The transition to smarter and more resilient power grids requires advanced testing and validation methodologies to ensure the reliability and interoperability of new technologies, equipment and protection schemes. This paper introduces a fully integrated Hardware-In-the-Loop (HIL) framework designed to bridge the gap between offline simulations and real-world implementations in smart grid environments. The framework enables the automation of Electromagnetic Transient (EMT) simulations, while publishing the the simulated results as Sampled Values (SV) compliant with the IEC 61850-9-2 light edition. By leveraging this integration, the framework enables bidirectional communication with real Intelligent Electronic Devices (IEDs) via Manufacturing Message Specification (MMS) protocol facilitating Common Format for Transient Data Exchange (COMTRADE) data retrieval and dynamic EMT simulation updates.

This article consists of presenting Astroroll, a Virtual Reality (VR) game in which the player assumes the role of the first wheelchairbound astronaut at the Lunar Station.This game was designed to be played only using an eye gaze to interact and a wheelchair to move, using a technique called Redirect Walk (RW).RW is a technique that aims to modify movement in VR in relation to the real world, making the path taken between the real world and the virtual world different.Using this technique correctly is possible to enlarge the virtual space and keep the gameplay in a short area in the real world.It was developed to be exhibited at Casa da Descoberta, the public science museum of the Fluminense Federal University (UFF), in the city of Niterói, in a space that demands a compact play area to accommodate all visitors.

Affective computing enables robots to understand, interpret, and respond to human emotions, and this is fundamental to creating more natural, human-like interactions.In healthcare, emotionally intelligent robots can reduce patient anxiety and improve treatment outcomes by providing empathic interactions.In educational settings, these robots can be used to create learning environments that are adaptive to students' emotions, enhancing the educational experience.Despite the good results presented in the literature with the use of affective social robots, their full use in the areas of education and healthcare has often been hindered due to their high cost.In addition, there is a need for open-source robotic platforms that can be customized according to the needs and resources of human-robot interaction (HRI) researchers, developers, and target users.This thesis presents a proposal for an open-source robotics framework and platform and the development of affective social robots, which includes a new affective social robot named FRED.It is an open-source affective human-robot interaction (AHRI) research platform.This platform can help HRI researchers conduct studies with social robots, especially those aimed at answering affective robotics-related questions involving affective understanding and affective generation.To serve as a proof of concept, the proposed framework was instantiated in two real-world scenarios on two different robotic platforms.Finally, we run an experiment with FRED using the framework as a research instrument in HRI.

The Internet of Multisensory, Multimedia, and Musical Things (Io3MT) bridges computer science, humanities, and arts, fostering transmedia services and creative applications.This demo research applies these principles alongside extended reality (XR) to enhance PhysioDrum, an immersive, multimodal system that blends physical and digital aspects to expand musical expression in virtual environments.Using a smart musical instrument (SMI) and electronic pedals as interfaces, users interact with a virtual drum kit through gestures while receiving haptic feedback.By integrating sound and multimedia elements, PhysioDrum aims to reduces cognitive load and the learning curve, merging traditional drumming practices with immersive XR.The demo emphasizes design strategies that enhance playability, accessibility, and creative potential for users of all skill levels.

Mulsemedia (Multiple Sensorial Media) authoring poses a considerable challenge as authors navigate the intricate task of identifying moments to activate sensory effects within multimedia content. A novel proposal is to integrate content recognition algorithms that use machine learning (ML) into authoring tools to alleviate the authoring effort. As author subjectivity is very important, it is imperative to allow users to define which sensory effects should be automatically extracted. This article conducts a twofold evaluation of the proposed semi-automatic authoring. The first is from a user perspective within the STEVE 2.0 mulsemedia authoring tool, employing the Goal–Question–Metric (GQM) methodology and a user feedback questionnaire. Our user evaluation indicates that users perceive the semi-automatic authoring approach as a positive enhancement to the authoring process. The second evaluation targets sensory effect recognition using two different content recognition modules, quantifying their automatic recognition capabilities against manual authoring. Metrics such as precision, recall, and F1 scores provide insights into the strengths and nuances of each module. Differences in label assignments underscore the need for ML module result combination methodologies. These evaluations contribute to a comprehensive understanding of the effectiveness of sensory effect recognition modules in enhancing mulsemedia content authoring.

