Evaluation of the GPS Accuracy of Tablets

Augmented Reality (AR) applications often require knowledge of the user’s position in some global coordinate system in order to draw the augmented content to its correct position on the screen. The most common method for coarse positioning is the Global Positioning System (GPS). One of the advantages of GPS is that GPS receivers can be found in almost every modern mobile device. This research was conducted in order to determine the accuracies of different GPS receivers. The tests included seven consumer-grade tablets, three external GPS modules and one professional-grade GPS receiver. All of the devices were tested with both static and mobile measurements. It was concluded that even the cheaper external GPS receivers were notably more accurate than the GPS receivers of the tested tablets. The absolute accuracy of the tablets is difficult to determine from the test results, since the results vary by a large margin between different measurements. The accuracy of the tested tablets in static measurements were between 0.30 meters and 13.75 meters.

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.