An integrated solution for QoS provision and congestion management in high-performance interconnection networks using deterministic source-based routing

A key element in any system based on several interconnected computing and/or storage nodes is the interconnection network. Currently, one of the main concerns of high-speed interconnection network designers is how to improve network performance while using the minimum number of network resources. In that sense, in this paper we describe an efficient switch architecture suitable for any interconnect technology implementing deterministic source-based routing. This switch architecture uses the same network resources to provide two issues that improve network performance: Congestion Management and QoS support. We also present results to compare the effectiveness of this architecture to those of other proposals typically used to provide these issues in this context. These results have been obtained for synthetic traffic and for traces from parallel benchmarks and video frames. From the results, we can conclude that in any traffic scenario, our proposal is as effective as the previous ones, while requiring fewer resources and thus being much more cost-effective.     

Analysis of Scalar Maps for the Segmentation of the Corpus Callosum in Diffusion Tensor Fields

Diffusion tensor imaging (DTI) is a powerful technique for imaging axonal anatomy in vivo and its automatic segmentation is important for quantitative analysis and visualization. Application of the watershed transform is a recent approach for robustly segmenting diffusion tensor images. Since an important step of the watershed-based segmentation is the gradient computation, this paper investigates scalar maps from DTI and their ability to enhance borders and, therefore, their usefulness in gradient calculation. A comparison between existing scalar maps is conducted in the context of segmentation. New diffusion scalar maps, inspired by mathematical morphology concepts are proposed and included in the comparison. The watershed transform is then applied to segment the corpus callosum, based on the computed scalar maps.     

Accessing movies based on emotional impact

Emotions play a central role in our daily lives, influencing the way we think and act, our health and sense of well-being, and films are by excellence the form of art that exploits our affective, perceptual and intellectual activity, holding the potential for a significant impact. Video is becoming a dominant and pervasive medium, and online video a growing entertainment activity on the web and iTV, mainly due to technological developments and the trends for media convergence. In addition, the improvement of new techniques for gathering emotional information about videos, both through content analysis or user implicit feedback through user physiological signals complemented in manual labeling from users, is revealing new ways for exploring emotional information in videos, films or TV series, and brings out new perspectives to enrich and personalize video access. In this work, we reflect on the power that emotions have in our lives, on the emotional impact of movies, and on how to address this emotional dimension in the way we classify and access movies, by exploring and evaluating the design of iFelt in its different ways to classify, access, browse and visualize movies based on their emotional impact.     

Statistical inference in a redesigned Radial Basis Function neural network

A Hybrid Learning Process method was fitted into a RBF. The resulting redesigned RBF intends to show how to test if the statistical assumptions are fulfilled and to apply statistical inference to the redesigned RBFNN bearing in mind that it allows to determine the relationship between a response (to a process) and one or more independent variables, testing how much each factor contributes to the total variation of the response is also feasible. The results show that statistical methods such as inference, Residual Analysis, and statistical metrics are all good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The foremost conclusion is that the resulting redesigned Radial Basis Function improved the accuracy of the model after using a Hybrid Learning Process; moreover, the new model also validates the statistical assumptions for using statistical inference and statistical analysis, satisfying the assumptions required for ANOVA to determine the statistical significance and the relationship between variables.     

Classifying patterns with missing values using Multi-Task Learning perceptrons

Datasets with missing values are frequent in real-world classification problems. It seems obvious that imputation of missing values can be considered as a series of secondary tasks, while classification is the main purpose of any machine dealing with these datasets. Consequently, Multi-Task Learning (MTL) schemes offer an interesting alternative approach to solve missing data problems. In this paper, we propose an MTL-based method for training and operating a modified Multi-Layer Perceptron (MLP) architecture to work in incomplete data contexts. The proposed approach achieves a balance between both classification and imputation by exploiting the advantages of MTL. Extensive experimental comparisons with well-known imputation algorithms show that this approach provides excellent results. The method is never worse than the traditional algorithms – an important robustness property – and, also, it clearly outperforms them in several problems.     

A novel automatic algorithm for the segmentation of the lumen of the carotid artery in ultrasound B-mode images

A novel algorithm is proposed for the segmentation of the lumen and bifurcation boundaries of the carotid artery in B-mode ultrasound images. It uses the image contrast characteristics of the lumen and bifurcation of the carotid artery in relation to other tissues and structures for their identification. The relevant ultrasound data regarding the artery presented in the input image is identified using morphologic operators and processed by an anisotropic diffusion filter for speckle noise removal. The information obtained is then used to define two initial contours, one corresponding to the lumen and the other one regarding the bifurcation boundaries, for the application of the Chan-Vese level set segmentation model. A set of longitudinal ultrasound B-mode grayscale images of the common carotid artery was acquired using a GE Healthcare Vivid-e ultrasound system. The results reveal that the new algorithm is effective and robust, and that its main advantage relies on the automatic identification of the carotid lumen, which overcomes the known limitations of the traditional algorithms.     

A review on the applications of type-2 fuzzy logic in classification and pattern recognition

In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented.     

Provision of awareness of learners’ emotions through visualizations in a computer interaction-based environment

One of the challenges of intelligent systems for education is to use low-level data collected in computer environments in the form of events or interactions to infer information with high-level significance using artificial intelligence techniques, and present it through visualizations in a meaningful and effective way. Among this information, emotional data is gaining track in by instructors in their educational activities. Many benefits can be obtained if an intelligent systems can bring teachers with knowledge about their learner’s emotions, learning causes, and learning relationships with emotions. In this paper, we propose and justify a set of visualizations for an intelligent system to provide awareness about the emotions of the learners to the instructor based on the learners’ interactions in their computers. We apply these learner’s affective visualizations in a programming course at University level with more than 300 students, and analyze and interpret the student’s emotional results in connection with the learning process.