This diploma thesis is concentrated to fully differential and non-differential fractional-order filters with active elements. It describes how we can obtain fractional-order fully-differential filters design from non-differential design, thesis describes realization in OrCad program and simulation of this project. The first part of the thesis concerns theoretical analysis of frequency filters, active elements and fractional-order filters. The second part of the thesis includes designs of filters and simulations of differential structures and rest of non-differential structures simulation. The following part of the thesis concerns practical realization and experimental measurment of differential fractional-order filter. In the last part of this project, thesis evaluates all results which were revealed in our simulations and experimental measurment.

Le chiffrement totalement homomorphe permet d’effectuer des calculs sur des données chiffrées sans fuite d’information sur celles-ci. Pour résumer, un utilisateur peut chiffrer des données, tandis qu’un serveur, qui n’a pas accès à la clé de déchiffrement, peut appliquer à l’aveugle un algorithme sur ces entrées. Le résultat final est lui aussi chiffré, et il ne peut être lu que par l’utilisateur qui possède la clé secrète. Dans cette thèse, nous présentons des nouvelles techniques et constructions pour le chiffrement totalement homomorphe qui sont motivées par des applications en apprentissage automatique, en portant une attention particulière au problème de l’inférence homomorphe, c’est-à-dire l’évaluation de modèles cognitifs déjà entrainé sur des données chiffrées. Premièrement, nous proposons un nouveau schéma de chiffrement totalement homomorphe adapté à l’évaluation de réseaux de neurones artificiels sur des données chiffrées. Notre schéma atteint une complexité qui est essentiellement indépendante du nombre de couches dans le réseau, alors que l’efficacité des schéma proposés précédemment dépend fortement de la topologie du réseau. Ensuite, nous présentons une nouvelle technique pour préserver la confidentialité du circuit pour le chiffrement totalement homomorphe. Ceci permet de cacher l’algorithme qui a été exécuté sur les données chiffrées, comme nécessaire pour protéger les modèles propriétaires d’apprentissage automatique. Notre mécanisme rajoute un coût supplémentaire très faible pour un niveau de sécurité égal. Ensemble, ces résultats renforcent les fondations du chiffrement totalement homomorphe efficace pour l’apprentissage automatique, et représentent un pas en avant vers l’apprentissage profond pratique préservant la confidentialité. Enfin, nous présentons et implémentons un protocole basé sur le chiffrement totalement homomorphe pour le problème de recherche d’information confidentielle, c’est-à-dire un scénario où un utilisateur envoie une requête à une base de donnée tenue par un serveur sans révéler cette requête. / Fully homomorphic encryption enables computation on encrypted data without leaking any information about the underlying data. In short, a party can encrypt some input data, while another party, that does not have access to the decryption key, can blindly perform some computation on this encrypted input. The final result is also encrypted, and it can be recovered only by the party that possesses the secret key. In this thesis, we present new techniques/designs for FHE that are motivated by applications to machine learning, with a particular attention to the problem of homomorphic inference, i.e., the evaluation of already trained cognitive models on encrypted data. First, we propose a novel FHE scheme that is tailored to evaluating neural networks on encrypted inputs. Our scheme achieves complexity that is essentially independent of the number of layers in the network, whereas the efficiency of previously proposed schemes strongly depends on the topology of the network. Second, we present a new technique for achieving circuit privacy for FHE. This allows us to hide the computation that is performed on the encrypted data, as is necessary to protect proprietary machine learning algorithms. Our mechanism incurs very small computational overhead while keeping the same security parameters. Together, these results strengthen the foundations of efficient FHE for machine learning, and pave the way towards practical privacy-preserving deep learning. Finally, we present and implement a protocol based on homomorphic encryption for the problem of private information retrieval, i.e., the scenario where a party wants to query a database held by another party without revealing the query itself.

