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Dissertations / Theses on the topic 'Synthetic LiDAR data generation'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Reje, Niklas. "Synthetic Data Generation for Anonymization." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276239.

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Because of regulations but also from a need to find willing participants for surveys, any released data needs to have some sort of privacy preservation. Privacy preservation, however, always requires some sort of reduction of the utility of the data, how much can vary with the method. Synthetic data generation seeks to be a privacy preserving alternative that keeps the privacy of the participants by generating new records that do not correspond to any real individuals/organizations but still preserve relationships and information within the original dataset. For a method to see wide adoption h

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Lee, Hyun Seung. "A HYBRID MODEL FOR DTM GENERATION FROM LIDAR DATA." MSSTATE, 2004. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11022004-053808/.

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This dissertation introduces an innovative technique to extract ground elevation models using small-footprint LIDAR data. This technique consists of a preprocessing step, ground modeling, and interpolation. In the preprocessing step, much of the non-terrain points are eliminated using a histogram-based clustering technique. Then, in the ground modeling stage, the information such as elevation and slope between nearest neighbor points is extracted. This step corresponds to an outlier detection process. In this stage, residuals and gradient indices for elevation and slope, are introduced. These

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Edhammer, Jens. "Rigid Body Physics for Synthetic Data Generation." Thesis, Linköpings universitet, Informationskodning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129808.

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For synthetic data generation with concave collision objects, two physics simu- lations techniques are investigated; convex decomposition of mesh models for globally concave collision results, used with the physics simulation library Bullet, and a GPU implemented rigid body solver using spherical decomposition and impulse based physics with a spatial sorting-based collision detection. Using the GPU solution for rigid body physics suggested in the thesis scenes con- taining large amounts of bodies results in a rigid body simulation up to 2 times faster than Bullet 2.83.

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Garcia, Torres Douglas. "Generation of Synthetic Data with Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254366.

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The aim of synthetic data generation is to provide data that is not real for cases where the use of real data is somehow limited. For example, when there is a need for larger volumes of data, when the data is sensitive to use, or simply when it is hard to get access to the real data. Traditional methods of synthetic data generation use techniques that do not intend to replicate important statistical properties of the original data. Properties such as the distribution, the patterns or the correlation between variables, are often omitted. Moreover, most of the existing tools and approaches requi

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Fowler, Lee Everett. "A Virtual pilot algorithm for synthetic HUMS data generation." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54473.

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Regime recognition is an important tool used in creation of usage spectra and fatigue loads analysis. While a variety of regime recognition algorithms have been developed and deployed to date, verification and validation (V&V) of such algorithms is still a labor intensive process that is largely subjective. The current V&V process for regime recognition codes involves a comparison of scripted flight test data to regime recognition algorithm outputs. This is problematic because scripted flight test data is expensive to obtain, may not accurately match the maneuver script, and is often used t

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Torfi, Amirsina. "Privacy-Preserving Synthetic Medical Data Generation with Deep Learning." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99856.

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Deep learning models demonstrated good performance in various domains such as ComputerVision and Natural Language Processing. However, the utilization of data-driven methods in healthcare raises privacy concerns, which creates limitations for collaborative research. A remedy to this problem is to generate and employ synthetic data to address privacy concerns. Existing methods for artificial data generation suffer from different limitations, such as being bound to particular use cases. Furthermore, their generalizability to real-world problems is controversial regarding the uncertainties in def

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Hammond, Patrick Douglas. "Deep Synthetic Noise Generation for RGB-D Data Augmentation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7516.

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Considerable effort has been devoted to finding reliable methods of correcting noisy RGB-D images captured with unreliable depth-sensing technologies. Supervised neural networks have been shown to be capable of RGB-D image correction, but require copious amounts of carefully-corrected ground-truth data to train effectively. Data collection is laborious and time-intensive, especially for large datasets, and generation of ground-truth training data tends to be subject to human error. It might be possible to train an effective method on a relatively smaller dataset using synthetically damaged dep

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Montanez, Andrew M. Eng Massachusetts Institute of Technology. "SDV : an open source library for synthetic data generation." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121631.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 105).<br>In this thesis, I designed three open source Python libraries with the intention of creating a robust system that can accurately generate synthetic data. The goals of this thesis were to separate the different componen

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Choudhury, Ananya. "WiSDM: a platform for crowd-sourced data acquisition, analytics, and synthetic data generation." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/72256.

