Abstract
Objective: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. Materials and methods: The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39 pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results and discussion: SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917 +/- 0.0024 and a F-Measure of 0.9925 +/- 0.0019, in comparison with ANN, which reached the values of 0.9847 +/- 0.0032 and 0.9852 +/- 0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. Conclusion: The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW.

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Abstract
The concept of smart cities grew with the need to rethink the use of urban spaces based on the constant technological advances and respecting sustainability. Today the urbanism and the methodologies to think about the city are changing, as citizens want more access to digital information on almost everything. Therefore, cities need to be planned and equipped with infrastructures that enable connectivity between the citizens’ devices and the digital information. This challenge raises technological problems, such as traffic management, in an attempt to guarantee fair network access to all users. Solutions based on wireless resource management and self-organizing networks are key when design the connectivity for these smart cities. This paper presents a study on forecasting the daily load of Wi-Fi city hotspots, taking also in consideration the weather conditions. This is particularly interesting to predict the network load and resource requirements needed to ensure proper quality of service is provided to the hotspot users. The study was performed in a Wi-Fi hotspot located in the city of Castelo Branco, Portugal. The results show the ARIMA model is capable of identifying and forecasting seasonality events for one week in advance including its capability to correlate the number of hotspot users with weather conditions. © 2018, Springer International Publishing AG.

Abstract
The increasing acceptance of WiFi has created unprecedented levels of congestion in the unlicensed frequency bands, especially in densely populated areas. This results mainly because of the unmanaged interference and uncoordinated operation between WiFi access points. Radio Environment Maps (REM) have been suggested as a support for coordination strategies that optimize the overall WiFi network performance. In this context, the main objective of this experiment is to assess the benefit of a coordinated management of radio resources in dense WiFi networks at 5 GHz band, using REMs for indoor scenarios. It was shown that REMs can detect the presence of interfering links on the network or coverage holes, and a suitable coordination strategy can use this information to reconfigure Access Points (AP) channel assignment and re-establish the client connection, at a cost of diminishing the aggregate throughput of the network. The technique of AP hand-off was tested to balance the load from one AP to another. Using REMs, the Radio Resource Management (RRM) strategy could reconfigure the network to optimize the client distribution among available APs. Although the aggregate throughput is lower after load balancing, the RRM could increase the throughput of the overloaded AP. © 2018 IEEE.

Abstract
The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, Based on two distinct architectures to detect anomalies: National Instruments acquisition board and an Arduino board with an EtherCAT Shield. With the implementation of a supervised learning model, both approaches corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults.