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This paper proposes a non-contact method for fault location in transmission lines, which is based on magnetic fields produced by current signals measured using magnetoresistive sensors installed only at transmission line terminals, under the phase conductors of the first transmission tower at both terminals or at the substations portals. The proposed method uses the Extended Kalman filter to process these measurements and is based on a travelling wave approach in order to perform the fault localization. This paper also describes the implementation and testing of the method, firstly introducing its overview, followed by an analysis of the magnetic fields produced by the current signals, as well as considerations on their measurement; secondly, detailing the Extended Kalman filter and the travelling wave approach; and, finally, presenting the results of the method with regards to simulations built using EMTP/ATP to evaluate its robustness under different conditions varying the fault resistance, fault inception angle, phases involved and fault location. The results indicate that the proposed method is robust and accurate.

A localizacao das vagas em grandes estacionamentos sempre foi alvo de problemas aos usuarios. Algumas solucoes existem para a resolucao deste problema, quando se opera em ambientes a ceu aberto, dada a facilidade em se utilizar recursos como o Sistema de Posicionamento Global (GPS- Global Positioning System), mas quando se trata de estacionamentos cobertos essa acao ainda encontra varios obstaculos como a interferencia entre os sinais proximos. O presente estudo apresenta um sistema inteligente para estacionamento em ambiente coberto capaz de indicar uma vaga para o usuario estacionar o veiculo e tambem oriente quanto ao local de estacionamento no retorno do usuario.

In this paper, we describe the use of problem-based learning in the project of a DC driver motor board for combat robot competitions. For that, a team of students from different engineering courses work together to acquire knowledge related to electrical circuits design, C program language for microcon - trollers, and printed circuit board manufacturing.

Much research in RoboCup 2D Soccer Simulation has used the Half Field Offense (HFO) environment. This work proposes a baseline approach for goalkeeper strategy using Reinforcement Learning on HFO. The proposed approach uses Sarsa with eligibility traces and Tile Coding for the discretization of state variables. Two comparative studies were conducted to validate the proposed baseline. First, a comparative study between the Agent2D's goalkeeper strategy and a random decision strategy was performed. The second comparative study verified the performance of the proposed approach against a random decision strategy. Wilcoxon's Signed-Rank test was used for measuring the statistical significance of performance differences. Experiments showed that the Agent2D's goalkeeper strategy is inferior to a random decision, and the proposed baseline delivers a performance superior to a random decision strategy with a confidence level of 95%.

This article presents the modeling of an antipodal Vivaldi antenna (AVA), Palm Tree Class, with radiating fractal slot edge (FSE), in the form of a Koch Square Fractal (SK), labeled as SK-FSE-AVA. The surface currents of the proposed antenna are analyzed, as well as the return loss, directivity, squint, side lobe level (SLL), and Main Lobe (ML) gain. Improvement on directivity is observed when compared to an AVA, with 8.41 dB of gain in the main lobe at 4 GHz, SLL of 11.70 dB, and the squint of 0.10°. SK-FSE-AVA has 9.23 dB of gain in the main lobe at 4 GHz, SLL of –13.50 dB, and the squint of 0.2°. This antenna could be used in ultra-wideband (UWB) systems for applications on Near-Field Microwave Imaging (NMI). To show the feasibility of this application, a 3D numerical simulation of a model containing lung tissue with a malignant tumor is presented, using the proposed antenna and the respective imaging results.

For software to be reliable and resilient, it is widely accepted that tests must be created and maintained alongside the software itself. One safeguard from vulnerabilities and failures in code is to ensure correct behavior on the boundaries between the input space sub-domains. So-called boundary value analysis (BVA) and boundary value testing (BVT) techniques aim to exercise those boundaries and increase test effectiveness. However, the concepts of BVA and BVT themselves are not generally well defined, and it is not clear how to identify relevant sub-domains, and thus the boundaries delineating them, given a specification. This has limited adoption and hindered automation. We clarify BVA and BVT and introduce Boundary Value Exploration (BVE) to describe techniques that support them by helping to detect and identify boundary inputs. Additionally, we propose two concrete BVE techniques based on information-theoretic distance functions: (i) an algorithm for boundary detection and (ii) the usage of software visualization to explore the behavior of the software under test and identify its boundary behavior. As an initial evaluation, we apply these techniques on a much used and well-tested date handling library. Our results reveal questionable behavior at boundaries highlighted by our techniques. In conclusion, we argue that the boundary value exploration that our techniques enable is a step towards automated boundary value analysis and testing, fostering their wider use and improving test effectiveness and efficiency.