This study introduces MCOF, a multi-camera, computer vision-based system designed to assist visually impaired individuals with mobility on university campuses. The system operates locally and achieves the following performance metrics: (i) detecting body points within 1.2. 10–2seconds, (ii) identifying people, objects, and animals in 2.4. 10–2seconds, and (iii) detecting movements in 5.1. 10−2seconds. These results were obtained using a GTX 1,660 GPU with up to 6 cameras (or a 6,112MB stream) running concurrently. According to the MCOF architecture, events trigger tickets that are sent to an external information system, which can then implement its own safety and personnel protocols. Additionally, MCOF includes modules to handle electrical and network failures and features an obstruction detection routine for the cameras.

Traditional audiovisual multimedia applications can be synchronized with sensory effects to further enhance the users' quality of experience. These applications are named mulsemedia applications and stimulate other human senses, such as touch, smell, and taste. Some delays may occur when rendering sensory effects due to specific device capabilities and communication between the mulsemedia presentation engine (formatter) and actuators. In this context, this work proposes the automatic preparation of sensory effects to avoid synchronization faults during a mulsemedia application presentation. The automatic preparation of sensory effects depends on the effect type to be rendered. As proof of concept, we implemented the automatic preparation in the Ginga-NCL presentation engine. Furthermore, we present some use cases of the preparation mechanisms applied to sensory effects. Finally, an evaluation of our proposal is presented.

One of the most prevalent types of cancer worldwide is breast cancer, a condition that tends to change the thermal pattern of the breasts. Thermographic images, a functional examination that considers temperature variation, emerge as an alternative for this disease since they consider body temperature variation to investigate anomalies. This examination has been widely investigated in studies for screening or diagnosing breast cancer. However, a limited number of studies investigate this examination to track the progress of cancer and assess tumor response to treatment. This study works in this context, exploring thermography during neoadjuvant treatment. In the proposed methodology, we first preprocess the thermal data and use the k-means unsupervised learning algorithm to identify the hottest regions. Subsequently, we build time series based on statistical measures and homogeneity measures among thermal captures to evaluate the patients’ tumor evolution. The results show that this approach indicates the treatment evolution correctly in at least 79% of the cases when observing only the statistical measures and 95% of the cases when combining the statistical and homogeneity measures on the patient evaluation process.

Voice is the fastest and most efficient method of communication among humans. Researchers believe that it can also be considered the most efficient means of communication between humans and machines. Voice can, in addition to providing useful information, inform us about the emotional state of the person who is speaking. For many applications, being able to identify the emotion is crucial, as it allows the application to adapt to the user. In the case of human-robot interaction, recognizing the user's emotion allows the robot to be more empathetic during interactions. In addition to other methods, such as recognition of facial expressions and recognition through body expressions, recognition of emotions through speech can be used as an additional component in identification the user's emotional state. This work proposes the recognition of emotion through speech using an approach based on image processing of the voice audio signal spectrogram. Two new features based on color histograms are proposed. One thousand six hundred audio files with phrases considering four types of emotions (angry, happy, neutral and sad) were processed and classified. These phrases were spoken by women half by a 64 years old (Subject- 64) and the rest by a 26 years old one (Subject-26). These files are a subset of the TESS (Toronto Emotional Speech Set) dataset. When processing subject-26's voice, an precision of 94.40 % and 91.90 % was achieved in detecting neutral and sad emotions, respectively. When processing subject-64's voice, an precision of 97.00 % was achieved for the angry emotion. The results obtained show the proposal great potential.

Mulsemedia (Multiple Sensorial Media) authoring is a complex task that requires the author to scan the media content to identify the moments to activate sensory effects. A novel proposal is to integrate content recognition algorithms into authoring tools to alleviate the authoring effort. Such algorithms could potentially replace the work of the human author when analyzing audiovisual content, by performing automatic extraction of sensory effects. Besides that, the semi-automatic method proposes to maintain the author subjectivity, allowing the author to define which sensory effects should be automatically extracted. This paper presents an evaluation of the proposed semi-automatic authoring considering the point of view of users. Experiments were done with the STEVE 2.0 mulsemedia authoring tool. Our work uses the GQM (Goal Question Metric) methodology, a questionnaire for collecting users' feedback, and analyzes the results. We conclude that users believe that the semi-automatic authoring is a positive addition to the authoring method.