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Human behavior is a key factor influencing the spread of infectious diseases. Individuals adapt their daily routine and typical behavior during the course of an epidemic -- the adaptation is based on their perception of risk of contracting the disease and its impact. As a result, it is desirable to collect behavioral data before and during a disease outbreak. Such data can help in creating better computer models that can, in turn, be used by epidemiologists and policy makers to better plan and respond to infectious disease outbreaks. However, traditional data collection methods are not well su

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Ali, Arslan Mehmet. "Generation and Bioinformatic Analysis of Synthetic Ago HITS-CLIP Data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-204891.

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Micro-RNAs (miRNAs) have been discovered to regulate messenger RNA (mRNA) translation and degradation. Various recent studies have been focused on miRNA target prediction, in order to get a better understanding of the rules and nature of miRNA regulation over mRNAs. In this project we aim to create a software module to identify miRNA target sites on mRNAs. As basis to this project, we refer to a study that identified a platform for miRNA-mRNA interaction in protein-RNA complexes in mouse brain (AGO HITS-CLIP study). We propose a probabilistic model of the data from this study, and generate syn

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Ljung, Mikael. "Synthetic Data Generation for the Financial Industry Using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301307.

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Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate i

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Knoors, Daan. "Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235640.

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When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. Over the last decade, academics have actively sought to find stronger definitions and methodologies to achieve data privacy while preserving the data utility. Differential privacy emerged and became the de facto standard for achieving data privacy and numerous techniques are continuously proposed based on this

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Knoors, Daan Josephus. "Utility of Differentially Private Synthetic Data Generation for High-Dimensional Databases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237424.

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When processing data that contains sensitive information, careful consideration is required with regard to privacy-preservation to prevent disclosure of confidential information. Privacy engineering enables one to extract valuable patterns, safely, without compromising anyone’s privacy. Over the last decade, academics have actively sought to find stronger definitions and methodologies to achieve data privacy while preserving the data utility. Differential privacy emerged and became the de facto standard for achieving data privacy and numerous techniques are continuously proposed based on this

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Mattila, Marianne. "Synthetic Image Generation Using GANs : Generating Class Specific Images of Bacterial Growth." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176402.

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Mastitis is the most common disease affecting Swedish milk cows. Automatic image classification can be useful for quickly classifying the bacteria causing this inflammation, in turn making it possible to start treatment more quickly. However, training an automatic classifier relies on the availability of data. Data collection can be a slow process, and GANs are a promising way to generate synthetic data to add plausible samples to an existing data set. The purpose of this thesis is to explore the usefulness of GANs for generating images of bacteria. This was done through researching existing l

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Momeni, Saba. "Synthetic data generation for machine learning to improve Microbleeds detection from MRI images." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/417201.

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Cerebral Microbleeds (CMB) are small chronic brain haemorrhages, known as paramagnetic blood products, and likely caused by structural abnormalities of the small vessels. The concept of CMB is primarily a radiological construct describing small MRI signal voids. They are often present with cerebrovascular disease, dementia, Alzheimer’s disease, and normal aging people. Substantial progress has been made in recent years, in developing MRI methodologies showing CMB, such as susceptibility-weighted imaging (SWI) which is sensitive to differences in tissue magnetic susceptibility. In SWI, residual

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Rashid, Sana. "On the use of hierarchical models for multiple imputation and synthetic data generation." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/412632/.

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Missing data are often imputed with plausible values when various analyses are performed. One popular approach employed to impute data is multiple imputation, which requires specification of a suitable imputation model. This thesis investigates the impact on multiply imputed hierarchical datasets when the imputation model is misspecified. The first issue studied is the presence of omitted variable bias. The same issue is then studied with a focus on the use of multiple imputation for creating synthetic data to protect data confidentiality. Here, the quality of multiply imputed datasets is stud

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Attouche, Lyes. "Data Generation for JSON Schema." Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLD037.

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JSON (JavaScript Object Notation) est un format d’échange de données largement utilisé, notamment dans le cadre des APIs web. Sa simplicité et sa légèreté en font un choix idéal pour l’échange de données entre clients et serveurs. JSON Schema constitue un vocabulaire puissant pour définir la structure, les contraintes et les règles de validation des données JSON, garantissant ainsi l’intégrité et la cohérence des informations tout en respectant les formats et la sémantique spécifiés. Cette synergie entre JSON et JSON Schema renforce la fiabilité de la gestion des données, permettant aux dévelo

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Ma, Qing. "High-Resolution X-Ray Image Generation from CT Data Using Super-Resolution." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42782.

