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Focuses on a study that introduced an approach to the stability analysis of a high-order digital phase-locked loop (PLL) for timing recovery in hard-disk-drive data transfer. Stability analysis in the Z domain; Stability analysis in the S domain; Stability analysis in the unified transform domain; Experimental results; Conclusion.

Deals with the conception of fuzzy logic microcontroller in supporting applications with higher computational demands while maintaining a low cost. Characteristics of fuzzy control; The use of a 16-bit microcontroller for fuzzy control; Evaluation of microcontroller architecture; Findings on fuzzy logic microcontrollers. INSET: Fuzzy logic controllers..

• Analytical games are a promising Knowledge Acquisition method. • Reliability Game data used in Bayesian Networks latent variable learning. • The work described aims at supporting multi-source fusion systems design. Knowledge Acquisition (KA) methods are of paramount importance in the design of intelligent systems. Research is ongoing to improve their effectiveness and efficiency. Analytical games appear to be a promising tool to support KA. In fact, in this paper we describe how analytical games could be used for Knowledge Engineering of Bayesian networks, through the presentation of the case study of the Reliability Game. This game has been developed with the aim of collecting data on the impact of meta-knowledge about sources of information upon human Situational Assessment in a maritime context. In this paper we describe the computational model obtained from the dataset and how the card positions, which reflect a player belief, can be easily converted in subjective probabilities and used to learn latent constructs, such as the source reliability, by applying the Expectation-Maximisation algorithm. [ABSTRACT FROM AUTHOR]

Availability of efficient development tools for data-rich IoT applications is becoming ever more important. Such tools should support cross-platform deployment and seamless and effective applicability in a variety of domains. In this view, we assessed the versatility of an edge-to-cloud system featuring Measurify, a framework for managing smart things. The framework exposes to developers a set of measurement-oriented resources that can be used in different contexts. The tool has been assessed in the development of end-to-end IoT applications in six Electronic and Information Technologies Engineering BSc theses that have highlighted the potential of such a system, both from a didactic and a professional point of view. The main design abstractions of the system (i.e., generic sensor configuration, simple language with chainable operations for processing data on the edge, seamless WiFi/GSM communication) allowed developers to be productive and focus on the application requirements and the high-level design choices needed to define the edge system (microcontroller and its sensors), avoiding the large set-up times necessary to start a solution from scratch. The experience also highlighted some usability issues that will be addressed in an upcoming release of the system. [ABSTRACT FROM AUTHOR]

Serious virtual worlds (SVWs) represent a great opportunity for learning and should embed a lot of high-quality contextualised information so that the player can take the most from his or her exploration of the environment. This requires defining new methodologies and tools for effective production. We have investigated this issue in implementing a couple of SVWs and abstracted a conceptual framework relying on the task-based learning pedagogical theory. The model defines games set in realistic SVWs enriched with embedded educational tasks. Tasks are simple trial activities that embody units of knowledge. The player can discover them in his or her SVW exploration and interact with them in order to construct meaning, build lasting memories and deepen understanding of the featured item(s). The model involves pedagogical task annotation—which allows decoupling the tasks, which can be reused in different VWs from the definition of their delivery strategy in the context of a specific VW, which is specified by the VW designer and automatically managed by the run-time engine. This approach simplifies the authoring work. The visual Creative Toolkit (CT) we have developed is being used for producing contents for the 15 reconstructed cultural cities that will be featured in the first version of the Travel in Europe VW. The paper described the overall framework and the details of the CT. It also presented an implemented example, briefly discussing the methodology we have followed for developing cultural heritage content. [ABSTRACT FROM AUTHOR]

The impending “intelligent car” scenario can have a great impact on improving the quality of life of a number of citizens by reducing wasted time and enhancing interaction with the environment. In this article, the authors describe their development of tour guides on palmtop computers that present tourists with high-quality multimedia information and services. An important added value of tour guides on palmtop computers is that they can assist the user in the real-time event (e.g., in a museum or while walking in a city) and during the in-car trip as well. However, the overall surrounding environment and the contextual tasks performed by the user are different. In-car use is challenging because it requires adapting the interaction modes of the user to the strict safety constraints of driving. The authors have explored these issues in the development of a tour guide for the Castellon region of Spain. In this article, they present ideas and discuss preliminary findings on their interface design. © 2005 Wiley Periodicals, Inc. Hum Factors Man 15: 461–476, 2005. [ABSTRACT FROM AUTHOR]

Describes the Venice Game project, which aims to explore how mobile gaming can help users enhance their experience of art and history through interactions with the heritage and people in an urban environment. Game architecture; Technical architecture; Tests.