The combination of both Network Function Virtualization (NFV) and Edge Computing facilitates the Quality of Service (QoS) fulfillment of latency-stringent applications by taking advantage of the proximity of the devices at the edge and the user, and provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models for 5G environments. However, to achieve those benefits, VNFs must be deployed so that system resource utilization is optimized and service QoS is maintained. In this context, the selection of the most suitable edge nodes to deploy VNFs is a challenging research issue because multiple — and often conflicting — objectives must be considered by the VNF placement algorithm. The challenges become greater when considering the high dynamism of the edge environment. The availability of edge nodes and users’ locations vary over time, making original allocation decisions ineffective. In this paper, we propose DSP, a Dynamic Smart VNF Placement algorithm that considers the dynamic nature of a 5G edge environment. Our proposal focuses on optimizing resource utilization with an online placement approach to handle continuously arriving service requests. Furthermore, we also account for user mobility providing an approach that reduces the performance impact caused by users’ movement. Additionally, a two-level resource-sharing mechanism is provided, minimizing the cost for the infrastructure provider with low impact on QoS.

Socially Assistive Robots (SARs) have successfully been used in various types of health therapies as non-pharmacological interventions. A SAR called EVA (Embodied Voice Assistant) is an open-source robotics platform intended to serve as a tool to support research in Human-Robot Interaction. The EVA robot was originally developed to assist in non-pharmacological interventions for people with Dementia and has more recently been applied for children with Autism Spectrum Disorder. EVA provides multimodal interactions such as verbal and non-verbal communication, facial recognition and light sensory effects. Although EVA uses low-cost hardware and open-source software, it is not always possible, or practical, to have a physical robot at hand, particularly during rapid iterative cycles of design and evaluation of therapies. Thus, our motivation to develop a simulator that allows testing the scripts of therapies to be enacted by the EVA robot. This work proposes EvaSIM (EVA Robot Simulator), a simulator that can interpret an EVA script code and emulate the multimodal interaction capabilities of the physical robot, such as Text-To-Speech, facial expression recognition, controlling light sensory effects, etc. Several EVA scripts were run using the simulator attesting that they have the same behaviour as the physical robot. EvaSIM can serve as a support tool in the teaching/learning process of the robot’s scripting language, enabling the training of technicians and therapists in script development and testing for the EVA robot.

The Internet of Musical Things (IoMusT) is an interdisciplinary area that aims to improve the relationship between musicians and their peers, as well as between musicians and audience members, creating new forms of interaction in concerts, studio productions, and music learning. Although emerging, this field already faces some challenges, such as lack of privacy and security, and mainly, lack of standardization and interoperability between its devices. Therefore, an environment design, called Sunflower, was proposed, which tries to contribute to solving the most recurrent problems in this area, specifying an architecture pattern, protocol, and sound features that aim to allow heterogeneity in these systems. Its practical implementation resulted in an interoperable, multimedia, and interactive environment. This paper, therefore, shows a demonstration of how Sunflower works in the accomplishment of an artistic presentation, also emphasizing its approach, the technologies that support it, and the advances it can bring to the area of IoMusT.

Several studies explore the use of thermography for screening or diagnosis of breast cancer. However, few investigate this examination to follow up breast cancer neoadjuvant treatment. This work explores this theme and proposes a computational methodology for monitoring breast cancer treatment response by creating time series from dynamic thermography examinations. In this approach, we use the unsupervised learning algorithm k-means to cluster breast temperature information and indicate the area used for creating time series. From each time series, we compute two variation measures. Our results are promising and show that these time series measures can indicate the patient's tumor evolution.

Advances in electronics have enabled the development of intelligent miniaturized biomedical sensors that can be used to monitor the human body. The use of wireless communication proved to be an alternative, which provides less discomfort to patients and good cost-benefit. In order to fully exploit the benefits of wireless technologies in e-health, a new type of wireless networks has emerged: Wireless Body Area Networks (WBANs). However, technical and social challenges must be addressed to enable their adoption. Some factors, such as the use of the human body as a propagation media, the effects of radiation on human tissue and human body movements, make WBANs a new paradigm of wireless communication networks. As a person moves, transmission channel quality may vary depending on body position, which may result in poor network performance. To meet the requirements of WBAN applications, while preserving the energy efficiency and the user's physical safety, this paper proposes B-Move, a WBAN transmission scheduler based on the movement of the human body. Our proposal was analyzed in Castalia Simulator and obtained results present improvements in packet delivery rate and energy efficiency, when compared to polling and contention-based medium access (CSMA/CA) used in the WBAN IEEE 802.15.6 standard.

