Abstract
Rheumatic heart disease is the result of damage to the heart valves, more often the mitral valve. The heart valves leaflets get inflamed, scarred and stretched which interrupts the normal blood flow, resulting into serious health condition. Measuring and quantifying clinically relevant features, like thickness, mobility and shape can help to analyze the functionality of the valve, identify early cases of disease and reduce the disease burden. To obtain these features, the first step is to automatically delineate the relevant structures, such as the anterior mitral valve leaflet, throughout the echocardiographic video. In this work, we proposed a near real time method to track the anterior mitral leaflet in ultrasound videos using the parasternal long axis view. The method is semi-automatic, requiring a manual delineation of the anterior mitral leaflet in the first frame of the video. The method uses mathematical morphological techniques to obtain the rough boundaries of the leaflet and are further refined by the localized active contour framework. The mobility of the leaflet was also obtained, providing us the base to analyze the functionality of the valve (opening and closing). The algorithm was tested on 67 videos with 6432 frames. It outperformed with respect to the time consumption (0.4 s/frame), with the extended modified Hausdorff distance error of 3.7 pixels and the improved tracking performance (less failure).

Abstract
MapReduce has become a popular programming model for processing and running large-scale data sets with a parallel, distributed paradigm on a cluster. Hadoop MapReduce is needed especially for large scale data like big data processing. In this paper, we work to modify the Hadoop MapReduce Algorithm and implement it to reduce processing time.  

Abstract
Cardiovascular diseases are the main cause of death in the world and its occurrence is closely related to arterial stiffness. Arterial stiffness is commonly evaluated by analysing the arterial pulse waveform and velocity, with electromechanical pressure transducers, in superficial arteries such as carotid, radial and femoral. In order to ease the acquisition procedure and increase the patients comfort during the measurements, new optical fibre techniques have been explored to be used in the reliable detection of arterial pulse waves, due to their small size, high sensitivity, electrical isolation and immunity to electromagnetic interference. More specifically, fibre Bragg gratings (FBGs) are refractive index modulated structures engraved in the core of an optical fibre, which have a well-defined resonance wavelength that varies with the strain conditions of the medium, known as Bragg wavelength. In this work, FBGs were embedded in a commercial resin, producing films that were used to assess the arterial pulse in superficial locations such as carotid, radial and foot dorsum. The technique proved to be a promising, comfortable and trustworthy way to assess the arterial pulses, with all the optical fibre use advantages, in a non-intrusive biomedical sensing procedure. Examples of possible applications of the developed structures are smart skin structures to monitor arterial cardiovascular parameters, in a stable and reliable way, throughout daily activities or even during exams with high electromagnetic fields, such as magnetic resonance imaging.

Abstract

Abstract
The moment immediately before the "add to cart" decision is very critical in online shopping. Drawing on theories of transfer, spreading activation and human-computer interaction, the superiority of markerless Augmented Reality (AR) and Marker-based augmented reality (M) over Conventional Interactive (CI) is hypothesized. Although those multimedia tools are not part of the product/brand motivating the consumer interest they interfere in the interactive performance of the ecommerce. 150 consumers in a lab experiment showed higher emotional response, interactive response and brand evaluation in M and AR than CI. Contrary to what was expected the usability results were the inverse. That is, usability of CI outperforms M and AR. Considering only AR and M interfaces their effect on psychological variables was not statistically significant. A sophisticated or a simple interface had no impact on intention to buy the target brand, but the brand recommendation improved from M to AR. The differing effect of those three interface systems was mediated by brand familiarity, perceived risk, opinion leadership and positive emotional traits.

Abstract
Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.