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You searched for subject:(spectral temporal data). Showing records 1 – 3 of 3 total matches.

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Iowa State University

1. Reising, Monica Marie. Modeling and discrimination for spectral-temporal data.

Degree: 2009, Iowa State University

This thesis focuses on applying statistical methods to spectral-temporal data obtained from point source events. This work arises from the need in some military and national defense applications to quickly detect, locate, and identify short duration "energetic" electromagnetic events providing particular characteristic patterns of evolution over time. The first article discusses model building for spectral-temporal data that have complete spectral and temporal information over an event's evolution. The goal of this work was to build models to serve as the basis for algorithms that can be used to distinguish between different types of electromagnetic events in real time. The second article discusses the preliminary design of an algorithm for real-time discrimination between different types of point source events based on spectral-temporal data. The development of the algorithm was based on data obtained from 3 classes of safety matches and from simulated data based on fitted models developed in the first article. The third and final article discusses important pratical considerations regarding the sensor and experimental set-up used in the previous two articles. If this line of inquiry is to be further developed, this article discusses some practical considerations that should be addressed when moving forward.

Subjects/Keywords: discrimination; psuedo-imager; spectral-temporal data; Statistics and Probability

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

APA (6th Edition):

Reising, M. M. (2009). Modeling and discrimination for spectral-temporal data. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/11901

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

Chicago Manual of Style (16th Edition):

Reising, Monica Marie. “Modeling and discrimination for spectral-temporal data.” 2009. Thesis, Iowa State University. Accessed September 22, 2020. https://lib.dr.iastate.edu/etd/11901.

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

MLA Handbook (7th Edition):

Reising, Monica Marie. “Modeling and discrimination for spectral-temporal data.” 2009. Web. 22 Sep 2020.

Vancouver:

Reising MM. Modeling and discrimination for spectral-temporal data. [Internet] [Thesis]. Iowa State University; 2009. [cited 2020 Sep 22]. Available from: https://lib.dr.iastate.edu/etd/11901.

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

Council of Science Editors:

Reising MM. Modeling and discrimination for spectral-temporal data. [Thesis]. Iowa State University; 2009. Available from: https://lib.dr.iastate.edu/etd/11901

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

2. Zurita Milla, R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.

Degree: 2008, NARCIS

Our environment is continuously undergoing change. This change takes place at several spatial and temporal scales and it is largely driven by anthropogenic activities. In order to protect our environment and to ensure a sustainable use of natural resources, a wide variety of national and international initiatives have been established. In this context, Earth observation sensors can provide a substantial amount of information about the biotic and abiotic conditions of our planet. For instance, high spatial resolution sensors, like Landsat TM, deliver data that can be used to produce maps of canopy properties and of land cover types. However, the use of this kind of sensors is not feasible for obtaining full coverage of large areas. Furthermore, high spatial resolution sensors generally do not provide sufficient temporal resolution for monitoring vegetation development during the year. This is especially true for areas having severe cloud coverage throughout the year. In this respect, coarse spatial resolution sensors, which deliver nearly daily data, have a higher chance of encountering cloud free areas. This facilitates large scale monitoring studies but at the expense of a lower spatial resolution providing images with potentially many mixed pixels. Recent developments in imaging devices resulted into a new kind of sensor that works at a medium spatial resolution while providing high temporal and spectral resolutions. The MEdium Resolution Imaging Spectrometer (MERIS) aboard the European Space Agency’s ENVISAT platform belongs to this category. MERIS measures the solar radiation reflected from the Earth’s surface in 15 narrow spectral bands and it has a revisit time of 2-3 days. This unprecedented spectral and temporal resolution has resulted in several land, water and atmospheric products. In addition, two vegetation indices have been specifically designed to monitor vegetated canopies using this sensor: the MERIS Terrestrial Chlorophyll index (MTCI) and the MERIS Global Vegetation Index (MGVI). However, the spatial resolution provided by this sensor – 300 m in full resolution (FR mode) – is not sufficient to accurately map and monitor heterogeneous and fragmented landscapes. This is why the synergic use of high spatial resolution and MERIS data is investigated in this thesis. More precisely, the objective of this thesis is to develop a multi-sensor and multi-resolution data fusion approach that allows mapping and monitoring of heterogeneous and highly fragmented landscapes using MERIS data. The Netherlands is selected as study area because of its mixed landscapes where patches of arable land, natural vegetation, forests, and water bodies can be found next to each other. Besides this, The Netherlands also suffers from frequent cloud coverage, which severely hampers operational mapping and monitoring with both high spatial and high temporal resolution. Chapter 1 outlines the challenges of mapping and monitoring heterogeneous and fragmented landscapes using data from the current optical Earth observing… Advisors/Committee Members: Wageningen University, Michael Schaepman, Jan Clevers.

Subjects/Keywords: remote sensing; cartografie; monitoring; landschap; gegevensverwerking; spectrale gegevens; variatie in de tijd; satellietbeelden; satellietkarteringen; geodata; Remote sensing en geografische informatiesystemen (algemeen); remote sensing; mapping; monitoring; landscape; data processing; spectral data; temporal variation; satellite imagery; satellite surveys; geodata; Remote Sensing and Geographical Information Systems (General)

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

APA (6th Edition):

Zurita Milla, R. (2008). Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. (Doctoral Dissertation). NARCIS. Retrieved from http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567

Chicago Manual of Style (16th Edition):

Zurita Milla, R. “Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.” 2008. Doctoral Dissertation, NARCIS. Accessed September 22, 2020. http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567.

