Abstract
The arrival of Unmanned Aerial Vehicles (UAV) to the consumer market has been changing the way we interact with our surroundings. Fields like cinema and photography are improving with the possibility of reaching unsafe areas, as much as industry related companies that can now supervise their infrastructures safely. Besides many other professional areas, this fever also got into sports and UAV racing became a new way to compete. There are limited alternatives to control a UAV, each one with its pros and cons. The most reliable is the conventional handheld controller, a bulky equipment that requires practice and dexterity to be a good pilot. Consequently, technologically illiterate individuals, users with low dexterity or hand malformations and elders are departed from this game breaking technology, either for professional or recreational purposes. To expand the control of UAV to more member of our society, a different control equipment should be developed. In this paper we propose a solution based in two lightweight hand worn devices sensitive to motion changes. These changes are used to detect modifications in the orientation of the device, which are then transmitted through Bluetooth Low Energy (BLE) to a mobile app that is responsible for their interpretation as simple input or input patterns. The result of this interpretation should be used as flight commands to control a UAV. Through simple and intuitive hand movements, users can accurately pilot a quadcopter. This alternative presents a new and easier approach to control UAV that decreases the time that is required to learn how to use it, with the outcomes of an accentuated learning curve. The results obtained with the usability tests performed with users with different capacities of interaction, confirmed the viability of this solution, as much as its simplicity and intuition in the control. © Springer Nature Switzerland AG 2019.

Abstract
The use of specialized accelerator circuits is a feasible solution to address performance and energy issues in embedded systems. This paper extends a previous field-programmable gate array-based approach that automatically generates pipelined customized loop accelerators (CLAs) from runtime instruction traces. Despite efficient acceleration, the approach suffered from high area and resource requirements when offloading a large number of kernels from the target application. This paper addresses this by enhancing the CLA with dynamic partial reconfiguration (DPR) support. Each kernel to accelerate is implemented as a variant of a reconfigurable area of the CLA which hosts all functional units and configuration memory. Evaluation of the proposed system is performed on a Virtex-7 device. We show, for a set of 21 kernels, that when comparing two CLAs capable of accelerating the same subset of kernels, the one which benefits from DPR can be up to 4.3x smaller. Resorting to DPR allows for the implementation of CLAs which support numerous kernels without a significant decrease in operating frequency and does not affect the initiation intervals at which kernels are scheduled. Finally, the area required by a CLA instance can be further reduced by increasing the IIs of the scheduled kernels.

Abstract
Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI (http://evoppi.i3s.up.pt) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

Abstract

Abstract
Through IoT, humans and objects can be connected seamlessly, to guaranty improved quality of service (QoS). IoT-driven e-Health systems benefit from such rich network setting, to transmit health information and deliver health services. It is expected to grow massively in scale, but for that to happen, several issues need to be addressed, including security and trust. Edge computing paradigms, such as Fog computing and Cloudlet, are already popular in IoT based e-Health domain. Fog nodes are leveraged to reduce latency between IoT devices and remote cloud computing infrastructure. In this work, we explain how Mobile edge-clouds, which is a less popular edge computing paradigm, can be employed to achieve similar or lower latency, at a lower cost. We also propose a lightweight mechanism for security and fairness in e-Health protocols that are based on mobile edge-clouds and other paradigms. Detailed simulation experiments show that the proposed method is scalable and can efficiently mitigate attacks that are targeted at e-Health information and the network.

Abstract
This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers. A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.