─Smart City is one area of application for the Internet of Things (IoT) and it has been attracting attention from both academia and industry. Cities will be composed by autonomous parts that communicate and provide services to each other. For instance, cars (autonomous or not) may be seen as a service that transports people from one point to another. Interactions between users and these kinds of services will grow, making it necessary to have digitization of all these parts of the Smart City. The Digital Twin (DT) concept proposes that real-world assets have a virtual representation connecting the physical world with the cyber world. This allows to track the whole life-cycle of this object as well as perform simulations with current or previously stored data. In this context, this work proposes the use of Digital Twin for enabling and supporting car-as-a-service (CaaS). A case study has been developed to demonstrate the modeling and the deployment of the Digital Twin, highlighting how this concept can be one of the key enablers for CaaS.

The digital twin (DT) is a virtual representation of a physical object, which has been proposed as one of the key concepts for Industry 4.0. The DT provides a virtual representation of products along their lifecycle that enables the prediction and optimization of the behavior of a production system and its components. A methodology design using model-driven engineering (MDE) is proposed that strives toward being both flexible and generic. This approach is presented at two levels: first, a DT is modeled as a composition of basic components that provide basic functionalities, such as identification, storage, communication, security, data management, human-machine interface (HMI), and simulation; second, an aggregated DT is defined as a hierarchical composition of other DTs. A generic reference architecture based on these concepts and a concrete implementation methodology are proposed using AutomationML. This methodology follows an MDE approach that supports most of the DT features currently proposed in the literature. A case study has been developed, the proposed ideas are being evaluated with industrial case studies, and some of the preliminary results are described in this article. With the case study, it is possible to verify that the proposed methodology supports the creation and the deployment process of a DT.

The production of food for all the population in the world became the biggest concern. The population continues to grow and the number of farmable lands has been decreasing. To make the lands more productive, fertilizers are used on a larger scale. To guarantee the quality of the product, particle size analysis are made by mechanical sieving. With the time, the wear-out of the sieving in the fertilizer industry the results of the particle size analysis will be erroneous. So the computer vision appears as an alternative that is non-invasive and less time-consuming. In this context, this paper has the objective to develop a grain surface simulator capable of generating virtual images with overlapping grains, since there is a difficulty to obtain annotated data of images of fertilizers. In order to validate the proposed simulator using a DIP algorithm, noises are added in the virtual images to compare with the reality in the industry, to show how well the particle size analysis with computer vision were handled towards adversities. The results of the overlapping analysis show that when the virtual image has a fewer number of grains, the DIP algorithm can identify the majority of grains, consequently with less error in the particle size analysis. Different noises, at different intensities, have their effects analyzed on the algorithm. As the analyzes in this study match with the reality showing the consequences, tendencies, and errors of the overlapping of grains and noises in the images, the simulator developed here matches with reality and is extremely useful to facilitate the study of complex cases of application of visual computing and digital image processing in particle size analysis of fertilizers.

In the fertilizer industry, granulometric analysis is an important quality control of the product. Usually, this process is done mechanically, through sieving, which makes the process slow. As an alternative to this, a methodology based on digital image processing for fertilizer classification is proposed. This approach allows to measure feret's diameter, area, volume and mass, important indicators to characterize fertilizers. The results obtained were validated with sieved samples and demonstrate the applicability of the method as a low cost alternative to the traditional method of granulometry of fertilizers.

The use of robots is increasing in different industries, as in the case of large metal structures. The use of mobile robots meets the needs of these industries: it can be easily moved in the production line, with gains in repeatability and process efficiency, reducing rework costs. The poor configuration of the robotic and welding system generates problems in the internal structure and the surface of the weld, thus compromising the final quality of the piece. Visual inspections are common to identify problems that may have occurred during these processes. Inspections that use x-rays, ultrasound, or thermal cameras require additional equipment and highly trained inspectors to analyze the results and detect problems. This work presents a computer vision system based on a passive monocular camera for analysis of weld bead textures. Images of weld beads with and without discontinuities are captured and a dimensional reduction algorithm known as Principal Component Analysis (PCA) is applied to select the main characteristics that describe each group. Afterward, the Support Vector Machine (SVM) supervised learning method is applied to recognize the patterns of image groups, making it possible to classify new images of weld beads as welds with or without discontinuities. The system makes use of the same camera coupled to the robot responsible for conducting the welding, without the need for additional sensors, and assists the welding inspector in the evaluation of the performed process.

As the number of network connected devices in an industrial system increases, the management and handling of all the information generated become a challenge. In this work we explore concepts of Cyber-Physical System model the virtual part of industrial devices (sensor, machines, CLPs) using the Digital Twin concept and propose an architecture based on web services for accessing their data. We present a case study where an Augmented Reality system access the Twin Model data via web services and display real-time information to the user. Moreover, we present a review of how the involved concepts, which have a strong computational background, relate to industrial applications and how they can expand the possibility of services and business models.

The generation of handwriting is a complex task. In order to accommodate for the large variations involved in handwritten words deformable templates need to be used. In this paper we propose a handwriting model, based on Active shape modeling (ASM). In a two-step generation process, a template-based ASM generates characters and a Gaussian mixture regression (GMR) model concatenates the generated characters. For real time generation of cursive handwriting an adaptation of Global local approximation (GLA) methodology is used to fit the generated models.