[pt] A Internet das Coisas (IoT) se propõe a interligar o mundo físico e a Internet, o que abre a possibilidade de desenvolvimento de diversas aplicações, principalmente na área da saúde. Essas aplicações requerem um grande número de sensores para coletar informações continuamente, gerando grandes fluxos de dados, muitas vezes excessivos, redundantes ou sem significado para as operações do sistema. Essa geração massiva de dados de sensores desperdiça recursos computacionais para adquirir, transmitir, armazenar e processar informações, levando à perda de eficiência desses sistemas ao longo do tempo. Além disso, os dispositivos IoT são projetados para serem pequenos e portáteis, alimentados por baterias, para maior mobilidade e interferência minimizada no ambiente monitorado. No entanto, esse design também resulta em restrições de consumo de energia, tornando a vida útil da bateria um desafio significativo que precisa ser enfrentado. Além disso, esses sistemas geralmente operam em ambientes imprevisíveis, o que pode gerar alarmes redundantes e insignificantes, tornando-os ineficazes. No entanto, um sistema auto-adaptativo que identifica e prevê riscos iminentes através de um sistema de pontuação de alertas antecipados (EWS) pode lidar com esses problemas. Devido ao seu baixo custo de processamento, a referência EWS pode ser incorporada em dispositivos vestíveis e sensores, permitindo um melhor gerenciamento das taxas de amostragem, transmissões, produção de alarmes e consumo de energia. Seguindo a ideia acima, esta tese apresenta uma solução que combina um sistema EWS com um algoritmo auto-adaptativo em aplicações IoT de monitoramento de pacientes. Desta forma, promovendo uma redução na aquisição e transmissão de dados , diminuindo alarmes não acionáveis e proporcionando economia de energia para esses dispositivos. Além disso, projetamos e desenvolvemos um protótipo de hardware capaz de embarcar nossa proposta, evidenciando a sua viabilidade técnica. Além disso, usando nosso protótipo, coletamos dados reais de consumo de energia dos componentes de hardware que foram usados durante nossas simulações com dados reais de pacientes provenientes de banco de dados públicos. Nossos experimentos demonstraram grandes benefícios com essa abordagem, reduzindo em 87 por cento os dados amostrados, em 99 por cento a carga total das mensagens transmitidas do dispositivo de monitoramento, 78 por cento dos alarmes e uma economia de energia de quase 82 por cento. No entanto, a fidelidade do monitoramento do estado clínico dos pacientes apresentou um erro absoluto total médio de 6,8 por cento (mais ou menos 5,5 por cento), mas minimizado para 3,8 por cento (mais ou menos 2,8 por cento) em uma configuração com menores ganhos na redução de dados. A perda de detecção total dos alarmes dependendo da configuração de frequências e janelas de tempo analisadas ficou entre 0,5 por cento e 9,5 por cento, com exatidão do tipo de alarme entre 89 por cento e 94 por cento. Concluindo, este trabalho apresenta uma abordagem para o uso mais eficiente de recursos computacionais, de comunicação e de energia para implementar aplicativos de monitoramento de pacientes baseados em IoT. / [en] The Internet of Things (IoT) proposes to connect the physical world to the Internet, which opens up the possibility of developing various applications, especially in healthcare. These applications require a huge number of sensors to collect information continuously, generating large data flows, often excessive, redundant, or without meaning for the system s operations. This massive generation of sensor data wastes computational resources to acquire, transmit, store, and process information, leading to the loss of efficiency of these systems over time. In addition, IoT devices are designed to be small and portable, powered by batteries, for increased mobility and minimized interference with the monitored environment. However, this design also results in energy consumption restrictions, making battery lifetime a significant challenge that needs to be addressed. Furthermore, these systems often operate in unpredictable environments, which can generate redundant and negligible alarms, rendering them ineffective. However, a self-adaptive system that identifies and predicts imminent risks using early-warning scores (EWS) can cope with these issues. Due to its low processing cost, EWS guidelines can be embedded in wearable and sensor devices, allowing better management of sampling rates, transmissions, alarm production, and energy consumption. Following the aforementioned idea, this thesis presents a solution combining EWS with a self-adaptive algorithm for IoT patient monitoring applications. Thus, promoting a reduction in data acquisition and transmission, decreasing non-actionable alarms, and providing energy savings for these devices. In addition, we designed and developed a hardware prototype capable of embedding our proposal, which attested to its technical feasibility. Moreover, using our wearable prototype, we collected the energy consumption data of hardware components and used them during our simulations with real patient data from public datasets. Our experiments demonstrated great benefits of our approach, reducing by 87 percent the sampled data, 99 percent the total payload of the transmitted messages from the monitoring device, 78 percent of the alarms, and an energy saving of almost 82 percent. However, the fidelity of monitoring the clinical status of patients showed a mean total absolute error of 6.8 percent (plus-minus 5.5 percent) but minimized to 3.8 percent (plus-minus 2.8 percent) in a configuration with lower data reduction gains. The total loss of alarm detection depends on the configuration of frequencies and time windows, remaining between 0.5 percent and 9.5 percent, with an accuracy of the type of alarm between 89 percent and 94 percent. In conclusion, this work presents an approach for more efficient use of computational, communication, and energy resources to implement IoT-based patient monitoring applications.

