Global ETD Search

1	Autentisering med OAuth 2.0 i SiteVision : Jämförelse mellan Java Portlets och WebApps Edin, Andreas January 2018 (has links) The aim of this project has been to explore alternative technical solutions for making own extensions in the CMS SiteVision. The purpose of these extensions is to retrieve data from an external API (Office 365) which requires OAuth 2.0 authentication. Additional, the alternative technical solutions have been evaluated and compared. The comparisons have been made based on criteria developed through interviews with professional IT-consultants. The purpose of the project has been to contribute to more efficient digitization, integration and individualization of datasystems. Within the project, an applied example (POC) has been created to show examples of how the technology can be used. In this example, Java Portlets have been used to implement the above functionality. WebApps in SiteVision have also been studied since this technology is an alternative to Java Portlets. The survey shows that it is fully possible to create a separate extension in SiteVision that performs authentication with OAuth 2.0 and then uses it to retrieve data from an external API. The results from the comparison between the two different Java Portlets and WebApps technologies show that there are pros and cons of each technique. The alternatives studied where comparable in performance. Individual circumstances can dictate which alternative is best. / Det övergripande syftet med detta projekt har varit att bidra till en effektiviserad digitalisering och individualisering. Målet för projektet har varit att undersöka alternativa tekniska lösningar för att göra egna tillägg i CMS:et SiteVision. Tillägg vars uppgift består i att hämta data från ett externt API (Office 365) som kräver autentisering med OAuth 2.0. Vidare har de alternativa tekniska lösningarna värderats och jämförts. Jämförelsen har gjorts utifrån kriterier som tagits fram genom intervjuer med utvecklare på ett IT-konsultbolag. Inom projektet har ett tillämpat exempel (POC) skapats för att visa exempel på hur tekniken kan användas. I detta exempel har Java Portlets använts för att implementera ovanstående funktionalitet. Även WebApps i SiteVision har studerats då den tekniken utgör ett alternativ till Java Portlets. Undersökningen visar att det är fullt möjligt att skapa ett eget tillägg i SiteVision som genomför autentisering med OAuth 2.0 och sedan använda denna för att hämta data från ett externt API. Resultaten från jämförelsen mellan de två olika teknikerna Java Portlets och WebApps visar att det finns för- och nackdelar med respektive teknik. Båda alternativen framstår som jämstarka i jämförelsen. De individuella omständigheterna kring ett framtida användande bör fälla avgörandet för vilken teknik som väljs. SiteVision Java Java Portlet CMS Content Management System OAuth 2.0 WebApps Cybercom SiteVision Java Java Portlet CMS Content Management System OAuth 2.0 WebApps Cybercom Software Engineering Programvaruteknik
2	Realtidsmultiplayerspel på IoT-backend / Real-time Multiplayer Game on IoT-backend Almqvist, Joel, Detterfelt, Björn, Håkansson, Tim, Kjellström, David, Löjdquist, Axel, Oskarsson, Joel, Wahid, Lieth, Wilkens, Alexander January 2018 (has links) This report presents a project carried out for the company Cybercom by eight students from Linköping University. The aim of the project has been to develop a real-time multiplayer game using an existing system for communication between different devices. The game has been developed as a web app that contains multiple game modes. The specific development methodology that has been used throughout the project is presented in this report. This methodology has been iterative, agile and followed a simplified version of the Scrum framework. The end result of the project is a well functioning product that directly creates value for the customer, but also allows for further development. / I denna rapport presenteras ett projekt för företaget Cybercom utfört av åtta studenter från Linköpings universitet. Projektet har gått ut på att utveckla ett realtidsspel som använder sig av ett existerande system för kommunikation mellan enheter. Spelet har utvecklats som en webbapplikation och innehåller flera olika spellägen. I det genomförda projektet har en modifierad, nedskalad variant av arbetsmetodiken Scrum följts och denna presenteras i rapporten. Utvecklingen har därmed varit iterativ och agil. Resultatet av projektet är en väl fungerande produkt som direkt skapar värde för kunden, men även tillåter smidig vidareutveckling. iot internet of things game mobile web javascript react cybercom sensors online multiplayer internet mobil spel flera spelare Computer Sciences Datavetenskap (datalogi)
3	Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison / Instanssegmentering av kategoriserat skräp samt hantering av obalanserat dataset Sievert, Rolf January 2021 (has links) Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) in conjunction with the Common Objects in Context precision and recall scores. TACO is an imbalanced dataset, and therefore imbalanced data-handling is addressed, exercising a second-order relation iterative stratified split, and additionally oversampling when training Mask R-CNN. Mask R-CNN without oversampling resulted in a segmentation of 0.127 mAP, and with oversampling 0.163 mAP. DetectoRS achieved 0.167 segmentation mAP, and improves the segmentation mAP of small objects most noticeably, with a factor of at least 2, which is important within the litter domain since small objects such as cigarettes are overrepresented. In contrast, oversampling with Mask R-CNN does not seem to improve the general precision of small and medium objects, but only improves the detection of large objects. It is concluded that DetectoRS improves results compared to Mask R-CNN, as well does oversampling. However, using a dataset that cannot have an all-class representation for train, validation, and test splits, together with an iterative stratification that does not guarantee all-class representations, makes it hard for future works to do exact comparisons to this study. Results are therefore approximate considering using all categories since 12 categories are missing from the test set, where 4 of those were impossible to split into train, validation, and test set. Further image collection and annotation to mitigate the imbalance would most noticeably improve results since results depend on class-averaged values. Doing oversampling with DetectoRS would also help improve results. There is also the option to combine the two datasets TACO and MJU-Waste to enforce training of more categories. Machine learning Multiclass Deep learning Instance segmentation Object segmentation Iterative stratification Mask R-CNN DetectoRS Imbalanced dataset Classification Detection Segmentation Litter Trash TACO COCO MMDetection Multinomial Cybercom AI Artificial intelligence Land-based litter Computer vision Maskininlärning Djupinlärning Instanssegmentering Objektsegmentering Mask R-CNN DetectoRS Obalanserat dataset Klassificering Detektion Segmentering Skräp TACO COCO MMDetection Multinomial Cybercom AI Artificiell intelligens Datorseende

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