Abstract
Noting recent advances in the field of image classification, where convolutional neural networks (CNNs) are used to classify images with high precision. This paper proposes a method of classifying breathing sounds using CNN, where it is trained and tested. To do this, a visual representation of each audio sample was made that allows identifying resources for classification, using the same techniques used to classify images with high precision.For this we used the technique known as Mel Frequency Cepstral Coefficients (MFCCs). For each audio file in the dataset, we extracted resources with MFCC which means we have an image representation for each audio sample. The method proposed in this article obtained results above 74%, in the classification of respiratory sounds used in the four classes available in the database used (Normal, crackles, wheezes, Both).

Abstract
This article reports a pedagogical experience developed within the scope of a Ph.D. program in Electrical and Computer Engineering with application to Education. Starting with a contextualization on the evolution of computers and Computational Thinking, the article describes the platform used in this study - code.org -, highlighting the strengths that captivate the students. In the Case Study topic, we describe the study carried out, starting with a description of the students involved, followed by a description of the process and the analysis of the results, ending with the evaluation process performed by the students. The article ends concluding that code.org is a valid option to develop computational thinking at early-ages. Copyright

Abstract
The increasing interest in connecting small sensors to the internet has led to the development of operating systems able to support all network, graphical and server functionalities over diverse embedded hardware. Globaltronic, a company based in Portugal, has developed an embedded computer called WiiPiiDo, powered by an ARM SoC (System on a Chip), which is highly specialized for IoT applications. It features NB-IoT - LTE Cat NB1 (Narrow Band IoT) to ensure robust connection to the Internet even in harsh conditions, and allows rapid development of complete IoT solutions for end-users. The development of a Linux image that exposes all the potential of the hardware platform is a must and will provide extra value to it. To create such an operating system, we examined the Yocto Project, which is a Linux building environment that is gaining a growing community of users, particularly enterprises. Nevertheless, Yocto is not the only choice for the embedded platform developer community. In fact, Armbian, a Debian/Ubuntu based distribution, appears as an popular alternative for embedded development in ARM boards. In this paper we show the steps we took from testing the first boot on the WiiPiiDo board until the development of the supporting operating system, finalizing with performance tests. We present a comparison of the two build systems that were used and report the results from the performance tests. © 2020, Springer Nature Switzerland AG.

Abstract
Intelligent agriculture in general, but especially when agricultural fields are very heterogeneous, requires a large number of sensors in order to obtain an effective control and thus increase productivity. This need becomes more evident in vineyards on the farms of the demarcated Douro region due to the specificities of the territory and the vineyards themselves. Thus, it is necessary to have low cost sensors which are, essentially, easy to install and maintain. In the present work, a node with these characteristics was developed, which, in addition, is low consumption and communicates wirelessly through a Long Rang Wide Area Network (LoRaWAN) network. To obtain an easy installation, a library of clusters was created for the LoRaWAN network and dedicated to sensors used in agriculture, especially those using an asynchronous serial protocol for intelligent sensors. Three nodes were developed and tested with sensors used in agriculture to measure several environmental parameters (soil and air temperature; wind speed, gust and direction; soil water content, water tension and electrical conductivity; solar radiation; precipitation; atmospheric and vapor pressure; relative humidity; and lightning strikes count). The three nodes send data to a server through an existing gateway on the farm. The data are decoded and sent to an Internet-of-Things analytics platform where it is aggregated, viewed and analyzed. Samples of the data collected are presented. The developed nodes are of small dimensions (85x65x35mm), thus making them easy to handle and install. Energy consumption depends on the distance to the gateway, and the number and type of sensors connected to each node. In the implemented cases, the maximum consumption was approximate to 400 mu A. The development of a cluster based library makes the node plug-and-play. The developed nodes will be a great step forward for the use of wireless sensors in smart agriculture in Douro vineyards.

Abstract
This project is being developed to access elder dependent people, inspired by a geriatric care management@home. According to the project’s main assessment, three stages have been completed and now, in the fourth stage, we are implementing a bio-network with several sensors that assists the caregiver in applying the therapeutic plan. At this point, we have collected data regarding the level of dependency of the elderly, and the risk of development of pressure ulcers, so that we can adapt the sensors according to their need. For example, sensors installed in a bed can warn the caregiver that it is time to change the position of the elderly patient. As such, after testing a prototype in laboratory, we installed the system in two homes where we are currently collecting data. © 2020, Springer Nature Switzerland AG.

Abstract
The physiotherapeutic process is widely discussed and of fundamental importance for the recovery of patients who suffer from any injury or adverse muscle condition. For best results, it is of primary importance that the patient maintains a pace of treatment and remains engaged in the activities required by the Physiotherapist. In this context, an approach that improves such engagement with concern for usability and acceptance by patients is explored in this article. A gamified platform was created, capable of capturing the time of exposure to the movement of squeezing a handgrip and expressing the patient's results in a swordsman-themed versus game, through the reading of neuromuscular signals captured by a MYO armband. © 2020 IEEE.