Abstract
The robotics field is widely used in the industrial domain, but nowadays several other domains could also take advantage of it. This interdisciplinary branch of engineering requires the use of human interfaces, efficient communication systems, high storage and processing capabilities, among other issues, to perform complex tasks. This paper aims to propose a cloud-based framework platform for robot operation in a hospital environment, addressing some challenges, such as communications security and processing/storage features. The recent developments in the artificial intelligence field and cloud resources sharing are allowing the penetration of robots in unstructured environments. However, some new challenges and solutions need to be tested in real environments. Our main contribution is to decrease the time-consumption related to processing and storage costs, associated with the physical processing resources of the robots. Also, the proposed methods provide an increase of the processing variables that are not yet present in the physical resources, such as in the case of robots with limited processing time or storage capabilities. This paper presents a platform based on Cloud Computing with services to support processing, storage and analytics applied to hospital environments. The proposed platform enables to achieve a decrease in the time-consumption, especially when it is intended to retrieve information about all robot activities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
In this paper the comparison between three convolutional neural networks, used for the control of bio-inspired multi-robots in a simulated environment, is performed through manual gestures captured in real time by a webcam. The neural networks are: VGG19, GoogLeNet and Alexnet. For the training of networks and control of robots, six gestures were used, each gesture corresponding to one action, collective and individual actions were defined, the simulation contains four bio-inspired robots. In this work the performance of the networks in the classification of gestures to control robots is compared. They proved to be efficient in the classification and control of agents, with Alexnet achieving an accuracy of 98.33%, VGG19 98.06% e Googlelenet 96.94%.

Abstract
This article discusses a method that performs gesture recognition, with the objective of extracting characteristics of the segmented hand, from dynamic images captured from a webcam and identifying signal patterns. With this method it is possible to manipulate simulated multirobots that perform specific movements. The method consists of the Continuously Adaptive Mean-SHIFT algorithm, followed by the Threshold segmentation algorithm and Deep Learning through Boltzmann restricted machines. As a result, an accuracy of 82.2%. © CLAWAR Association.

Abstract
The objective of the present study was to investigate the influence of demineralization solution on the optical properties of chicken femoral samples. Biomaterials based on bone have gained importance in clinical applications due to their properties as better osseointegration and biocompatibility. Biomateriais (bone substitute) are essentials to auxiliary in treatment of diseases related to bones such as bone density disorder, low bone mineral mass and the deterioration of bone tissue. Our data shows that integrating sphere technique permits to determinate significant difference in optical properties between healthy and demineralized samples. In this work, the optical properties of bone samples from chicken femur have been measured over the wavelength range 700–1000 nm.

Abstract
This paper describes a project-based learning example used to promote academic success on electrical engineering, BSc and MSc degrees. The project aims to implement an aerial drone devoted to environmental surveillance, through identification and prevention of forest fires, effects of floods, and industrial flammable gases leaks to the atmosphere using an extensive set of gas sensors. The project was carried out using a hexacopter drone platform and combines knowledge of different areas, namely electronics, communications, and instrumentation. The wireless transmission of the acquired data is the main focus of the project. The results obtained from this project-based learning educational experience show that a practical-learning approach usage in conjunction with highly motivating topics promotes academic success and improves the understanding of theoretical concepts. Students increased their knowledge and skills during the problem resolution, and achieved a real solution according to their options. Moreover, this approach provides students an extensive learning experience on current technologies, modules and programming languages. © Springer Nature Switzerland AG 2019.

Abstract
This paper presents a novel radio localization system based on radio signal strength measurements to estimate the location of animals on a farm. The system uses low-cost mobile devices based on a microcontroller, a radio transceiver and an antenna. The developed solution works on the 433 MHz band and comprises innovative protocols that use the radio wake-up concept in order to minimize the mobile unit energy consumption, promoting that way its autonomy. A software application was developed to track the animals on a real-time basis using the Internet of Things paradigm. © 2019 IEEE.