The subject of the Diploma thesis was the city library in Prerov. Solved plot is located on the square of Prerov rebellion, near the historical center, on the pedestrian route from the center to one of the largest residential areas of the city. Urban solution is based on the nature of the site, which determines the spatial park and building of cinema named Hvezda. The proposed building complements unused corner opposite the church at the northern edge of the territory back somewhat from the road front space and creating public place respecting the imaginary street line on the east side of the building defined cinema. The building is divided vertically into 2 parts - a larger platform with a green roof that moves freely in the park and is its part, second part is the main corner rectangular compound libraries 'growing up' from the green platform.

The transformation of cities into smart cities is a common occurrence today, and one of the reasons is urbanization. Technology contributes to the ability to deal with new problems that arise. The number of IoT platforms has increased due to their flexibility and value addition regarding interoperability, processing of IoT data, and analysis capability. An IoT platform's capabilities are desirable, not least to municipalities today. Through IoT devices, such as sensors, the municipality can increase their awareness of the city’s surroundings and stay up to date through better insight into trends or new challenges. However, IoT is in its infancy compared to other markets, and there is a limited understanding of the IoT subject in municipal contexts. These aspects contribute to difficulties when municipalities take on trends for smart city initiatives. There is still a lack of design and development guidelines regarding ICT solutions to enable the development of a holistic smart city. The few current smart city deployments also make it challenging to validate which technology should be adopted to achieve the vision for the smart city. Therefore, the study investigates how a central IoT platform might benefit municipal departments in urban development projects. The case study conducted within the City of Stockholm generated ten benefits that might occur if the central IoT platform were available within the municipal departments' urban development projects. The benefits were divided into three themes: ·         Information and data management ·         Reasons to use IoT data ·         Interoperability between the central IoT platform and systems

Drones have a new emerging use case: performing in shows and live events. This master thesis has been driven by an artistic project invited to take part in a full-scale operatic performance in the Croatian National Theatre Ivan Zajc in Rijeka, Croatia, in 2019. This project merges technological research with ancient theatrical and operatic traditions by using drones as an opera choir. After describing the process of designing and building a fleet of quadrotors equipped with speakers, we present a reacting and interacting motion planning strategy based on potential fields. We analyse and evaluate our drone design with its control strategy on simulation and on a real drone. / Droner har ett nytt framväxande användarfall: att delta i show- och liveevenemang. Detta examensarbete har drivits av ett konstnärligt projekt som inbjudits att delta i ett fullskaligt opera-uppträdande i den kroatiska nationalteatern Ivan Zajc i Rijeka, Kroatien, 2019. Detta projekt förenar teknisk forskning med gamla teatraliska och opera-traditioner genom att använda droner som en operakör. Efter att ha beskrivit processen att designa och bygga en flotta quadrotors utrustade med högtalare presenterar vi en reagerande och interaktiv rörelseplaneringsstrategi baserad på potentiella fält. Vi analyserar och utvärderar vår drone-design med sin kontrollstrategi för simulering och på en riktig drone.

This research investigates the Training Effectiveness of a low-cost, PC-based training system when compared with two modes (motion and no motion) of a cab training system with large screen for various aviation flying tasks. While much research on this topic has been done in the past, advances in technology have significantly altered what is considered a "low-cost" "simulator." The technology advances have in effect increased the ability of a "low-cost" "simulator" to deliver desired experiences to the user. These "simulators" often are nothing more than PC training system, with only notional representations of the actual aircraft. This research considers the use of such training systems in training for a highly complex and dynamic task situation, that task being a search and rescue mission. A search and rescue mission is far more complex task than those studied for possible "low-cost" simulation substitution in the past. To address that aspect, one mode of the cab involves motion in two degrees of freedom. The results of this research advances the body of literature on the capability of "low-cost" simulation to deliver the experiences necessary to learn highly complex tasks associated with search and rescue as well as further clarify the extent to which a motion platform aides in flight training. This research utilizes available platforms provided by the US Army Research, Development and Engineering Command Simulation and Training Technology Center. Additionally, all the participants in the research are in training to be helicopter pilots. Participants were randomly assigned to one of three training configurations: a) Cab with motion turned ON, b) Cab with motion turned OFF and c) PC-based simulator. Training effectiveness is evaluated using measures for learning, task performance, and human factors. Statistically significant results are shown for the Cab with Motion and the Cab with No Motion configurations.