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Synthetic X-ray or digitally reconstructed radiographs (DRRs) are simulated X-ray images projected from computed tomography (CT) data that are commonly used for CT and real X-Ray image registration. High-quality synthetic X-ray images can facilitate various applications such as guiding images for virtual reality (VR) simulation and training data for deep learning methods such as creating CT data from X-Ray images. It is challenging to generate high-quality synthetic X-ray images from CT slices, especially in various view angles, due to gaps between CT slices, high computational cost, and the

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Wang, Chao. "Point clouds and thermal data fusion for automated gbXML-based building geometry model generation." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/54008.

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Existing residential and small commercial buildings now represent the greatest opportunity to improve building energy efficiency. Building energy simulation analysis is becoming increasingly important because the analysis results can assist the decision makers to make decisions on improving building energy efficiency and reducing environmental impacts. However, manually measuring as-is conditions of building envelops including geometry and thermal value is still a labor-intensive, costly, and slow process. Thus, the primary objective of this research was to automatically collect and extract th

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Arcidiacono, Claudio Salvatore. "An empirical study on synthetic image generation techniques for object detectors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235502.

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Convolutional Neural Networks are a very powerful machine learning tool that outperformed other techniques in image recognition tasks. The biggest drawback of this method is the massive amount of training data required, since producing training data for image recognition tasks is very labor intensive. To tackle this issue, different techniques have been proposed to generate synthetic training data automatically. These synthetic data generation techniques can be grouped in two categories: the first category generates synthetic images using computer graphic software and CAD models of the objects

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Rončka, Martin. "Material Artefact Generation." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399191.

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Ne vždy je jednoduché získání dostatečně velké a kvalitní datové sady s obrázky zřetelných artefaktů, ať už kvůli nedostatku ze strany zdroje dat nebo složitosti tvorby anotací. To platí například pro radiologii, nebo také strojírenství. Abychom mohli využít moderní uznávané metody strojového učení které se využívají pro klasifikaci, segmentaci a detekci defektů, je potřeba aby byla datová sada dostatečně velká a vyvážená. Pro malé datové sady čelíme problémům jako je přeučení a slabost dat, které způsobují nesprávnou klasifikaci na úkor málo reprezentovaných tříd. Tato práce se zabývá prozkou

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Roc, Roc David. "Above-ground biomass estimation in boreal productive forests using Sentinel-1 data." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-172942.

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Estimation of biomass has high importance for economic, ecologic and climatic reasons due to the multiple ecosystem services offered by forested landscapes. Measurements that are taken in the field incur personal and economic costs. Nevertheless, biomass surveying based on remote sensing techniques offer efficiency thanks to covering large areas. The European Space Agency (ESA) Sentinel-1 satellite offers promising capabilities for above-ground biomass (AGB) estimation through synthetic aperture radar (SAR) based microwave remote sensing. In this study, experimental AGB estimations based on Se

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Lundberg, Gustav. "Automatic map generation from nation-wide data sources using deep learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170759.

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The last decade has seen great advances within the field of artificial intelligence. One of the most noteworthy areas is that of deep learning, which is nowadays used in everything from self driving cars to automated cancer screening. During the same time, the amount of spatial data encompassing not only two but three dimensions has also grown and whole cities and countries are being scanned. Combining these two technological advances enables the creation of detailed maps with a multitude of applications, civilian as well as military.This thesis aims at combining two data sources covering most

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Holder, Martin Friedrich [Verfasser], Hermann [Akademischer Betreuer] Winner, and Erwin [Akademischer Betreuer] Biebl. "Synthetic Generation of Radar Sensor Data for Virtual Validation of Autonomous Driving / Martin Friedrich Holder ; Hermann Winner, Erwin Biebl." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1233429426/34.

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Depoy, Randy S. Jr. "Mitigating atmospheric phase errors in SAL data." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1610632181418557.

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Šejvlová, Ludmila. "Porovnání přístupů ke generování umělých dat." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-358804.