The number of people diagnosed with anxiety disorders has been increasing in recent years. The correct diagnosis of such disorders is not always a trivial task, sometimes forcing an individual to consult with many clinicians and performing several medical exams. Post-traumatic Stress Disorder (PTSD) is a disorder related to experienced events, which presented a certain degree of threat to an individual. When experiencing situations that refer to past events, an individual may present reactions that trigger physiological changes in the organism such as tachycardia or bradycardia. Many disorders have common symptoms, and identifying these subtleties results in greater diagnosis efficiency and effectiveness. Artificial Intelligence (AI) and Data Mining (DM) techniques have helped specialists in the diagnosis and prevention of diseases and disorders. In our research, we aim at finding new biomarkers to diagnose PTSD analyzing physiological signals with DM techniques. In this paper, we used a dataset from an experiment with civilians that were recently exposed to traumatic events related to violence. Those individuals completed a questionnaire that evaluates the impact of such events through PCL (PTSD Disorder Checklist for DSM-IV) scale. Heart rate and skin conductance signals were collected while viewing emotional and neutral stimuli images. We applied DM techniques and classification algorithms to evaluate and maximize PCL score prediction performance considering those physiological signal data. The best result was obtained with SMO algorithm with its hyperparameters values suggested by an auto-learning procedure, presenting an accuracy of 85.45% (p-value = 0.001), precision of 0.8 (p-value = 0.001), recall of 0.5714 and F-Measure of 0.6667 (p-value = 0.001).

As recent research indicates that virtual games are beneficial to the player's brain health, games have been used to aid aging-related disease treatment. There are also studies that imply that sensory effects improve user's Quality of Experience. Combining both concepts, this paper proposes the Stroop Game, a cognitive game inspired by the Stroop test, which uses light sensory effects. The Stroop Game was developed for the Brazilian Digital TV System using the Ginga-NCL middleware, which implements the ITU H.761 standard. Evaluating the Stroop Game implementation with elderly users, it indicates that the game proposed in this paper was well received by the users and also that the light sensory effect makes it more attractive to them.

Breast cancer is the second most common cancer in the world. Early diagnosis and treatment increase the patient's chances of healing. The temperature of cancerous tissues is generally higher than that of healthy neighbouring tissues, making thermography an option to be considered in screening strategies for this type of cancer. In this paper, we propose a computational method for breast Dynamic Infrared Thermography images analysis for screening patients with abnormalities in the breast, using supervised and unsupervised machine learning techniques. An abnormality may be a benign tumor or a malignant tumor (cancer). As performance measure, we use the area under ROC curve, sensitivity, specificity and accuracy. The best results are achieved by K-Star classifier, obtaining an accuracy equal to 98.57%. The results confirm the potential of the proposed method for screening patients with abnormalities in the breast.

Electronic medical records (EMRs) are highly sensitive information shared among peers to keep up-to-date patient history. Providing security, privacy, and availability to these sensitive data is a challenge because, typically, after data publication the patient loses control over them. In this paper, we propose a blockchain-based approach to secure EMR for healthcare applications, where access control is patient-centric. Our proposal keeps encrypted EMRs in the blockchain, and the patient shares the decryption key only with healthcare professionals in which he/she trusts. Blockchain allows untrusted node, in a distributed peer-to-peer network to correctly and verifiably interact with each other, without any reliable intermediary. We investigate the scalability of our approach through simulations. Results show that it scales well since increasing the number of nodes in the network implies a linear increase in the size of the stored chain. Results also reveal that the time for inserting a new EMR in the blockchain remains low even when the number of nodes in the network increases.

HTTP streaming has become a practical technique to distribute multimedia content. This work proposes an adaptation method that improves the user's quality of experience when they consume video streaming over HTTP, compared to other methods considered state of the art. Based on the study of existing adaptation methods, we propose a new approach called Relative Smoothed Throughput (RST) method. Experiments with varying bandwidth conditions and multiple flows show that RST is able to outperform state of the art methods in efficiency.

In multimedia applications, a short delay in content delivery may cause synchronization failures among media objects, decreasing the presentation quality and user quality of experience. Moreover, some applications incorporate sensory effects to audiovisual content, which are called mulsemedia applications, imposing even more challenges on application execution control. In order to lessen these problems, document authors or presentation systems can prepare media content and sensory effects in advance. This paper proposes a new operation type over media objects and sensory effects, represented as a new event type in multimedia and mulsemedia models, named preparation event. When sensory effects are modeled as first-class entities, mulsemedia authors may specify relationships that trigger actuator device preparation to ensure that sensory effects will be presented at the correct moment. In order to validate our proposal, some use cases of the preparation event in NCL (Nested Context Language) and Lua applications have been developed and are discussed in the paper. NCL is the standard authoring language for the Brazilian digital TV system declarative middleware, named Ginga-NCL, and also ITU-T H.761 standard for IPTV systems.