This study evaluates the use of a wheelchair balance board. The balance board was created as a sensory stimulation tool for users with various disabilities. It was originally designed to create vestibular stimulation for the person after they were loaded on. This study was used as a way to test if the balance board could be used for other things such as physical therapy and exercise. Ten able-bodied individuals were used to show the functionality of the device. They were asked to perform six different abdominal exercises while the muscle activity of their rectus abdominis and external obliques was measured using EMG electrodes. The exercises performed included: abdominal crunch, reverse crunch, full vertical crunch, torso twist, seated crunch, and sitting abdominal bend down. The last three exercises were performed on both stable ground and on the wheelchair balance board. This study found that the balance board did not cause any negative effects in terms of the abdominal muscle activation. In some cases it actually increased the muscle activation compared to the stable and floor exercises. There were no cases where the balance board caused a decrease in the amount of muscle activation in comparison to the floor exercises. The highest values for any muscle group activated in the entire study was found to be on the balance board during the sitting abdominal bend down. This demonstrates that the balance board shows promise as a tool for stimulating muscles not traditionally activated for people in a wheelchair.

Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received RF signal, and automated modulation classification (AMC), determining the modulation scheme of a received RF transmission. Both tasks have a number of algorithms that are effective on simulated data, but struggle to generalize to data collected in the real-world, partially due to the lack of available datasets upon which to train models and understand their limitations. This thesis covers the practical considerations for systems that can create high-quality datasets for RFML tasks, how variances from real-world effects in these datasets affect RFML algorithm performance, and how well models developed from these datasets are able to generalize and adapt across different receiver hardware platforms. Moreover, this thesis presents a proof-of-concept system for large-scale and efficient data generation, proven through the design and implementation of a custom platform capable of coordinating transmissions from nearly a hundred Software-Defined Radios (SDRs). This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful transfer between SDRs of trained models for both SEI and AMC algorithms. / Master of Science / Radio Frequency Machine Learning (RFML) is the application of machine learning techniques to solve problems having to do with radio signals as an alternative to traditional signal processing techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a received signal, and automated modulation classification (AMC), determining the data encoding format of a received RF transmission. Both tasks have practical limitations related to the real-world collection of RF training data. This thesis presents a proof-of-concept for large-scale, efficient data generation and management, as proven through the design and construction of a custom platform capable of coordinating transmissions from nearly a hundred radios. This platform was used to rapidly perform experiments in both RFML performance sensitivity analysis and successful cross-radio transfer of trained behaviors.

The thesis purpose is to gather recommendations to adapt participatory design to elderly users, through the involvement of an association of elderly museum volunteers. The outcome is the result of a participatory process that included forms, interviews, cultural probes, and workshops where the volunteers and designer collaborated tightly to explore volunteers’ needs and find solutions to address them. This process led to the design of a platform that empowers volunteers' work and recognizes its value. The platform includes sections managed by the volunteers to archive information about the museum pieces, share organized activities, and receive feedback from visitors to improve their work.

Energy consumption is one of the most constrained requirements for the development and implementation of wireless sensor networks. Many design aspects affect energy consumption, ranging from the hardware components, operations of the sensors, the communication protocols, the application algorithms, duty cycles and others. Efficient simulation tool can be used to estimate the contribution to energy consumption of all of these factors, and significantly decrease the efforts and time spent to choose the right solution that fits best to a particular application. In this work we present design space exploration methodology for ultra low power embedded systems and wireless sensor networks. The methodology takes inspiration from Platform Based Design (PBD) paradigm and defines separate abstraction layers for all system aspects that directly contribute power consumption of target applications. To support presented methodology we built a SystemC-based discrete event simulation framework, called “PASES”, that provides power-aware simulation and analysis of wireless sensor networks and sensor nodes. Its modular architecture allows flexible, extensible and rapid modeling of custom HW platforms, SW application models, communication protocols, energy sources, environment dynamics and nodes mobility. Based on the feedback gained from PASES, the optimal and energy-efficient solution for the specific project of interest can be selected. The proposed approach improves state-of-the-art by providing fast and reliable power-aware system-level exploration for a wide range of custom applications

