Search

Toys are play products designed for leisure and social play activities. Today, play products increasingly incorporate hardware and software computation, often connecting to online services and other computing devices, thus being commonly referred to as "smart toys." Accordingly, such products can also be categorized as user interfaces as they allow human interaction with digital contents using the physical toys' inputs and outputs. Here we propose the idea of Toy-User-Interfaces (ToyUI) and relate them to the domains of human-computer interaction, mixed reality, and the Internet of Things. We surveyed and categorized a multitude of ToyUIs, gathering information from research papers and toy companies by performing both a systematic mapping (covering research from 2008 to 2017) and an industrial mapping (covering releases from 2012 to 2017). The resulting 297 items were then analyzed according to our classification model, being divided into four categories, eight genres, and 22 setups, considering the play features and the interface features of such items. The classification model and the analysis of the results allowed us to identify six trends in design and technology for ToyUI. Ultimately, our research findings may guide the future of ToyUI projects for researchers, designers, educators, therapists, and end-users. [ABSTRACT FROM AUTHOR]

The player should enjoy games through the game controller of their preference and keep the best possible game experience regardless their option. This article proposes a novel classification model for game interaction techniques from the player perspective and with direct control of a single humanoid avatar based on three characteristics: Effort, Duration, and Context. Combining the three characteristics, we classify the game interaction techniques into nine isomorphic categories. We conducted three experiments. The first (N = 57) and second (N = 45) were a paired quantitative comparison evaluation method, allowing the volunteer to play the same game level twice, changing only the interaction technique for one action. This aimed at validating our initial proposal with users. The third (N = 100) was also a comparative test. The volunteer played the same stage twice using the button-interaction based version as a reference against one of another four device-based versions. The goal was to extend the proposed classification model to other interaction devices to check if the interaction metaphor was kept coherent. In all three experiments, we chose the 202 participants as a convenience sample. The users' experiments support all the research hypotheses. We conclude that, if our proposed guideline with our interaction technique classification model is applied, it may expect a positive impact on the players' experiences. [ABSTRACT FROM AUTHOR]

This paper describes a methodology for navigation and exploration assistance intended to enhance user satisfaction when exploring three-dimensional virtual environments. The complexity of such environments often makes navigation and information retrieval difficult, making it necessary to add assistance components to the world in order to turn it easier to manipulate. This methodology uses three-dimensional "intelligent" avatars as interactive guides, along with information-based navigation strategies. The intelligence of the avatars is represented through physical features, behaviors, and knowledge about the user and the environment. These components establish the avatar's architecture. Content personalization according to the user's interest, navigation assistance according to the desired content, and avatar guides that make the virtual place more realistic and pleasant are proposed in order to involve users. A three-dimensional model of the Guggenheim Museum Bilbao, in Spain, is presented as a prototype for the validation of this methodology. In the museum, the guide is represented as a fish that, according to the user's preferences, assumes gender and age. The avatar swims through the museum, following navigation routes that lead through exhibitions previously chosen by the visitor. [ABSTRACT FROM AUTHOR]

A patient with chronic myeloid leukemia developed hypercalcemia as a presenting sign of the accelerated phase of the disease. Ultrasound of the neck showed a large hypodense mass connected to the thyroid gland, which was thought to be a parathyorid tumor and the cause of the hypercalcemia. Histology of the surgically removed mass revealed a chloroma. The patient’s course was complicated by respiratory failure and metastatic calcinosis of the lung, an unusual finding in hypercalcemia of short duration. [ABSTRACT FROM AUTHOR]

Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil's National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO. [ABSTRACT FROM AUTHOR]

Background: Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario. Objective: The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment. Method: We use clinical and sociodemographic data from pregnant women that were assisted by a social program in Pernambuco, Brazil, named Mãe Coruja Pernambucana Program (PMCP). Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion. Results: The AdaBoost-BODS-Expert model, an Adaptive Boosting (AdaBoost) model that used attributes selected by health experts, presented the best results in terms of evaluation metrics and acceptance by health experts from PMCP. By using this model, the results are more reliable and allows adoption on a daily usage to classify possible outcomes of congenital syphilis using clinical and sociodemographic data. [ABSTRACT FROM AUTHOR]