Silicon-on-insulator is a key technology which ensures continuation of Moore’s law. This document investigates the impact of orientation, confinement, and strain on the electronic structure of thin silicon slabs using density functional theory. Moreover a systematic comparison of FDSOI device characteristics using parameters of both the default bulk material and that of the studied slab material is also performed. The comparative study of low index orientations show that confinement not only widens the band gap but also transforms the band gap type. Moreover, it is found that for thin silicon layers, strain can alter band gap and band gap type. By summarizing the findings for different crystal orientations, we demonstrate that the consideration of the electronic structure of strained and confined silicon is of high relevance for modelling actual devices with ultra thin body.

[pt] A crescente disponibilidade de dados de sensoriamento remoto vem criando novas oportunidades e desafios em aplicações de monitoramento de processos naturais e antropogénicos em escala global. Nos últimos anos, as técnicas de aprendizado profundo tornaram-se o estado da arte na análise de dados de sensoriamento remoto devido sobretudo à sua capacidade de aprender automaticamente atributos discriminativos a partir de grandes volumes de dados. Um dos problemas chave em análise de imagens é a segmentação semântica, também conhecida como rotulação de pixels. Trata-se de atribuir uma classe a cada sítio de imagem. As chamadas redes totalmente convolucionais de prestam a esta função. Os anos recentes têm testemunhado inúmeras propostas de arquiteturas de redes totalmente convolucionais que têm sido adaptadas para a segmentação de dados de observação da Terra. O presente trabalho avalias cinco arquiteturas de redes totalmente convolucionais que representam o estado da arte em segmentação semântica de imagens de sensoriamento remoto. A avaliação considera dados provenientes de diferentes plataformas: veículos aéreos não tripulados, aeronaves e satélites. Cada um destes dados refere-se a aplicações diferentes: segmentação de espécie arbórea, segmentação de telhados e desmatamento. O desempenho das redes é avaliado experimentalmente em termos de acurácia e da carga computacional associada. O estudo também avalia os benefícios da utilização do Campos Aleatórios Condicionais (CRF) como etapa de pósprocessamento para melhorar a acurácia dos mapas de segmentação. / [en] The increasing availability of remote sensing data has created new opportunities and challenges for monitoring natural and anthropogenic processes on a global scale. In recent years, deep learning techniques have become state of the art in remote sensing data analysis, mainly due to their ability to learn discriminative attributes from large volumes of data automatically. One of the critical problems in image analysis is the semantic segmentation, also known as pixel labeling. It involves assigning a class to each image site. The so-called fully convolutional networks are specifically designed for this task. Recent years have witnessed numerous proposals for fully convolutional network architectures that have been adapted for the segmentation of Earth observation data. The present work evaluates five fully convolutional network architectures that represent the state of the art in semantic segmentation of remote sensing images. The assessment considers data from different platforms: unmanned aerial vehicles, airplanes, and satellites. Three applications are addressed: segmentation of tree species, segmentation of roofs, and deforestation. The performance of the networks is evaluated experimentally in terms of accuracy and the associated computational load. The study also assesses the benefits of using Conditional Random Fields (CRF) as a post-processing step to improve the accuracy of segmentation maps.

<div>This study explores the perceptions of critical stakeholders as to the viability of a fully remote apprenticeship delivery system (FRADS), as well as its ability to serve as a functionally equivalent path of inclusion for access-limited populations. One of the first recorded pedagogical models, apprenticeship was also one of the first to be regulated. The effectiveness of the method of training a novice to enter the adult world of work through apprenticeship is undisputed, when it is conducted in a manner approximate to that from which it derived: a process that occurs over time, with continuous interaction between novice and expert. Despite millennia of practice, and a few emerging programs called Virtual Apprenticeships, the critical real-time skills-based mentoring component (on the job instruction/training, or OJI/OJT) of the modern apprenticeship is still only carried out fully in face-to-face programs. With the move to work-from-home (WFH) resulting from the global COVID-19 pandemic of 2020, assessing the viability of a FRADS is timely. This qualitative exploratory study is a first step in the discussion. Bounded by the parameters of the U.S. Certified Apprenticeship Guidelines for Registered Apprenticeships and the constructs of viability and functional equivalence, participants of three critical stakeholder groups—policy makers, service managers, and front-line service workers—offer their pre-pandemic perceptions of the construct of a FRADS. Guided by the work of Jahoda, et al., (1957), Northrop (1949,1959), and Swedberg (2018), this qualitative exploratory methodology identified perceptual data points that are then compared against a framework of viability derived from IEG’s Service Delivery Evaluation Framework (Caceres, et al., 2016). And, because this represents a large systems change (LSC), I included aspects of Weiner’s (2009) Organizational Readiness for Change—valance and efficacy—as additional indicators of potential viability. Stakeholders examined key components of IEG’s evaluative criteria applied to a face-to-face apprenticeship as a functionally equivalent, technology-mediated apprenticeship delivery system. Additional stakeholder perceptions, mid-pandemic, along with a review of scholarly articles, media reports, and Department of Labor statistics concerning the impact of the WFH mandates foreground the gap a purposeful FRADS might fill. Analysis of some of the findings are represented in a preliminary process map. </div>

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The following article describes the development, validation and series introduction of a novel highly integrated smart electrohydraulic 4-quadrant displacement machine. Starting in 2012, an unique unit consisting of a hydraulic internal gear machine combined with a newly developed electric machine with integrated electronic unit was created. The developed unit aims at the application in fully active automotive chassis in combination with hydraulic shock absorbers. The very special requirements of this application resulted in a new development with numerous detailed solutions which are described below. Parallel and interacting with the product development, all new series assembly and testing devices tailored to this product was developed.

The cooling systems in a gas turbine are especially important as the turbine blades and vanes are exposed to extreme temperatures. The relatively cool air is extracted from the compressors and fed to the turbines to cool the turbine blades. The manufacturing of these blades and channels used to cool is especially complicated using conventional manufacturing techniques. Additive Manufacturing (AM) gives the designer much more freedom to design core components. The AM technique currently explored is the Selective Laser Melting process (SLM). The surface area is exposed to the cooling airflow by using lattice structures which can be manufactured at relative ease using AM. This thesis will provide some insights into using AM parts for the cooling, by analyzing the pressure drop that could be expected from superalloys that are manufactured using AM. The surface roughness is an inherent property of the AM components therefore it would be interesting to analyze a turbulent flow through AM channels (CM247LC and INCONEL 939). The thesis deals with turbulent flows as the airflow used for cooling in the gas turbine is most likely turbulent.  The friction factor (Darcy–Weisbach friction factor) is used to relate the impact of the surface roughness to the pressure drop. The results from the previous experiments are contrasted as the flow in the previous experiments was assumed to be fully developed but in reality, it was not. And the accuracy of the previous results to the actual fully developed flow will shed some light on the feasibility of the flow analysis techniques used in the previous experiments. It is found that the previous experimental results for the CM247LC TPs have good agreement with current experimental results but INCONEL 939 exhibits significant deviation. The possible reasons for the deviations are directly linked to the assumptions made to calculate the minor losses. The Test Pieces (TP) analyzed in this thesis have varying length to diameter (L/D) ratios and the impact of the variation of different L/D ratios is analyzed along with varying pressure ratios. Where the flow resistance increases with an increase in L/D and pressure ratio. The technique to accommodate the compressibility of the airflow is also explored in this thesis. Finally, reasons for the manifestation of anomalies are discussed. The probability of the compressibility effects of the airflow on the anomalies was found to be quite high, and concluding remarks are provided.