The paper deals with the hot issue of entrepreneurship education and describes the rationale behind the gamified and collaborative courses for university students conceived, developed and deployed in the framework of the eSG (stimulating entrepreneurship through Serious Games) project, funded under the EU lifelong learning (LLP) Programme. The project aims to help students becoming familiar, mainly through practice, with basic concepts of entrepreneurship and company management and to stimulate the emergence of their entrepreneurial attitudes. In the framework of the project specific courses mainly grounded on the concepts of gamification and collaboration were designed and carried out in three different partner countries: Italy, Spain and the Netherlands. The main objectives of the courses are presented in this paper and a theoretical model supporting the choice of Serious Games is shown which keeps into account usability, pedagogy and the entrepreneurship skills expressed by state of the art models. [ABSTRACT FROM AUTHOR]

There are currently a number of models, frameworks and methodologies for serious games analysis and design that provide useful interpretations of the possibilities and limitations offered by serious games. However, these tools focus mostly on high-level aspects and requirements and do not help understand how such high-level requirements can be concretely satisfied. In this paper, we present a conceptual model, called Activity Theory-based Model of Serious Games (ATMSG), that aims to fill this gap. ATMSG supports a systematic and detailed representation of educational serious games, depicting the ways that game elements are connected to each other throughout the game, and how these elements contribute to the achievement of the desired pedagogical goals. Three evaluation studies indicate that ATMSG helped participants, particularly those with gaming experience, identify and understand the roles of each component in the game and recognize the game's educational objectives. [ABSTRACT FROM AUTHOR]

Although there is a consensus on the instructional potential of Serious Games ( SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance () European Network of Excellence on Serious Games, which has a focus upon pedagogy-driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of gameplay, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics- Game Mechanics ( LM-GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in an SG. The LM-GM model includes a set of pre-defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories. Designers and analysts can exploit these mechanics to draw the LM-GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework's support in determining how gameplay and pedagogy intertwine in an SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state-of-the-art framework. [ABSTRACT FROM AUTHOR]

Abstract: A 3D reconstruction of an urban area for Virtual Worlds (VWs) or Serious Games (SGs) requires a careful trade-off among the 3D models'' realism, weight and development time. This has led us to the definition of an algorithm – the Architectonic Style Area (ASA) algorithm – for procedural generation of buildings in an urban area, based on the concept of “architectonic likelihood”, that intends to give players the sense of being in a particular city or even specific area of a city. This has a cognitive foundation in the fact that, after a visit, a tourist typically does not remember every single palace of a city, but remembers – beside the most important monuments and buildings – its general features and its architectonic styles. The algorithm extrudes 2D maps and composes façades by statistically assembling sample images of architectonic components from the target city. Components are classified in an ontology based on the classic principles of architecture. The algorithm relies on rules that encode the semantics of the ontology. The paper describes the algorithm and the related easy-to-use visual authoring tool. It also shows the results in two different projects: a SG for the promotion of the cultural heritage and a 3D movie. Results show that the approach allows keeping the realism of every single building at good detail and of the overall scene, while dropping a huge work of image collection and processing for the textures. Comparisons are investigated with regard to state of the art urban modeling methods, highlighting the differences and possible synergies. [Copyright &y& Elsevier]

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness. [ABSTRACT FROM AUTHOR]

Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users' privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused. [ABSTRACT FROM AUTHOR]

While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners' intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight the importance of the reference methodology to theoretically inform and coherently manage all the steps of the project and the need for effective and efficient tools, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts. [ABSTRACT FROM AUTHOR]

This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a linear kernel, k-Nearest Neighbors (K-NN), and Decision Tree (DT)), and exploits STM X-Cube-AI to implement Artificial Neural Networks (ANNs) on STM32 Nucleo boards. We investigated the performance of these algorithms on six embedded boards and six datasets (four classifications and two regression). Our analysis—which aims to plug a gap in the literature—shows that the target platforms allow us to achieve the same performance score as a desktop machine, with a similar time latency. ANN performs better than the other algorithms in most cases, with no difference among the target devices. We observed that increasing the depth of an NN improves performance, up to a saturation level. k-NN performs similarly to ANN and, in one case, even better, but requires all the training sets to be kept in the inference phase, posing a significant memory demand, which can be afforded only by high-end edge devices. DT performance has a larger variance across datasets. In general, several factors impact performance in different ways across datasets. This highlights the importance of a framework like ELM, which is able to train and compare different algorithms. To support the developer community, ELM is released on an open-source basis. [ABSTRACT FROM AUTHOR]