Les dernières décennies ont donné lieux à d'énormes progrès dans la simulation numérique des phénomènes physiques. D'une part grâce au raffinement des méthodes de discrétisation des équations aux dérivées partielles. Et d'autre part grâce à l'explosion de la puissance de calcul disponible. Pourtant, de nombreux problèmes soulevés en ingénierie tels que les simulations multi-physiques, les problèmes d'optimisation et de contrôle restent souvent hors de portée. Le dénominateur commun de ces problèmes est le fléau des dimensions. Un simple problème tridimensionnel requiert des centaines de millions de points de discrétisation auxquels il faut souvent ajouter des milliers de pas de temps pour capturer des dynamiques complexes. L'avènement des supercalculateurs permet de générer des simulations de plus en plus fines au prix de données gigantesques qui sont régulièrement de l'ordre du pétaoctet. Malgré tout, cela n'autorise pas une résolution ``exacte'' des problèmes requérant l'utilisation de plusieurs paramètres. L'une des voies envisagées pour résoudre ces difficultés est de proposer des représentations ne souffrant plus du fléau de la dimension. Ces représentations que l'on appelle séparées sont en fait un changement de paradigme. Elles vont convertir des objets tensoriels dont la croissance est exponentielle $n^d$ en fonction du nombre de dimensions $d$ en une représentation approchée dont la taille est linéaire en $d$. Pour le traitement des données tensorielles, une vaste littérature a émergé ces dernières années dans le domaine des mathématiques appliquées.Afin de faciliter leurs utilisations dans la communauté des mécaniciens et en particulier pour la simulation en mécanique des fluides, ce manuscrit présente dans un vocabulaire rigoureux mais accessible les formats de représentation des tenseurs et propose une étude détaillée des algorithmes de décomposition de données qui y sont associées. L'accent est porté sur l'utilisation de ces méthodes, aussi la bibliothèque de calcul texttt{pydecomp} développée est utilisée pour comparer l'efficacité de ces méthodes sur un ensemble de cas qui se veut représentatif. La seconde partie de ce manuscrit met en avant l'étude de l'écoulement dans une cavité entraînée à haut nombre de Reynolds. Cet écoulement propose une physique très riche (séquence de bifurcation de Hopf) qui doit être étudiée en amont de la construction de modèle réduit. Cette étude est enrichie par l'utilisation de la décomposition orthogonale aux valeurs propres (POD). Enfin une approche de construction ``physique'', qui diffère notablement des développements récents pour les modèles d'ordre réduit, est proposée. La connaissance détaillée de l'écoulement permet de construire un modèle réduit simple basé sur la mise à l'échelle des fréquences d'oscillation (time-scaling) et des techniques d'interpolation classiques (Lagrange,..). / Numerical simulation has experienced tremendous improvements in the last decadesdriven by massive growth of computing power. Exascale computing has beenachieved this year and will allow solving ever more complex problems. But suchlarge systems produce colossal amounts of data which leads to its own difficulties.Moreover, many engineering problems such as multiphysics or optimisation andcontrol, require far more power that any computer architecture could achievewithin the current scientific computing paradigm. In this thesis, we proposeto shift the paradigm in order to break the curse of dimensionality byintroducing decomposition and building reduced order models (ROM) for complexfluid flows.This manuscript is organized into two parts. The first one proposes an extendedreview of data reduction techniques and intends to bridge between appliedmathematics community and the computational mechanics one. Thus, foundingbivariate separation is studied, including discussions on the equivalence ofproper orthogonal decomposition (POD, continuous framework) and singular valuedecomposition (SVD, discrete matrices). Then a wide review of tensor formats andtheir approximation is proposed. Such work has already been provided in theliterature but either on separate papers or into a purely applied mathematicsframework. Here, we offer to the data enthusiast scientist a comparison ofCanonical, Tucker, Hierarchical and Tensor train formats including theirapproximation algorithms. Their relative benefits are studied both theoreticallyand numerically