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Rozprawy doktorskie na temat „Classification: Advanced Methods”

Autor: Grafiati

Data publikacji: 27 lipca 2024

Data aktualizacji: 31 lipca 2025

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1

Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367947.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordabil

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Zeggada, Abdallah. "Advanced classification methods for UAV imagery." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2943/1/thesis_disclaimer.pdf.

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The rapid technological advancement manifested lately in the remote sensing acquisition platforms has triggered many benefits in favor of automated territory control and monitoring. In particular, unmanned aerial vehicles (UAVs) technology has drawn a lot of attention, providing an efficient solution especially in real-time applications. This is mainly motivated by their capacity to collect extremely high resolution (EHR) data over inaccessible areas and limited coverage zones, thanks to their small size and rapidly deployable flight capability, notwithstanding their ease of use and affordabil

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Villa, Alberto. "Advanced spectral unmixing and classification methods for hyperspectral remote sensing data." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00767250.

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La thèse propose des nouvelles techniques pour la classification et le démelange spectraldes images obtenus par télédétection iperspectrale. Les problèmes liées au données (notammenttrès grande dimensionalité, présence de mélanges des pixels) ont été considerés et destechniques innovantes pour résoudre ces problèmes. Nouvelles méthodes de classi_cationavancées basées sur l'utilisation des méthodes traditionnel de réduction des dimension etl'integration de l'information spatiale ont été développés. De plus, les méthodes de démelangespectral ont été utilisés conjointement pour ameliorer la class

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Bergamasco, Luca. "Advanced Deep-Learning Methods For Automatic Change Detection and Classification of Multitemporal Remote-Sensing Images." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342100.

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Deep-Learning (DL) methods have been widely used for Remote Sensing (RS) applications in the last few years, and they allow improving the analysis of the temporal information in bi-temporal and multi-temporal RS images. DL methods use RS data to classify geographical areas or find changes occurring over time. DL methods exploit multi-sensor or multi-temporal data to retrieve results more accurately than single-source or single-date processing. However, the State-of-the-Art DL methods exploit the heterogeneous information provided by these data by focusing the analysis either on the spatial inf

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Mehner, Henny. "The potential of high spatial resolution remote sensing for mapping upland vegetation using advanced classification methods." Thesis, University of Newcastle Upon Tyne, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417524.

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Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.

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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present n

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Preethy, Byju Akshara. "Advanced Methods for Content Based Image Retrieval and Scene Classification in JPEG 2000 Compressed Remote Sensing Image Archives." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/281771.

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Recent advances in satellite imaging technologies have paved its way to the RS big data era. Efficient storage, management and utilization of massive amounts of data is one of the major challenges faced by the remote sensing (RS) community. To minimize the storage requirements and speed up the transmission rate, RS images are compressed before archiving. Accordingly, developing efficient Content Based Image Retrieval (CBIR) and scene classification techniques to effectively utilize these huge volume of data is one among the most researched areas in RS. With the continual growth in the volume o

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Preethy, Byju Akshara. "Advanced Methods for Content Based Image Retrieval and Scene Classification in JPEG 2000 Compressed Remote Sensing Image Archives." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/281771.

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Recent advances in satellite imaging technologies have paved its way to the RS big data era. Efficient storage, management and utilization of massive amounts of data is one of the major challenges faced by the remote sensing (RS) community. To minimize the storage requirements and speed up the transmission rate, RS images are compressed before archiving. Accordingly, developing efficient Content Based Image Retrieval (CBIR) and scene classification techniques to effectively utilize these huge volume of data is one among the most researched areas in RS. With the continual growth in the volume o

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Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, Università degli studi di Trento, 2019. https://hdl.handle.net/11572/369121.

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The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniqu

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Harikumar, Aravind. "Advanced methods for tree species classification and biophysical parameter estimation using crown geometric information in high density LiDAR data." Doctoral thesis, University of Trento, 2019. http://eprints-phd.biblio.unitn.it/3782/1/PhD_Thesis_Harikumar.pdf.

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The ecological, climatic and economic influence of forests makes them an essential natural resource to be studied, preserved, and managed. Forest inventorying using single sensor data has a huge economic advantage over multi-sensor data. Remote sensing of forests using high density multi-return small footprint Light Detection and Ranging (LiDAR) data is becoming a cost-effective method to automatic estimation of forest parameters at the Individual Tree Crown (ITC) level. Individual tree detection and delineation techniques form the basis for ITC level parameter estimation. However SoA techniq

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11

Feng, Zao. "Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/9463.

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Liska, Adam J. "Homology-Based Functional Proteomics By Mass Spectrometry and Advanced Informatic Methods." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2003. http://nbn-resolving.de/urn:nbn:de:swb:14-1071757497859-43887.