Simulating new protocols for e-health systems is very important, as it allows an initial evaluation before a real implementation is made. On the other hand, network simulators do not offer proper support to represent medical applications or components to facilitate running simulations modeling e-health applications. The lack of simulators that specify the sensor type and its communication requirements make real experiments harder. Aiming at fulfilling this gap, this paper proposes the use of ISO/IEEE 11073 standard for Personal Health Devices (X73-PHD) in e-health network simulations, representing realistic medical applications and investigating the behavior of medical devices (sensors or actuators) in Wireless Body Area Network (WBAN) scenarios. We developed a free and open-source implementation of X73-PHD for Castalia Simulator, providing five different PHD types to act like real ISO/IEEE 11073 devices in WBAN simulations. Our implementation supports Agent-initiated mode, where PHDs take the initiative to send measurements to the hub. Our implementation also supports the unconfirmed communication mode and the confirmed communication mode, where the receiver sends an acknowledgment to the sender every time it receives a packet. Simulation results showed that the confirmed communication mode did not perform well in WBANs when the interval between transmissions is too small, due to the long period of timeout proposed in the X73-PHD standard. Therefore, we propose a new extension to the confirmed mode standard that decreases the overhead of control packets over the network, using smaller timeouts and delivering more packets.

There is a great number of image applications, where the first step in the analysis is to associate the same point in two or more images that were taken from different viewpoints or at different times. This procedure is called image registration (IR). This work proposes a new efficient method for IR based on tensor calculus that overtakes the usual assumption of brightness consistency among the involved images. The main idea is to define transformations in serial images by using tensor behavior on coordinate transformations. It combines the simplicity and efficiency of the computer implementation with the correctness and elegance of the physic foundations. Moreover, it introduces a complete different way for management of this very important application of image processing: the use of the principal axis idea. The significant advantage of the proposed method is its promptitude and robustness for use in medical images in a way the as far as we known have never considered before. Experimental results in real examinations demonstrate that the proposed algorithm is highly accurate and outperforms a combination of the most updated IR approaches.

This paper proposes an interactive multimedia authoring tool called STEVE (Spatio-Temporal View Editor) and a new multimedia model called SIMM (Simple Interactive Multimedia Model). STEVE aims at allowing users with no knowledge of multimedia authoring languages and models to create hypermedia applications for web and digital TV systems in a user-friendly way. Compared with existing multimedia authoring tools, STEVE is the unique tool that allows ordinary users to export hypermedia applications to HTML5 and NCL documents. STEVE uses an event-based temporal synchronization model called SIMM that exactly fits its needs. SIMM provides high-level temporal, spatial and interactivity relations to make authoring with STEVE easier. Usability tests show that, according to users, STEVE allowed them to create multimedia applications and export them as HTML5 and NCL documents in a few minutes without programming.

Aging is a worldwide phenomenon and represents a growing concern for public health systems. In this context, neurodegenerative diseases like Alzheimer's Disease (AD) have a high prevalence among the elderly. Early diagnosis of AD allows early treatment and improves patient's quality of life. In this paper, we propose a clinical decision support system (CDSS) to aid physicians in diagnosis of AD and related disorders: Dementia (D) and Mild Cognitive Impairment (MCI). For each case, the system exhibits the most probable diagnosis, the most relevant health records and unobserved health records that could confirm such diagnosis. Moreover, the system has the ability to refine its decision model using the final diagnosis reported by physicians. The system can offer to physicians a friendly user interface designed for smartphones. The proposed decision support system is flexible and adaptable to different contexts, since it allows new neuropsychological tests to be included and its decision model to be adapted automatically. Clinical cases from Center for Alzheimer's Disease (CAD) of Federal University of Rio de Janeiro (UFRJ) were used as the training dataset for the proposed supervised learning method. Preliminary tests using clinical cases from Antônio Pedro University Hospital (HUAP) of Fluminense Federal University (UFF) showed that the proposed CDSS decision model achieves good performance (accuracy of 0.94 for D and 0.85 for MCI) by a computational method that evaluates several classifiers and selects the best for each mental disorder.

Multimedia applications are usually composed by audiovisual content. Traditional multimedia conceptual models, and consequently declarative multimedia authoring languages, do not support the definition of multiple sensory effects. Multiple sensorial media (mulsemedia) applications consider the use of sensory effects that can stimulate touch, smell and taste, in addition to hearing and sight. Therefore, mulsemedia applications have been usually developed using general-purpose programming languages. In order to fill in this gap, this paper proposes an approach for modeling sensory effects as first-class entities, enabling multimedia applications to synchronize sensorial media to interactive audiovisual content in a high-level specification. Thus, complete descriptions of mulsemedia applications will be made possible with multimedia models and languages. In order to validate our ideas, an interactive mulsemedia application example is presented and specified with NCL (Nested Context Language) and Lua. Lua components are used for translating sensory effect high-level attributes to MPEG-V SEM (Sensory Effect Metadata) files. A sensory effect simulator was developed to receive SEM files and simulate mulsemedia application rendering.