thanks to the python library texttt{pydecomp} that wasdeveloped during this thesis. A careful analysis of the link between continuousand discrete methods is performed. Finally, we conclude that for mostapplications ST-HOSVD is best when the number of dimensions $d$ lower than fourand TT-SVD (or their POD equivalent) when $d$ grows larger.The second part is centered on a complex fluid dynamics flow, in particular thesingular lid driven cavity at high Reynolds number. This flow exhibits a seriesof Hopf bifurcation which are known to be hard to capture accurately which iswhy a detailed analysis was performed both with classical tools and POD. Oncethis flow has been characterized, emph{time-scaling}, a new ``physics based''interpolation ROM is presented on internal and external flows. This methodsgives encouraging results while excluding recent advanced developments in thearea such as EIM or Grassmann manifold interpolation.

The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic analysis. Result of the work is a design and implementation of summarization module for Python language. Final part of the work contains evaluation of summaries generated by implemented summarization methods and their subjective comparison of the author.

This thesis is a collaboration with Stockholm Globe Arena Fastigher AB (SGAF) and focuses on a case study involving the multi-functional arenas Avicii Arena and Annexet in Stockholm, Sweden. The objective of this study is to investigate Key Performance Indicators (KPI) that can sufficiently measure and evaluate monthly and yearly electricity, heating and cooling consumption while considering events and visitors. Data regarding visitor and event, electricity, heating and cooling were provided by companies in agreement with SGAF, which is handled in a Data Reduction. This study identified four different KPIs to evaluate energy consumption dynamics; KPI 1: Energy consumption per event day, KPI 2: Energy consumption per visitor, KPI 3: Load Factor and KPI 4: Occupancy rate. The results showed that the monthly KPI 1 and 2 values exhibited unpredictable fluctuations hindering its ability to assess energy consumption patterns. In contrast, the annual KPI 1 and 2 were able to effectively evaluate the energy consumption which uncovered that the electricity consumption in the venues is on a downward trend. However, the heating and cooling consumption remained stagnant during the same timeframe. KPI 3 and 4 displayed efficient operation of the energy systems. Moreover, all four KPIs revealed that the energy consumption is influenced by other factor beyond visitors and events. A subsequent Correlation Analysis unveiled two additional factors, outdoor temperature and event types, affects the energy consumption which suggests potential areas for future research. / Detta examensarbete ar ett samarbete med Stockholm Globe Arena Fastigheter AB (SGAF) och fokuserar på en fallstudie som involverar de multi-funktionella arenorna Avicii Arena och Annexet i Stockholm, Sverige. Målet med denna studie är att undersöka Nyckeltal som kan mäta och utvärdera månatlig och årlig elektricitetsförbrukning, värmeförbrukning och kylförbrukning med hänsyn till evenemang och besökare. Data avseende besökare och evenemang, elförbrukning, värmeförbrukning och kylförbrukning tillhandahölls av företag i samförstånd med SGAF som hanterades i en Data Reduktion. Denna studie identifierade fyra olika nyckeltal för utvärdering av energiförbrukningen; Nyckeltal 1: Energiförbrukning per evenemangsdag, Nyckeltal 2: Energiförbrukning per besökare, Nyckeltal 3: Belastningsfaktor och Nyckeltal 4: Beläggningsgrad. Resultaten visar att de månatliga nyckeltalen 1 och 2 uppvisade förutsägbara fluktuationer som hindrade dess förmåga att bedöma energiförbrukningsmönster. Den årliga nyckeltalen 1 och 2 kunde effektivt utvärdera energiförbrukningen vilket avslöjade att elförbrukningen i anläggningarna minskar. Dock, påvisade värmeförbrukningen och kylförbrukningen oförändrade under samma tidsperiod. Nyckeltal 3 och 4 uppvisade att energisystemen i anläggningarna körs på ett effektivt sätt. Vidare, visade samtliga fyra nyckeltal att energiförbrukningen påverkas av andra faktorer utöver besökare och evenemang. En efterföljande korrelationsanalys påvisar att två ytterligare faktorer, utomhus temperatur och evenemangstyper, påverkar energiförbrukningen vilket antyder nya potentiella forskningsområden.

