Abstract
The advent of small and low-cost Unmanned Aerial Vehicles (UAVs) is paving the way to use swarms of UAVs to perform missions such as aerial video monitoring and infrastructure inspection. Within a swarm, UAVs communicate by means of a Flying Multi-hop Network (FMN), which due to its dynamics induces frequent changes of network topology and quality of the links. Recently, UAVs have also been used to provide Internet access and enhance the capacity of existing networks in Temporary Events. This brings up additional routing challenges not yet addressed, in order to provide always-on and high capacity paths able to meet the Quality of Service expected by the users. This paper presents RedeFINE, a centralized routing solution for FMNs that selects high-capacity paths between UAVs and avoids communications disruptions, by defining in advance the forwarding tables and the instants they shall be updated in the UAVs; this represents a major step forward with respect to traditional routing protocols. The performance evaluation of RedeFINE shows promising results, especially regarding Throughput and Packet Delivery Ratio, when compared with state of the art routing solutions.

Abstract
The performance of Resonant Tunnelling Diode (RTD) oscillators with an optical window is evaluated experimentally, in the transmission of advanced modulation formats using electrical and optical modulation, for the first time. Additionally, the impact of phase noise in the transmission performance is also assessed.

Abstract
The high permittivity of InP substrates has been a limiting factor for the bandwidth and efficiency of antennas fabricated in this material. In this manuscript we propose an elliptical monopole, monolithically fabricated in InP, fed by a CPW line. The suggested topology was simulated using HFSS finite element method. Input reflection coefficient measurements were performed on the monopole to validate the proposed antenna. Simulated and measured -10 dB bandwidths of 27 and 24 GHz were obtained, respectively. The peak simulated efficiency and realized gain were 95.37% and 4.6 dBi.

Abstract
Technological advances are pushing into the mass market innovative wearable devices featuring increasing processing and sensing capacity, non-intrusiveness and ubiquitous use. Sensors built-in those devices, enable acquiring different types of data and by taking advantage of the available processing power, it is possible to run intelligent applications that process the sensed data to offer added -value to the user in multiple domains. Although not new to the modern society, it is unquestionable that the present exercise boom is rapidly spreading across all age groups. However, in a great majority of cases, people perform their physical activity on their own, either due to time or budget constraints and may easily get discouraged if they do not see results or perform exercises inadequately. This paper presents an application, running on a wearable device, aiming at operating as a personal trainer that validates a set of proposed exercises in a sports' session. The developed solution uses inertial sensors of an Android Wear smartwatch and, based on a set of pattern recognition algorithms, detects the rate of success in the execution of a planned workout. The fact that all processing can be executed on the device is a differentiator factor to other existing solutions.

Abstract
Machine Learning (ML), presently the major research area within Artificial Intelligence, aims at developing tools that can learn, approximately on their own, from data. ML tools learn, through a training phase, to perform some association between some input data and some output evaluation of it. When the input data is audio or visual media (i.e. akin to sensory information) and the output corresponds to some interpretation of it, the process may be described as Synthetic Cognition (SC). Presently ML (or SC) research is heterogeneous, comprising a broad set of disconnected initiatives which develop no systematic efforts for cooperation or integration of their achievements, and no standards exist to facilitate that. The training datasets (base sensory data and targeted interpretation), which are very labour intensive to produce, are also built employing ad-hoc structures and (metadata) formats, have very narrow expressive objectives and thus enable no true interoperability or standardisation. Our work contributes to overcome this fragility by putting forward: a specification for a standard ML dataset repository, describing how it internally stores the different components of datasets, and how it interfaces with external services; and a tool for the comprehensive structuring of ML datasets, defining them as Synthetic Cognitive Experience (SCE) records, which interweave the base audio-visual sensory data with multilevel interpretative information. A standardised structure to express the different components of the datasets and their interrelations will promote re-usability, resulting on the availability of a very large pool of datasets for a myriad of application domains. Our work thus contributes to: the universal interpretability and reusability of ML datasets; greatly easing the acquisition and sharing of training and testing datasets within the ML research community; facilitating the comparison of results from different ML tools; accelerating the overall research process. © MIR Labs.

Abstract
Over the last years, Deep Learning has become one of the most popular research fields of Artificial Intelligence. Several approaches have been developed to address conventional challenges of AI. In computer vision, these methods provide the means to solve tasks like image classification, object identification and extraction of features. In this paper, some approaches to face detection and recognition are presented and analyzed, in order to identify the one with the best performance. The main objective is to automate the annotation of a large dataset and to avoid the costy and time-consuming process of content annotation. The approach follows the concept of incremental learning and a R-CNN model was implemented. Tests were conducted with the objective of detecting and recognizing one personality within image and video content. Results coming from this initial automatic process are then made available to an auxiliary tool that enables further validation of the annotations prior to uploading them to the archive. Tests show that, even with a small size dataset, the results obtained are satisfactory. © 2020, Springer Nature Switzerland AG.