Abstract
The Internet of Things is progressively getting broader, evolving its scope while creating new markets and adding more to the existing ones. However, both generation and analysis of large amounts of data, which are integral to this concept, may require the proper protection and privacy-awareness of some sensitive information. In order to control the access to this data, allowing devices to verify the reliability of their own interactions with other endpoints of the network is a crucial step to ensure this required safeness. Through the implementation of a blockchain-based Public Key Infrastructure connected to the Keybase platform, it is possible to achieve a simple protocol that binds devices’ public keys to their owner accounts, which are respectively supported by identity proofs. The records of this blockchain represent digital signatures performed by this Keybase users on their respective devices’ public keys, claiming their ownership. Resorting to this distributed and decentralized PKI, any device is able to autonomously verify the entity in control of a certain node of the network and prevent future interactions with unverified parties. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
Phase coherence methods have been proposed to improve the delay-and-sum (DAS) beamforming in terms of contrast and spatial resolution. However, they could be equally beneficial for speckle tracking echocadiography, given the higher variance they introduce in the speckle texture. The aim of this study was to compare, in a close-to-clinical scenario, the B-mode and speckle tracking performance of the DAS beamformer and 4 phase coherence methods: generalized coherence factor, phase coherence factor, sign coherence factor and short lag spatial coherence. Both simulation and experimental imaging of a tissue mimicking phantom were used to assess classical imaging metrics, whereas in-vivo imaging was performed to evaluate myocardial visibility and tissue tracking. Results showed improved resolution and contrast from the coherence beamformers, as well as a reduction of clutter noise, especially in the near field. Similarly, apical strain curves were more reliably estimated following coherence processing. Overall, these methods seem to better derive both morphological and functional imaging, although no method outperformed in all scenarios. © 2018 IEEE.

Abstract
In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration.

Abstract
Road pavement conditions influence the daily lives of both drivers and passengers. Anomalies in road pavement can cause discomfort, increase stress, cause mechanical failures in vehicles and compromise safety of road users. Detecting and surveying road condition/anomalies requires expensive and specially designed equipment and vehicles, that cost considerable amounts of money, and require specialized workers to operate them. As an alternative, an emergent sensing paradigm is being discussed as a promising mechanism for collecting large-scale real-world data. In this paper we describe our experience on the design, implementation and deployment of a cloud based road anomaly information management service, that combines Collaborative Mobile Sensing and data-mining approaches, to provide a practical solution for detecting, identifying and managing road anomaly information. Additionally, we identify technical challenges and propose guidelines that may help to improve this type of services and applications. © 2018 IEEE.

Abstract
The growing economic importance of the biomass-for-bioenergy in Europe motivates research on biomass supply chain design and planning. The temporally and geographically fragmented availability of woody biomass makes it particularly relevant to find cost-effective solutions for biomass production, storage and transportation up to the consumption facility. This paper addresses tactical decisions related with optimal allocation of wood chips from forest residues at forest sites to terminals and power plants. The emphasis is on a "hot-system" with synchronized chipping and chips transportation at the roadside. Thus, decisions related with the assignment of chippers to forest sites are also considered. We extend existing studies by considering the impact of the wood chips energy content variation in the logistics planning. This is a key issue in biomass-for-bioenergy supply chains. The higher the moisture content of wood chips, the lower its net caloric value and therefore, a larger amount of chips is needed to meet the contracted demand. We propose a Mixed Integer Programming (MIP) model to solve this problem to optimality. Results of applying the model in a biomass supply chain case in Finland are presented. Results suggest that a 20% improvement in the supplier profit can be obtained with the proposed approach when compared with a baseline situation that relies on empirical estimates for a fixed and known moisture content in the end of an obliged storage age.