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Functional characterization of biochemically-isolated proteins is a central task in the biochemical and genetic description of the biology of cells and tissues. Protein identification by mass spectrometry consists of associating an isolated protein with a specific gene or protein sequence in silico, thus inferring its specific biochemical function based upon previous characterizations of that protein or a similar protein having that sequence identity. By performing this analysis on a large scale in conjunction with biochemical experiments, novel biological knowledge can be developed. The study

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MAGGIOLO, LUCA. "Deep Learning and Advanced Statistical Methods for Domain Adaptation and Classiﬁcation of Remote Sensing Images". Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1070050.

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In the recent years, remote sensing has faced a huge evolution. The constantly growing availability of remote sensing data has opened up new opportunities and laid the foundations for many new challenges. The continuous space missions and new constellations of satellites allow in fact more and more frequent acquisitions, at increasingly higher spatial resolutions, and at an almost total coverage of the globe. The availability of such an huge amount data has highlighted the need for automatic techniques capable of processing the data and exploiting all the available information. Meanwhile, the

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Klein, Joachim, Christel Baier, Philipp Chrszon, et al. "Advances in probabilistic model checking with PRISM." Springer, 2018. https://tud.qucosa.de/id/qucosa%3A74265.

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The popular model checker PRISM has been successfully used for the modeling and analysis of complex probabilistic systems. As one way to tackle the challenging state explosion problem, PRISM supports symbolic storage and manipulation using multi-terminal binary decision diagrams for representing the models and in the computations. However, it lacks automated heuristics for variable reordering, even though it is well known that the order of BDD variables plays a crucial role for compact representations and efficient computations. In this article, we present a collection of extensions to PRISM.

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Klein, Joachim, Christel Baier, Philipp Chrszon, et al. "Advances in Symbolic Probabilistic Model Checking with PRISM." Springer, 2016. https://tud.qucosa.de/id/qucosa%3A74267.

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For modeling and reasoning about complex systems, symbolic methods provide a prominent way to tackle the state explosion problem. It is well known that for symbolic approaches based on binary decision diagrams (BDD), the ordering of BDD variables plays a crucial role for compact representations and efficient computations. We have extended the popular probabilistic model checker PRISM with support for automatic variable reordering in its multi-terminal-BDD-based engines and report on benchmark results. Our extensions additionally allow the user to manually control the variable ordering at a fin

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16

Heidernätsch, Mario, Michael Bauer, Daniela Täuber, Günter Radons, and Christian von Borcyskowski. "An advanced method of tracking temporarily invisible particles in video imaging." Diffusion fundamentals 11 (2009) 111, S. 1-2, 2009. https://ul.qucosa.de/id/qucosa%3A14085.

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17

Fried, Andrea, and Volker Linss. "Towards an Advanced Impact Analysis of Intangible Resources in Organisations." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501203.

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The paper refers to the discussion of measuring and assessing knowledge capital. In particular, the interconnectedness of the intangible resources in organizations is not well represented in the methodical approaches. Moreover, the identification of driver resources which is strongly connected with this question is far from being solved in a satisfactory manner. Therefore, this article reviews existing methods of the scenario analysis in view of the performance measurement discussion and contributes towards an advanced analysis of resources in organizations.

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Linss, Volker, and Andrea Fried. "Advanced Impact Analysis: the ADVIAN® method - an enhanced approach for the analysis of impact strengths with the consideration of indirect relations." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200900908.

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An enhanced approach for the impact analysis is presented. Impact analyses play an important role in future research analysis as part of the scenario techniques in the strategic management field. Nowadays, they are also applied for the description of mutual relationships of tangible and intangible resources in organisations. The new method is based on currently existing methods using a cross impact matrix and overcomes some of their drawbacks. Indirect impacts are considered together with their impact strengths. A modification of the impact matrix is not necessary. Simple examples show that th

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19

Lienemann, Kai [Verfasser]. "Advanced ensemble methods for automatic classification of 1H-NMR spectra / von Kai Lienemann." 2010. http://d-nb.info/1008219770/34.

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20

Christen, Victor. "Advanced Methods for Entity Linking in the Life Sciences." 2020. https://ul.qucosa.de/id/qucosa%3A73504.

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The amount of knowledge increases rapidly due to the increasing number of available data sources. However, the autonomy of data sources and the resulting heterogeneity prevent comprehensive data analysis and applications. Data integration aims to overcome heterogeneity by unifying different data sources and enriching unstructured data. The enrichment of data consists of different subtasks, amongst other the annotation process. The annotation process links document phrases to terms of a standardized vocabulary. Annotated documents enable effective retrieval methods, comparability of differen

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21

Fischer, André. "Advanced Cluster Methods for Correlated-Electron Systems." Doctoral thesis, 2015. https://tud.qucosa.de/id/qucosa%3A29127.

