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The objective of this thesis is creating a design of a redundant secure VPN WAN network for Medical Emergency Service of Pardubice region. The starting point of this thesis is the analysis of the current state of the corporate computer network, which was evaluated as not satisfying. The result is a design of WAN network, which satisfies the requirements of the investor. The solution also includes the project of implementation and financial calculation of the project.

The thesis focuses on the management and functionality of the Network Operations Center, whose function is to maintain optimal network operations on various platforms, media and communication channels. The department is in a company that develops AAA game titles. The first part defines the theoretical basis. The next section introduces the company together with the analysis of the functioning of the department and communication. The last part is devoted to the design of the organizational structure, which includes the process of creating a new job. The process involves the division of activities, the recruitment process and the economic appreciation itself.

<p>Through the computing advance and GPU (Graphics Processing Unit) availability for math calculation, the deep learning field becomes more popular and prevalent. Object detection with deep learning, which is the part of image processing, plays an important role in automatic vehicle drive and computer vision. Object detection includes object localization and object classification. Object localization involves that the computer looks through the image and gives the correct coordinates to localize the object. Object classification is that the computer classification targets into different categories. The traditional image object detection pipeline idea is from Fast/Faster R-CNN [32] [58]. The region proposal network generates the contained objects areas and put them into classifier. The first step is the object localization while the second step is the object classification. The time cost for this pipeline function is not efficient. Aiming to address this problem, You Only Look Once (YOLO) [4] network is born. YOLO is the single neural network end-to-end pipeline with the image processing speed being 45 frames per second in real time for network prediction. In this thesis, the convolution neural networks are introduced, including the state of art convolutional neural networks in recently years. YOLO implementation details are illustrated step by step. We adopt the YOLO network for our applications since the YOLO network has the faster convergence rate in training and provides high accuracy and it is the end to end architecture, which makes networks easy to optimize and train. </p>

Analysis of Inferred Social Networks While the social network analysis (SNA) is not a new science branch, thanks to the boom of social media platforms in recent years new methods and approaches appear with increasing frequency. However, not all datasets have network structure visible at first glance. We believe that every reasonable interconnected system of data hides a social network, which can be inferred using specific methods. In this thesis we examine such social network, inferred from the real-world data of a smaller bank. We also review some of the most commonly used methods in SNA and then apply them on our complex network, expecting to find structures typical for traditional social networks.

<div>Advances in virtualization technologies have revolutionized the design of the core of cellular networks. However, the adoption of microservice design patterns and migration of services from purpose-built hardware to virtualized hardware has adversely affected the delivery of latency-sensitive services.</div><div><br></div><div>In this dissertation, we make a case for cloud-native (microservice container packaged) network functions in the cellular core by proposing domain knowledge-driven, traffic-aware, orchestration frameworks to make network placement decisions. We begin by evaluating the suitability of virtualization technologies for the cellular core and demonstrating that container-driven deployments can significantly outperform other virtualization technologies such as Virtual Machines for control and data plane applications.</div><div><br></div><div>To support the deployment of latency-sensitive applications on virtualized hardware, we propose using Virtual Network Function (VNF) bundles (aggregates) to handle transactions. Specifically, we design Invenio to leverage a combination of network traces and domain knowledge to identify VNFs involved in processing a specific transaction, which are then collocated by a traffic-aware orchestrator. By ensuring that a user request is processed by a single aggregate of collocated VNFs, Invenio can significantly reduce end-to-end latencies and improve user experience.</div><div><br></div><div>Finally, to understand the challenges in using container-driven deployments in real-world applications, we develop and evaluate a novel caller-ID spoofing detection solution in Voice over LTE (VoLTE) calls. Our proposed solution, NASCENT, cross validates the caller-ID used during voice-call signaling with a previously authenticated caller-ID to detect caller-ID spoofing. Our evaluation with traditional and container-driven deployments shows that container-driven deployment can not only support complex cellular services but also outperform traditional deployments.</div><div><br></div>