Building information extraction and reconstruction from satellite images is an essential task for many applications related to 3D city modeling, planning, disaster management, navigation, and decision-making. Building information can be obtained and interpreted from several data, like terrestrial measurements, airplane surveys, and space-borne imagery. However, the latter acquisition method outperforms the others in terms of cost and worldwide coverage: Space-borne platforms can provide imagery of remote places, which are inaccessible to other missions, at any time. Because the manual interpretation of high-resolution satellite image is tedious and time consuming, its automatic analysis continues to be an intense field of research. At times however, it is difficult to understand complex scenes with dense placement of buildings, where parts of buildings may be occluded by vegetation or other surrounding constructions, making their extraction or reconstruction even more difficult. Incorporation of several data sources representing different modalities may facilitate the problem. The goal of this dissertation is to integrate multiple high-resolution remote sensing data sources for automatic satellite imagery interpretation with emphasis on building information extraction and refinement, which challenges are addressed in the following: Building footprint extraction from Very High-Resolution (VHR) satellite images is an important but highly challenging task, due to the large diversity of building appearances and relatively low spatial resolution of satellite data compared to airborne data. Many algorithms are built on spectral-based or appearance-based criteria from single or fused data sources, to perform the building footprint extraction. The input features for these algorithms are usually manually extracted, which limits their accuracy. Based on the advantages of recently developed Fully Convolutional Networks (FCNs), i.e., the automatic extraction of relevant features and dense classification of images, an end-to-end framework is proposed which effectively combines the spectral and height information from red, green, and blue (RGB), pan-chromatic (PAN), and normalized Digital Surface Model (nDSM) image data and automatically generates a full resolution binary building mask. The proposed architecture consists of three parallel networks merged at a late stage, which helps in propagating fine detailed information from earlier layers to higher levels, in order to produce an output with high-quality building outlines. The performance of the model is examined on new unseen data to demonstrate its generalization capacity. The availability of detailed Digital Surface Models (DSMs) generated by dense matching and representing the elevation surface of the Earth can improve the analysis and interpretation of complex urban scenarios. The generation of DSMs from VHR optical stereo satellite imagery leads to high-resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, a methodology based on conditional Generative Adversarial Network (cGAN) is developed for generating a good-quality Level of Detail (LoD) 2 like DSM with enhanced 3D object shapes directly from the low-quality photogrammetric half-meter resolution satellite DSM input. Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. Therefore, an observation of such influences for important remote sensing applications such as realistic elevation model generation and roof type classification from stereo half-meter resolution satellite DSMs, is demonstrated in this work. Recently published deep learning architectures for both tasks are investigated and a new end-to-end cGAN-based network is developed, which combines different models that provide the best results for their individual tasks. To benefit from information provided by multiple data sources, a different cGAN-based work-flow is proposed where the generative part consists of two encoders and a common decoder which blends the intensity and height information within one network for the DSM refinement task. The inputs to the introduced network are single-channel photogrammetric DSMs with continuous values and pan-chromatic half-meter resolution satellite images. Information fusion from different modalities helps in propagating fine details, completes inaccurate or missing 3D information about building forms, and improves the building boundaries, making them more rectilinear. Lastly, additional comparison between the proposed methodologies for DSM enhancements is made to discuss and verify the most beneficial work-flow and applicability of the resulting DSMs for different remote sensing approaches.

The subject of this master thesis is focused to the refining of the cast structure of gamma-TiAl–2Nb alloy using cyclic heat treatment and to the analysis of the grain refining mechanism. Structure evolution after applied cycles of heat treatment was characterized using light, laser and electron microscopy and using microhardness tests. Application of five heat treatment cycles during which two phase transformations (eutectoid and alpha-recrystallization reactions) repeatedly took place resulted in refining of the cast columnar structure having the mean grain size 512 microns to fully lamellar structure containing gamma and alpha2 phases having the mean grain size 229 microns. Lamellae thickness of gamma was not changed while the thickness of alpha2 phase decreased, up to 78 nm. Refining of alpha2 phase resulted in the increase of the microhardness by 20 %. The recrystallized cast structure obtained by cyclic heat treatment and the knowledge on the mechanisms of the refining the structure were compared with the literature data and were discussed in order to propose more efficient procedure for refining thermal treatment of cast TiAl alloys.