Human-robot collaboration has gained a notable prominence in Industry 4.0, as the use of collaborative robots increases efficiency and productivity in the automation process. However, it is necessary to consider the use of mechanisms that increase security in these environments, as the literature reports that risk situations may exist in the context of human-robot collaboration. One of the strategies that can be adopted is the visual recognition of the collaboration environment using machine learning techniques, which can automatically identify what is happening in the scene and what may happen in the future. In this work, we are proposing a new framework that is capable of detecting robotic arm keypoints commonly used in Industry 4.0. In addition to detecting, the proposed framework is able to predict the future movement of these robotic arms, thus providing relevant information that can be considered in the recognition of the human-robot collaboration scenario. The proposed framework has two main modules. The first one contains a convolutional neural network based on self-calibrated convolutions enabling better discriminative feature extraction and the support of extreme learning machine neural networks with different kernels for predicting robotic arm keypoints. The second module is composed of deep recurrent learning models, such as long short-term memory and gated recurrent unit. These models are able to predict future robotic arm keypoints. All experiments were evaluated using the mean squared error metric. Results show that the proposed framework is capable of detecting and predicting with low error, contributing to the mitigation of risks in human-robot collaboration. In addition, it was possible to verify that the use of convolutional neural networks in conjunction with extreme learning machines can offer a lower detection error in a regression task (e.g., keypoint detection), something that, as far as the authors are aware of, is not yet known, nor had been evaluated previously in the literature. [ABSTRACT FROM AUTHOR]

Background: The Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn.Methods: The Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system.Results: The main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system.Conclusions: The proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time. [ABSTRACT FROM AUTHOR]

Background: The Brazilian healthcare system is a large and complex system, specially considering its mixed public and private funding. The incidence of syphilis has increased in the last four years, in spite of the presence of an effective and available treatment. Furthermore, syphilis takes part in a group of disorders of compulsory notification to the public health surveillance. The epidemiological implications are especially important during pregnancy since it can lead to complications, related to prematurity stillbirth and miscarriage, in addition to congenital syphilis, characterized by multisystem involved in the newborn. Methods: The Action Research methodology was applied to address the complexity of the syphilis surveillance scenario in Pernambuco, Brazil. Iterative learning cycles were used, resulting in six cycles, followed by a formal validation of an operational version of the syphilis Trigram visualisation at the end of the process. The original data source was analyzed and prepared to be used without any new data or change in the ordinary procedure of the current system. Results: The main result of this work is the production of a Syphilis Trigram: a domain-specific infographic for presenting gestational data and birth data. The second contribution of this work is the Average Trigram, an organized pie chart which synthesizes the Syphilis Trigram relationship in an aggregated way. The visualization of both graphics is presented in an Infographic User Interface, a tool that gathers an infographic broad visualization aspect to data visualization. These interfaces also gather selections and filters tools to assist and refine the presented information. The user can experience a specific case-by-case view, in addition to an aggregated perspective according to the cities monitored by the system. Conclusions: The proposed domain-specific visualization amplifies the understanding of each syphilis case and the overall characteristics of cases of a chosen city. This new information produced by the Trigram can help clarify the reinfection/relapse cases, optimize resource allocation and enhance the syphilis healthcare policies without the need of new data. Thus, this enables the health surveillance professionals to see the broad tendency, understand the key patterns through visualization, and take action in a feasible time. [ABSTRACT FROM AUTHOR]

Self-composing networks dynamically emerge to offer added-value services. A simple dynamic configuration should allow the birth of this new brand of networks—networks with a soul. They are closer to end users, turning them into casier to set up. We examine two emerging networking technologies that take network dynamic configuration a step further than what we are used to. These are Universal Plug and Play (UPnP) networks and Ambient Networks (ANs). Both technologies permit proximity-triggered seamless networking and are the focus of separate working groups. After introducing UPnP, we look in more detail at the design and management aspects of ambient networks. Though not all networks may be designed to be self-composing, we believe the path is set for this course. We show that we have the technology, the way we use it needs to evolve. [ABSTRACT FROM AUTHOR]