In this paper, we define a fractional version of the quaternion Fourier number transform (QFNT). With this purpose, we first study the eigenstructure of the QFNT; this is used to obtain the eigendecomposition of the corresponding transform matrix, from which the fractional QFNT can be computed. A multiple parameter version of this transform is then employed to perform encryption of color images. Preliminary results suggest that the proposed scheme is secure, while allowing to deal with all color channels in a holistic manner and take into account possible interactions between them.

Automated graphical user interface (GUI) tests can reduce manual testing activities and increase test frequency. This motivates the conversion of manual test cases into automated GUI tests. However, it is not clear whether such automation is cost-effective given that GUI automation scripts add to the code base and demand maintenance as a system evolves. In this paper, we introduce a method for estimating maintenance cost and Return on Investment (ROI) for Automated GUI Testing (AGT). The method utilizes the existing source code change history and has the potential to be used for the evaluation of other testing or quality assurance automation technologies. We evaluate the method for a real-world, industrial software system and compare two fundamentally different AGT frameworks, namely Selenium and EyeAutomate, to estimate and compare their ROI. We also report on their defect-finding capabilities and usability. The quantitative data is complemented by interviews with employees at the company the study has been conducted at. The method was successfully applied, and estimated maintenance cost and ROI for both frameworks are reported. Overall, the study supports earlier results showing that implementation time is the leading cost for introducing AGT. The findings further suggest that, while EyeAutomate tests are significantly faster to implement, Selenium tests require more of a programming background but less maintenance.

This paper aims to answer the following research question: "How to build an efficient kick strategy for agents in the 2D Simulation League?". The robot soccer provides an opportunity for students and professionals to apply their concepts of intelligent agent development. One of the main challenges of this game is to decide when a player must kick the ball to the goal. The proposed solution to solve this question is a data mining approach. The solution consists of three components: 1) use of the Random Forest technique as a classifier, 2) enrichment of the database through the construction of new variables and 3) Features Selection. In order to validate the proposed solution, a comparative study between the original kick strategy of a base team and the solution proposed was conducted. Experiments showed that the proposed approach delivers a performance superior. The results showed that the proposed policy reached a winning rate of 65% against 28% of the original.

Ontologies play an important role across several domains as they represent and define categories, properties and relationships among concepts, data and entities existing in those domains. Specially in Robotics, ontologies can be used as a standard way to represent and share knowledge and reasoning among autonomous agents. CORA (core ontology or robotics and automation) is a standard ontology developed by the IEEE ORA working group mapping main concepts and axioms from the robotics and automation (R&A) domain. After CORA has been approved as an official IEEE standard, ORA was split in different sub-groups addressing, among other topics, task representation (RTR) and autonomous robots architectures (AuR). This paper presents a simulation study on a multirobot reconnaissance application intended to serve as proof-ofconcept on the feasibility of the CORA ontology and its related architecture. We show how robots and tasks are represented and dynamically mapped into the ontology, as well discuss some technical aspects of our simulation when dealing with the proposed architecture. Our results show the proposed ontology (CORA) and its associated reference architecture (ROA) are able to be correctly mapped during application runtime.

Bots that support software development ("DevBots") are seen as a promising approach to deal with the ever-increasing complexity of modern software engineering and development. Existing DevBots are already able to relieve developers from routine tasks such as building project images or keeping dependencies up-to-date. However, advances in machine learning and artificial intelligence hold the promise of future, significantly more advanced, DevBots. In this paper, we introduce the terminology of contemporary and ideal DevBots. Contemporary DevBots represent the current state of practice, which we characterise using a facet-based taxonomy. We exemplify this taxonomy using 11 existing, industrial-strength bots. We further provide a vision and definition of future (ideal) DevBots, which are not only autonomous, but also adaptive, as well as technically and socially competent. These properties may allow ideal DevBots to act more akin to artificial team mates than simple development tools.

This paper presents the design, development and evaluation of a software tool to assist the localisation of root causes of test case failures in distributed embedded systems, specifically vehicle systems controlled by a network of electronic control units (ECUs). We use data visualising to provide sensible information from a large number of test execution logs from large-scale software integration testing under a continuous integration process. Our goal is to allow more efficient root-cause identification of failures and foster a continuous feedback loop in the fault localisation process. We evaluate our solution in-situ at the Research and Development division of Volvo Car Corporation (VCC). Our prototype helps the failure debugging procedures by presenting clear and concise data and by allowing stakeholders to filter and control which information is displayed. Moreover, it encourages a systematic and continuous analysis of the current state of testing by aggregating and categorising historical data from test harnesses to identify patterns and trends in test results.

