Manual deployments of software is a tedious, repetitive and non-scaling method of deploying software.Continuous Delivery is a practice that enables automated deployment of software in a rapid fashion at the click of a button.When deciding whether to start using a new practice, software companies need to make an assessment from a cost-benefit perspective.This thesis compares automated deployments through Continuous Delivery with manual deployments from a cost perspective.The comparison is done at a small software company where two tools for Continuous Delivery are chosen based on requirements imposed by the company. The tools, Octopus Deploy and Azure DevOps, are cost efficient to different degrees.Octopus is cost efficient if several deployments per week are necessary, particularly if many deployment targets are involved.Azure DevOps is quickly cost efficient in most cases due to its pricing scheme, only needing roughly one deployment per week for few deployment targets, and a couple of deployments per year for many deployment targets.The initial cost of having a paid employee set up the tool needs to be paid off, but is easily done within a year using weekly deployments with a small number of deployment targets.

This thesis addresses a multi-agent search problem where several agents, equipped with sensors and communication devices, search an unknown area. Lack of information about the search space is modeled as an uncertainty density distribution. A sequential deploy and search (SDS) strategy is formulated where the agents are first deployed to maximize single step search effectiveness. To achieve an optimal deployment, a multi-center objective function defined using the Voronoi cells and the uncertainty distribution is optimized. It is shown that the critical points of this objective function are the centroids of the Voronoi cells. A proportional control law is proposed that makes the agents move to their respective “centroids”. Assuming agents to be first order dynamical systems and using LaSalle's invariance principle, it is shown that the closed-loop system converges globally asymptotically to the critical points. It is also shown that the sequential deploy and search strategy is spatially distributed with respect to the Delaunay graph corresponding to any given agent configuration. Next, a combined deploy and search (CDS) strategy is proposed where, instead of first deploying agents and then performing the search, the agents engage in search operation as they move toward the centroids. This strategy gives rise to shorter agent trajectories compared to the SDS strategy. Then the problem is formulated with practical constraints such as sensor range limits and limit on maximum speed of the agents. A few issues relating to implementation of the proposed search strategies are also addressed. Finally, the assumption of homogeneous agents is relaxed and agents equipped with sensors with heterogeneous capabilities are considered. A generalized Voronoi partitioning scheme is proposed and used to formulate a heterogeneous locational optimization problem. In this problem the agents are deployed in the search space optimizing the sensor effectiveness. As earlier, the two search strategies are proposed. Simulation experiments are carried out to validate the performance of the proposed search strategies. The simulation results indicate that both the proposed search strategies perform quite well even when the conditions deviated from the nominal. It is also shown that the combined deploy and search strategy leads to shorter and smoother trajectories than those of the sequential deploy and search strategy with the same parameters.

Configurable computing machines (CCMs) offer high-performance application acceleration with custom hardware. They are also dynamically reconfigurable and give significant internal visibility. Such features are useful throughout the design, debug, and deploy stages of CCM-based application development. However traditional, monolithic design tools do not offer adequate support for all of these development stages. This thesis describes a specification for a reconfigurable computing application framework (RCAF) which is more suitable for CCM application development. It also describes an implementation of such an RCAF. This RCAF improves the efficiency of application design and debugging. It also establishes an application architecture framework which helps to build up not only the hardware design, but also the application software and user interface. Applications built using this small, deployable RCAF may also perform significantly better due to the dynamic hardware reconfiguration features included with the RCAF.