Conventional wireless techniques are becoming inadequate for beyond fifth-generation (5G) networks due to latency and bandwidth considerations. To increase the wireless network throughput and improve wireless communication systems’ error performance, we propose physical layer network coding (PNC) in an Intelligent Reflecting Surface (IRS)-assisted environment. We consider an IRS-aided butterfly network, where we propose an algorithm for obtaining the optimal IRS phases. Also, analytic expressions for the bit error rate (BER) are derived. The numerical results demonstrate that the scheme proposed in this thesis significantly enhances the BER performance. The proposed scheme is compared to traditional network coding without IRS. For instance, at a target BER of 10−3, 28 dB and 0.75 dB signal to noise ratio (SNR) gains are achieved at the relay and destination node of the 32-element IRS-assisted butterfly network model.

Vehicle to Vehicle (V2V) communication provides a flexible and real-time information dissemination mechanism through various applications of Intelligent Transportation Systems (ITS). Achieving seamless connectivity through multi-hop vehicular communication with sparse network is a challenging issue. In this paper, we have studied this multi-hop vehicular connectivity in an urban scenario using GPS traces obtained from San Francisco Yellow cabs. Our current work describes a new algorithm for the analysis of topological properties like connectivity and partitions for any kind of vehicular or mobile computing environment. The novel approach uses bitwise manipulation of sparse matrix with an efficient storage technique for determining multi-hop connectivity. The computation mechanism can be further scaled to parallel processing environment. The main contribution of this research is threefold. First, developing an efficient algorithm to quantify multi-hop connectivity with the aid of bitwise manipulation of sparse matrix. Second, investigating the time varying nature of multi-hop vehicular connectivity and dynamic network partitioning of the topology. Third, deriving a mathematical model for calculating message propagation rate in an urban environment.

Machine learning generally requires large amounts of data, however data is often limited. On the whole the amount of data needed grows with the complexity of the problem to be solved. Utilising transfer learning, data augmentation and problem reduction, acceptable performance can be achieved with limited data for a multitude of tasks. The goal of this master project is to develop an artificial neural network-based model for food waste analysis, an area in which large quantities of data is not yet readily available. Given two images an algorithm is expected to identify what has changed in the image, ignore the uncharged areas even though they might contain objects which can be classified and finally classify the change. The approach chosen in this project was to attempt to reduce the problem the machine learning algorithm has to solve by subtracting the images before they are handled by the neural network. In theory this should resolve both object localisation and filtering of uninteresting objects, which only leaves classification to the neural network. Such a procedure significantly simplifies the task to be resolved by the neural network, which results in reduced need for training data as well as keeping the process of gathering data relatively simple and fast. Several models were assessed and theories of adaptation of the neural network to this particular task were evaluated. Test accuracy of at best 78.9% was achieved with a limited dataset of about 1000 images with 10 different classes. This performance was accomplished by a siamese neural network based on VGG19 utilising triplet loss and training data using subtraction as a basis for ground truth mask creation, which was multiplied with the image containing the changed object. / Maskininlärning kräver generellt mycket data, men stora mängder data står inte alltid till förfogande. Generellt ökar behovet av data med problemets komplexitet. Med hjälp av överföringsinlärning, dataaugumentation och problemreduktion kan dock acceptabel prestanda erhållas på begränsad datamängd för flera uppgifter.  Målet med denna masteruppsats är att ta fram en modell baserad på artificiella neurala nätverk för matavfallsanalys, ett område inom vilket stora mängder data ännu inte finns tillgängligt. Givet två bilder väntas en algoritm identifiera vad som ändrats i bilden, ignorera de oförändrade områdena även om dessa innehåller objekt som kan klassificeras och slutligen klassificera ändringen. Tillvägagångssättet som valdes var att försöka reducera problemet som maskininlärningsalgoritmen, i detta fall ett artificiellt neuralt nätverk, behöver hantera genom att subtrahera bilderna innan de hanterades av det neurala nätverket. I teorin bör detta ta hand om både objektslokaliseringen och filtreringen av ointressanta objekt, vilket endast lämnar klassificeringen till det neurala nätverket. Ett sådant tillvägagångssätt förenklar problemet som det neurala nätverket behöver lösa avsevärt och resulterar i minskat behov av träningsdata, samtidigt som datainsamling hålls relativt snabbt och simpelt.  Flera olika modeller utvärderades och teorier om specialanpassningar av neurala nätverk för denna uppgift evaluerades. En testnoggrannhet på som bäst 78.9% uppnåddes med begränsad datamängd om ca 1000 bilder med 10 klasser. Denna prestation erhölls med ett siamesiskt neuralt nätverk baserat på VGG19 med tripletförlust och träningsdata som använde subtraktion av bilder som grund för framställning av grundsanningsmasker (eng. Ground truth masks) multiplicerade med bilden innehållande förändringen.

