Tractography is widely used in the brain connectivity study from diffusion magnetic resonance imaging data. However, lack of ground truth and plenty of anatomically implausible streamlines in the tractograms have caused challenges and concerns in the use of tractograms such as brain connectivity study. Tractogram filtering methods have been developed to remove the faulty connections. In this study, we focus on one of these filtering methods, Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), which tries to find a set of streamlines that best reconstruct the diffusion magnetic resonance imaging data with global optimization approach. There are biases with this method when assessing individual streamlines. So a method named randomized COMMIT(rCOMMIT) is proposed to obtain multiple assessments for each streamline. The acceptance rate from this method is introduced to the streamlines and divides them into three groups, which are regarded as pseudo ground truth from rCOMMIT. Therefore, the neural networks are able to train on the pseudo ground truth on classification tasks. The trained classifiers distinguish the obtained groups of plausible and implausible streamlines with accuracy around 77%. Following the same methodology, the results from rCOMMIT and randomized SIFT are compared. The intersections between two methods are analyzed with neural networks as well, which achieve accuracy around 87% in binary task between plausible and implausible streamlines.

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Användningen och behovet av rekommendationssystem i digitala tjänster har växt i takt med att utbudet i dessa blivit allt större och svårare för användare att navigera i. Rekommendationssystem används idag i allt ifrån E-handel till musikoch filmstreaming. För att förse användare med rekommendationer på objekt används en mängd olika väl beprövade algoritmer, filtreringsmetoder och datainsamlingsmetoder. Att applicera dessa i livestreamingtjänster ställer nya krav på systemen eftersom innehållet byts ut mer frekvent, helt nytt innehåll tillkommer regelbundet och explicit data samt metadata är sällan tillräcklig för att ta fram träffsäkra rekommendationer. I en fallstudie med företaget Liveguide undersöks hur rekommendationssystem kan appliceras i livestreamingtjänster med avseende på de utmaningar och krav som finns. Metoder presenteras där aktuella lösningar testas, utvärderas och anpassas till att fungera bra i livestreamingsammanhang. Slutligen föreslås tre modeller för rekommendationssystem som tagits fram utifrån det resultat metoderna leder till. För att tillfredsställa de identifierade utmaningarna inom området visade sig hybrida, mångsidiga modeller fördelaktiga i livestreaming. / The usage and demand of recommender systems in digital services has increased in line with their huge range of products, making it more difficult for users to navigate through the content. Recommender systems are used in a wide scope of digital services ranging from E-commerce to music and film streaming. In order to provide users with recommendations on objects, a variety of algorithms, filtering methods and methods of data collections are being used. Applying these in live streaming services puts new demands on such systems since the content is replaced frequently and new objects added regularly. Furthermore, livestreaming services often lack explicit data and metadata, making recommendations less accurate. In a case study with Liveguide, recommender systems are evaluated, focusing on whether they are applicable to live streaming services, respecting requirements and demands on such systems. Methods are presented which tests, evaluates and adapts existing solutions to fit in well in context of live streaming. Finally, three models for recommender systems are suggested, based on the methods result. In order to satisfy the identified challenges, hybrid models turned out to be preferable in the context.

This thesis analyzes the process of deblurring of degraded images generated by space-variant nonlinear image systems with Gaussian observation noise. The restoration of blurred images is performed by using two methods / a modified version of the Optimum Decoding Based Smoothing Algorithm and the Bootstrap Filter Algorithm which is a version of Particle Filtering methods. A computer software called MATLAB is used for performing the simulations of image estimation. The results of some simulations for various observation and image models are presented.

This thesis concerns Precision Agriculture (PA) technology which involves methods developed to optimise grain yield by examining data quality and modelling protein/yield relationship of wheat and sorghum fields in central and southern Queensland. An important part of developing strategies to optimisise grain yield is the understanding of PA technology. This covers major aspects of PA which includes all the components of Site- Specific Crop Management System (SSCM). These components are 1. Spatial referencing, 2. Crop, soil and climate monitoring, 3. Attribute mapping, 4. Decision suppport systems and 5. Differential action. Understanding how all five components fit into PA significantly aids the development of data analysis methods. The development of PA is dependent on the collection, analysis and interpretation of information. A preliminary data analysis step is described which covers both non-spatial and spatial data analysis methods. The non-spatial analysis involves plotting methods (maps, histograms), standard distribution and statistical summary (mean, standard deviation). The spatial analysis covers both undirected and directional variogram analyses. In addition to the data analysis, a theoretical investigation into GPS error is given. GPS plays a major role in the development of PA. A number of sources of errors affect the GPS and therefore effect the positioning measurements. Therefore, an understanding of the distribution of the errors and how they are related to each other over time is needed to complement the understanding of the nature of the data. Understanding the error distribution and the data give useful insights for model assumptions in regard to position measurement errors. A review of filtering methods is given and new methods are developed, namely, strip analysis and a double harvesting algoritm. These methods are designed specifically for controlled traffic and normal traffic respectively but can be applied to all kinds of yield monitoring data. The data resulting from the strip analysis and double harvesting algorithm are used in investigating the relationship between on-the-go yield and protein. The strategy is to use protein and yield in determining decisions with respect to nitrogen managements. The agronomic assumption is that protein and yield have a significant relationship based on plot trials. We investigate whether there is any significant relationship between protein and yield at the local level to warrent this kind of assumption. Understanding PA technology and being aware of the sources of errors that exist in data collection and data analysis are all very important in the steps of developing management decision strategies.