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Current EO satellite missions providing imagery in the VIS- TIR region at high spatial resolution offer the possibility to estimate a number of important parameters to monitor volcanic activities at worldwide level. Therefore, space missions contribute in understanding the ongoing phenomena to mitigate the volcanic risk when population and infrastructures are exposed. New hyperspectral and thermal missions which could provide images at high spatial and temporal resolution are under study in cooperation between Space Agencies (NASA, ASI, ESA etc.) and hopefully will improve the monitoring capabilities of active volcanoes in highly populated areas. In the present work we describe several results obtained in the last years by EO satellite missions for the volcanic risk. While the satellite data provide a wide description of scenes, in situ and proximal measurements are crucial to retrieve gas emissions and analyze surface characteristics at very high spatial resolution. Data acquired from drones (UAV) and thermal cameras in volcanic areas are presented and discussed. Moreover, this work shows the need of validation process for satellite products comparing with measurements collected during field campaigns.

Software systems naturally evolve, and this evolution often brings design problems that cause system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among modules. The early detection of these smells is important for developers, because they can plan ahead for maintenance or refactoring efforts, thus preventing system degradation. Existing tools for identifying architectural smells can detect the smells once they exist in the source code. This means that their undesired dependencies are already created. In this work, we explore a forward-looking approach that is able to infer groups of likely module dependencies that can anticipate architectural smells in a future system version. Our approach considers the current module structure as a network, along with information from previous versions, and applies link prediction techniques (from the field of social network analysis). In particular, we focus on dependency-related smells, such as Cyclic Dependency and Hub-like Dependency, which fit well with the link prediction model. An initial evaluation with two open-source projects shows that, under certain considerations, the predictions of our approach are satisfactory. Furthermore, the approach can be extended to other types of dependency-based smells or metrics.

In this work was used a methodology for the magnetic simulation of power transformers for 12-pulse rectifiers. For the magnetic simulation of transformers the software ANSYS Maxwell was used, while for the model of circuits the software ANSYS Simplorer was used. Parameters such as electric losses in active components due to harmonics were analyzed. In final part of this work a comparison is made between results in simplified conditions compared with the realistic operation conditions of transformer connected to a 12-pulse rectifier.

Glaucoma is an asymptomatic eye disease and one of the major causes of irreversible blindness worldwide. For this reason, there have been significant advances in automatic screening tools for early detection. In this paper, an automatic glaucoma diagnosis algorithm based on retinal fundus image is presented. This algorithm uses anatomical characteristics such as the position of the vessels and the cup within the optic nerve. Using several color spaces and the Stochastic Watershed transformation, different characteristics of the optic nerve were analyzed in order to distinguish between a normal and a glaucomatous fundus. The proposed algorithm was evaluated on 53 images (24 normal and 29 glaucomatous images). The specificity and sensitivity obtained by the proposed algorithm are 0.81 and 0.87 using Luv color space, which means considerable performance in diagnosis systems.

Code smells are a popular mechanism to identify structural design problems in software systems. Since it is generally not feasible to f x all the smells arising in the code, some of them are often postponed by developer s to be resolved in the future. One reason for this decision is that the improvement of the code structure, to achieve modifability goals, requires extra effort from developer s. Therefore, they might not always spend this additional effort, particularly when they are focused on delivering customer-visible features. This postponement of code smells are seen as a source of technical debt. Furthermore, not all the code smells may be urgent to f x in the context of the system's modifability and business goals. While there are a number of tools to detect smells, they do not allow developer s to discover the most urgent smells according to their goals. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The tool is fexible to allow developer s to add new smell detection strategies and to prioritize smells, and groups of smells, based on the confguration of their manifold criteria. To illustrate this fexibility, we present an application example of our tool. The results suggest that our tool can be easily extended to be aligned with the developer's goals.

Developing high-quality requirements specifications often demands a thoughtful analysis and an adequate level of expertise from analysts. Although requirements modeling techniques provide mechanisms for abstraction and clarity, fostering the reuse of shared functionality (e.g., via UML relationships for use cases), they are seldom employed in practice. A particular quality problem of textual requirements, such as use cases, is that of having duplicate pieces of functionality scattered across the specifications. Duplicate functionality can sometimes improve readability for end users, but hinders development-related tasks such as effort estimation, feature prioritization and maintenance, among others. Unfortunately, inspecting textual requirements by hand in order to deal with redundant functionality can be an arduous, time-consuming and error-prone activity for analysts. In this context, we introduce a novel approach called ReqAligner that aids analysts to spot signs of duplication in use cases in an automated fashion. To do so, ReqAligner combines several text processing techniques, such as a use-case-aware classifier and a customized algorithm for sequence alignment. Essentially, the classifier converts the use cases into an abstract representation that consists of sequences of semantic actions, and then these sequences are compared pairwise in order to identify action matches, which become possible duplications. We have applied our technique to five real-world specifications, achieving promising results and identifying many sources of duplication in the use cases.

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

Over the last years, several techniques to evaluatemodifiability of software architectureshave been developed. One of such techniques is change impact analysis (CIA), which aids developers in assessing the effects of change scenarios on architectural modules. However, CIA does not take into account the pattern structures behind those modules. In architectural frameworks, such as the Enterprise Java Beans (EJB) architecture, the use of patterns is a key practice to achieve modifiability goals. Although patterns can be easily understood individually, when an application combines several pattern instances the analysis is not straightforward. In practice, many EJB designsare assessed in an ad-hoc manner and relying on the developers' experience. A way of dealing with this problem is through the integration of modifiabiliy analysis models and patterns. We propose a knowledge-based approach that explicitly links the EJB patterns to a scenario-based analysis for multi-tier architectures. Specifically, we have developed a modifiability reasoning framework that reifies the EJB patterns present in a given design solution and, for a set of predetermined scenarios, the reasoning framework identifies which architectural elements can be affected by the scenarios. The reasoning framework outputs metrics for each of the scenarios regarding specific EJB tiers. The main contribution of this approach is that assists developers to evaluate EJB alternatives, providing quantitativeinformation about the modifiability implications of their decisions. A preliminary evaluation has shown that the the reasoning framework is viable to analyze EJB designs.

The buck converter is an electronic device widely used in power electric applications. This converter has a nonlinear performance in consequence of the switching transistor effects which it uses. Implement a classic controller for this converter results very complex. Hence, fuzzy controllers are used to obtain a better steady state performance of the system. This paper presents a multi-objective optimization to ensure a steady state error (e ss )

The recent advances in the fields such as modeling bio-mechanics of living tissues, haptic technologies, computational capacity, and graphics realism have created conditions necessary in order to develop effective surgical training using virtual environments. However, virtual simulators need to meet two requirements, they need to be real-time and highly realistic. The most expensive computational task in a surgical simulator is that of the physical model. The physical model is the component responsible to simulate the deformation of the anatomical structures and the most important factor in order to obtain realism. In this paper we present a novel approach to virtual surgery. The novelty comes in two forms: specifically a highly realistic mass-spring model, and a GPU based technique, and analysis, that provides a nearly 80x speedup over serial execution and 20x speedup over CPU based parallel execution.

Rapidly changing technology is one of the key triggers of system evolution. Some examples are: physically relocating a data center, replacement of infrastructure such as migrating from an in-house broker to CORBA, moving to a new architectural approach such as migrating from client-server to a service-oriented architecture. At a high level, the goals of such an evolution are easy to describe. While the end goals of the evolution are typically captured and known, the key architecturally significant requirements that guide the actual evolution tasks are often unexplored. At best, they are tucked under maintainability and/or modifiability concerns. In this paper, we argue that eliciting and using architecturally significant requirements of an evolution has a potential to significantly improve the quality of the evolution effort. We focus on elicitation and representation techniques of architecturally significant evolution requirements, and demonstrate their use in analysis for evolution planning.

