With the recent developments in augmented reality (AR) technologies comes an increased interest in the use of smart glasses for hands-on training. Whether this interest is turned into market success depends at the least on whether the interaction with smart AR glasses satisfies users, an aspect of AR use that so far has received little attention. With this contribution, we seek to change this. The objective of the article, therefore, is to investigate user satisfaction in AR applied to three cases of practical use. User satisfaction of AR can be broken down into satisfaction with the interaction and satisfaction with the delivery device. A total of 142 participants from three different industrial sectors contributed to this study, namely, aeronautics, medicine, and astronautics. In our analysis, we investigated the influence of different factors, such as age, gender, level of education, level of Internet knowledge, and the roles of the participants in the different sectors. Even though users were not familiar with the smart glasses, results show that general computer knowledge has a positive effect on user satisfaction. Further analysis using two-factor interactions showed that there is no significant interaction between the different factors and user satisfaction. The results of the study affirm that the questionnaires developed for user satisfaction of smart glasses and the AR application performed well, but leave room for improvement.

EISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes (PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of 30 observations days, corresponding to 56,250 data samples. We manually labeled the data into three different categories: PMSE, Ionospheric background, and Background noise. For segmentation, we employed random forests on a set of simple features. These features include: altitude derivative, time derivative, mean, median, standard deviation, minimum, and maximum values corresponding to neighborhood sizes ranging from 3 by 3 to 11 by 11 pixels. Next, in order to reduce the model bias and variance, we employed a method that decreases the weight applied to pixel labels with large uncertainty. Our results indicate that, first, it is possible to segment PMSE from the data using random forests. Second, the weighted-down labels technique improves the performance of the random forests method.

Cloud computing, which is a relatively new approach to distributed computing, uses cloud infrastructures that automatically scale to support an application's hardware requirements, and therefore, must have high reliability in order to meet user expectations. To achieve a high level of quality and reliability, cloud platforms and infrastructures must be tested thoroughly. Typically, cloud providers have individual approaches to infrastructure testing, and there are no widely accepted methods of infrastructure testing that are currently available. An important theoretical and practical problem in this area is the development of such methods, including testing application programming interfaces (APIs), which are direct links between the client code and the infrastructure it runs upon. In this paper, an approach for testing cloud platforms and infrastructures is suggested. An intelligent framework is presented, which accelerates testing and provides for parallel development of test cases. This framework is a cloud application, which contains plugins for testing APIs of cloud platforms. A prototype framework for testing Google App Engine has been created to show demonstrated applicability of the suggested approach.

Under natural viewing conditions humans tend to fixate on specific parts of the image that interests them naturally. Understanding the mechanisms of the human visual attention may benefit numerous applications in a various fields of engineering, marketing and art such as image quality evaluation, label design, human computer interaction, etc.Saliency map, proposed by Dirk Walther et al., represents the regions which are more prominent than other regions in terms of low level image properties such as intensity, color and orientation. We propose in this paper to modify this saliency map algorithm to account for one high-level feature, human faces, to better mimic the natural human attention and compare it to gaze maps obtained experimentally. The fixations of the gaze map are compared with the most salient regions of the saliency map. The factors that influence the relationship between the saliency maps and gaze maps are analyzed. Gaze map analysis was done for 20 test subjects using eye tracking device as they were shown a set of 190 images.

We propose new accurate predictive models for the delay, power, and area of buffered interconnects to enable a more effective system-level design exploration with existing and future nanometer technology processes. We show that our models are significantly more accurate than previous models - essentially matching sign-off analyses. We integrate our models in the COSI-OCC communication synthesis infrastructure and show how they impact the feasibility and optimality of the network-on-chip architectures that are synthesized by this tool.