Declarative multimedia documents represent the description of multimedia applications in terms of media items and relationships among them. Relationships specify how media items are dynamically arranged in time and space during runtime. Although a declarative approach usually facilitates the authoring task, authors can still make mistakes due to incorrect use of language constructs or inconsistent or missing relationships in a document. In order to properly support multimedia application authoring, it is important to provide tools with validation capabilities. Document validation can indicate possible inconsistencies in a given document to an author so that it can be revised before deployment. Although very useful, multimedia validation tools are not often provided by authoring tools. This work proposes a multimedia validation approach that relies on a formal model called Simple Hypermedia Model ( SHM ). SHM is used for representing a document for the purpose of validation. An SHM document is validated using a hybrid approach based on two complementary techniques. The first one captures the document’s spatio-temporal layout in terms of its state throughout its execution by means of a rewrite theory, and validation is performed through model-checking . The second one captures the document’s layout in terms of intervals and event occurrences by means of Satisfiability Modulo Theories (SMT) formulas, and validation is performed through SMT solving. Due to different characteristics of both approaches, each validation technique complements the other in terms of expressiveness of SHM and tests to be checked. We briefly present validation tools that use our approach. They were evaluated with real NCL documents and by usability tests.

The worldwide aging phenomenon is a growing concern. Alzheimer's disease (AD) has a high prevalence in the elderly. In this paper, we present a clinical decision support system for aiding the diagnosis of AD and related disorders. We describe system's main components and architecture, which is based on a mobile web-based platform. Its predictive model is based on Bayesian networks designed considering AD diagnosis criteria, trained and tested with the patient database of the Center for Alzheimer's Disease and Related Disorder at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil. Patient database attributes are composed by predisposal factors, demographic data, assessment scales, symptoms and signs. When the system indicates a patient diagnosis, it provides: the most probable diagnosis, health data that lead to such diagnosis and, in case of low certainty factor, unobserved health data that should be collected to confirm or refuse the initial diagnostic hypothesis. Preliminary usability tests indicate potential use of the system in clinical practice.

The inherent complexity of Wireless Mesh Networks (WMNs) makes management and configuration tasks difficult, specially for fault detection and diagnosis. In addition, manual inspections are extremely costly and require a highly skilled workforce, thus becoming impractical as the problem scales. To address this issue, this paper proposes a solution that makes use of machine learning techniques for automated fault detection and diagnosis (FDD) on solar-powered Wireless Mesh Networks (WMNs). We have used the Knowledge Discovery in Databases (KDD) methodology and a pre-defined dictionary of failures based on our previous experience with the deployment of WMNs. Thereafter, the problem was solved as a pattern classification problem. Several classification algorithms were evaluated, such as Naive Bayes, Support Vector Machine (SVM), Decision Table, k-Nearest Neighbors (k-NN) and C4.5. The SVM presented the best results, achieving a 90.59% overall accuracy during training and over 85% in validation tests.

In smart grids, the broad use of Distributed Energy Resources (DERs) in distribution networks introduces the need for protective relaying schemes similar to those used in high-voltage networks. The introduction of intermittent DERs, as solar panels, also requires more autonomic and dynamic SCADA networks. Hence, power systems experience an increased demand for resilience in the distribution communication network. However, resilience methods currently in use still cannot meet those protection requirements. This work proposes ARES, a framework for autonomic and resilient communication for smart grids. ARES provides resilient, robust, and flexible communication for smart grids with Software Defined Network (SDN). Our proposal also provides autonomic services for SCADA that can improve smart grid application performance and efficiency. ARES fault resilience module is implemented and tested using Mininet 2.2.1 and RYU controller and presents maximum recovery time of 610 microseconds, which is an important advance compared to other proposals. In addition, ARES is transparent to end devices, keeping compatibility with legacy measurement and actuation devices.

Advances in cloud computing have enabled cloud gaming systems. In those systems, game logic and rendering is processed remotely in a cloud server and audio and video outputs are streamed to a thin client with limited computing capabilities, such as mobile phones, low-powered computers or even smart TVs. There are still many challenges involved in the task of providing cloud games, especially when trying to reduce server encoding time and video bitrate. In this work, we present a novel optimization technique based on image layers caching. Our technique allows that previously encoded layers are reused reducing encoding workload. In addition, only newly encoded layers are streamed, reducing the overall bitrate sent from server to client. We achieved around 23% stream size reduction with about 5% encoding time increase for cases where background cache usage is maximized.