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In this thesis, quantum cluster methods are used to calculate electronic properties of correlated-electron systems. A special focus lies in the determination of the ground state properties of a 3/4 filled triangular lattice within the one-band Hubbard model. At this filling, the electronic density of states exhibits a so-called van Hove singularity and the Fermi surface becomes perfectly nested, causing an instability towards a variety of spin-density-wave (SDW) and superconducting states. While chiral d+id-wave superconductivity has been proposed as the ground state in the weak coupling limit

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22

Reichenbach, Jonas. "Credit scoring with advanced analytics: applying machine learning methods for credit risk assessment at the Frankfurter sparkasse." Master's thesis, 2018. http://hdl.handle.net/10362/49557.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management<br>The need for controlling and managing credit risk obliges financial institutions to constantly reconsider their credit scoring methods. In the recent years, machine learning has shown improvement over the common traditional methods for the application of credit scoring. Even small improvements in prediction quality are of great interest for the financial institutions. In this thesis classification methods are appli

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Reed, Eric R. "Development of advanced methods for large-scale transcriptomic profiling and application to screening of metabolism disrupting compounds." Thesis, 2020. https://hdl.handle.net/2144/41943.

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High-throughput transcriptomic profiling has become a ubiquitous tool to assay an organism transcriptome and to characterize gene expression patterns in different cellular states or disease conditions, as well as in response to molecular and pharmacologic perturbations. Refinements to data preparation techniques have enabled integration of transcriptomic profiling into large-scale biomedical studies, generally devised to elucidate phenotypic factors contributing to transcriptional differences across a cohort of interest. Understanding these factors and the mechanisms through which they contrib

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Schindler, Rose. "Effective Prevention for Children: Conceptual and Methodological Advances." Doctoral thesis, 2015. https://monarch.qucosa.de/id/qucosa%3A20382.

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This dissertation addresses various methodological and conceptual challenges of prevention programs for preschool children. It focuses on two major topics, (1) methodological guidelines for longitudinal studies in the context of prevention projects, and (2) analyses of emotional development and moral emotions. After a brief introduction to the research questions in Chapter 1, Chapters 2 and 3 address the methodological branch of my research, and Chapters 4 to 6 will analyze several aspects of moral development and moral emotions. In the final Chapter 7, all findings are summarized in view of t

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Scharf, Florian. "Advances in the analysis of event-related potential data with factor analytic methods." 2019. https://ul.qucosa.de/id/qucosa%3A33711.

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Researchers are often interested in comparing brain activity between experimental contexts. Event-related potentials (ERPs) are a common electrophysiological measure of brain activity that is time-locked to an event (e.g., a stimulus presented to the participant). A variety of decomposition methods has been used for ERP data among them temporal exploratory factor analysis (EFA). Essentially, temporal EFA decomposes the ERP waveform into a set of latent factors where the factor loadings reﬂect the time courses of the latent factors, and the amplitudes are represented by the factor scores. An i

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Mitin, Dmitriy. "Advanced scanning magnetoresistive microscopy as a multifunctional magnetic characterization method." Doctoral thesis, 2016. https://monarch.qucosa.de/id/qucosa%3A20700.

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Advanced scanning magnetoresistive microscopy (SMRM) — a robust magnetic imaging and probing technique — is presented. It utilizes conventional recording heads of a hard disk drive as sensors. The spatial resolution of modern tunneling magnetoresistive sensors is nowadays comparable with more commonly used magnetic force microscopes. Important advantages of SMRM are the ability to detect pure magnetic signals directly proportional to the out-of-plane magnetic stray field, negligible sensor stray fields, and the ability to apply local bipolar magnetic field pulses up to 10 kOe with bandwidths

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Haustein, Rocco. "Die Exportabhängigkeit südwestsächsischer Industrie-KMU und internationale Mitarbeiterqualifikationen: Eine qualitative Untersuchung anhand ausgewählter Unternehmen sowie industrienaher Dachorganisationen und Verbände." Doctoral thesis, 2013. https://monarch.qucosa.de/id/qucosa%3A20020.

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Die Region Südwestsachsen wird von kleinen und mittleren Industrieunternehmen (Industrie-KMU) geprägt. In der vorliegenden Arbeit wird gezeigt, dass der wirtschaftliche Erfolg dieser Unternehmen maßgeblich von deren Exportgeschäft abhängt. Eine solche große Bedeutung des Außenhandelsgeschäfts verlangt von den Beschäftigten der Unternehmen spezielle Qualifikationen ab. Diese Dissertationsschrift versucht, diese sog. „internationalen Qualifikationen“ durch die Untersuchung ausgewählter Unternehmen und industrienaher Dachorganisationen und Verbände sowohl zu charakterisieren als auch qualifikator

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