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The diffusion of new digital technologies renders digital transformation relevant to nearly every economic activity sector, including in the agriculture sector. Farming and how farmers work is changing, the use of Information and Communications Technology (ICT) together with the increased use of the Internet of Things (IoT) is developing a concept that is called Smart Farming. Smart Farming benefits are tremendous, smart data can be used for seed traits and to treat soil conditions, the use of new technologies can offer unprecedented conveniences and improve the management and quality of agriculture farming. The use of new information systems and services will be more and more used to sustain and improve operations, competitiveness, and profitability in the agriculture sector. However, the massive use of technology comes with inherent security risks and vulner-abilities, and the sector finds itself targeted as never before. In this paper, using an empirical methodology, are highlighted some reflections regarding the security challenges that Smart Farming systems face.

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The purpose of this work is to explore the design principles for a Real-Time Robotic Multi Camera Vision System, in a case study involving a real world competition of autonomous driving. Design practices from vision and real-time research areas are applied into a Real-Time Robotic Vision application, thus exemplifying good algorithm design practices, the advantages of employing the "zero copy one pass" methodology and associated trade-offs leading to the selection of a controller platform. The vision tasks under study are: (i) recognition of a "flat" signal; and (ii) track following, requiring 3D reconstruction. This research firstly improves the used algorithms for the mentioned tasks and finally selects the controller hardware. Optimization for the shown algorithms yielded from 1.5 times to 190 times improvements, always with acceptable quality for the target application, with algorithm optimization being more important on lower computing power platforms. Results also include a 3-cm and five-degree accuracy for lane tracking and 100% accuracy for signalling panel recognition, which are better than most results found in the literature for this application. Clear results comparing different PC platforms for the mentioned Robotic Vision tasks are also shown, demonstrating trade-offs between accuracy and computing power, leading to the proper choice of control platform. The presented design principles are portable to other applications, where Real-Time constraints exist.

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This Special Issue on Electrical and Computer Engineering includes selected papers from the 1st edition of the Symposium on Electrical and Computer Engineering (ECE 2015), one of the symposia included in the 1st Doctoral Congress in Engineering, held at FEUP, 11-12 June, 2015. ECE 2015 was an important forum for presenting the research activities of ECE students, particularly from the Doctoral Program in Electrical and Computer Engineering, at FEUP (PDEEC). ECE 2015 received a total of 42 two-page abstracts. The review process was carried out by members of the Symposium Scientific Committee and other reviewers. Each abstract was reviewed by at least two reviewers, and checked by the Program Committee. 37 abstracts were finally accepted and appear in the Symposium book of abstracts. From the 37 abstracts, 17 were presented in four oral sessions, and 20 in one poster session. We were very pleased to include two keynote talks: “The Internet of Things - Latest Trends and Future Perspectives” by Carlos Azeredo Leme, University of Lisbon, Portugal; “A Perspective on Virtual Radio Access Networks” by Luís M. Correia, University of Lisbon, Portugal. Six papers were invited to submit extended versions to this special issue, that were further reviewed and published in this issue.We would like to sincerely thank the authors for submitting these extended versions, and we thank the special issue reviewers for the careful evaluation and feedback provided to the authors. We also would like to express our gratitude to Luís Miguel Costa, for supporting the organization of this Special issue of the U.Porto Journal of Engineering.Finally, we are very pleased to give the readership of this Special Issue on ECE examples of the research developed by PDEEC students, from a vast area covered by the Electrical and Computer Engineering at FEUP and at the associated research institutes and research centers.

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This paper presents the positioning, navigation and awareness (PNA) system developed for the Underwater Robotic Mining System of the VAMOS! project [1]. It describes the main components of the VAMOS! system, the PNA sensors in each of those components, the global architecture of the PNA system, and its main subsystems: Position and Navigation, Real-time Mine Modeling, 3D Virtual reality HMI and Real-time grade system. General results and lessons learn during the first mining field trial in Lee Moor, Devon, UK during the months of September and October 2017 are presented.

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