Sepsis is a life-threatening condition characterized by organ dysfunction due to an uncontrolled response to infection. Despite decades of research, the mortality rate remains high, emphasizing the need for an improved understanding of sepsis pathophysiology and improvements in preclinical animal research. Recently, extracellular histones, major mediators of organ dysfunction and death, have emerged as a potential therapeutic target for sepsis. In this thesis, we reported that the ability of heparin to neutralize the cytotoxic and procoagulant effects of histones is size-dependent but independent of the antithrombin- binding pentasaccharide. In contrast, the ability of heparin to neutralize histone-mediated impairment of activated protein C generation is independent of size and anticoagulant activity. These findings suggest that heparin variants may have differential therapeutic potential in vascular disease states that are associated with elevated levels of histones. Before testing the therapeutic efficacy of the heparin variants in vivo, we aimed to develop and standardize a murine model of sepsis that can be utilized in a multi-center platform. As one of the lead sites for the National Preclinical Sepsis Platform (NPSP), we optimized a 72-hour model of abdominal sepsis using supportive treatments. As sepsis predominately impacts the elderly, we also explored the impact of aging on the host response to sepsis using our fecal induced peritonitis (FIP) model. Aged FIP mice exhibited a higher mortality rate compared to young FIP mice. The worsened organ injury and poor survival in aged mice may be attributed to heightened inflammation in aged mice. We also observed trends in increased bacterial loads, increased coagulation, elevated cell free DNA, and decreased ADAMTS13 activity in aged septic mice. These findings help to improve our understanding of how aging impacts the host response to sepsis, which may be translated into therapeutic strategies that considers advanced age as a risk factor for sepsis. / Thesis / Candidate in Philosophy

Researchers have made significant progress in information extraction from short documents in the last few years, including social media interaction, news articles, and email excerpts. This research aims to extract technical entities like hardware resources, computing platforms, compute time, programming language, and libraries from scholarly research articles. Research articles are generally long documents having both salient as well as non-salient entities. Analyzing the cross-sectional relation, filtering the relevant information, measuring the saliency of mentioned entities, and extracting novel entities are some of the technical challenges involved in this research. This work presents a detailed study about the performance, effectiveness, and scalability of rule-based weakly supervised algorithms. We also develop an automated end-to-end Research Entity and Relationship Extractor (E2R Extractor). Additionally, we perform a comprehensive study about the effectiveness of existing deep learning-based information extraction tools like Dygie, Dygie++, SciREX. The research also contributes a dataset containing novel entities annotated in BILUO format and represents the baseline results using the E2R extractor on the proposed dataset. The results indicate that the E2R extractor successfully extracts salient entities from research articles. / Master of Science / Information extraction is a process of automatically extracting meaningful information from unstructured text such as articles, news feeds and presenting it in a structured format. Researchers have made significant progress in this domain over the past few years. However, their work primarily focuses on short documents such as social media interactions, news articles, email excerpts, and not on long documents such as scholarly articles and research papers. Long documents contain a lot of redundant data, so filtering and extracting meaningful information is quite challenging. This work focuses on extracting entities such as hardware resources, compute platforms, and programming languages used in scholarly articles. We present a deep learning-based model to extract such entities from research articles and research papers. We evaluate the performance of our deep learning model against simple rule-based algorithms and other state-of-the-art models for extracting the desired entities. Our work also contributes a labeled dataset containing the entities mentioned above and results obtained on this dataset using our deep learning model.