MLA Handbook (7th Edition):

Zurita Milla, R. “Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data.” 2008. Web. 22 Sep 2020.

Vancouver:

Zurita Milla R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. [Internet] [Doctoral dissertation]. NARCIS; 2008. [cited 2020 Sep 22]. Available from: http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567.

Council of Science Editors:

Zurita Milla R. Mapping and monitoring heterogeneous landscapes: spatial, spectral and temporal unmixing of MERIS data. [Doctoral Dissertation]. NARCIS; 2008. Available from: http://library.wur.nl/WebQuery/wurpubs/368567 ; urn:nbn:nl:ui:32-368567 ; urn:nbn:nl:ui:32-368567 ; http://library.wur.nl/WebQuery/wurpubs/368567

3. Huang, Yongsheng. Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health.

Degree: PhD, Bioinformatics, 2011, University of Michigan

This dissertation develops methods of integrative statistical learning to studies of two human diseases - respiratory infectious diseases and leukemia. It concerns integrating statistically principled approaches to connect data with knowledge for improved understanding of diseases. A wide spectrum of temporal and high-dimensional biological and medical datasets were considered. The first question studied in this thesis examined host responses to viral insult. In a human challenge study, eight transcriptional response patterns were identified in hosts experimentally challenged with influenza H3N2/Wisconsin viruses. These patterns are highly correlated with and predictive of symptoms. A non-passive asymptomatic state was revealed and associated with subclinical infections. The findings were validated and extended to three additional viral pathogens (influenza H1N1, Rhinovirus, and RSV). Their differences and similarities were compared and contrasted. Statistical models were developed for exposure detection and risk stratification. Experimental validations have been performed by collaborators at the Duke University. The second question studied in this thesis investigated the regulatory roles of Hoxa9 and Meis1 in hematopoiesis and leukemia. Methods were developed to characterize their global in vivo binding patterns and to identify their functional cofactors and collaborators. The combinatorial effects of these factors were modeled and related to specific epigenetic signatures. A new biological model was proposed to explain their synergistic functions in leukemic transformation. Experimental validations have been performed by members of the Hess laboratory. Motivated by problems encountered in these studies, two algorithms were developed to identify spatial and temporal patterns from high-throughput data. The first method determines temporal relationships between gene pathways during disease progression. It performs spectral analysis on graph Laplacian-embedded significance measures of pathway activity. The second algorithm proposes probabilistic modeling of protein binding events. Based on information geometry theory, it applies hypothesis testing coupled with jackknife-bias correction to characterize protein-protein interactions. Experimental validations were shown for both algorithms. In conclusion, this dissertation addressed issues in the design of statistical methods to identify characteristic and predictive features of human diseases. It demonstrated the effectiveness of integrating simple techniques in bioinformatics analysis. Several bioinformatics tools were developed to facilitate the analysis of high-dimensional time-series datasets. Advisors/Committee Members: Hero Iii, Alfred O. (committee member), Hess, Jay L. (committee member), Burns Jr., Daniel M. (committee member), Omenn, Gilbert S. (committee member), Shedden, Kerby A. (committee member).

Subjects/Keywords: Integrative Statistical Learning in High-dimensional Time-series Data; Host Transcriptional Responses to Respiratory Viral Pathogens; Role of Hoxa9 in Leukemic Transformation; Spectral Analysis of Temporal Pathway Activity Using Graph Lapalacian; Information Geometric Analysis of Motif Profiles in ChIP-sequencing; Predictive Modeling and Classification in High-dimensional and Temporal Data; Biomedical Engineering; Genetics; Microbiology and Immunology; Pathology; Science (General); Statistics and Numeric Data; Health Sciences; Science

Spectral Analysis Of Temporal Gene Pathway Activation During Influenza Virus-induced Symptomatic… …spatial and temporal patterns from high-throughput data. The first method determines temporal… …over-complicating the model. – An spectral method for studying temporal disease dynamics. In… …Chapter V, we develop an algorithm to perform spectral analysis of temporal gene pathway… …preparation for submission in 2011. Y. Huang, A. Rao, A. Hero III. Spectral Analysis of Temporal… 

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

APA (6th Edition):

Huang, Y. (2011). Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/84435

Chicago Manual of Style (16th Edition):

Huang, Yongsheng. “Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health.” 2011. Doctoral Dissertation, University of Michigan. Accessed September 22, 2020. http://hdl.handle.net/2027.42/84435.

MLA Handbook (7th Edition):

Huang, Yongsheng. “Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health.” 2011. Web. 22 Sep 2020.

Vancouver:

Huang Y. Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health. [Internet] [Doctoral dissertation]. University of Michigan; 2011. [cited 2020 Sep 22]. Available from: http://hdl.handle.net/2027.42/84435.

Council of Science Editors:

Huang Y. Integrative Statistical Learning with Applications to Predicting Features of Diseases and Health. [Doctoral Dissertation]. University of Michigan; 2011. Available from: http://hdl.handle.net/2027.42/84435

.