Reorganização do layout e reajustamento dos recursos humanos de um armazém

O projeto descrito no presente relatório, desenvolvido no contexto do Mestrado em Logística da APNOR, surgiu com vista a colmatar as necessidades identificadas pela empresa acolhedora na área da armazenagem. A empresa identificou como objetivo principal a reorganização do armazém de modo a satisfazer em tempo útil os pedidos efetuados pela unidade de produção. Em simultâneo foi identificada a necessidade de ajustar os recursos humanos do armazém às necessidades do mesmo. Durante o último ano de atividade verificou-se um crescimento exponencial do volume de produção, assim como do número de clientes. O crescimento acelerado dificultou a adaptação das estruturas da empresa à nova realidade, designadamente no que respeita à disposição do armazém, ao fluxo de informação e físico entre o armazém e a produção, assim como um correto dimensionamento do número de colaboradores afetos ao armazém. No início do estágio foi efetuado um acompanhamento próximo dos colaboradores e das atividades desenvolvidas, por forma a encontrar os principais pontos de estrangulamento dentro do armazém e na relação entre o armazém e a produção. Durante o acompanhamento averiguou-se que a principal dificuldade era a capacidade do armazém em albergar a mercadoria remetida pelos clientes, o tempo dispensado nos percursos efetuados no armazém para a preparação das encomendas e o abastecimento da produção em tempo útil.

An RF LC Q-enhanced CMOS iter using integrated inductors with layout optimization

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Analysable software language translations

Dissertação para obtenção do Grau de Doutor em Engenharia Informática

Customer Lifetime Value: a framework for application in the insurance industry - Building a Business Process to Generate and Maintain an Automatic Estimation Agent

Research Project submited as partial fulfilment for the Master Degree in Statistics and Information Management

Multipacket reception in LTE femtocell networks

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Automatic information retrieval through text-mining

The dissertation presented for obtaining the Master’s Degree in Electrical Engineering and Computer Science, at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Automated Generation of Indoor Accessibility Information for Mobility-Impaired Individuals

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study.

Automatic delimitation of the clinical region of interest in ultra-wide field of view images of the retina

Retinal ultra-wide field of view images (fundus images) provides the visu-alization of a large part of the retina though, artifacts may appear in those images. Eyelashes and eyelids often cover the clinical region of interest and worse, eye-lashes can be mistaken with arteries and/or veins when those images are put through automatic diagnosis or segmentation software creating, in those cases, the appearance of false positives results. Correcting this problem, the first step in the development of qualified auto-matic diseases diagnosis programs can be done and in that way the development of an objective tool to assess diseases eradicating the human error from those processes can also be achieved. In this work the development of a tool that automatically delimitates the clinical region of interest is proposed by retrieving features from the images that will be analyzed by an automatic classifier. This automatic classifier will evaluate the information and will decide which part of the image is of interest and which part contains artifacts. The results were validated by implementing a software in C# language and validated through a statistical analysis. From those results it was confirmed that the methodology presented is capable of detecting artifacts and selecting the clin-ical region of interest in fundus images of the retina.

Automatic extraction of concepts from texts and applications

The extraction of relevant terms from texts is an extensively researched task in Text- Mining. Relevant terms have been applied in areas such as Information Retrieval or document clustering and classification. However, relevance has a rather fuzzy nature since the classification of some terms as relevant or not relevant is not consensual. For instance, while words such as "president" and "republic" are generally considered relevant by human evaluators, and words like "the" and "or" are not, terms such as "read" and "finish" gather no consensus about their semantic and informativeness. Concepts, on the other hand, have a less fuzzy nature. Therefore, instead of deciding on the relevance of a term during the extraction phase, as most extractors do, I propose to first extract, from texts, what I have called generic concepts (all concepts) and postpone the decision about relevance for downstream applications, accordingly to their needs. For instance, a keyword extractor may assume that the most relevant keywords are the most frequent concepts on the documents. Moreover, most statistical extractors are incapable of extracting single-word and multi-word expressions using the same methodology. These factors led to the development of the ConceptExtractor, a statistical and language-independent methodology which is explained in Part I of this thesis. In Part II, I will show that the automatic extraction of concepts has great applicability. For instance, for the extraction of keywords from documents, using the Tf-Idf metric only on concepts yields better results than using Tf-Idf without concepts, specially for multi-words. In addition, since concepts can be semantically related to other concepts, this allows us to build implicit document descriptors. These applications led to published work. Finally, I will present some work that, although not published yet, is briefly discussed in this document.

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

Detection of purkinje images for automatic positioning of fixation target and interferometric measurements of anterior eye chamber

In cataract surgery, the eye’s natural lens is removed because it has gone opaque and doesn’t allow clear vision any longer. To maintain the eye’s optical power, a new artificial lens must be inserted. Called Intraocular Lens (IOL), it needs to be modelled in order to have the correct refractive power to substitute the natural lens. Calculating the refractive power of this substitution lens requires precise anterior eye chamber measurements. An interferometry equipment, the AC Master from Zeiss Meditec, AG, was in use for half a year to perform these measurements. A Low Coherence Interferometry (LCI) measurement beam is aligned with the eye’s optical axis, for precise measurements of anterior eye chamber distances. The eye follows a fixation target in order to make the visual axis align with the optical axis. Performance problems occurred, however, at this step. Therefore, there was a necessity to develop a new procedure that ensures better alignment between the eye’s visual and optical axes, allowing a more user friendly and versatile procedure, and eventually automatizing the whole process. With this instrument, the alignment between the eye’s optical and visual axes is detected when Purkinje reflections I and III are overlapped, as the eye follows a fixation target. In this project, image analysis is used to detect these Purkinje reflections’ positions, eventually automatically detecting when they overlap. Automatic detection of the third Purkinje reflection of an eye following a fixation target is possible with some restrictions. Each pair of detected third Purkinje reflections is used in automatically calculating an acceptable starting position for the fixation target, required for precise measurements of anterior eye chamber distances.

Instrumental Fado: a generative interactive system

Fado was listed as UNESCO Intangible Cultural Heritage in 2011. This dissertation describes a theoretical model, as well as an automatic system, able to generate instrumental music based on the musics and vocal sounds typically associated with fado’s practice. A description of the phenomenon of fado, its musics and vocal sounds, based on ethnographic, historical sources and empirical data is presented. The data includes the creation of a digital corpus, of musical transcriptions, identified as fado, and statistical analysis via music information retrieval techniques. The second part consists in the formulation of a theory and the coding of a symbolic model, as a proof of concept, for the automatic generation of instrumental music based on the one in the corpus.

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.