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The diploma thesis deals with synthetic data, selected approaches to their generation together with a practical task of data generation. The goal of the thesis is to describe the selected approaches to data generation, capture their key advantages and disadvantages and compare the individual approaches to each other. The practical part of the thesis describes generation of synthetic data for teaching knowledge discovery using databases. The thesis includes a basic description of synthetic data and thoroughly explains the process of their generation. The approaches selected for further examinat

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Beck, Ferdinand [Verfasser], and András [Akademischer Betreuer] Bárdossy. "Generation of spatially correlated synthetic rainfall time series in high temporal resolution : a data driven approach / Ferdinand Beck. Betreuer: András Bárdossy." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2013. http://d-nb.info/1032171367/34.

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Gutiérrez, Antuñano Miguel Ángel. "Doppler wind LIDAR systems data processing and applications : an overview towards developing the new generation of wind remote-sensing sensors for off-shore wind farms." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667245.

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This Ph.D. thesis addresses remote sensing of the atmosphere by means of lidar and S-band clear-air weather radar, and related data signal processing. Active remote sensing by means of these instruments offers unprecedented capabilities of spatial and temporal resolutions for vertical atmospheric profiling and the retrieval of key optical and physical atmospheric products in an increasing environmental regulatory framework. The first goal is this Ph.D. concerns the estimation of error bounds in the inversion of the profile of the atmospheric backscatter coefficient from elastic lidar signal

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Gwinnett, Claire M. B. "The Use of Inexperienced Personnel in the Analysis of Synthetic Textile Fibres using Polarized Light Microscopy for the Generation of Data Suitable for the Production of a Synthetic Fibres Database." Thesis, Staffordshire University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522253.

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Nord, Sofia. "Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302644.

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Large datasets are a crucial requirement to achieve high performance, accuracy, and generalisation for any machine learning task, such as prediction or anomaly detection, However, it is not uncommon for datasets to be small or imbalanced since gathering data can be difficult, time-consuming, and expensive. In the task of collecting vehicle sensor time series data, in particular when the vehicle has an abnormal behaviour, these struggles are present and may hinder the automotive industry in its development. Synthetic data generation has become a growing interest among researchers in several fie

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Kříž, Blažej. "Framework pro tvorbu generátorů dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236623.

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This master's thesis is focused on the problem of data generation. At the beginning, it presents several applications for data generation and describes the data generation process. Then it deals with development of framework for data generators and demonstrational application for validating the framework.

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Malec, Stanislaw. "Semantic Segmentation with Carla Simulator." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105287.

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Autonomous vehicles perform semantic segmentation to orient themselves, but training neural networks for semantic segmentation requires large amounts of labeled data. A hand-labeled real-life dataset requires considerable effort to create, so we instead turn to virtual simulators where the segmented labels are known to generate large datasets virtually for free. This work investigates how effective synthetic datasets are in driving scenarios by collecting a dataset from a simulator and testing it against a real-life hand-labeled dataset. We show that we can get a model up and running faster by

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Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this pu

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Haq, Ikram. "Fraud detection for online banking for scalable and distributed data." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171977.

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Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric

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Lecomte-Denis, François. "Amélioration des procédures guidées par fluoroscopie à l'aide d'un réseau de neurones pour le recalage déformable des organes." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD062.

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Dans les interventions guidées par fluoroscopie, le manque de contraste empêche la visualisation directe des structures anatomiques essentielles.Les solutions existantes présentent des inconvénients significatifs: l'utilisation de CBCT augmente l'exposition aux radiations,tandis que les agents de contraste présentent des risques de toxicité pour les patients.Les techniques de recalage fluoroscopie-CT pourraient résoudre ces problèmes,mais la littérature existante s'est principalement concentrée sur la compensation du mouvement respiratoire.Or, pendant les interventions, l'action des cliniciens

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Proença, Fernando Roberto. "Geração de dados espaciais vagos baseada em modelos exatos." Universidade Federal de São Carlos, 2013. https://repositorio.ufscar.br/handle/ufscar/531.

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Made available in DSpace on 2016-06-02T19:06:05Z (GMT). No. of bitstreams: 1 5287.pdf: 3924606 bytes, checksum: 935b5a09df26eb1b41df901a189a6e2a (MD5) Previous issue date: 2013-05-29<br>Universidade Federal de Sao Carlos<br>Geographic information systems with the aid of spatial databases store and manage crisp spatial data (or exact spatial data), whose shapes (boundaries) are well defined and have a precise location in space. However, several spatial data do not have precisely known boundaries or have an uncertain location in space, which are called vague spatial data. The boundaries of a g

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Barrère, Killian. "Architectures de Transformer légères pour la reconnaissance de textes manuscrits anciens." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0017.

