Abstract
Lung function tests are critical for diagnosis and monitoring of asthma and other respiratory diseases. Monitoring of lung function, in the absence of a healthcare professional, is very challenging but may be obtained through Smart Devices if_automated quality assessment systems guarantee the proper technique during the forced expiratory manoeuvre. This paper describes the evaluation of one such system that uses the microphone of smart devices, regarding the initial effort of forced expiratory manoeuvres using the Back Extrapolated Volume. A health professional recorded microphone spirometry in 55 children (5-10 years), using a mobile game engineered for the purpose, and registered its quality. At least one acceptable manoeuvre was achieved for 96% of the children using a featured threshold. Using a stricter threshold of 5% of forced vital capacity, it was possible to ensure at least one acceptable manoeuvre for 69%. While the obtained results are comparable to findings in literature for regular spirometry in this age group, further work is required before we can determine whether the proposed algorithm is effective in real life. Copyright

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.

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.