Abstract
Given the dynamic nature of cardiac function, correct temporal alignment of pre-operative models and intra-operative images is crucial for augmented reality in cardiac image-guided interventions. As such, the current study focuses on the development of an image-based strategy for temporal alignment of multimodal cardiac imaging sequences, such as cine Magnetic Resonance Imaging (MRI) or 3D Ultrasound (US). First, we derive a robust, modality-independent signal from the image sequences, estimated by computing the normalized cross-correlation between each frame in the temporal sequence and the end-diastolic frame. This signal is a resembler for the left-ventricle (LV) volume curve over time, whose variation indicates different temporal landmarks of the cardiac cycle. We then perform the temporal alignment of these surrogate signals derived from MRI and US sequences of the same patient through Dynamic Time Warping (DTW), allowing to synchronize both sequences. The proposed framework was evaluated in 98 patients, which have undergone both 3D +t MRI and US scans. The end-systolic frame could be accurately estimated as the minimum of the image-derived surrogate signal, presenting a relative error of 1 : 6 +/- 1 : 9% and 4 : 0 +/- 4 : 2% for the MRI and US sequences, respectively, thus supporting its association with key temporal instants of the cardiac cycle. The use of DTW reduces the desynchronization of the cardiac events in MRI and US sequences, allowing to temporally align multimodal cardiac imaging sequences. Overall, a generic, fast and accurate method for temporal synchronization of MRI and US sequences of the same patient was introduced. This approach could be straightforwardly used for the correct temporal alignment of pre-operative MRI information and intra-operative US images.

Abstract
The main purpose of this work was to design, develop, and construct a simple desktop AC susceptometer to monitor in situ and in real time the coprecipitation synthesis of magnetic nanoparticles. The design incorporates one pair of identical pick-up sensing coils and one pair of Helmholtz coils. The picked up signal is detected by a lock-in SR850 amplifier that measures the in-and out-of-phase signals. The apparatus also includes a stirrer with 45 degrees-angle blades to promote the fast homogenization of the reaction mixture. Our susceptometer has been successfully used to monitor the coprecipitation reaction for the synthesis of iron oxide nanoparticles. (C) 2015 AIP Publishing LLC.

Abstract
In recent years, mobile devices and applications have known a growth that is unprecedented in any other technological field, reaching virtually all aspects of our lives including sports, leisure, social relationships or health. This paper describes the development of an environment to assess auditory emotional recognition based on a mobile application. The primary aim of this work is to provide a valuable instrument that can be used both in research and clinical settings, responding to the strong need of validated measures of emotional processing in Portugal. The secondary aim is to study behavioral features, acquired unobtrusively from the interaction of the participant with the device, in search for a relationship with medical conditions, cognitive impairments, auditory emotional recognition or sociodemographic indicators. This will establish the foundation for the prediction of such aspects based on the analysis of people's interaction with technological devices, providing new potentially interesting diagnostic tools.

Abstract

Abstract
Systems such as social networks, search engines or trading platforms operate geographically distant sites that continuously generate streams of events at high-rate. Such events can be access logs to web servers, feeds of messages from participants of a social network, or financial data, among others. The ability to timely detect trends and popularity variations is of paramount importance in such systems. In particular, determining what are the most popular events across all sites allows to capture the most relevant information in near real-time and quickly adapt the system to the load. This paper presents TOPiCo, a protocol that computes the most popular events across geo-distributed sites in a low cost, bandwidth-efficient and timely manner. TOPiCo starts by building the set of most popular events locally at each site. Then, it disseminates only events that have a chance to be among the most popular ones across all sites, significantly reducing the required bandwidth. We give a correctness proof of our algorithm and evaluate TOPiCo using a real-world trace of more than 240 million events spread across 32 sites. Our empirical results shows that (i) TOPiCo is timely and cost-efficient for detecting popular events in a large-scale setting, (ii) it adapts dynamically to the distribution of the events, and (iii) our protocol is particularly efficient for skewed distributions.

Abstract