Abstract
A buildings resilience to seismic activity can be increased by providing ways for the structure to dynamically counteract the effect of the Earth's crust movements. This ability is fundamental in certain regions of the globe, where earthquakes are more frequent, and can be achieved using different strategies. State-of-the-art anti-seismic buildings have, embedded on their structure, mostly passive actuators such as base isolation, Tuned Mass Dampers (TMD) and viscous dampers that can be used to reduce the effect of seismic or even wind induced vibrations. The main disadvantage of this type of building vibration reduction strategies concerns their inability to adapt their properties in accordance to both the excitation signal or structural behaviour. This adaption capability can be promoted by adding to the building active type actuators operating under a closed-loop. However, these systems are substantially larger than passive type solutions and require a considerable amount of energy that may not be available during a severe earthquake due to power grid failure. An intermediate solution between these two extremes is the introduction of semi-active actuators such as magneto-rheological dampers. The inclusion of magneto-rheological actuators is among one of the most promising semi-active techniques. However, the overall performance of this strategy depends on several aspects such as the actuators number and location within the structure and the vibration sensors network. It can be the case where the installation leads to a non-collocated system which presents additional challenges to control. This paper proposes to tackle the problem of controlling the vibration of a non-collocated three-storey building by means of a brain-emotional controller tuned using an evolutionary algorithm. This controller will be used to adjust the stiffness coefficient of a magneto-rheological actuator such that the building's frame oscillation under earthquake excitation, is mitigated. The obtained results suggest that, using this control strategy, it is possible to reduce the building vibration to secure levels.

Abstract
O presente artigo expõe o estudo de aplicabilidade do ensaio edométrico a uma areia siltosa, existente na cidade de Bragança, sendo que objetiva a aplicabilidade do ensaio aos solos de granulometria mais fina da região, com a ressalva de que a curva granulométrica destes se afasta de forma substancial das curvas granulométricas dos solos que por norma são alvo do ensaio edométrico. Objetiva assim o artigo, a aplicabilidade do ensaio aos solos mais finos oriundos de uma região com predominância de maciço residual granítico. Para tal, foram recolhidas amostras de dois solos com acentuada percentagem de finos, no contexto da predominância de solos da região, nomeadamente amostras indeformadas de uma areia siltosa, e amostras remexidas de um solo, posteriormente peneirado, com 100% de passados no peneiro nº 10, cuja granulometria corresponderá a um silte de elevada plasticidade. Devido à maior predominância de finos no silte de elevada plasticidade, o comportamento mecânico deste solo, mais condicionado por forças de natureza eletroquímica, representativo do comportamento dos solos finos, servirá de termo comparativo ao comportamento mecânico das amostras indeformadas de areia siltosa, com comportamento menos dependente destas forças, permitindo assim evidenciar a aplicabilidade do ensaio a solos de amostras indeformadas, com menor percentagem de finos, com comportamento gravítico mais acentuado e, portanto, mais atípicos a este ensaio devido à predominância de grossos na sua constituição. Dos resultados obtidos para a areia siltosa, verificou-se um elevado valor do assentamento imediato, após aplicação de cada ciclo de carga. Dado que a correta identificação do início do processo de consolidação carece de rigor no registo dos tempos de assentamento, fundamental para aplicação das metodologias de cálculo do ensaio, evidencia-se a necessidade de automatização de registo que minimize erros por parte de operadores humanos. Assim o recurso a um sistema de aquisição de dados revelou-se fundamental na identificação do início da consolidação primária, permitindo assim o alargar do espetro de aplicabilidade do ensaio.;This paper exposes the applicability study of the oedometer test to an existing silty sand soil, from the city of Braganza, with the purpose to verify the applicability of this test to soils with fines in his composition, in a region with predominance of granite residual soils, considering that the granulometric curve of these soils are substantially different from the granulometric curves of the soils that are usually tested. For this, two types of soils were collected, namely intact samples of a silty sand, and a disturbed soil sample whose grains size distribution curve built into laboratory corresponds to a high plasticity silt. Due to the greater predominance of fines in the high plasticity silt, the mechanical behaviour of this soil, more conditioned by forces of an electrochemical nature, serves as a comparative term to the mechanical behaviour of the undisturbed samples of the silty sand, with a behaviour less dependent on these forces, thus allowing to show applicability of the test to soils with lower percentage of fines, with a more pronounced gravitational behaviour and, therefore, more atypical to the oedometer test. From the results obtained for silty sand, a high value of the immediate settlement was verified after each applied load cycle. Thus this observation, the beginning of the consolidation process demands high accurate recording of the settlement that clearly identify the time that primary consolidation begins. In this way, the implementation of the data acquisition system, allowing the correct reading of the consolidation beginning, minimizing human errors, expands the spectrum of soils existing in the region that can be study by the consolidation problematics point of view.