This paper proposes LIBR, a routing protocol for Wireless Mesh Networks specially designed for linear topologies, i.e., topologies in which nodes are placed following a well-defined line-based pattern. This kind of topology may arise, for example, in power transmission lines, pipelines, roads, and railway lines. By exploring the intrinsic characteristics of these linear topologies, LIBR can achieve good performance and scalability, while being simple and incurring in low overhead for the network. Even employing a heuristic next hop selection procedure, in our simulation results, we found that LIBR was able improve network goodput in typical linear topologies up to 22%, when compared to traditional ad hoc routing protocols. Our analysis suggests that this improvement comes from a number of reasons, including better sharing of network resources (such as nodes' queues and the wireless medium itself). Finally, due to its heuristic nature, LIBR results in lower levels of control overhead, increasing the network energetic efficiency in 14% in our simulations.

A multimedia document authoring system should provide analysis and validation tools that help authors find and correct mistakes before document deployment. Although very useful, multimedia validation tools are not often provided. Spatial validation of multimedia documents may be performed over the initial position of media items before presentation starts. However, such an approach does not lead to ideal results when media item placement changes over time. Some document authoring languages allow the definition of spatio-temporal relationships among media items and they can be moved or resized during runtime. Current validation approaches do not verify dynamic spatio-temporal relationships. This paper presents a novel approach for spatio-temporal validation of multimedia documents. We model the document state, extending the Simple Hypermedia Model (SHM), comprising media item positioning during the whole document presentation. Mapping between document states represent time lapse or user interaction. We also define a set of atomic formulas upon which the author's expectations related to the spatio-temporal layout can be described and analyzed.

With current advances in the deployment of testbeds for Future Internet (FI), a new challenge arises: identity management in a globally distributed environment. In this context, it is necessary to understand local and federated models of identity management to integrate testbeds. This paper presents the design and implementation of a module for credential translation that enables a user of an academic authentication and authorization (A&A) federation, such as CAFe (the Brazilian Federated Academic Community), to access the FI testbed federation. The proposed model supports the integration of testbed federations and academic federations. The proposal generates X.509 certificates and other standard credentials used in the testbed federation, following the SFA standard, based on user attributes obtained from the A&A federation (CAFe). The developed module also allows an attribute-based access control, denying or allowing a user access according to his/her attributes obtained from CAFe. Other contributions are based on facilities for the user to delegate his SFA credential to an experimenter control interface. The study was conducted using a real experimentation laboratory (GIDLab), in which mirrors of the CAFe federation and of the MySlice platform were set up to allow the comparison of security features of our scheme to other proposals.

Testing Digital TV applications is not a simple task. DTV applications either need to be transmitted by a TV broadcaster or someone with an equipment capable of generating a DTV signal with the application embedded. Alternatively, an interactive TV application developer may use a virtual execution environment, like a virtual set-top box installed in a computer, which implements the digital TV middleware standard. In both cases, the application usually does not reach a large number of final users, and developers may not be motivated to continue working with digital TV interactive content. On the other hand, HTML5 support for multimedia content will certainly attract multimedia authors to web development. Considering this scenario, this work proposes an alternative way of presenting a digital TV application developed in NCL for the Ginga declarative middleware, translating it into HTML5 web pages, so it can be presented using a common web browser. The translation tool is called NCL4WEB. Like HTML, NCL is XML-based, so NCL4WEB is based on XSLT stylesheets. It transforms NCL elements into HTML5 elements and a set of JavaScript functions that implement synchronization relationships among media objects, including user interaction. Using NCL4WEB, NCL developers are able to publish their interactive TV applications on the web. It is transparent for final users to access HTML5 or NCL content using a web browser.

This work presents an admission control mechanism for multi-hop wireless mesh networks based on the IEEE 802.11 standard and the OLSR routing protocol. This mechanism, called CAC-OLSR, aims at ensuring that traffic flows with quality of service (QoS) requirements, especially voice and video, are only admitted in the mesh network if it has available resources in order to provide flow requirements. In addition, QoS requirements of previously admitted traffic flows cannot be violated. The proposal was evaluated with NS-2 and Evalvid simulations.

