Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

Análise comparativa entre a promoção para arrendamento e venda de imóveis de habitação

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Materials selection for a set of multiple parts considering manufacturing costs and weight reduction with structural isoperformance using direct multisearch optimization

Materials selection is a matter of great importance to engineering design and software tools are valuable to inform decisions in the early stages of product development. However, when a set of alternative materials is available for the different parts a product is made of, the question of what optimal material mix to choose for a group of parts is not trivial. The engineer/designer therefore goes about this in a part-by-part procedure. Optimizing each part per se can lead to a global sub-optimal solution from the product point of view. An optimization procedure to deal with products with multiple parts, each with discrete design variables, and able to determine the optimal solution assuming different objectives is therefore needed. To solve this multiobjective optimization problem, a new routine based on Direct MultiSearch (DMS) algorithm is created. Results from the Pareto front can help the designer to align his/hers materials selection for a complete set of materials with product attribute objectives, depending on the relative importance of each objective.

Application of GIS-based multi-criteria analysis for site selection of aquifer recharge with reclaimed water

Reclaimed water from small wastewater treatment facilities in the rural areas of the Beira Interior region (Portugal) may constitute an alternative water source for aquifer recharge. A 21-month monitoring period in a constructed wetland treatment system has shown that 21,500 m(3) year(-1) of treated wastewater (reclaimed water) could be used for aquifer recharge. A GIS-based multi-criteria analysis was performed, combining ten thematic maps and economic, environmental and technical criteria, in order to produce a suitability map for the location of sites for reclaimed water infiltration. The areas chosen for aquifer recharge with infiltration basins are mainly composed of anthrosol with more than 1 m deep and fine sand texture, which allows an average infiltration velocity of up to 1 m d(-1). These characteristics will provide a final polishing treatment of the reclaimed water after infiltration (soil aquifer treatment (SAT)), suitable for the removal of the residual load (trace organics, nutrients, heavy metals and pathogens). The risk of groundwater contamination is low since the water table in the anthrosol areas ranges from 10 m to 50 m. Oil the other hand, these depths allow a guaranteed unsaturated area suitable for SAT. An area of 13,944 ha was selected for study, but only 1607 ha are suitable for reclaimed water infiltration. Approximately 1280 m(2) were considered enough to set up 4 infiltration basins to work in flooding and drying cycles.

Stacked pin Devices for Imaging Applications

In this paper we present results on the optimization of device architectures for colour and imaging applications, using a device with a TCO/pinpi'n/TCO configuration. The effect of the applied voltage on the color selectivity is discussed. Results show that the spectral response curves demonstrate rather good separation between the red, green and blue basic colors. Combining the information obtained under positive and negative applied bias a colour image is acquired without colour filters or pixel architecture. A low level image processing algorithm is used for the colour image reconstruction.

IOPT Petri Net State Space Generation Algorithm with Maximal-Step Execution Semantics

This paper presents an algorithm to efficiently generate the state-space of systems specified using the IOPT Petri-net modeling formalism. IOPT nets are a non-autonomous Petri-net class, based on Place-Transition nets with an extended set of features designed to allow the rapid prototyping and synthesis of system controllers through an existing hardware-software co-design framework. To obtain coherent and deterministic operation, IOPT nets use a maximal-step execution semantics where, in a single execution step, all enabled transitions will fire simultaneously. This fact increases the resulting state-space complexity and can cause an arc "explosion" effect. Real-world applications, with several million states, will reach a higher order of magnitude number of arcs, leading to the need for high performance state-space generator algorithms. The proposed algorithm applies a compilation approach to read a PNML file containing one IOPT model and automatically generate an optimized C program to calculate the corresponding state-space.

Comparação do cálculo de dose com os algoritmos Analytical Anisotropic Algorithm e Pencil Beam Convolution na patologia de pulmão

Mestrado em Radioterapia.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization. (C) 2013 Elsevier Ltd. All rights reserved.

Comparação entre o pencil beam convolution algorithm e o analytical anisotropic algorithm em tumores de mama

Mestrado em Radioterapia

OsRMC, a negative regulator of salt stress response in rice, is regulated by two AP2/ERF transcription factors

High salinity causes remarkable losses in rice productivity worldwide mainly because it inhibits growth and reduces grain yield. To cope with environmental changes, plants evolved several adaptive mechanisms, which involve the regulation of many stress-responsive genes. Among these, we have chosen OsRMC to study its transcriptional regulation in rice seedlings subjected to high salinity. Its transcription was highly induced by salt treatment and showed a stress-dose-dependent pattern. OsRMC encodes a receptor-like kinase described as a negative regulator of salt stress responses in rice. To investigate how OsRMC is regulated in response to high salinity, a salt-induced rice cDNA expression library was constructed and subsequently screened using the yeast one-hybrid system and the OsRMC promoter as bait. Thereby, two transcription factors (TFs), OsEREBP1 and OsEREBP2, belonging to the AP2/ERF family were identified. Both TFs were shown to bind to the same GCC-like DNA motif in OsRMC promoter and to negatively regulate its gene expression. The identified TFs were characterized regarding their gene expression under different abiotic stress conditions. This study revealed that OsEREBP1 transcript level is not significantly affected by salt, ABA or severe cold (5 °C) and is only slightly regulated by drought and moderate cold. On the other hand, the OsEREBP2 transcript level increased after cold, ABA, drought and high salinity treatments, indicating that OsEREBP2 may play a central role mediating the response to different abiotic stresses. Gene expression analysis in rice varieties with contrasting salt tolerance further suggests that OsEREBP2 is involved in salt stress response in rice.

Localização ótima de apoios em estruturas do tipo viga utilizando o método dos algoritmos genéticos

Dissertação de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica Ramo de Manutenção e Produção

Modelo de data mining para deteção de embolias pulmonares

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores