The thesis is focused on the role of assurance within project management standards. Firstly, the theoretical role of assurance was established based on the performed research. Three main areas of interest have been identified: Assurance over business, the project itself and the product. The role established in the theoretical part of the work was subsequently compared to information systems development methodologies and project management standards. From the comparison with AUP, Scrum and FDD methodologies, it seems that the better assurance is defined, the longer the feedback cycle is. During the comparison with the three most widespread project management standards - IPMA, PRINCE2 and PMBOK - various areas have been identified where these are not fully compliant with the theoretical role of assurance. Additions to IPMA and PMBOK have been created to support the compliance with the theoretically established role of assurance, fulfilling the objective set for the practical part of the work and providing benefits to IT project management professionals that struggle to deliver quality products while following one of the aforementioned standards.

The main goal of this diploma thesis is the proposal to use agile development procedures of Scrum framework with UML modeling language applicable for creating websites of small scale. The emphasis is especially put on creating a practically usable type model of the websites. The theoretical part of the thesis has two main chapters. In the "Agile methodology" chapter are listed principles of agile approach to creating software. The next part of this chapter is focus on agile methodologies (Scrum, XP, ASD, FDD, DSDM, LD and Crystal). In the "UML" chapter is introduced modeling language UML and its principles. The next part of this chapter is focus on UML diagrams used in practical part of this thesis (use case diagram, class diagram, sequence diagram, activity diagram and deployment diagram). The practical part of this thesis focuses on the design type model for creating websites of small scale. The type model is based both on the theoretical part of this work and practical experience of the author. In the first part is introduced scheme of model, where is shown the mutual relationship of individual phases. In the next part of this chapter are discussed the individual phases where is used the principle of agile development framework Scrum and examples of use of the UML diagrams.

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Multi-standard RF front-end is a critical part of legacy and future emerging mobile architectures, where the size, the efficiency, and the integration of the elements in the RF front-end will affect the network key performance indicators (KPIs). This chapter discusses power amplifier design for both handset and base station applications for 5G and beyond. Also, this chapter deals with filter-antenna design for 5G applications that include a synthesis-based approach, differentially driven reconfigurable planar filter-antenna, and an insensitive phased array antenna with air-filled slot-loop resonators.

Tato disertační práce se zabývá využitím programovatelných hradlových polí (FPGA) v diagnostice měničů, využívajících spínaných IGBT tranzistorů. Je zaměřena na budiče těchto výkonových tranzistorů a jejich struktury. Přechodné jevy veličin, jako jsou IG, VGE, VCE během procesu přepínání (zapnutí, vypnutí), mohou poukazovat na degradaci IGBT. Pro měření a monitorování těchto veličin byla navržena nová architektura budiče IGBT. Rychlé měření a monitorování během přepínacího děje vyžaduje vysokou vzorkovací frekvenci. Proto jsou navrhovány paralelní vysokorychlostní AD převodníky (> 50 MSPS). Práce je zaměřena převážně na návrh zařízení s FPGA včetně hardware a software. Byla navržena nová deska plošných spojů s FPGA, která plní požadované funkce, jako je řízení IGBT pomocí vícenásobných paralelních koncových stupňů, monitorování a diagnostiku, a propojení s řídicí jednotkou měniče.

Les réseaux radio actuelles utilisent le spectre inefficacement, car une bande de fréquence est allouée de façon permanente à une technologie spécifique. Vu que le spectre est une ressource limitée, cette attribution statique ne pourra bientôt plus combler les besoins des systèmes de transmission qui ne cessent de croître. On peut toutefois optimiser l'utilisation du spectre en permettant des transmissions secondaires (SU) dans les espaces libres du primaire (PU). Cette vision constitue l'objectif principal de la radio cognitive. Nous proposons d'évaluer les stratégies de transmission pour la coexistence des systèmes primaires (PU) et SU dans les mêmes réseaux. Plus concrètement, nous nous focalisons sur un scénario spatial interweave en émettant les signaux SU dans les espaces vides du PU à l'aide d'un précodeur linéaire. Néanmoins, ce précodage nécessite une connaissance a priori des canaux interférents. L'échange d'informations entre le PU et le SU étant proscrit, nous exploitons l'hypothèse de la réciprocité du canal. Cette hypothèse compense l'absence de coopération, mais elle n'est pas si évidente à exploiter en pratique à cause des perturbations des circuits radio fréquence. Nous suggérons de compenser ces perturbations par des méthodes de calibration relative. Nous proposons ensuite une implémentation temps-réel des solutions sur une plateforme LTE. Pour finir, nous généralisons l'approche RC à un système de transmission multi-utilisateurs, à travers une combinaison des techniques RC et massive MIMO, cette approche constitue s'établit comme une solution à la progression exponentielle du trafic.

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>