Abstract
The fourth author name was missed in the original publication. The correct list of authors should read as “Hugo Fernandes, Paulo Costa, Vitor Filipe, Hugo Paredes, João Barroso”. It has been corrected in this erratum. The original article has been updated. © 2017 Springer-Verlag GmbH Germany

Abstract

Abstract
The capability of drones to perform autonomous missions has led retail companies to use them for deliveries, saving time and human resources. In these services, the delivery depends on the Global Positioning System (GPS) to define an approximate landing point. However, the landscape can interfere with the satellite signal (e.g., tall buildings), reducing the accuracy of this approach. Changes in the environment can also invalidate the security of a previously defined landing site (e.g., irregular terrain, swimming pool). Therefore, the main goal of this work is to improve the process of goods delivery using drones, focusing on the detection of the potential receiver. We developed a solution that has been improved along its iterative assessment composed of five test scenarios. The built prototype complements the GPS through Computer Vision (CV) algorithms, based on Convolutional Neural Networks (CNN), running in a Raspberry Pi 3 with a Pi NoIR Camera (i.e., No InfraRed-without infrared filter). The experiments were performed with the models Single Shot Detector (SSD) MobileNet-V2, and SSDLite-MobileNet-V2. The best results were obtained in the afternoon, with the SSDLite architecture, for distances and heights between 2.5-10 m, with recalls from 59%-76%. The results confirm that a low computing power and cost-effective system can perform aerial human detection, estimating the landing position without an additional visual marker.

Abstract
The great potential of Unmanned Vehicles (UV) for services is supported by the evolution of drones and their flexible ability to help us performing dangerous, boring and difficult tasks. The common communication solutions e.g., radio, consider a pilot and a vehicle in the same location. Besides, the alternatives based on Ground Control Stations (GCS) are difficult to configure, which makes it difficult for a common user and a developer to use and/or implement new services for drones. This work proposes a solution with a new messaging protocol to overcome the previous problems, allowing controlling a UV in a remote location after a simple configuration. The implementation of this approach is based on a cloud platform, responsible for UV and user management, and on TCP/IP WebSockets for the user to control remotely located vehicles, anytime and anywhere. This research starts with the analysis of the prior work regarding UV communication technologies and the available GCS. Minding the identified problems, we designed an architecture that represents a cloud platform and multiple users and UVs in different networks. To prototype this architecture, we developed the user interface, the platform and the UV control module. The functional assessment considers a generic simplified and controlled scenario, focusing on the real-time control and video stream. The results confirmed the possibility to control a UV in a different location, in real-time, with an average response time of 25ms. This work provides valuable insights regarding the communication in the fields of anytime and anywhere device control and vehicle-based services. © 2020 IEEE.

Abstract
AppVox is a mobile application that provides support for children with speech and language impairments in their speech therapy sessions, while also allowing autonomous training at home. The application simulates a vocalizer with an audio stimulus feature, which can be used to train and amend the pronunciation of specific words through repetition. In this paper, we aim to present the development of the application as an assistive technology option, by adding new features to the vocalizer as well as assessing it as a usable option for daily training interaction for children with speech and language impairments. In this regard, we invited 15 children with speech and language impairments and 20 with no impairments to perform training activities with the application. Likewise, we asked three speech therapists and three usability experts to interact, assess, and give their feedback. In this assessment, we include the following parameters: successful conclusion of the training tasks (effectiveness); number of errors made, as well as number and type of difficulties found (efficiency); and the acceptance and level of comfort in completing the requested tasks (satisfaction). Overall, the results showed that children conclude the training tasks successfully and helped to improve their language and speech capabilities. Therapists and children gave positive feedback to the AppVox interface. © 2019 John Wiley & Sons, Ltd