Abstract
In this work we focus on the influence of salt water as the medium between two coupling coils to design a wireless power transfer system. An electrical circuit model and an adequate characterization approach is presented to account for the power losses in the conductive medium. Optimum values for the load and efficiency of the power link are determined. Experimental results are provided to compare the performance of the coupling coils between different coupling mediums (air, fresh and salt water).

Abstract
Triboelectric nanogenerators (TENGs) have been recognized as a promising harvesting technology to satisfy the power requirements of some marine activities. Our current work addresses the TENG design for the purpose of energy harvesting in the sea. In this paper we present a prototype currently under development, in which spheres made of a triboelectric material move within a limited volume due to external mechanical excitation. The spheres move along a printed circuit board having several electrodes and a PDMS film deposited on top, producing numerous voltage spikes. Experimental results are provided with time-domain wave forms and power characterization of the TENG.

Abstract

Abstract
The creation of 3D complete models of the woman breast that aggregate radiological and surface information is a crucial step for the development of surgery planning tools in the context of breast cancer. This requires the registration of interior and surface data of the breast, which has to recover large breast deformations caused by the different poses of the patient during data acquisition and has to deal with the lack of landmarks between both modalities, apart from the nipple. In this paper, the registration of Magnetic Resonance Imaging exams and 3D surface data reconstructed from Kinect (TM) acquisitions is explored using a biomechanical modelling of breast pose transformations combined with a free form deformation to finely match the data. The results are promising, with an average euclidean distance between the matched data of 0.81 +/- 0.09 mm being achieved.

Abstract
The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation.

Abstract
Particle tracking applied to video passive microrheology is conventionally done through methods that are far from being automatic. Creating mechanisms that decode the image set properties and correctly detect the tracer beads, to find their trajectories, is fundamental to facilitate microrheology studies. In this work, the adequacy of two particle detection methods - a Radial Symmetry-based approach and Gaussian fitting - for microrheology setups is tested, both on a synthetic database and on real data. Results show that it is possible to automate the particle tracking process in this scope, while ensuring high detection accuracy and sub-pixel precision, crucial for an adequate characterization of microrheology studies.