Towards Engaging Intangible Holographic Public Displays

Public displays are some of the most challenging interfaces to design because of two key characteristics. First, the experience should be engaging, to attract and maintain users’ attention. Second, the interaction with the display should be natural, meaning that users should be able to receive the desired output with little or no training. Holographic displays are increasingly popular in public spaces such as museums and concert halls but there is little published research on users’ experiences with such displays. Previous research has suggested both tangible and intangible inputs as engaging and natural options for holographic displays, but there is no conclusive evidence on their relative merits. Hence, we run a study to investigate the user experience with a holographic display comparing the level of engagement and feeling of natural experience in the interacting process. We used a mix of surveys, interviews, video recordings, and task-based metrics to measure users’ performance on a specific task, the perceived usability, and levels of engagement and satisfaction. Our findings suggest that a tangible input was reported as more natural than the intangible one, however, both tangible and intangible inputs were found to be equally engaging. The latter findings contribute to the efforts of designing intangible public holographic displays and other interactive systems that take into consideration health safety issues, especially during the Covid-19 pandemic era in which contamination can be established with tangible and physical interaction between users and public displays, yet without affecting the level of engagement compared to the tangible experience

Modeling and control of a satellite’s geostationary orbit

In this Thesis a complete model of the dynamics describing the orbit of a geostationary satellite has been developed by using the Euler-Hill equations of relative motion. Following that, a system has been developed to control the satellite’s motion, which was the main objective of this work. Typically, this is classic problem in formation flight when the objective is to follow a “leader” or an analogous formation. On a similar basis, the idea developed and applied in this Thesis, was to control the satellite in order to minimize the distance from the satellite to the “leader” which in this case, is considered as a point orbiting in an ideal trajectory, irrespective of external or internal influences or disturbances. Real satellites are influenced by disturbances. Consequently, models causing those disturbances were developed. Finally, the satellite’s trajectory has been controlled using optimum and robust control design methods such as an LQ regulator and an H-infinity optimal control synthesis approach.Validerat; 20101217 (root

Neural networks to investigate the effects of smoking and alcohol abuse on the risk for preeclampsia

Following the application of a large number of neural network schemes that have been applied to a large data base of pregnant women, aiming at generating a predictor for the risk of preeclampsia occurrence at an early stage, we investigated the importance of the parameters of smoking and alcohol intake on the classification yield. A number of feedforward neural structures, both standard multilayer and multi-slab, were tried for the prediction. The database was composed of 6838 cases of pregnant women in UK, provided by the Harris Birthright Research Centre for Fetal Medicine in London. For each subject, 24 parameters were measured or recorded. Out of these, 15 parameters were considered as the most influential at characterizing the risk of preeclampsia occurrence, including the characteristics on whether the pregnant woman was an active smoker or not, and on whether she was consuming alcohol. The same data were applied to the same neural architecture, after excluding the information on smoking and alcohol, in order to study the importance of these two parameters on the correct classification yield. It has been found that both information parameters, were needed in order to achieve a correct classification as high as 83.6% of preeclampsia cases in the whole dataset, and of 93.8% in the test set. The preeclampsia cases prediction, for the totally unknown verification test, was 100%. When information on smoking and alcohol intake were not used, the results deteriorated significantl

Computational Modeling of Visual Selective Attention Based on Correlation and Synchronization of Neural Activity

Abstract Within the broad area of computational intelligence, it is of great importance to develop new computational models of human behaviour aspects. In this report we look into the recently suggested theory that neural synchronization of activity in different areas of the brain occurs when people attend to external visual stimuli. Furthermore, it is suspected that this cross-area synchrony may be a general mechanism for regulating information flow through the brain. We investigate the plausibility of this hypothesis by implementing a computational model of visual selective attention that is guided by endogenous and exogenous goals (i.e., what is known as top down and bottom-up attention). The theoretical structure of this model is based on the temporal correlation of neural activity that was initially proposed b