<p>  </p> <p>The proper classification and disposal of waste are crucial in reducing environmental impacts and promoting sustainability. Several solid waste classification systems have been developed over the years, ranging from manual sorting to mechanical and automated sorting. Manual sorting is the oldest and most commonly used method, but it is time-consuming and labor-intensive. Mechanical sorting is a more efficient and cost-effective method, but it is not always accurate, and it requires constant maintenance. Automated sorting systems use different types of sensors and algorithms to classify waste, making them more accurate and efficient than manual and mechanical sorting systems. In this thesis, we propose the development of an intelligent solid waste detection, classification and tracking system using artificial deep learning techniques. To address the limited samples in the TrashNetV2 dataset and enhance model performance, a data augmentation process was implemented. This process aimed to prevent overfitting and mitigate data scarcity issues while improving the model's robustness. Various augmentation techniques were employed, including random rotation within a range of -20° to 20° to account for different orientations of the recycled materials. A random blur effect of up to 1.5 pixels was used to simulate slight variations in image quality that can arise during image acquisition. Horizontal and vertical flipping of images were applied randomly to accommodate potential variations in the appearance of recycled materials based on their orientation within the image. Additionally, the images were randomly scaled to 416 by 416 pixels, maintaining a consistent image size while increasing the dataset's overall size. Further variability was introduced through random cropping, with a minimum zoom level of 0% and a maximum zoom level of 25%. Lastly, hue variations within a range of -20° to 20° were randomly introduced to replicate lighting condition variations that may occur during image acquisition. These augmentation techniques collectively aimed to improve the dataset's diversity and the model's performance. In this study, YOLOv8, EfficientNet-B0 and VGG16 architectures were evaluated, and stochastic gradient descent (SGD) and Adam were used as the optimizer. Although, SGD provided better test accuracies compared to Adam. </p> <p>Among the three models, YOLOv8 showed the best performance, with the highest average precision mAP of 96.5%. YOLOv8 emerges as the top performer, with ROC values varying from 92.70% (Metal) to 98.40% (Cardboard). Therefore, the YOLOv8 model outperforms both VGG16 and EfficientNet in terms of ROC values and mAP. The findings demonstrate that our novel classifier tracker system made of YOLOv8, and supervision algorithms surpass conventional deep learning methods in terms of precision, resilience, and generalization ability. Our contribution to waste management is in the development and implementation of an intelligent solid waste detection, classification, and tracking system using computer vision and deep learning techniques. By utilizing computer vision and deep learning algorithms, our system can accurately detect, classify, and localize various types of solid waste on a moving conveyor, including cardboard, glass, metal, paper, and plastic. This can significantly improve the efficiency and accuracy of waste sorting processes.</p> <p>This research provides a promising solution for detection, classification, localization, and tracking of solid waste materials in real time system, which can be further integrated into existing waste management systems. Through comprehensive experimentation and analysis, we demonstrate the superiority of our approach over traditional methods, with higher accuracy and faster processing times. Our findings provide a compelling case for the implementation of intelligent solid waste sorting.</p>

To create, update, or deploy a mobile application, a collection of hand-operated works must be satisfied. In this project, regardless of the mobile application's code and its core functionalities, which can be an e-book, an application, or even a mobile game, we will study how to automate, visualize and simplify the following manual procedures: 1.Create a remote Git repository for the mobile application. 2.Constructing or altering the mobile application's configuration or graphical contents. 3.Push all changes to the remote Git repository. 4.Deploy or distribute the mobile application from its Git repository after each push. / För att skapa, uppdatera eller distribuera en mobilapplikation måste en samling handstyrda verk uppfyllas. I detta projekt, oavsett mobilapplikationens kod och dess kärnfunktioner, som kan vara en e-bok, en applikation eller till och med ett mobilspel, kommer vi att studera hur man automatiserar, visualiserar och förenklar följande manuella procedurer: 1. Skapa ett avlägset Git -arkiv för mobilapplikationen. 2.Konstruera eller ändra mobilapplikationens konfiguration eller grafiska innehåll. 3.Push alla ändringar i det externa Git -arkivet. 4. Distribuera mobilappen från sitt Git -arkiv efter varje ändring.

In this diploma thesis is designed replica Hanacky estate from the 19th century. The whole structure includes not only a replica of the estate but also including the reconstruction of a barn or shelter. Hanacky estate will be part of Zoopark Vyškov and will serve as an environmental education center. Thesis I deal with a construction technological project. I focus here on the transport links, building-site cell site equipment, calculation of water and electricity, design tools and machinery, calculation, budget and schedule, the use of machines and workers, limitky. I deal with the technological regulations innovative technology. Furthermore, it is the control and test plans, security, ecology, design contract for work. In the last part of my thesis I deal with natural building materials.