The network function virtualization (NFV) paradigm is an emerging technology that provides network flexibility by allowing the allocation of network functions over commodity hardware, like legacy servers in an IT infrastructure. In comparison with traditional network functions, implemented by dedicated hardware, the use of NFV reduces the operating and capital expenses and improves service deployment. In some scenarios, a complete network service can be composed of several functions, following a specific order, known as a service function chain (SFC). SFC placement is a complex task, already proved to be NP-hard. Moreover, in highly distributed scenarios, the network performance can also be impacted by other factors, such as traffic oscillations and high delays. Therefore, a given SFC placement strategy must be carefully developed to meet the network operator service constraints. In this paper, we present a systematic review of SFC placement advances in distributed scenarios. Differently from the current literature, we examine works over the last 10 years which addressed this problem while focusing on distributed scenarios. We then discuss the main scenarios where SFC placement has been deployed, as well as the several techniques used to create the placement strategies. We also present the main goals considered to create SFC placement strategies and highlight the metrics used to evaluate them. [ABSTRACT FROM AUTHOR]

Background: Although considerable success in reducing the incidence of malaria has been achieved in Brazil in recent years, an increase in the proportion of cases caused by the harder-to-eliminate Plasmodium vivax parasite can be noted. Recurrences in P. vivax malaria cases are due to new mosquito-bite infections, drug resistance or especially from relapses arising from hypnozoites. As such, new innovative surveillance strategies are needed. The aim of this study was to develop an infographic visualization tool to improve individual-level malaria surveillance focused on malaria elimination in the Brazilian Amazon. Methods: Action Research methodology was employed to deal with the complex malaria surveillance problem in the Amazon region. Iterative cycles were used, totalling four cycles with a formal validation of an operational version of the Malaria Trigram tool at the end of the process. Further probabilistic data linkage was carried out so that information on the same patients could be linked, allowing for follow-up analysis since the official system was not planned in such way that includes this purpose. Results: An infographic user interface was developed for the Malaria Trigram that incorporates all the visual and descriptive power of the Trigram concept. It is a multidimensional and interactive historical representation of malaria cases per patient over time and provides visual input to decision-makers on recurrences of malaria. Conclusions: The Malaria Trigram is aimed to help public health professionals and policy makers to recognise and analyse different types of patterns in malaria events, including recurrences and reinfections, based on the current Brazilian health surveillance system, the SIVEP-Malária system, with no additional primary data collection or change in the current process. By using the Malaria Trigram, it is possible to plan and coordinate interventions for malaria elimination that are integrated with other parallel actions in the Brazilian Amazon region, such as vector control management, effective drug and vaccine deployment strategies. [ABSTRACT FROM AUTHOR]

With the popularity of mobile devices, the next generation of mobile networks has faced several challenges. Different applications have been emerged, with different requirements. Offering an infrastructure that meets different types of applications with specific requirements is one of these issues. In addition, due to user mobility, the traffic generated by the mobile devices in a specific location is not constant, making it difficult to reach the optimal resource allocation. In this context, network function virtualization (NFV) can be used to deploy the telecommunication stacks as virtual functions running on commodity hardware to meet users' requirements such as performance and availability. However, the deployment of virtual functions can be a complex task. To select the best placement strategy that reduces the resource usage, at the same time keeps the performance and availability of network functions is a complex task, already proven to be an NP-hard problem. Therefore, in this paper, we formulate the NFV placement as a multi-objective problem, where the risk associated with the placement and energy consumption are taken into consideration. We propose the usage of two optimization algorithms, NSGA-II and GDE3, to solve this problem. These algorithms were taken into consideration because both work with multi-objective problems and present good performance. We consider a triathlon circuit scenario based on real data from the Ironman route as an use case to evaluate and compare the algorithms. The results show that GDE3 is able to attend both objectives (minimize failure and minimize energy consumption), while the NSGA-II prioritizes energy consumption. [ABSTRACT FROM AUTHOR]