Candida albicans est un champignon commensal de l’homme, mais aussi l'un des agents pathogènes fongiques les plus répandus. C. albicans alterne entre formes levure et filamenteuses (pseudohyphes ou hyphes), une transition morphologique déclenchée par divers signaux environnementaux et jouant un rôle important dans la virulence. Un réseau de régulation complexe, faisant intervenir de nombreux facteurs de transcription, gouverne la transition levure-hyphe. L’objectif de ce travail de thèse était d’étudier la fonction et l’interaction avec ce réseau de régulation de deux facteurs de transcription, Skn7 et Orf19 .217. L’identification des circuits de transcription gouvernés par Skn7, par des approches globales d’étude de la transcription et de la fixation à la chromatine, indique un double rôle dual dans la régulation de la réponse au stress osmotique/oxydatif et de la croissance filamenteuse. L’analyse des interactions génétiques a révélé des relations fonctionnelles entre Skn7 et les principaux régulateurs de la morphogenèse. En outre, Skn7 est indispensable pour limiter l'accumulation d'espèces réactives à l'oxygène (ROS) pendant la croissance filamenteuse sur milieu solide. La caractérisation de mutants spécifiques de Skn7 a mis en évidence un découplage de cette dernière fonction et de la fonction de Skn7 dans la morphogénèse.Orf19.217 est impliqué dans la virulence de C. albicans, mais sa fonction biologique reste incertaine. Notre étude montre que Orf19.217 régule divers processus, tels que la morphogenèse, la modification de la paroi cellulaire et la réponse au stress. D'autres expériences sont en cours pour élucider son rôle dans la virulence / Candida albicans is a diploid fungus, commensal of most healthy individuals, but also one of the most prevalent human fungal pathogens. C. albicans has the ability to switch between the unicellular yeast form and filamentous forms (pseudohyphae or hyphae). This transition is triggered by various environmental cues and plays important roles in C. albicans virulence. An intricate regulatory network involving many transcription factors controls the yeast-to-hypha transition. The aim of this PhD was to explore the function of two transcription factors, Skn7 and Orf19.217, whose overexpression triggers filamentation independently of hypha-inducing cues, and their interplay with the morphogenetic regulatory network. Mapping of the Skn7 transcriptional circuitry, through combination of genome-wide expression and location technologies, pointed to a dual regulatory role encompassing osmotic/oxidative stress response and filamentous growth. Genetic interaction analyses revealed close functional interactions between Skn7 and master regulators of morphogenesis. Furthermore, Skn7 was crucial for limiting the accumulation of reactive oxygen species (ROS) during filamentous growth on solid medium. Interestingly, functional domain mapping using site-directed mutagenesis allowed decoupling of Skn7 function in morphogenesis from protection against intracellular ROS. Orf19.217 was previously implicated in C. albicans virulence, but its biological function remains unclear. Preliminary data showed that Orf19.217 regulates several processes, including morphogenesis, cell wall modifications and stress response. Further experiments are ongoing to elucidate the role of this gene in virulence