Abstract
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.

Abstract
This paper presents an optical fiber sensor, that uses surface plasmon resonance on metallic wires to directly and simultaneously measure both the refractive index and the temperature. The sensor is constituted by gold wires on a D-type fiber engineered, using numerical simulations based on the finite-element method to support plasmon modes with strong dependencies to either one of the measured parameters. In particular, the influence of the temperature on the structure of the plasmon modes results from contributions from the thermooptic effect in the fiber core and sensing layer, and phononelectron scattering along with electron-electron scattering in the metal wire. The performance of the sensor is evaluated in terms of its sensitivity and resolution.

Abstract
Smart devices and Internet of Things (IoT) technologies are becoming each day more common. At the same time, besides the exponentially increasing demand to analyze the produced data, there is an evolving trend to perform the data analysis closer to the data sources, particularly at the Fog and Edge levels. In this sense, the development of testbeds that can, e.g., simulate smart devices in IoT environments, are important to explore and develop the technologies to enable the complete realization of such IoT concepts. This paper describes the digitization of an electric motor, through the incorporation of sensing and an analytical computational environment, towards the development of a testbed for IoT and Big Data technologies. The smart electric motor testbed provides real-time data streams, enabling a continuous monitoring of its operation along all the device life-cycle through advanced data analytics. Furthermore, the paper discusses how specific data analytics features fit the different IoT layers, while preliminary experiments demonstrate the testbed potentials.

Abstract
Automatic segmentation of breast is an important step in the context of providing a planning tool for breast cancer conservative treatment, being important to segment completely the breast region in an objective way; however, current methodologies need user interaction or detect breast contour partially. In this paper, we propose a methodology to detect the complete breast contour, including the pectoral muscle, using multi-modality data. Exterior contour is obtained from 3D reconstructed data acquired from low-cost RGB-D sensors, and the interior contour (pectoral muscle) is obtained from Magnetic Resonance Imaging (MRI) data. Quantitative evaluation indicates that the proposed methodology performs an acceptable detection of breast contour, which is also confirmed by visual evaluation.

Abstract
The proposed technique consists in an optical fiber resonator interrogated for sensor characterization, implementing an alternative technique for dynamic range improvement. Such technique relies on the analysis of an added-signal caused by signal saturation, which occurs due to the broadening of the laser pulse. A wide study for different pulse widths is presented in this work, namely for 100 ns, 5 mu s and 20 mu s, being the last one related to the emergence of an added-signal for the proposed configuration. The behavior of the waveform in the presence of an intensity sensor is also characterized.

Abstract
The Network-on-Chip (NoC) architecture is an interconnect network with a good performance and scalability potential. Thus, it comes as no surprise that NoCs are among the most popular interconnect mediums in nowadays available many-core platforms. Over the years, the real-time community has been attempting to make NoCs amenable to the real-time analysis. One such approach advocates to employ virtual channels. Virtual channels are hardware resources that can be used as an infrastructure to facilitate flit-level preemptions between communication traffic flows. This gives the possibility to implement priority-preemptive arbitration policies in routers, which is a promising step towards deriving real-time guarantees for NoC traffic. So far, various aspects of priority-preemptive NoCs were studied, such as arbitration, priority assignment, routing, and workload mapping. Due to a potentially large solution space, the majority of available techniques are heuristic-centric, that is, either pure heuristics, or heuristic-based search strategies are used. Such approaches may lead to an inefficient use of hardware resources, and may cause a resource over-provisioning as well as unnecessarily high design-cost expenses. Motivated by this reality, we take a different approach, and propose an integer linear program to solve the problems of priority assignment and routing of NoC traffic. The proposed method finds optimal routes and priorities, but also allows to reduce the search space (and the computation time) by fixing either priorities or routes, and derive optimal values for remaining parameters. This framework is used to experimentally evaluate both the scalability of the proposed method, as well as the efficiency of existing priority assignment and routing techniques.