With the emergence of new applications driven by the popularization of mobile devices, the next generation of mobile networks faces challenges to meet different requirements. Virtual Network Functions (VNFs) have been deployed to minimize operational costs and make network management more flexible. In this sense, strategies for VNF placement can impact different metrics of interest. Invoking and visiting VNFs in a specific execution order may be required for different use cases, resulting in a complete network service called Service Function Chain (SFC). The SFC placement problem is to define a feasible path in the physical infrastructure whose nodes and edges meet the computational and bandwidth requirements for the VNFs and virtual links, respectively. It has already been proved that this process is NP-hard and it is difficult to find an optimal solution to this problem. Therefore, in this paper, we propose the use of meta-heuristics to solve the SFC placement problem in cellular networks. We consider a triathlon competition leading to different mobility patterns. We collected real data about the competitors to simulate their movements through the scenario as well as the measured signal quality of the network. We formulate the SFC placement problem as a multi-objective problem where we try to minimize the placement cost and the total SFC delay. To solve the problem, we propose the use of two algorithms, NSGA-II and GDE3, which compare two different greedy approaches that prioritize the different optimization metrics considered in this work. Our results show that the meta-heuristics provide better results for each of the metrics. For all competition stages, GDE3 presented a slightly lower placement costs than NSGA-II, while NSGA-II had a lower delay in some scenarios. [ABSTRACT FROM AUTHOR]

Today, most innovation on Information Technology and Communication is cloud-centric and an increasing number of organizations believe that this transition is ever more unavoidable. With this increased demand for Cloud services, providers are facing many challenges regarding how to avoid outages and optimization of resource management since they impact directly in costs and profits. In this paper, we propose the cost-based allocation (CBA), a resource allocation system that takes into consideration the minimum availability level required by the user, and the minimum cost to allocate resources while complying with the service availability forum redundancy models. Results show that, considering occupation and cost metrics, our CBA algorithm presents the best overall performance between evaluated strategies. [ABSTRACT FROM AUTHOR]

Energy consumption represents a considerable concern for industries in terms of cost and the need to adopt future green energy sources. As a result, several recurring research initiatives seek to reduce it. In this work, we propose a data-based approach to optimize the overall process of fiberglass manufacturing, aiming to reduce the energy consumption of an industrial boiler. Initially, the process parameters with the most significant impact on the plant's energy consumption were identified. A machine-learning model was developed using the historical factory data collected for these parameters. Then, we formally relate these parameters to the energy consumption of the analyzed system. With this, an approach based on genetic algorithms was used to search for the ideal values of the model's input parameters that optimize energy consumption for particular production demands. As a result, we have formulated an operational approach that effectively guides the daily functioning of the modeled system, contributing to the reduction of energy consumption per unit of production. We show that simple learning techniques, such as decision trees, can efficiently model this class of problems. This is a crucial design requirement as the tool can be deployed on IoT devices with limited processing resources. The application of our study made it possible to save thousands of dollars in boiler energy consumption costs. The results show that monthly savings can reach $8440. [ABSTRACT FROM AUTHOR]

Cloud data center providers benefit from software-defined infrastructure once it promotes flexibility, automation, and scalability. The new paradigm of software-defined infrastructure helps facing current management challenges of a large-scale infrastructure, and guarantying service level agreements with established availability levels. Assessing the availability of a data center remains a complex task as it requires gathering information of a complex infrastructure and generating accurate models to estimate its availability. This paper covers this gap by proposing a methodology to automatically acquire data center hardware configuration to assess, through models, its availability. The proposed methodology leverages the emerging standardized Redfish API and relevant modeling frameworks. Through such approach, we analyzed the availability benefits of migrating from a conventional data center infrastructure (named Performance Optimization Data center (POD) with redundant servers) to a next-generation virtual Performance Optimized Data center (named virtual POD (vPOD) composed of a pool of disaggregated hardware resources). Results show that vPOD improves availability compared to conventional data center configurations. [ABSTRACT FROM AUTHOR]

Intelligent industry and Internet of Things (IoT) rely on sensors to monitor the surroundings and allow information exchange. More sensors deployed in an environment offer additional benefits, such as increased availability and more accurate observations, as they generate information from different perspectives. Nonetheless, dealing with large amounts of data and multiple input sources to deduct unified information often requires considerable processing, communication, and energy resources. To address such a problem, this work examines intelligent agents that cooperate to generate a unified output in a Multi-Agent System (MAS) environment. The proposed approach presents a fuzzy scheduler that evaluates the processing and accuracy level to determine the relevance of each agent by comparing its information with the unified output. The proposal orchestrates when an agent can have its processing suspended. Our evaluations demonstrate that this proposal improves service level metrics, such as the rate of video frames processed per second for an increasing number of agents. We consider three experimental setups using tracking agents. First, we evaluate the threshold and how long an agent should remain idle when its observations are irrelevant. The second experiment considers an increasing number of agents. Agent scheduling achieves a frame processing rate 12.5 times faster while suffering a 6% accuracy loss for 20 agents. The third experiment focuses on using different tracking algorithms simultaneously. In this case, compared to when no agent scheduling is applied, there is a 23.9 fold increase in the frame processing rate while losing some accuracy, limited to around 6%. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed the birth of major applications of wireless communication technology, further supported by the increasing capabilities of portable devices, low cost and ubiquitous presence. Despite radio technology diversity, a great deal of existing research focuses on a single and isolated wireless technology at a time, where homogeneous elements are identified by IP addresses. This work presents a heterogeneous technology routing (HTR) Framework, targeted towards scenarios where the heterogeneity of devices and networking technologies is present. Our contribution is many fold. It consists of a framework, which encompasses a process for bootstrapping networks, a routing protocol capable of dealing with multiple network interfaces, and a tuning with multipath extensions. We evaluate the performance of the bootstrap, routing and multipath mechanisms by way of simulation and an actual testbed implementation. The multipath evaluation simulates HTR networks with WiMAX, 3GPP LTE and Wi-Fi support. Results show that our proposal can effectively improve the data delivery ratio for ad-hoc networks and that it reduces the end-to-end delay without major impact on network energy consumption. As part of HTR tuning, we investigate next the impacts of tuning the HELLO refresh interval timer on route convergence and its subsequent energy consumption reduction during this phase. We also compare our tuned HTR with the widely used optimized link state routing protocol. Results show that varying the HELLO refresh interval can improve the convergence time and reduce the energy consumption without major impact on network behavior. Our proposal also includes a new distributed address allocation algorithm, namely, the dynamic node configuration protocol (DNCP). This paper conducts a comparative analysis between the Prime, Prophet and the DNCP schemes using static and dynamic topologies in terms of network setup time, energy consumption and control message overhead. Results show that the DNCP had a lower battery power consumption and less control message overhead while it slightly suffers with regard to setup. [ABSTRACT FROM AUTHOR]

SUMMARY Programming language concepts have inspired some networking design decisions. For example, concepts such as object encapsulation and interface invocation have been borrowed, at the time of their adoption, from an already well established object oriented programming paradigm. The authors suggest in this paper that it may be time again to revisit emerging software engineering programming paradigms to learn from them. More specifically, this paper discusses the practical tangling problem, embedded in conventional layer-coupling (linking) network software design and highlighted by recent research proposals for cross layer design. The adopted solution is based on the aspect-oriented programming paradigm. We show its programming efficiency, limitations and role in the seamless enforcement of multiple policy scenarios while emphasizing little design changes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Current GPU computational power enables the execution of complex and parallel algorithms, such as ray tracing techniques supported by kD-trees for 3D scene rendering in real time. This work describes in detail the study and implementation of eight different kD-tree traversal algorithms using the parallel framework NVIDIA Compute Unified Device Architecture, in order to point their pros and cons regarding performance, memory consumption, branch divergencies and scalability on multiple GPUs. In addition, two new algorithms are proposed by the authors based on this analysis, aiming to performance improvement. Both of them are capable of reaching speedup gains up to 3 × when compared to recent and optimized parallel traversal implementations. As a consequence, interactive frame rates are possible for scenes with 1,408 × 768 pixels of resolution and 3.6 million primitives. [ABSTRACT FROM AUTHOR]