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En reconnaissance d’écriture manuscrite, les architectures Transformer permettent de faibles taux d’erreur, mais sont difficiles à entraîner avec le peu de données annotées disponibles. Dans ce manuscrit, nous proposons des architectures Transformer légères adaptées aux données limitées. Nous introduisons une architecture rapide basée sur un encodeur Transformer, et traitant jusqu’à 60 pages par seconde. Nous proposons aussi des architectures utilisant un décodeur Transformer pour inclure l’apprentissage de la langue dans la reconnaissance des caractères. Pour entraîner efficacement nos archit

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Toure, Almamy. "Collection, analysis and harnessing of communication flows for cyber-attack detection." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. http://www.theses.fr/2024UPHF0023.

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La complexité croissante des cyberattaques, caractérisée par une diversification des techniques d'attaque, une expansion des surfaces d'attaque et une interconnexion croissante d'applications avec Internet, rend impérative la gestion du trafic réseau en milieu professionnel. Les entreprises de tous types collectent et analysent les flux réseau et les journaux de logs pour assurer la sécurité des données échangées et prévenir la compromission des systèmes d'information. Cependant, les techniques de collecte et de traitement des données du trafic réseau varient d'un jeu de données à l'autre, et

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Salem, Mostafa. "Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/668990.

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This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework for the detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF). Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MR images. The model was trained end-to-end and simultaneously learned both the DFs

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Yu-WeiLiu and 劉囿維. "Quality Assessment of DEM Generation From Airborne LiDAR Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/04064303682794660930.

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碩士<br>國立成功大學<br>測量及空間資訊學系碩博士班<br>98<br>Airborne LiDAR has become the primary technology of DEM generation. In order to produce DEM from airborne LiDAR data, the major process is filtering non-ground points. Mane filtering methods have been developed. All filters are designed to keep as many correct ground points as possible. However, there is no perfect filter yet. Every filter more or less misclassifies some non ground points to ground point, or the other way round. Owing to the widespread application of DEM, how to assess the quality of a DEM product is an important topic. For this reason, t

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Peng, Miao-Hsiang, and 彭淼祥. "DTM Generation and Error Assessment for Airborne LIDAR Data." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/63873034056790503590.

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博士<br>國立交通大學<br>土木工程系所<br>94<br>Airborne light detection and ranging (LIDAR) technology has become a leading method for producing digital terrain models (DTMs) that are important to many GIS-related analyses and applications. In generating a digital terrain model, removing non-terrain measurements from LIDAR datasets has proven to be an important task. In this dissertation, a series of filters are developed to remove non-terrain LIDAR measurements. It is difficult to accurately extract the terrain surface in areas of rugged relief or discontinuous topography. This research applies adaptive

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Tang, Mei-Hua, and 湯美華. "True Orthoimage Generation Using Airborne Lidar Data and Topographic Maps." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/17621038530359797613.

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碩士<br>國立成功大學<br>測量工程學系碩博士班<br>94<br>The generation of true orthoimages is a procedure to rectify photographs of perspective projection to the images of orthogonal projection. True orthoimages provide correct shape and position of ground objects and correct measurement of angles and distances as a traditional line-based map. Therefore, they can serve as a background of a thematic map and as a means to developing and updating thematic maps. True orthoimages can be applied in many fields, such as city planning and river basin monitoring, etc. Complete correction of relief displacement for true or

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Kai-Hsuan, Chan, and 詹凱軒. "Automatic Generation of Building Model from Ground-Based LIDAR Data." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/32041068860695995694.

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碩士<br>國立政治大學<br>資訊科學學系<br>95<br>Ground-based LIDAR system can be used to detect the surface of the buildings on the earth. In general, it produces large amount of high-precision point cloud data. These data include not only the three-dimensional space information, but also the color information. However, the number of point cloud data is huge and is difficult to be displayed efficiently. It’s necessary to use efficient data processing techniques in order to display these point cloud data in real-time. In this research, we construct the three-dimensional building model using the key points sel

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Chou, Fu-Chen, and 周富晨. "An Adaptive Point Cloud Filtering Algorithm for DEM Generation from Airborne Lidar Data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/07520162336469440714.

