomon Decoding. [Masters Thesis]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/166067

Open Access Theses and Dissertations