A generating matrix is a matrix such that, when multiplied by an eigenvector of a discrete transform, a new eigenvector is obtained. In this paper, we introduce a family of generating matrices of DFT eigenvectors. We demonstrate that, if a specific initial set of eigenvectors is chosen, using the referred family of matrices, a Hermite-Gaussian-like DFT eigenbasis is obtained. Such an eigenbasis is then employed to define a discrete fractional Fourier transform which numerically approximates the corresponding continuous transform.

The cosine number transform (CNT) is a cosine-like number-theoretic transform, which has been employed as the basis for multimedia security schemes. In this paper, we propose hardware architectures for computing an 8-point CNT. The architectures include a pipelined approach and are based on a recently introduced fast algorithm, which has been demonstrated to be more efficient than the direct computation of the CNT. We quantify such an efficiency by considering several aspects inherent to modular arithmetic and comparing metrics obtained from field-programmable gate array (FPGA) implementations of the proposed architectures.

This study intends to help data mining developers to get better performance when obtaining the most frequent N-grams in Text Mining projects. The process of building new variables is one of the oldest and still challenging problems in Data Mining projects. The most frequent N-grams are commonly used as input variables in Text Mining projects. The N-grams represent the occurrence of N items in sequence in a given text. The items can be letters or words. This paper presents a performance comparison between the two main approaches of data storage, relational and NoSQL databases in the task of obtaining the most frequent N-grams. Validation of the study was executed using a database from a known benchmark from an international competition organized by PAN@CLEF 2013. The one-tailed paired t-test showed that NoSQL approach is statistically superior to the relational approach with a confidence level of 95%.

This paper presents a methodology for low-voltage AC surge protective device (SPD) coordination. As motivation, the required nuclear plant reliability and safety criteria, in which the surge protection coordination can assume an important role, are emphasized. Due to the characteristics of the protection system, and the surge phenomena, an iterative methodology is applied. The SPD selection and specifications are based on the equipment immunity level, insulation and on SPD characteristics, like absorbed energy. Some results are presented and discussed, showing the suitability of the proposed methodology.

As agile methods become more pervasive, agile practices are applied to more large-scale systems with a scope that goes beyond pure software. The expansion of agile in these contexts provides benefits, but creates new challenges. Widespread use of agile has changed the way we must think about practices both in Requirements Engineering (RE) and in System Testing (ST). Our experience shows that many challenges in the application of large-scale agile development relate to either RE or ST, and in particular to the alignment between these areas. In this paper we present large-scale agile-related challenges from a multiple case study which relate to REST alignment. We map our challenges to an existing framework for REST alignment, and make an initial attempt to suggest agile RE practices from the literature which may alleviate these challenges. Our results show that the interviewed companies need to first adopt more agile RE practices to enhance REST alignment and then leverage agile testing. Future work will look more towards evaluating these best practices.

In this paper, we propose a method for obtaining eigenvectors of discrete cosine and sine transforms of types I and IV. The approach is based on constructing an initial eigenvector of one of such trigonometric transforms and a generating matrix. Multiplying powers of the matrix by the initial eigenvector, new eigenvectors are obtained. It is shown how the generated eigenvectors can be used in the fractionalization of the respective transform. Finally, we illustrate the applicability of the developed theory in the scenario of image encryption.

This paper presents an approach combining concepts such as Internet of Things (IoT) and Cloud Computing to create digital assistive technology focused on the urban space context. We propose the convergence of Smart City, Urban Computing, Accessibility and Universal Design in order to provide a citizen experience of independence and autonomy in the city for a range of people that frequently depend on the aid of others to perform some activities that are ordinary for most people: identifying objects inside a subway station or localizing the right position of a pharmacy in a street.

RAM-based neural networks, despite their speed and hardware implementability, have a strong drawback in representing continuous input variables that affects its performance. Usually these networks require value discretization followed by binary encoding to access the binary memory addresses where the learned content is stored. Added to being a cumbersome process, binary encoding does not usually preserve monotonic distance relations when mapping the original input space. That imposes a difficulty for the learning system to create basins of attraction thus affecting its generalization power. This paper proposes a RAM-based neuron, the PAN model, with true continuous input handling capability which performs a polynomial approximation of the input variables on supervised learning tasks. The proposed model is applied in a single layer neural network architecture coined PAN n-tuple classifier similar to that previously proposed by Adeodato and Oliveira Neto. The network combines the polynomial approximation of the neuron with subspace sampling of the n-tuple classifier under competitive learning. Experimental comparison with 10-fold cross-validation on 2 Proben1 binary decision problems against the MultiLayer Perceptron shows equivalent performance at 0.05 significance level measured by the AUC_ROC and Max_KS metrics.