Abstract
This paper exposes the applicability study of the oedometer test to an existing silty sand soil, from the city of Braganza, with the purpose to verify the applicability of this test to soils with fines in his composition, in a region with predominance of granite residual soils, considering that the granulometric curve of these soils are substantially different from the granulometric curves of the soils that are usually tested. For this, two types of soils were collected, namely intact samples of a silty sand, and a disturbed soil sample whose grains size distribution curve built into laboratory corresponds to a high plasticity silt. Due to the greater predominance of fines in the high plasticity silt, the mechanical behaviour of this soil, more conditioned by forces of an electrochemical nature, serves as a comparative term to the mechanical behaviour of the undisturbed samples of the silty sand, with a behaviour less dependent on these forces, thus allowing to show applicability of the test to soils with lower percentage of fines, with a more pronounced gravitational behaviour and, therefore, more atypical to the oedometer test. From the results obtained for silty sand, a high value of the immediate settlement was verified after each applied load cycle. Thus this observation, the beginning of the consolidation process demands high accurate recording of the settlement that clearly identify the time that primary consolidation begins. In this way, the implementation of the data acquisition system, allowing the correct reading of the consolidation beginning, minimizing human errors, expands the spectrum of soils existing in the region that can be study by the consolidation problematics point of view.

Abstract
Optical character recognition (OCR) is the process by which the textual content of an image is converted into strings. Localization is the problem of figuring out where one is in a given environment. In this work we approach the application of OCR in robot localization. We develop and test a vision based localization system that is capable of detecting room identification signs present in the environment, recognizing their textual contents and apply them to determine its location referent to a topological map of the environment. A sign detection method based on image segmentation by color and corner detection by contour analysis is developed. The recognition of characters is performed with the application of an open-source OCR engine. Localization is performed through the comparison of sign readings with the textual information embedded in the topological representation of the environment. The algorithm was tested in a dataset of images acquired in a corridor. Experimental results show that the system successfully determines its localization in 83.33% of tested cases. © 2019 IEEE.

Abstract
The photogrammetry, 3D reconstruction from images, is an old technique but it's potentials could only be seen after the development of computers and digital photographs. Nowadays it has many applications, as creating scenarios for games, acquiring human expressions, roof inspection, stockpile measurement, high voltage transformer inspection, etc. As new technologies appear, new applications to photogrammetry are created. In this paper the use of available open and closed-source algorithms for 3D reconstruction and texturization is investigated. To achieve this goal, images of a fountain from several points-of-view were used. Next a comparison between several open and closed-source algorithms was performed, evaluating the number of faces, time consumption, RAM memory, GPU memory and the generated textured 3D models. The results obtained demonstrate that with the right setup, current open-source algorithms can achieve results near or better than proprietary software. Regarding the comparison, 3Dflow and MeshRecon presented the most accurate textured 3D models. When comparing quantitative measures, though, MeshRecon presented a slightly better performance in time consumption, but 3Dflow had a better RAM memory usage and a lower quantity of faces with a similar level of details. © 2019 IEEE.

Abstract
This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio.