Midgard: um middleware baseado em componentes e orientado a recursos para redes de sensores sem fio

On the last years, several middleware platforms for Wireless Sensor Networks (WSN) were proposed. Most of these platforms does not consider issues of how integrate components from generic middleware architectures. Many requirements need to be considered in a middleware design for WSN and the design, in this case, it is possibility to modify the source code of the middleware without changing the external behavior of the middleware. Thus, it is desired that there is a middleware generic architecture that is able to offer an optimal configuration according to the requirements of the application. The adoption of middleware based in component model consists of a promising approach because it allows a better abstraction, low coupling, modularization and management features built-in middleware. Another problem present in current middleware consists of treatment of interoperability with external networks to sensor networks, such as Web. Most current middleware lacks the functionality to access the data provided by the WSN via the World Wide Web in order to treat these data as Web resources, and they can be accessed through protocols already adopted the World Wide Web. Thus, this work presents the Midgard, a component-based middleware specifically designed for WSNs, which adopts the architectural patterns microkernel and REST. The microkernel architectural complements the component model, since microkernel can be understood as a component that encapsulates the core system and it is responsible for initializing the core services only when needed, as well as remove them when are no more needed. Already REST defines a standardized way of communication between different applications based on standards adopted by the Web and enables him to treat WSN data as web resources, allowing them to be accessed through protocol already adopted in the World Wide Web. The main goals of Midgard are: (i) to provide easy Web access to data generated by WSN, exposing such data as Web resources, following the principles of Web of Things paradigm and (ii) to provide WSN application developer with capabilities to instantiate only specific services required by the application, thus generating a customized middleware and saving node resources. The Midgard allows use the WSN as Web resources and still provide a cohesive and weakly coupled software architecture, addressing interoperability and customization. In addition, Midgard provides two services needed for most WSN applications: (i) configuration and (ii) inspection and adaptation services. New services can be implemented by others and easily incorporated into the middleware, because of its flexible and extensible architecture. According to the assessment, the Midgard provides interoperability between the WSN and external networks, such as web, as well as between different applications within a single WSN. In addition, we assessed the memory consumption, the application image size, the size of messages exchanged in the network, and response time, overhead and scalability on Midgard. During the evaluation, the Midgard proved satisfies their goals and shown to be scalable without consuming resources prohibitively

Arquitetura com elevada taxa de processamento e reduzida largura de banda de memória para a estimação de movimento em vídeos digitais

Nowadays several electronics devices support digital videos. Some examples of these devices are cellphones, digital cameras, video cameras and digital televisions. However, raw videos present a huge amount of data, millions of bits, for their representation as the way they were captured. To store them in its primary form it would be necessary a huge amount of disk space and a huge bandwidth to allow the transmission of these data. The video compression becomes essential to make possible information storage and transmission. Motion Estimation is a technique used in the video coder that explores the temporal redundancy present in video sequences to reduce the amount of data necessary to represent the information. This work presents a hardware architecture of a motion estimation module for high resolution videos according to H.264/AVC standard. The H.264/AVC is the most advanced video coder standard, with several new features which allow it to achieve high compression rates. The architecture presented in this work was developed to provide a high data reuse. The data reuse schema adopted reduces the bandwidth required to execute motion estimation. The motion estimation is the task responsible for the largest share of the gains obtained with the H.264/AVC standard so this module is essential for final video coder performance. This work is included in Rede H.264 project which aims to develop Brazilian technology for Brazilian System of Digital Television

Avaliação do Uso de Smartphones na Interação com as Aplicações da Televisão Digital Brasileira

Brazil is going through the process from analogical transmission to digital transmission. This new technology, in addition to providing a high quality audio and video, also allows applications to execute on television. Equipment called Set-Top Box are needed to allow the reception of this new signal and create the appropriate environment necessary to execute applications. At first, the only way to interact with these applications is given by remote control. However, the remote control has serious usability problems when used to interact with some types of applications. This research suggests a software resources implementation capable to create a environment that allows a smartphone to interact with applications. Besides this implementation, is performed a comparative study between use remote controle and smartphones to interact with applications of digital television, taking into account parameters related to usability. After analysis of data collected by the comparative study is possible to identify which device provides an interactive experience more interesting for users

Estendendo CRefine para o suporte de táticas de refinamento

The use of increasingly complex software applications is demanding greater investment in the development of such systems to ensure applications with better quality. Therefore, new techniques are being used in Software Engineering, thus making the development process more effective. Among these new approaches, we highlight Formal Methods, which use formal languages that are strongly based on mathematics and have a well-defined semantics and syntax. One of these languages is Circus, which can be used to model concurrent systems. It was developed from the union of concepts from two other specification languages: Z, which specifies systems with complex data, and CSP, which is normally used to model concurrent systems. Circus has an associated refinement calculus, which can be used to develop software in a precise and stepwise fashion. Each step is justified by the application of a refinement law (possibly with the discharge of proof obligations). Sometimes, the same laws can be applied in the same manner in different developments or even in different parts of a single development. A strategy to optimize this calculus is to formalise these application as a refinement tactic, which can then be used as a single transformation rule. CRefine was developed to support the Circus refinement calculus. However, before the work presented here, it did not provide support for refinement tactics. The aim of this work is to provide tool support for refinement tactics. For that, we develop a new module in CRefine, which automates the process of defining and applying refinement tactics that are formalised in the tactic language ArcAngelC. Finally, we validate the extension by applying the new module in a case study, which used the refinement tactics in a refinement strategy for verification of SPARK Ada implementations of control systems. In this work, we apply our module in the first two phases of this strategy

Um estudo algorítmico da programação da intervenção de sondas de produção

This work approaches the Scheduling Workover Rigs Problem (SWRP) to maintain the wells of an oil field, although difficult to resolve, is extremely important economical, technical and environmental. A mathematical formulation of this problem is presented, where an algorithmic approach was developed. The problem can be considered to find the best scheduling service to the wells by the workover rigs, taking into account the minimization of the composition related to the costs of the workover rigs and the total loss of oil suffered by the wells. This problem is similar to the Vehicle Routing Problem (VRP), which is classified as belonging to the NP-hard class. The goal of this research is to develop an algorithmic approach to solve the SWRP, using the fundamentals of metaheuristics like Memetic Algorithm and GRASP. Instances are generated for the tests to analyze the computational performance of the approaches mentioned above, using data that are close to reality. Thereafter, is performed a comparison of performance and quality of the results obtained by each one of techniques used

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.

Especificação do tipo intervalar parametrizado em CASL

The interval datatype applications in several areas is important to construct a interval type reusable, i.e., a interval constructor can be applied to any datatype and get intervals this datatype. Since the interval is, of certain form, a set of elements limited for two bounds, left and right, with a order notions, then it s reasonable that interval constructor enclose datatypes with partial order. On the order hand, what we want is work with interval of any datatype like this we work with this datatype then. it s important to guarantee the properties of the datatype when maps to interval of this datatype. Thus, the interval constructor get a theory to parametrized interval type, i.e., a interval with generics parameters (for example rational, real, complex). Sometimes, the interval application in some algebras doesn t guarantee the mainutenance of their properties, for example, when we use interval of real, that satisfies the field properties, it doesn t guarantee the distributivity propertie. A form to surpass this problem Santiago introduced the local equality theory that weakened the notion of strong equality, and thus, allowing some properties are local keeped, what can be discard before. The interval arithmetic generalization aim to apply the interval constructor on ordered algebras weakened for local equality with the purpose of the keep their properties. How the intervals are important in applications with continuous data, it s interesting specify that theory using a specification language that supply a system development using intervals of form disciplined, trustworth and safe. Currently, the algebraic specification language, based in math models, have been use to that intention often. We choose CASL (Common Algebraic Specification Language) among others languages because CASL has several characteristics excellent to parametrized interval type, such as, provide parcialiy and parametrization

Análise das medidas de boa e má diversidade na construção de comitês de classificadores através de metaheurísticas de otimização multiobjetivo

Committees of classifiers may be used to improve the accuracy of classification systems, in other words, different classifiers used to solve the same problem can be combined for creating a system of greater accuracy, called committees of classifiers. To that this to succeed is necessary that the classifiers make mistakes on different objects of the problem so that the errors of a classifier are ignored by the others correct classifiers when applying the method of combination of the committee. The characteristic of classifiers of err on different objects is called diversity. However, most measures of diversity could not describe this importance. Recently, were proposed two measures of the diversity (good and bad diversity) with the aim of helping to generate more accurate committees. This paper performs an experimental analysis of these measures applied directly on the building of the committees of classifiers. The method of construction adopted is modeled as a search problem by the set of characteristics of the databases of the problem and the best set of committee members in order to find the committee of classifiers to produce the most accurate classification. This problem is solved by metaheuristic optimization techniques, in their mono and multi-objective versions. Analyzes are performed to verify if use or add the measures of good diversity and bad diversity in the optimization objectives creates more accurate committees. Thus, the contribution of this study is to determine whether the measures of good diversity and bad diversity can be used in mono-objective and multi-objective optimization techniques as optimization objectives for building committees of classifiers more accurate than those built by the same process, but using only the accuracy classification as objective of optimization

Cloud Integrator: uma plataforma para composição de serviços em ambientes de computação em nuvem

With the advance of the Cloud Computing paradigm, a single service offered by a cloud platform may not be enough to meet all the application requirements. To fulfill such requirements, it may be necessary, instead of a single service, a composition of services that aggregates services provided by different cloud platforms. In order to generate aggregated value for the user, this composition of services provided by several Cloud Computing platforms requires a solution in terms of platforms integration, which encompasses the manipulation of a wide number of noninteroperable APIs and protocols from different platform vendors. In this scenario, this work presents Cloud Integrator, a middleware platform for composing services provided by different Cloud Computing platforms. Besides providing an environment that facilitates the development and execution of applications that use such services, Cloud Integrator works as a mediator by providing mechanisms for building applications through composition and selection of semantic Web services that take into account metadata about the services, such as QoS (Quality of Service), prices, etc. Moreover, the proposed middleware platform provides an adaptation mechanism that can be triggered in case of failure or quality degradation of one or more services used by the running application in order to ensure its quality and availability. In this work, through a case study that consists of an application that use services provided by different cloud platforms, Cloud Integrator is evaluated in terms of the efficiency of the performed service composition, selection and adaptation processes, as well as the potential of using this middleware in heterogeneous computational clouds scenarios

GT4CCI: uma abordagem baseada em grounded theory para a identificação de interesses transversais em documentos de requisitos

When crosscutting concerns identification is performed from the beginning of development, on the activities involved in requirements engineering, there are many gains in terms of quality, cost and efficiency throughout the lifecycle of software development. This early identification supports the evolution of requirements, detects possible flaws in the requirements specification, improves traceability among requirements, provides better software modularity and prevents possible rework. However, despite these several advantages, the crosscutting concerns identification over requirements engineering faces several difficulties such as the lack of systematization and tools that support it. Furthermore, it is difficult to justify why some concerns are identified as crosscutting or not, since this identification is, most often, made without any methodology that systematizes and bases it. In this context, this paper proposes an approach based on Grounded Theory, called GT4CCI, for systematizing and basing the process of identifying crosscutting concerns in the initial stages of the software development process in the requirements document. Grounded Theory is a renowned methodology for qualitative analysis of data. Through the use of GT4CCI it is possible to better understand, track and document concerns, adding gains in terms of quality, reliability and modularity of the entire lifecycle of software

Uma investigação de algoritmos exatos e metaheurísticos aplicados ao nonograma

Nonogram is a logical puzzle whose associated decision problem is NP-complete. It has applications in pattern recognition problems and data compression, among others. The puzzle consists in determining an assignment of colors to pixels distributed in a N  M matrix that satisfies line and column constraints. A Nonogram is encoded by a vector whose elements specify the number of pixels in each row and column of a figure without specifying their coordinates. This work presents exact and heuristic approaches to solve Nonograms. The depth first search was one of the chosen exact approaches because it is a typical example of brute search algorithm that is easy to implement. Another implemented exact approach was based on the Las Vegas algorithm, so that we intend to investigate whether the randomness introduce by the Las Vegas-based algorithm would be an advantage over the depth first search. The Nonogram is also transformed into a Constraint Satisfaction Problem. Three heuristics approaches are proposed: a Tabu Search and two memetic algorithms. A new function to calculate the objective function is proposed. The approaches are applied on 234 instances, the size of the instances ranging from 5 x 5 to 100 x 100 size, and including logical and random Nonograms

Um framework conceitual para apoiar a instrumentação da avaliação formativa da aprendizagem em jogos digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Instanciação e avaliação de um framework para a geração dinâmica de processos

One way to deal with the high complexity of current software systems is through selfadaptive systems. Self-adaptive system must be able to monitor themselves and their environment, analyzing the monitored data to determine the need for adaptation, decide how the adaptation will be performed, and finally, make the necessary adjustments. One way to perform the adaptation of a system is generating, at runtime, the process that will perform the adaptation. One advantage of this approach is the possibility to take into account features that can only be evaluated at runtime, such as the emergence of new components that allow new architectural arrangements which were not foreseen at design time. In this work we have as main objective the use of a framework for dynamic generation of processes to generate architectural adaptation plans on OSGi environment. Our main interest is evaluate how this framework for dynamic generation of processes behave in new environments

Uso de confiabilidade na rotulação de exemplos em problemas de classificação multirrótulo com aprendizado semissupervisionado

The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; this is represented by small percentage of labeled data (supervised learning) combined with a quantity of label and non-labeled examples (unsupervised learning) during the training phase, which reduces, therefore, the need for a large quantity of labeled instances when only small dataset of labeled instances is available for training. A commom problem in semi-supervised learning is as random selection of instances, since most of paper use a random selection technique which can cause a negative impact. Much of machine learning methods treat single-label problems, in other words, problems where a given set of data are associated with a single class; however, through the requirement existent to classify data in a lot of domain, or more than one class, this classification as called multi-label classification. This work presents an experimental analysis of the results obtained using semissupervised learning in troubles of multi-label classification using reliability parameter as an aid in the classification data. Thus, the use of techniques of semissupervised learning and besides methods of multi-label classification, were essential to show the results

Avaliando a robustez e manutenibilidade do comportamento excepcional de aplicações C#

Mainstream programming languages provide built-in exception handling mechanisms to support robust and maintainable implementation of exception handling in software systems. Most of these modern languages, such as C#, Ruby, Python and many others, are often claimed to have more appropriated exception handling mechanisms. They reduce programming constraints on exception handling to favor agile changes in the source code. These languages provide what we call maintenance-driven exception handling mechanisms. It is expected that the adoption of these mechanisms improve software maintainability without hindering software robustness. However, there is still little empirical knowledge about the impact that adopting these mechanisms have on software robustness. This work addresses this gap by conducting an empirical study aimed at understanding the relationship between changes in C# programs and their robustness. In particular, we evaluated how changes in the normal and exceptional code were related to exception handling faults. We applied a change impact analysis and a control flow analysis in 100 versions of 16 C# programs. The results showed that: (i) most of the problems hindering software robustness in those programs are caused by changes in the normal code, (ii) many potential faults were introduced even when improving exception handling in C# code, and (iii) faults are often facilitated by the maintenance-driven flexibility of the exception handling mechanism. Moreover, we present a series of change scenarios that decrease the program robustness