Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL2DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The importance of planktonic microbial communities is well acknowledged, since they are fundamental for several natural processes of aquatic ecosystems. Microorganisms naturally control the flux of nutrients, and also degrade and recycle anthropogenic organic and inorganic contaminants. Nevertheless, climate change effects and/or the runoff of nutrients/pollutants can affect the equilibrium of natural microbial communities influencing the occurrence of microbial pathogens and/or microbial toxin producers, which can compromise ecosystem environmental status. Therefore, improved microbial plankton monitoring is essential to better understand how these communities respond to environmental shifts. The study of marine microbial communities typically involves highly cost and time-consuming sampling procedures, which can limit the frequency of sampling and data availability. In this context, we developed and validated an in situ autonomous biosampler (IS-ABS) able to collect/concentrate in situ planktonic communities of different size fractions (targeting prokaryotes and unicellular eukaryotes) for posterior genomic, metagenomic, and/or transcriptomic analysis at a home laboratory. The IS-ABS field prototype is a small size and compact system able to operate up to 150 m depth. Water is pumped by a micropump (TCS MG2000) through a hydraulic circuit that allows in situ filtration of environmental water in one or more Sterivex filters placed in a filter cartridge. The IS-ABS also includes an application to program sampling definitions, allowing pre-setting configuration of the sampling. The efficiency of the IS-ABS was tested against traditional laboratory filtration standardized protocols. Results showed a good performance in terms of DNA recovery, as well as prokaryotic (16S rDNA) and eukaryotic (18S rDNA) community diversity analysis, using either methodologies. The IS-ABS automates the process of collecting environmental DNA, and is suitable for integration in water observation systems, what will contribute to substantially increase biological surveillances. Also, the use of highly sensitive genomic approaches allows a further study of the diversity and functions of whole or specific microbial communities.

Abstract
The development of Low-Density Fan-Out (LDFO), formerly Wafer Level Fan-Out (WLFO), platforms to encompass the requirements of potential new markets and applications such as the Internet of Things (IoT) is crucial to maintain LDFO as the leading Fan-Out technology. This drives the development of a new set of capabilities in the current standard LDFO process flow to break through the existing technology boundaries. One of the most widely discussed advantages of LDFO packaging is heterogeneous high-density system integration in a package. LDFO System in Package (LDFO SiP) integrates active dies, passive components and even already-packaged components using other packaging technologies. This heterogeneous integration is based on a wide range of different geometries and materials placed inside the LDFOSiP with high accuracy. Ultimately, heterogeneous integration will be fundamental to achieve new levels of miniaturization. However, multi-die solutions face several challenges such as bare-die availability, passives integration, antenna integration, low power budget, test complexity and reliability. Package research and development (R&D) must overcome all of these issues to build a product with high volume manufacturability. The wafer level SiP (WLSiP) technology required to enable the new features and processes needs to be ready for high volume manufacturing of new products at high yield and reasonable cost. This paper presents the approaches used to effectively enable LDFO SiPs (WLSiPs): 1. A pre-formed vias solution is employed to connect front to back side of the package, including development for high accuracy via bar placement. 2. A wafer front-side to back-side redistribution layer (RDL) alignment solution was developed. 3. Space requirement reduction between components to achieve the smallest possible package. 4. Miniaturized Bluetooth antenna integration in the RDL. 5. Creation of a stacking concept (vertical connections to create a modular system that enables easy addition of new features to the final product). Inside the package (excluding the area reserved for the antenna), components are densely packed: several sensors, power management components, radio communication and all required passives are incorporated into a single WLSiP. Connecting all these features to create a component that works by connecting only a single battery required implementing a double sided, multi-layer RDL, while maintaining the ability to create a 3D solution by stacking vertical connections for several other solutions. The result is an approach that easily adapts the system to a variety of customers' needs. The work done is part of the collaborative COMPETE2020-PT2020 funding program under "IoTiP-Internet of Thing in Package" project no 017763, Projetos de I&DT Empresas em CoPromocao.

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
Zooplankton plays a key -role on Earth's ecosystem, emerging in the oceans and rivers in great quantities and diversity, making it an important and rather common topic on scientific studies. Given the numbers of different species it is not only necessary to study their numbers but also their classification. In this paper a possible solution for the zooplankton in situ detection and classification problem in real-time is proposed using a portable deep learning approach based on Convolutional Neural Networks deployed on INESC TEC's MarinEye system. For detection a Single Shot Detection model with MobileNet was used, and ZooplanktoNet for classification. System portability is guaranteed with the use of MovidiusTMNeural Compute Stick as the deep learning motor.

Abstract
For the context of a mobile tracking system, an underwater acoustic positioning system was developed, using three hydrophones to compute the direction of an acoustic source relative to an Autonomous Surface Vehicle (ASV). The paper presents an algorithm for the Direction of Arrival (DoA) of an acoustic source, which allows to estimate its position. Preliminary results will be shown in this paper relative to the detection and identification (ID) of the acoustic sources, as well as an analysis of the proposed algorithm. The solution allows the position estimation of an acoustic source, which can be used in tracking solutions. The system can be applied in an ASV or fixed buoys, as long as the baseline's hydrophones are at equal angular distances. The main objective is to track targets with the DoA algorithm as well to estimate their position, improving what was done in [1].