It is known that fission yeast Schizosaccharomyces pombe maintains its nuclear envelope during mitosis and it undergoes an interesting shape change during cell division - from a spherical via an ellipsoidal and a peanut-like to a dumb-bell shape. However, the biomechanical system behind this amazing transformation is still not understood. What we know is, that the shape must change due to forces acting on the membrane surrounding the nucleus and the microtubule based mitotic spindle is thought to play a key role. To estimate the locations and directions of the forces, the shape of the nucleus was recorded by confocal light microscopy. But such data is often inhomogeneously labeled with gaps in the boundary, making classical segmentation impractical. In order to accurately determine the shape we developed a global parametric shape description method, based on a Fourier coordinate expansion. The method implicitly assumes a closed and smooth surface. We will calculate the geometrical properties of the 2-dimensional shape and extend it to 3-dimensional properties, assuming rotational symmetry. Using a mechanical model for the lipid bilayer and the so called Helfrich-Canham free energy we want to calculate the minimum energy shape while respecting system-specific constraints to the surface and the enclosed volume. Comparing it with the observed shape leads to the forces. This provides the needed research tools to study forces based on images.

At the beginning, the work describes DNS and SSL/TLS protocols, it mainly deals with communication between devices using these protocols. Then we'll talk about data preprocessing and data cleaning. Furthermore, the thesis deals with basic data mining techniques such as data classification, association rules, information retrieval, regression analysis and cluster analysis. The next chapter we can read something about how to identify mobile devices on the network. We will evaluate data sets that contain collected data from communication between the above mentioned protocols, which will be used in the practical part. After that, we finally get to the design of a system for analyzing network communication data. We will describe the libraries, which we used and the entire system implementation. We will perform a large number of experiments, which we will finally evaluate.

It is known that fission yeast Schizosaccharomyces pombe maintains its nuclear envelope during mitosis and it undergoes an interesting shape change during cell division - from a spherical via an ellipsoidal and a peanut-like to a dumb-bell shape. However, the biomechanical system behind this amazing transformation is still not understood. What we know is, that the shape must change due to forces acting on the membrane surrounding the nucleus and the microtubule based mitotic spindle is thought to play a key role. To estimate the locations and directions of the forces, the shape of the nucleus was recorded by confocal light microscopy. But such data is often inhomogeneously labeled with gaps in the boundary, making classical segmentation impractical. In order to accurately determine the shape we developed a global parametric shape description method, based on a Fourier coordinate expansion. The method implicitly assumes a closed and smooth surface. We will calculate the geometrical properties of the 2-dimensional shape and extend it to 3-dimensional properties, assuming rotational symmetry. Using a mechanical model for the lipid bilayer and the so called Helfrich-Canham free energy we want to calculate the minimum energy shape while respecting system-specific constraints to the surface and the enclosed volume. Comparing it with the observed shape leads to the forces. This provides the needed research tools to study forces based on images.