Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Avaliação e controlo da qualidade da água de albufeiras com base em modelos dinâmicos

A problemática relacionada com a modelação da qualidade da água de albufeiras pode ser abordada de diversos pontos de vista. Neste trabalho recorre-se a metodologias de resolução de problemas que emanam da Área Cientifica da Inteligência Artificial, assim como a ferramentas utilizadas na procura de soluções como as Árvores de Decisão, as Redes Neuronais Artificiais e a Aproximação de Vizinhanças. Actualmente os métodos de avaliação da qualidade da água são muito restritivos já que não permitem aferir a qualidade da água em tempo real. O desenvolvimento de modelos de previsão baseados em técnicas de Descoberta de Conhecimento em Bases de Dados, mostrou ser uma alternativa tendo em vista um comportamento pró-activo que pode contribuir decisivamente para diagnosticar, preservar e requalificar as albufeiras. No decurso do trabalho, foi utilizada a aprendizagem não-supervisionada tendo em vista estudar a dinâmica das albufeiras sendo descritos dois comportamentos distintos, relacionados com a época do ano. ABSTRACT: The problems related to the modelling of water quality in reservoirs can be approached from different viewpoints. This work resorts to methods of resolving problems emanating from the Scientific Area of Artificial lntelligence as well as to tools used in the search for solutions such as Decision Trees, Artificial Neural Networks and Nearest-Neighbour Method. Currently, the methods for assessing water quality are very restrictive because they do not indicate the water quality in real time. The development of forecasting models, based on techniques of Knowledge Discovery in Databases, shows to be an alternative in view of a pro-active behavior that may contribute to diagnose, maintain and requalify the water bodies. ln this work. unsupervised learning was used to study the dynamics of reservoirs, being described two distinct behaviors, related to the time of year.

Transition to complete synchronization in phase-coupled oscillators with nearest neighbor coupling

We investigate synchronization in a Kuramoto-like model with nearest neighbor coupling. Upon analyzing the behavior of individual oscillators at the onset of complete synchronization, we show that the time interval between bursts in the time dependence of the frequencies of the oscillators exhibits universal scaling and blows up at the critical coupling strength. We also bring out a key mechanism that leads to phase locking. Finally, we deduce forms for the phases and frequencies at the onset of complete synchronization.

Studies of solute self-association by sedimentation equilibrium: allowance for effects of thermodynamic non-ideality beyond the consequences of nearest-neighbor interactions

A sedimentation equilibrium study of a-chymotrypsin self-association in acetate-chloride buffer, pH 4.1 I 0.05, has been used to illustrate determination of a dimerization constant under conditions where thermodynamic non-ideality is manifested beyond the consequences of nearest-neighbor interactions. Because the expressions for the experimentally determinable interaction parameters comprise a mixture of equilibrium constant and excluded volume terms, the assignment of reasonable magnitudes to the relevant virial coefficients describing non-associative cluster formation is essential for the evaluation of a reliable estimate of the dimerization constant. Determination of these excluded volume parameters by numerical integration over the potential-of-mean-force is shown to be preferable to their calculation by approximate analytical solutions of the integral for this relatively small enzyme monomer with high net charge (+ 10) under conditions of low ionic strength (0.05 M). (C) 2001 Elsevier Science B.V. All rights reserved.

Neighbour list optimization for real LTE radio network

With the increasing complexity of current networks, it became evident the need for Self-Organizing Networks (SON), which aims to automate most of the associated radio planning and optimization tasks. Within SON, this paper aims to optimize the Neighbour Cell List (NCL) for Long Term Evolution (LTE) evolved NodeBs (eNBs). An algorithm composed by three decisions were were developed: distance-based, Radio Frequency (RF) measurement-based and Handover (HO) stats-based. The distance-based decision, proposes a new NCL taking account the eNB location and interference tiers, based in the quadrants method. The last two algorithms consider signal strength measurements and HO statistics, respectively; they also define a ranking to each eNB and neighbour relation addition/removal based on user defined constraints. The algorithms were developed and implemented over an already existent radio network optimization professional tool. Several case studies were produced using real data from a Portuguese LTE mobile operator. © 2014 IEEE.

Shade avoidance: phytochrome signalling and other aboveground neighbour detection cues.

Plants compete with neighbouring vegetation for limited resources. In competition for light, plants adjust their architecture to bring the leaves higher in the vegetation where more light is available than in the lower strata. These architectural responses include accelerated elongation of the hypocotyl, internodes and petioles, upward leaf movement (hyponasty), and reduced shoot branching and are collectively referred to as the shade avoidance syndrome. This review discusses various cues that plants use to detect the presence and proximity of neighbouring competitors and respond to with the shade avoidance syndrome. These cues include light quality and quantity signals, mechanical stimulation, and plant-emitted volatile chemicals. We will outline current knowledge about each of these signals individually and discuss their possible interactions. In conclusion, we will make a case for a whole-plant, ecophysiology approach to identify the relative importance of the various neighbour detection cues and their possible interactions in determining plant performance during competition.

The ‘Dys-Appearing’ Body in Doris Lessing’s The Diary of a Good Neighbour and Margaret Forster’s Have the Men Had Enough?

If the old body is usually read as a synonym of fragility and upcoming illness, even though not the case for most elderly citizens, the reality is that the longer we live, the increased probability of being affected by different illnesses cannot be eluded or denied. In Doris Lessing’s The Diary of a Good Neighbour and Margaret Forster’s Have the Men Had Enough? the reader is invited to participate in the day-to-day routines of two aged female protagonists, as well as to empathize with their inner feelings as they go through their last life stage. In fact, their ‘dys-appearing’ bodies, marked by their respective terminal illnesses, force these characters to grow closer to those around them and to accept the help of their families and friends, despite their desire to keep their free will and independence until the very end. The analysis of the two novels within the framework of ageing studies aims to show the contradictions existing between a growing ageing society and the negative cultural connotations of old age in Western society and the need to revise them.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.