Star formation is a complex hierarchical process that witnesses the transfer of mass among a range of scales from large diffuse molecular clouds to crowded clumps and finally down to prestellar cores. The final stage of this process has prestellar cores actively accreting matter while undergoing gravitational collapse on their way to becoming main sequence stars. This thesis presents multi wavelength submillimeter observations of the Perseus molecular cloud using 160 μm, 250 μm, 350 μm, and 500 μm maps of thermal dust emission from the Herschel space observatory. Additionally C18O J = 3 → 2 spectral line emission is observed in four star forming clumps within Perseus using the James Clerk Maxwell Telescope. Spectral line emission allows for the separation of material along the line of sight. Prestellar core mass is derived from observational maps using various source finding algorithms. The mass is overestimated when compared to prestellar core mass found from spectral line data. This overestimation can be mitigated with careful selection of source finding algorithm and background removal. Further, the prestellar core mass derived from spectral line data was the closest match to the initial stellar mass function over dust maps. However, both the spectral line masses and dust map masses do not agree with the IMF confirming a star forming efficiency factor in the evolutionary step between prestellar core and main sequence star. Lastly, a filamentary analysis finds that high mass stars preferentially form in crowded regions close to, or contained within, filament structure. / Thesis / Master of Science (MSc) / Star formation is a complex hierarchical process that witnesses the transfer of mass among a range of scales from large diffuse molecular clouds to crowded clumps and finally down to prestellar cores. The final stage of this process has prestellar cores actively accreting matter while undergoing gravitational collapse on their way to becoming main sequence stars. This thesis presents multi wavelength submillimeter observations of the Perseus molecular cloud using 160 μm, 250 μm, 350 μm, and 500 μm maps of thermal dust emission from the Herschel space observatory. Additionally carbon monoxide spectral line emission is observed in four star forming clumps within Perseus using the James Clerk Maxwell Telescope. Spectral line emission allows for the separation of material along the line of sight. Prestellar core mass is derived from observational maps using various source finding algorithms. The mass is overestimated when compared to prestellar core mass found from spectral line data. This overestimation can be mitigated with careful selection of source finding algorithm and background removal. Further, the prestellar core mass derived from spectral line data was the closest match to the initial stellar mass function over dust maps. However, both the spectral line masses and dust map masses do not agree with the IMF confirming a star forming efficiency factor in the evolutionary step between prestellar core and main sequence star. Lastly, a filamentary analysis finds that high mass stars preferentially form in crowded regions close to, or contained within, filament structure.

There are a lot of stand-alone and mobile platforms using transmitters today. Some want to be found while others do not. In our modern society there is a great demand of mobility and communication abilities. This means that several mobile platforms could potentially carry a sensor to record incoming signals to be used in Direction Finding. This thesis identifies the possibility to determine the direction to a transmitter with randomly placed sensors. By conducting a literature review well-known methods such as Time Difference Of Arrival (TDOA) and MUltiple SIgnal Classification (MUSIC) where chosen as methods in this analysis. The methods are applied on two antenna arrays, an Uniform Circular Array (UCA) and a Random Circular Array (RCA). The RCA is generated with randomly placed sensors. The performance in the Direction Of Arrival (DOA) is investigated in presence of time synchronization error and with different numbers of elements, radius and Signal to Noise Ratio (SNR). The ambiguity in the arrays is also investigated to insure a ambiguity-free DOA estimation. The results from this analysis identifies that the accuracy in the DOA estimation is dependent on the number of elements, SNR, the elements positions and the radius of the DF array. Furthermore, the accuracy of a UCA is greater than a RCA when the elements are randomly distributed within the area of a circle with radius R. Finally, it has shown that if time synchronization error occurs between the sensors, then the MUSIC method the accuracy will decrease greatly. / Det finns många individer och mobila platformar som använder sändare idag. Vissa vill bli hittade, andra inte. I vårat moderna samhälle är det en stor efterfrågan på rörlighet och kommunikationsmöjligheter. Detta innebär att många mobila plattformar skulle kunna spela in signaler för att användas i radiopejling. Denna uppsats identifierar möjligheten att bestämma riktningen till en sändare med slumpmässigt placerade sensorer. Genom litteraturstudien identifierades de välkända riktningsmetoder som Time Difference Of Arrivial (TDOA) och MUltiple SIgnal Classification (MUSIC) som vidare valdes som metoder i denna analys. Två antennstrukturer används i analyserna. Den ena är en Uniform Circular Array (UCA) och den andra är en Random Circular Array (RCA). RCA är genererad med slumpmässigt utplacerade sensorer. Prestandan i riktningsuppskattningen undersöks när det existerar ett tidssynkroniseringsfel, olika antal sensorer i antennstrukturerna, varierande radier och olika signaloch brusförhållanden.Ä ventvetydigheter undersöks i strukturerna för att säkerställa att en entydig riktningsbestämning kan utföras. Resultaten implicerar att noggrannheten i riktningsbestämningen är beroende avantalet element, SNR, elementens position och radien i antennmatrisen. Utöver detta visar resultaten att en UCA har högre noggrannhet än en RCA då elementen är slumpmässigt utplacerade inom en cirkelradie, R. Slutligen, om tidssynkroniseringsfel uppstår mellan sensorerna kommer detta resultera i minskad noggrannhet när MUSIC metoden tillämpas.

