Abstract
In recent years, the intense urbanization, and consequently the traffic congestion, has been a major concern of large cities. In this context, the development of smart parkings is a suitable solution to deal with this problem. However, the complexity and requirements imposed by such large-scale systems are an obstacle to its easy implementation. In this sense, it is fundamental to adopt emergent ICT and Artificial Intelligence technologies that are capable to address the imposed requirements. Multi-Agent Systems (MAS) is a suitable approach to face this challenge by providing modularity, flexibility, reconfigurability and fast response to condition change based on its decentralized nature. The use of such agent-based solutions to control physical assets, create novel systems entitled Cyber-Physical Systems (CPS) where the interconnection between the cyber and the physical parts is a crucial issue. This paper focuses the interface between the software agents of a smart parking system with the physical control devices of the parking spots. For this purpose, different interface practices were implemented and tested, considering different interaction schemes and technologies. These alternative interface practices were analyzed taking into consideration the response time, scalability and re-usability parameters.

Abstract
The advances in wireless communications are still very limited when intended to be used on Underwater Communication Systems mainly due to the adverse proprieties of the submarine channel to the acoustic and radio frequency (RF) waves propagation. This work describes the development and characterization of a polyvinylidene difluoride ultrasound transducer to be used as an emitter in underwater wireless communications. The transducer has a beam up to 10 degrees x 70 degrees degrees and a usable frequency band up to 1 MHz. The transducer was designed using Finite Elements Methods and compared with real measurements. Pool trials show a transmitting voltage response (TVR) of approximately 150 dB re mu Pa/V@1 m from 750 kHz to 1 MHz. Sea trials were carried in Ria Formosa, Faro (Portugal) over a 15 m source-receiver communication link. All the signals were successfully detected by cross-correlation using 10 chirp signals between 10 to 900 kHz.

Abstract
Industry 4.0 paradigm is a reality in the digitization of industrial processes and physical assets, as well as their integration into digital ecosystems with several suppliers of the value chain. In particular, Industry 4.0 is the technological evolution of embedded systems applied to Cyber-Physical Systems (CPSs). With this, a shift from the current paradigm of centralization to a more decentralized production, supported by Industrial Internet of Things (IIoT), is implied. The work reported in this paper focuses on the development of smart devices (SmartBoxes), based on low-cost hardware such as Raspberry Pi and also platforms certified for industrial applications, such as NI CompactRIO. Both platforms adopted the OPC-UA architecture to collect data from the shop-floor and convert it into OPC-UA Data Access standard for further integration in the proposed CPPS. Tests were also performed with the MQTT protocol for monitorization. Each SmartBox is capable of real-time applications that run on OPC-UA and MQTT, allowing easy interaction between supervisory systems and physical assets. © 2019 IEEE.

Abstract
Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperahility turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.

Abstract
The use of fossil fuel vehicles is one of the factors responsible for the degradation of air quality in urban areas. In order to reduce levels of air pollution in metropolitan areas, several countries have encouraged the use of electric vehicles in the cities. However, due to the high investment costs in this class of vehicles, it is expected that the spatial distribution of electric vehicles' adopters will be heterogeneous. The additional charging power required by electric vehicles' batteries can change operation and expansion planning of power distribution utilities. In addition, urban planning agencies should analyze the most suitable locations for the construction of electric vehicle recharging stations. Thus, in order to provide information for the planning of electric mobility services in the city, this paper presents a spatiotemporal model for estimating the rate of electric vehicles' adopters per subareas. Results are spatial databases that can be viewed in geographic information systems to observe regions with greater expectancy of residential electric vehicle adopters. These outcomes can help utilities to develop new services that ground on the rising availability of electric mobility in urban zones.

Abstract
This article describes a Kernel Principal Component Regressor (KPCR) to identify Auto Regressive eXogenous (ARX) Linear Parmeter Varying (LPV) models. The new method differs from the Least Squares Support Vector Machines (LS-SVM) algorithm in the regularisation of the Least Squares (LS) problem, since the KPCR only keeps the principal components of the Gram matrix while LS-SVM performs the inversion of the same matrix after adding a regularisation factor. Also, in this new approach, the LS problem is formulated in the primal space but it ends up being solved in the dual space overcoming the fact that the regressors are unknown. The method is assessed and compared to the LS-SVM approach through 2 Monte Carlo (MC) experiments. Every experiment consists of 100 runs of a simulated example, and a different noise level is used in each experiment,with Signal to Noise Ratios of 20db and 10db, respectively. The obtained results are twofold, first the performance of the new method is comparable to the LS-SVM, for both noise levels, although the required calculations are much faster for the KPCR. Second, this new method reduces the dimension of the primal space and may convey a way of knowing the number of basis functions required in the Kernel. Furthermore, having a structure very similar to LS-SVM makes it possible to use this method in other types of models, e.g. the LPV state-space model identification.