Wireless mesh network management is more complex than wired network management mainly because of network resource constraints and link quality variability. Therefore, monitoring solutions for wired networks do not achieve satisfactory performance in mesh networks. There are several techniques and tools proposed in the literature for wireless mesh network management. Each of those techniques and tools usually includes a set of specific functionalities leading to the use of more than one together in order to manage a real mesh network. This article discusses a set of requirements of an integrated platform for wireless mesh network management. This work also presents MeshAdmin, a management platform that fulfills a subset of the presented requirements. In order to evaluate MeshAdmin monitoring traffic overhead, this work presents performance tests done in a real mesh testbed.

This paper presents EDITEC, a graphical editor for hypermedia composite templates that can be used for authoring interactive TV programs. EDITEC templates are based on the XTemplate 3.0 language. EDITEC was designed for offering a user-friendly visual graphical approach. It provides several options for representing iteration structures and a graphical interface for creating basic XPath expressions. The editor provides a multiple-view environment, giving the user a complete control of the composite template during the authoring process. Composite templates can be used in NCL programs for embedding spatio-temporal semantics into NCL contexts. NCL is the standard declarative language used for the production of interactive applications in the Brazilian digital TV system and ITU H.761 IPTV services.

This paper addresses the image registration problem applying genetic algorithms. The image registration's objective is the definition of a mapping that best match two set of points or images. In this work the point matching problem was addressed employing a method based on nearest-neighbor. The mapping was handled by affine transformations. Experiments were conducted using three 2D synthetic point-sets with different affine transformations and noise. The results were compared against other optimization techniques. The similarity of two point-sets is measured using the Euclidean distance between matched points.

This paper discusses wireless mesh network topology visualization tools and their requirements. It presents an interactive tool for visualizing mesh network topologies. The proposed tool uses the SVG — Scalable Vector Graphics — web standard, which is based on XML, to build the network map. In a wireless mesh network, link quality varies quite often, therefore the visualization tool dynamically displays the quality metric of each link, in addition to presenting router configuration information and other network statistics. This work also introduces a configuration tool designed to adapt the topology view to different wireless mesh environments.

The non-invasive in vivo nature of magnetic resonance imaging (MRI) makes it the modality of choice of many neuroanatomical imaging studies. This paper discusses automatic brain structure segmentation based on anatomic atlas. Our goal is to use image-processing algorithms and previous knowledge statistical models for segmentation and labeling of brain regions in order to support radiologists to make clinical diagnosis. Practical experiments show the results of brain tissue classification process and automatic region labeling in order to segment accurately the hippocampus and measure its volume. Hippocampus volumetric information can be useful to evaluate patients with Alzheimer's disease. The final goal of this work is computer-aided diagnosis for brain diseases.

This paper presents the main new features of Nested Context Language (NCL) version 2.0. NCL 2.0 is a modular and declarative hypermedia language, whose modules can be combined to other languages, such as SMIL, to provide new facilities. Among the NCL 2.0 new features, we can highlight the support for handling hypermedia relations as first-class entities, through the definition of hypermedia connectors, and the possibility of specifying any semantics for a hypermedia composition, using the concept of composition templates. Another important goal of this paper is to describe a framework to facilitate the development of NCL parsing and processing tools. Based on this framework, the paper comments several implemented compilers, which allow, for instance, the conversion of NCL documents into SMIL specifications.

This paper proposes XConnector, a language for the creation of complex hypermedia relations with causal or constraint semantics. XConnector allows the definition of relations independently of which resources are related. Another feature is the specification of relation libraries, providing reuse in relationship definition. The main goal is to improve linking languages or the linking modules of hypermedia authoring languages in order to provide multimedia synchronization capabilities using links. Following this direction, an extension to W3C XLink is proposed, incorporating XConnector facilities.

This paper presents a detailed comparison between the structural elements and definitions provided by Hypermedia Authoring Languages and Architecture Description Languages (ADL). ADLs are formal languages that can be used for representing a software architecture. Although it may look trivial to make a direct correspondence between ADL and hypermedia structural entities, such as components to nodes and connectors to links, interesting differences can be identified when observing them more closely. Based on the comparison results, a structural meta-model that can be specialized for use in both domains is proposed. Furthermore, the paper also presents an example of how the meta-model can be used for describing hypermedia document structures, showing how some features found in ADLs can be applied to hypermedia authoring languages. Our final goal is to integrate the contributions of document engineering and software architecture engineering and take advantage of the advances of one area in the other one. The current paper is the first step towards this direction.

The paper proposes a visualization tool for the structure of World-Wide Web documents using fisheye-view techniques. Besides helping user navigation, this graphical browser may play an important role in Web information searching. In order to build structural views more efficiently, a set of rules for Web-site authoring, based on nested composition concepts, are proposed to help logically organizing documents.