Aircraft in the US are equipped with Emergency Locator Transmitters (ELTs). In emergency situations these beacons are activated, providing a radio signal that can be used to locate the aircraft. Recent developments in UAV technologies have enabled mini-UAVs (5-foot wingspan) to possess a high level of autonomy. Due to the small size of these aircraft they are human-packable and can be easily transported and deployed in the field. Using a custom-built Radio Direction Finder, we gathered readings from a known transmitter and used them to compare various Bayesian reasoning-based filtering algorithms. Using a custom-developed simulator, we were able to test and evaluate filtering and control methods. In most non-trivial conditions we found that the Sequential Importance Resampling (SIR) Particle Filter worked best. The filtering and control algorithms presented can be extended to other problems that involve UAV control and tracking with noisy non-linear sensor behavior.

Due to complexity in software, manual testing is not enough to cover all relevant behaviours of it. A different approach to this problem is Symbolic Execution. Symbolic Execution is a software testing technique that tests all possible inputs of a program in the hopes of finding all bugs. Due to the often exponential increase in possible program paths, Symbolic Execution usually cannot exhaustively test a program. To nevertheless cover the most important or error prone areas of a program, search strategies that prioritize these areas are used. Such search strategies navigate the program execution tree, analysing which paths seem interesting enough to execute and which to prune. These strategies are typically grouped into two categories, general purpose searchers, with no specific target but the aim to cover the whole program and targeted searchers which can be directed towards specific areas of interest. To analyse how different searching strategies in Symbolic Execution affect the finding of errors and how they can be combined to improve the general outcome, the experiments conducted consist of several different searchers and combinations of them, each run on the same set of test targets. This set of test targets contains amongst others one of the most heavily tested sets of open source tools, the GNU Coreutils. With these, the different strategies are compared in distinct categories like the total number of errors found or the percentage of covered code. With the results from this thesis the potential of targeted searchers is shown, with an example implementation of the Pathscore-Relevance strategy. Further, the results obtained from the conducted experiments endorse the use of combinations of search strategies. It is also shown that, even simple combinations of strategies can be highly effective. For example, interleaving strategies can provide good results even if the underlying searchers might not perform well by themselves. / På grund av programvarukomplexitet är manuell testning inte tillräcklig för att täcka alla relevanta beteenden av programvaror. Ett annat tillvägagångssätt till detta problem är Symbolisk Exekvering (Symbolic Execution). Symbolisk Exekvering är en mjukvarutestningsteknik som testar alla möjliga inmatningari ett program i hopp om att hitta alla buggar. På grund av den ofta exponentiella ökningeni möjliga programsökvägar kan Symbolisk Exekvering vanligen inte uttömmande testa ettprogram. För att ändå täcka de viktigaste eller felbenägna områdena i ett program, används sökstrategier som prioriterar dessa områden. Sådana sökstrategier navigerar i programexekveringsträdet genom att analysera vilka sökvägar som verkar intressanta nog att utföra och vilka att beskära. Dessa strategier grupperas vanligtvis i två kategorier, sökare med allmänt syfte, utan något specifikt mål förutom att täcka hela programmet, och riktade sökare som kan riktas mot specifika intresseområden. För att analysera hur olika sökstrategier i Symbolisk Exekvering påverkar upptäckandetav fel och hur de kan kombineras för att förbättra det allmänna utfallet, bestod de experimentsom utfördes av flera olika sökare och kombinationer av dem, som alla kördes på samma uppsättning av testmål. Denna uppsättning av testmål innehöll bland annat en av de mest testade uppsättningarna av öppen källkod-verktyg, GNU Coreutils. Med dessa jämfördes de olika strategierna i distinkta kategorier såsom det totala antalet fel som hittats eller procenttalet av täckt kod. Med resultaten från denna avhandling visas potentialen hos riktade sökare, med ett exempeli form av implementeringen av Pathscore-Relevance strategin. Vidare stöder resultaten som erhållits från de utförda experimenten användningen av sökstrategikombinationer. Det visas också att även enkla kombinationer av strategier kan vara mycket effektiva.Interleaving-strategier kan till exempel ge bra resultat även om de underliggande sökarna kanske inte fungerar bra själva.

