Real-time tracking and reporting of dynamic events in hierarchical wireless sensor networks

Wireless Sensor Networks (WSNs) are highly distributed systems in which resource allocation (bandwidth, memory) must be performed efficiently to provide a minimum acceptable Quality of Service (QoS) to the regions where critical events occur. In fact, if resources are statically assigned independently from the location and instant of the events, these resources will definitely be misused. In other words, it is more efficient to dynamically grant more resources to sensor nodes affected by critical events, thus providing better network resource management and reducing endto- end delays of event notification and tracking. In this paper, we discuss the use of a WSN management architecture based on the active network management paradigm to provide the real-time tracking and reporting of dynamic events while ensuring efficient resource utilization. The active network management paradigm allows packets to transport not only data, but also program scripts that will be executed in the nodes to dynamically modify the operation of the network. This presumes the use of a runtime execution environment (middleware) in each node to interpret the script. We consider hierarchical (e.g. cluster-tree, two-tiered architecture) WSN topologies since they have been used to improve the timing performance of WSNs as they support deterministic medium access control protocols.

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

Fuzzy clustering applied to a demand response model in a smatr grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.

Effects of oxidante acid treatments on carbon-templated hierarchical SAPO-11 materials: synthesis, characterization and catalytic evaluation in n-decane hydroiosomerization

Hierarchical SAPO-11 was synthesized using a commercial Merck carbon as template. Oxidant acid treatments were performed on the carbon matrix in order to investigate its influence on the properties of SAPO-11. Structural, textural and acidic properties of the different materials were evaluated by XRD, SEM, N-2 adsorption, pyridine adsorption followed by IR spectroscopy and thermal analyses. The catalytic behavior of the materials (with 0.5 wt.% Pt, introduced by mechanic mixture with Pt/Al2O3), were studied in the hydroisomerization of n-decane. The hierarchical samples showed higher yields in monobranched isomers than typical microporous SAPO-11, as a direct consequence of the modification on both porosity and acidity, the later one being the most predominant. (C) 2014 Elsevier B.V. All rights reserved.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Paired indices for clustering evaluation correction for agreement by chance

In the present paper we focus on the performance of clustering algorithms using indices of paired agreement to measure the accordance between clusters and an a priori known structure. We specifically propose a method to correct all indices considered for agreement by chance - the adjusted indices are meant to provide a realistic measure of clustering performance. The proposed method enables the correction of virtually any index - overcoming previous limitations known in the literature - and provides very precise results. We use simulated datasets under diverse scenarios and discuss the pertinence of our proposal which is particularly relevant when poorly separated clusters are considered. Finally we compare the performance of EM and KMeans algorithms, within each of the simulated scenarios and generally conclude that EM generally yields best results.

Fuzzy clustering não supervisionado na detecção automática de regiões de upwelling a partir de mapas de temperatura da superfície oceânica

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Hierarchical wrinkling on elastometric Janus spheres

Hierarchical wrinkling on elastomeric Janus spheres is permanently imprinted by swelling, for different lengths of time, followed by drying the particles in an appropriate solvent. First-order buckling with a spatial periodicity (lambda(11)) of the order of a few microns and hierarchical structures comprising of 2nd order buckling with a spatial periodicity (lambda(12)) of the order of hundreds of nanometers have been obtained. The 2nd order buckling features result from a Grinfeld surface instability due to the diffusion of the solvent and the presence of sol molecules.

Clustering de dados biomédicos com algoritmos baseados em critérios entrópicos

A procura de padrões nos dados de modo a formar grupos é conhecida como aglomeração de dados ou clustering, sendo uma das tarefas mais realizadas em mineração de dados e reconhecimento de padrões. Nesta dissertação é abordado o conceito de entropia e são usados algoritmos com critérios entrópicos para fazer clustering em dados biomédicos. O uso da entropia para efetuar clustering é relativamente recente e surge numa tentativa da utilização da capacidade que a entropia possui de extrair da distribuição dos dados informação de ordem superior, para usá-la como o critério na formação de grupos (clusters) ou então para complementar/melhorar algoritmos existentes, numa busca de obtenção de melhores resultados. Alguns trabalhos envolvendo o uso de algoritmos baseados em critérios entrópicos demonstraram resultados positivos na análise de dados reais. Neste trabalho, exploraram-se alguns algoritmos baseados em critérios entrópicos e a sua aplicabilidade a dados biomédicos, numa tentativa de avaliar a adequação destes algoritmos a este tipo de dados. Os resultados dos algoritmos testados são comparados com os obtidos por outros algoritmos mais “convencionais" como o k-médias, os algoritmos de spectral clustering e um algoritmo baseado em densidade.

Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam

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

Performance assessment of a wind energy conversion system using a hierarchical controller structure

This paper deals with a hierarchical structure composed by an event-based supervisor in a higher level and two distinct proportional integral (PI) controllers in a lower level. The controllers are applied to a variable speed wind energy conversion system with doubly-fed induction generator, namely, the fuzzy PI control and the fractional-order PI control. The event-based supervisor analyses the operation state of the wind energy conversion system among four possible operational states: park, start-up, generating or brake and sends the operation state to the controllers in the lower level. In start-up state, the controllers only act on electric torque while pitch angle is equal to zero. In generating state, the controllers must act on the pitch angle of the blades in order to maintain the electric power around the nominal value, thus ensuring that the safety conditions required for integration in the electric grid are met. Comparisons between fuzzy PI and fractional-order PI pitch controllers applied to a wind turbine benchmark model are given and simulation results by Matlab/Simulink are shown. From the results regarding the closed loop point of view, fuzzy PI controller allows a smoother response at the expense of larger number of variations of the pitch angle, implying frequent switches between operational states. On the other hand fractional-order PI controller allows an oscillatory response with less control effort, reducing switches between operational states. (C) 2015 Elsevier Ltd. All rights reserved.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.

Classificação e agregação automática de notícias desportivas

Mestrado em Engenharia Informática - Área de Especialização em Arquiteturas, Sistemas e Redes