We introduce a GPU grid-based data structure for massively parallel nearest neighbor searches for dynamic point clouds. The implementation provides real-time performance and it is executed on GPU, both grid construction and nearest neighbors (approximate or exact) searches. This minimizes the memory transfer between device and system memories, improving overall performance. The proposed algorithm may be used across different applications with static and dynamic scenarios. Moreover, our data structure supports three-dimensional point clouds and given its dynamic nature, the user can change the data structure's parameters at runtime. The same applies to the number of neighbors to be found. Performance comparisons were made against previous works, endorsing the benefits of our solution. Finally, we were able to develop a real-time Point-Based Rendering application for validation of the data structure. Its drawbacks and data distribution's impact on performance were analysed and some directions for further investigation are given. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a review of the Photorealistic Augmented Reality field and proposes a solution for interactively rendering virtual objects into dynamic real scenes in a photorealistic way. This solution features a rendering pipeline that comprises techniques regarding illumination, reflectance model, shadowing, composition, and camera effects. The techniques are chained in a flexible way, allowing the user to choose which techniques are to be enabled. An environment map generation procedure was developed and allows virtual objects to exhibit coherent effects such as color bleeding and specular reflection, even when the real objects are moved. The range of materials that can be rendered was widened by extending Lafortune''s Spatial BRDF. The implemented infrastructure is offered as an authoring toolkit that consists of an API and a material editor tool. The aim of this authoring toolkit is to increase development productivity of Photorealistic Augmented Reality applications. The proposed solution was evaluated by taking into account visual and performance metrics. It allowed consistent rendering of dynamic scenes and photorealistic materials. The frame rate obtained was suitable to Augmented Reality applications when there were few virtual objects in the scene. [Copyright &y& Elsevier]

This paper presents two novel frameworks for session admission control and resource reservation in the context of next generation mobile and cellular networks. We also devised a special scheme that avoids per-user reservation signaling overhead in order to meet scalability requirements needed for next generation multi-access networks. The first proposal, Distributed Call Admission Control with Aggregate Resource Reservation (VR), uses mobility prediction based on mobile positioning system location information and takes into account the expected bandwidth to be used by calls handing off to and from neighboring cells within a configurable estimation time window. In conjunction, a novel concept called virtual reservation has been devised to prevent per-user reservation. Our second proposal, Local Call Admission Control and Time Series-based Resource Reservation, takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell a user is seeking admission to. To this end, we suggest the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and Autoregressive Integrated Moving Average (ARIMA) that uses the Box and Jenkins methodology. It is worth to emphasize that the use of bandwidth prediction based on ARIMA technique still exist for wireless networks. The novelty of our approach is to build an adaptive framework based on ARIMA technique that takes into account the measured handoff dropping probability in order to tuning the prediction time window size so increasing the prediction accuracy. The proposed schemes are compared through simulations with the fixed guard channel (GC) and other optimized dynamic reservation-based proposals present in the literature. The results show that our schemes outperform many others and that the simpler local proposal based on TL can grant nearly similar levels of handoff dropping probability as compared to those from more the complex distributed approach. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of a large number of small devices with computational power, wireless communication and sensing capability. These networks have been developed for a wide range of applications, such as habitat monitoring, object tracking, precision agriculture, building monitoring and military systems. Meanwhile, middleware systems have also been proposed in to facilitate both the development of these applications and provide common application services. The development of middleware for sensor networks, however, places new challenges on middleware developers due to the low availability of resources and processing capacity of the sensor nodes. In this context, this paper presents the design and implementation of a middleware for WSN named Mires. Mires incorporates characteristics of message-oriented middleware by allowing applications communicate in a publish/subscribe way. In order to illustrate the proposed middleware, we have also developed an environment-monitoring application and a data aggregation service. [ABSTRACT FROM AUTHOR]

This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration. [ABSTRACT FROM AUTHOR]

In a cloud computing environment, dynamic resource allocation and reallocation are keys for accommodating unpredictable demands and, ultimately, contribute to investment return. This article discusses this process in the context of distributed clouds, which are seen as systems where application developers can selectively lease geographically distributed resources. This article highlights and categorizes the main challenges inherent to the resource allocation process particular to distributed clouds, offering a stepwise view of this process that covers the initial modeling phase through to the optimization phase. [ABSTRACT FROM PUBLISHER]