Robots are increasingly utilized in industry for their capability to perform repetitive,complex tasks in environments unsuitable for humans. This surge in robotic applicationshas spurred research into Multi-Robot Systems (MRS), which aim to tackle complex tasksrequiring collaboration among multiple robots, thereby boosting overall efficiency. However,MRS introduces multifaceted challenges that span various domains, including robot perception,localization, task assignment, communication, and control. This dissertation delves into theintricate aspects of task assignment and path planning within MRS.The first area of focus is on multi-robot navigation, specifically addressing the limitationsinherent in current Multi-Agent Path Finding (MAPF) models. Traditional MAPF solutionstend to oversimplify, treating robots as holonomic units on grid maps. While this approachis impractical in real-world settings where robots have distinct geometries and kinematicrestrictions, it is important to note that even in its simplified form, MAPF is categorized as anNP-hard problem. The complexity inherent in MAPF becomes even more pronounced whenextending these models to non-holonomic robots, underscoring the significant computationalchallenges involved. To address these challenges, this thesis introduces a novel MAPF solverdesigned for non-holonomic, heterogeneous robots. This solver integrates the hybrid A*algorithm, accommodating kinematic constraints, with a conflict-based search (CBS) for efficientconflict resolution. A depth-first search approach in the conflict tree is utilized to accelerate theidentification of viable solutions.The second research direction explores synergizing task assignment with path-finding inMRS. While there is substantial research in both decentralized and centralized task assignmentstrategies, integrating these with path-finding remains underexplored. This dissertation evaluatesdecoupled methods for sequentially resolving task assignment and MAPF challenges. Oneproposed method combines the Hungarian algorithm and a Traveling Salesman Problem (TSP)solver for swift, albeit suboptimal, task allocation. Subsequently, robot paths are generatedindependently, under the assumption of collision-free navigation. During actual navigation, aNonlinear Model Predictive Controller (NMPC) is deployed for dynamic collision avoidance. Analternative approach seeks optimal solutions by conceptualizing task assignment as a MultipleTraveling Salesman Problem (MTSP), solved using a simulated annealing algorithm. In tandem,CBS is iteratively applied to minimize the cumulative path costs of the robots.

This work further develops the way-finding model first proposed by Pearson and Kosicki (2017) which examines the flow of information in the digital age. Way-finding systems are online systems that help individuals find information—i.e. social media, search engines, email, etc. Using a grounded theory methodology, this new framework was explored in greater detail. Way-finding theory was created using the context of the elaboration likelihood model, gatekeeping theory, algorithmic gatekeepers, and the existence of the filter bubble phenomenon. This study establishes the three basic pillars of way-finding theory: the user’s mindset when accessing way-finding systems, the perception of how popular way-finding systems function, and the perception of the information personalization process—particularly regarding algorithmic gatekeepers and their roles in creating the filter bubble phenomenon. These pillars and the relationships that exist between each constitute way-finding theory.

The global migration crisis has brought to light many issues related to integrating migrants intotheir host countries. Employment is a critical component of integration, as it provides individualswith financial stability, a sense of purpose, and a means of contributing to their new society.However, employment outcomes for migrants vary widely depending on factors such as theircountry of origin, education level, language skills, and discrimination. In Sweden, MiddleEastern and Sub-Saharan African migrants face significant barriers to employment, which canlimit their ability to integrate into Swedish society. This thesis focused on and explored theemployment outcomes for Middle Eastern and Sub-Saharan African migrants in Malmo, Sweden.In a qualitative design and purposive sampling, 20 participants from Middle Eastern and Sub-Saharan African living in Malmo were interviewed using semi-structured interviews in threeweeks. The findings revealed that the means of finding employment, discrimination, skillsrecognition, and social networks as key factors influencing employment among immigrants inMalmo, Sweden. A short overview of the challenges and policies implemented to improve theiremployment prospects was presented.

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