Active materials are capable of responding in a controlled way to different external physical or chemical stimulus by changing some of their properties. These materials can be used to design and develop sensors, actuators and multifunctional systems with a large number of applications for developing medical devices (for both surgery and implants). On the other hand, Bruxism is a health problem consisting on grinding or tightly clenching the upper and lower teeth, and both phenomena lead to wear of the teeth and produce a noise during the night that is sufficiently loud to disturb the sleep of anyone sharing the bedroom. The tension produced also causes problems in the muscles, tissues and other structures surrounding the jaw, ear pain, headaches, lesions to the teeth and disorders in the jaw joints. For an early, rapid, effective and economical diagnosis of bruxism, we propose the use of instrumented splints based on the use of active materials that detect and record the intensity and duration of interdentally pressure episodes. This paper introduces the design, manufacture and testing of using instrumented splint for bruxism detection. The contribution of this work is basically focused on using piezoelectric polymeric sensors, taking advantage of their reduced thickness, and avoiding the alteration of the patient's bite. In addition, the system allows a quantitative assessment of intraoral pressure that consequently allows the diagnosis of bruxism behaviour at an early stage, aiming to perform corrective actions before irreversible dental wear appears. The first trials performed allowed to demonstrate the feasibility of the system, which also includes wireless communication capabilities for improving on telemedicine tasks.

A key aspect of architecture-centric development is the traceability of design documentation. In particular, architects should be always aware of the relationships between the architectural model “as documented” and its corresponding implementation model. The problem is that these two models usually diverge from each other over time, due to factors such as new requirements, refactorings, etc. Therefore, tool assistance is very important to assess the level of conformance between architectural documentation and implementation. In this paper, we present a tool approach called ArchSync that helps architects to conciliate architectural documentation expressed through Use-Case Maps with Java source code, as modifications are being made on the code. ArchSync relies on a heuristic that incrementally detects inconsistencies with respect to the architectural prescriptions, based on the analysis of system execution traces. ArchSync can also give suggestions for re-synchronization. Results of two case-studies showing the applicability of the approach are reported.

Architecture evolution, a key aspect of software evolution, is typically done in an ad hoc manner, guided only by the competence of the architect performing it. This process lacks the rigor of an engineering discipline. In this paper, we argue that architecture evolution must be engineered - based on rational decisions that are supported by formal models and objective analyses. We believe that evolutions of a restricted form - close-ended evolution, where the starting and ending design points are known a priori - are amenable to being engineered. We discuss some of the key challenges in engineering close-ended evolution. We present a conceptual framework in which an architecture evolutionary trajectory is modeled as a sequence of steps, each captured by an operator. The goal of our framework is to support exploration and objective evaluation of different evolutionary trajectories. We conclude with open research questions in developing this framework.

until now the use of equivalent circuit for induction motor has been limited for steady state analysis. The equivalent circuits for dynamic simulation proposed until now, are not able to obtain the transient response of the induction motor, because they lack of mechanical component representations. Complex non-linear matrices are used instead. Here electrical analogy of mechanical system is integrated with d-q equivalent circuit derivates from the stator frame of reference to create a complete equivalent circuit for three-phase induction motor which is suitable for dynamic simulation. Finally this paper presents results that validate the proposed circuit by comparing it with that obtained with mathematical models.

This paper presents a maximum power tracking method for DC motor fed from photovoltaic modules (PVM) through step-down power converter. This method employs positive feedback of motor speed; therefore, only DC motor speed measurements are required. The designed control law is based on mathematical model of DC motor and power converter, and also, variations of optimal maximum power point (MPP) are considered at different values of irradiance. Optimal MPP is approximated by method known as linear reoriented coordinates method (LRCM). Finally, a simple analog circuit is designed and implemented, which calculates optimal duty ratio for power converter according to DC motor speed.

Locating software faults is a problematic activity in many systems. Existing tool approaches usually work close to the system implementation, requiring the developer to perform tedious code analyses in which the amount of information she must manage is usually overwhelming. This problem calls for approaches able to work at higher abstraction levels than code. In this context, we present a tool approach, called FLABot, to assist fault-localization tasks. A novelty of FLABot is that it reasons about faults using software architecture information. Based on Use-case-maps and system logs, FLABot performs a heuristic search for possible faulty functions in the architecture, and then maps these functions to code sections. This allows the developer to quickly navigate large systems and spot code regions that may contain faults, which can be further debugged using conventional techniques. Our preliminary experiments have shown that FLABot is practical and reduces the efforts for discovering faults.