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碩士<br>國立成功大學<br>測量工程學系碩博士班<br>92<br>DEM generation is the primary application of airborne Lidar. The point cloud provided by airborne Lidar not only represents the terrain surface, but also contains buildings, vegetation, or other ground objects. The major process of generating DEM from airborne Lidar is to filter out non-ground points from the point cloud data. The purpose of this study is to propose an adaptive filtering algorithm for DEM generation using airborne Lidar data. 　　The filtering algorithm is based on the principle of morphological filtering theory. To make the algorithm adapti

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Ho, Hsin-Yu, and 何心瑜. "The Production Process and Quality Management Schemes for DEM Generation from Airborne LiDAR Data." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/13907033581496801008.

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碩士<br>國立交通大學<br>土木工程系所<br>94<br>Airborne LiDAR has become one of the primary methods used for DEM generation. This study investigates the production process and quality management scheme for DEM generation from airborne LiDAR data. The quality of airborne LiDAR produced by DEM is currently evaluated by identifying the planimetric and height position accuracies with ground checkpoints. Because there are only a limited number of checkpoints, and the process is usually conducted during the final stage, a step-by-step quality management scheme during the flow of DEM production would be beneficial

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Holder, Martin Friedrich. "Synthetic Generation of Radar Sensor Data for Virtual Validation of Autonomous Driving." Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/17545/1/Dissertation_Martin_Holder_2021.pdf.

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The first accidents in otherwise promising deployments of autonomous driving fleets has underscored the importance of safety certification. Safety certification is expensive, especially when conducted via real-world driving. As such, high expectations are placed on virtual testing as an economic alternative. Autonomous driving functionality often relies heavily on radar sensors, but adequately modeling these radar sensors presents a particular challenge. While automotive simulation techniques have improved, there have yet to be systematic evaluations to prove that radar simulation models d

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Rosenberg, Kathrine Joan. "Stochastic modelling of rainfall and generation of synthetic rainfall data at Mawson Lakes." 2004. http://arrow.unisa.edu.au:8081/1959.8/24949.

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Mawson Lakes is a new suburban housing development, situated 12 kms from the city of Adelaide in South Australia. The developers, the Mawson Lakes Joint Venture (MLJV), and the local council, the City of Salisbury, intend to capture all stormwater entering the site and recondition all wastewater. The water will then be supplied to residents and businesses for non-potable usage. Modelling the behaviour of the Mawson Lakes catchment under extreme conditions such as drought and prolonged periods of high rainfall will allow the project team to determine optimal water management strategies for the

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Chen, Yen-Ting, and 陳彥廷. "On the utility of differentially private synthetic data generation and differentially private model release." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394031%22.&searchmode=basic.

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碩士<br>國立中興大學<br>資訊科學與工程學系所<br>107<br>In recent years, with the widespread use of neural networks, a large amount of personal data is being collected. To protect the model from leaking private information, it combines with differential privacy to achieve the goal. In our work, we use the following two methods to achieve a balance between utility and privacy. The first method, which is called “Clean features with sloppy training”, adds noise to protect the sensitive data during training and finally generates a classifier. We use DP-SGD proposed by Abadi [1] and PATE proposed by Nicolas [2] for e

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Nunes, Rui Jose Silva Oliveira. "Procedural Generation of Synthetic Forest Environments to Train Machine Learning Algorithms." Master's thesis, 2021. http://hdl.handle.net/10316/97981.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>O campo de Machine Learning está a evoluir a um ritmo frenético e novas soluções e conceitos estão a ser desenvolvidos todos os dias. Estas descobertas estão principalmente a ser impulsionadas pelo Deep Learning. Os modelos de Deep Learning são inspirados no cérebro humano e apresentam uma estrutura em camadas de neurónios. Estas redes são capazes de aprender a partir de exemplos e os modelos apresentam ótimos resultados e uma capacidade de aprendizagem impressio

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PAPA, Mario. "Geolocating Low-Earth-Orbit satellite data from next-generation millimeter-wave radiometers using natural targets." Doctoral thesis, 2021. http://hdl.handle.net/11573/1485754.

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The main goal of this work is to perform the geolocation error assessment of the channel imagery at 183.31 GHz of the Special Sensor Microwave Imager/Sounder (SSMIS). The frequency around 183.31 GHz still represents the highest channel frequency of current spaceborne microwave and millimeter-wave radiometers. The latter will be extended to frequencies up to 664 GHz, as in the case of EUMETSAT Ice Cloud Imager (ICI). This use of submillimeter observations unfortunately prevents a straightforward geolocation error assessment using landmark-based techniques. This work uses SSMIS data at 183.31 GH

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