Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

A network of integrate and fire neurons for visual selection

Biological systems have facility to capture salient object(s) in a given scene, but it is still a difficult task to be accomplished by artificial vision systems. In this paper a visual selection mechanism based on the integrate and fire neural network is proposed. The model not only can discriminate objects in a given visual scene, but also can deliver focus of attention to the salient object. Moreover, it processes a combination of relevant features of an input scene, such as intensity, color, orientation, and the contrast of them. In comparison to other visual selection approaches, this model presents several interesting features. It is able to capture attention of objects in complex forms, including those linearly nonseparable. Moreover, computer simulations show that the model produces results similar to those observed in natural vision systems.

Chaotic synchronization in general network topology for scene segmentation

Chaotic synchronization has been discovered to be an important property of neural activities, which in turn has encouraged many researchers to develop chaotic neural networks for scene and data analysis. In this paper, we study the synchronization role of coupled chaotic oscillators in networks of general topology. Specifically, a rigorous proof is presented to show that a large number of oscillators with arbitrary geometrical connections can be synchronized by providing a sufficiently strong coupling strength. Moreover, the results presented in this paper not only are valid to a wide class of chaotic oscillators, but also cover the parameter mismatch case. Finally, we show how the obtained result can be applied to construct an oscillatory network for scene segmentation.

SZNAJD MODEL AND PROPORTIONAL ELECTIONS: THE ROLE OF THE TOPOLOGY OF THE NETWORK

The Sznajd model (SM) has been employed with success in the last years to describe opinion propagation in a community. In particular, it has been claimed that its transient is able to reproduce some scale properties observed in data of proportional elections, in different countries, if the community structure (the network) is scale-free. In this work, we investigate the properties of the transient of a particular version of the SM, introduced by Bernardes and co-authors in 2002. We studied the behavior of the model in networks of different topologies through the time evolution of an order parameter known as interface density, and concluded that regular lattices with high dimensionality also leads to a power-law distribution of the number of candidates with v votes. Also, we show that the particular absorbing state achieved in the stationary state (or else, the winner candidate), is related to a particular feature of the model, that may not be realistic in all situations.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.

A complex network-based approach for boundary shape analysis

This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.

From the road network database to a graph for localization purposes

The problems of finding best facility locations require complete and accurate road network with the corresponding population data in a specific area. However the data obtained in road network databases usually do not fit in this usage. In this paper we propose our procedure of converting the road network database to a road graph which could be used in localization problems. The road network data come from the National road data base in Sweden. The graph derived is cleaned, and reduced to a suitable level for localization problems. The population points are also processed in ordered to match with that graph. The reduction of the graph is done maintaining most of the accuracy for distance measures in the network.

Does road network density matter in optimally locating facilities?

Optimal location on the transport infrastructure is the preferable requirement for many decision making processes. Most studies have focused on evaluating performances of optimally locate p facilities by minimizing their distances to a geographically distributed demand (n) when p and n vary. The optimal locations are also sensitive to geographical context such as road network, especially when they are asymmetrically distributed in the plane. The influence of alternating road network density is however not a very well-studied problem especially when it is applied in a real world context. This paper aims to investigate how the density level of the road network affects finding optimal location by solving the specific case of p-median location problem. A denser network is found needed when a higher number of facilities are to locate. The best solution will not always be obtained in the most detailed network but in a middle density level. The solutions do not further improve or improve insignificantly as the density exceeds 12,000 nodes, some solutions even deteriorate. The hierarchy of the different densities of network can be used according to location and transportation purposes and increase the efficiency of heuristic methods. The method in this study can be applied to other location-allocation problem in transportation analysis where the road network density can be differentiated. 

Protocols between programs and proofs

In this paper we describe a new protocol that we call the Curry-Howard protocol between a theory and the programs extracted from it. This protocol leads to the expansion of the theory and the production of more powerful programs. The methodology we use for automatically extracting “correct” programs from proofs is a development of the well-known Curry-Howard process. Program extraction has been developed by many authors, but our presentation is ultimately aimed at a practical, usable system and has a number of novel features. These include 1. a very simple and natural mimicking of ordinary mathematical practice and likewise the use of established computer programs when we obtain programs from formal proofs, and 2. a conceptual distinction between programs on the one hand, and proofs of theorems that yield programs on the other. An implementation of our methodology is the Fred system. As an example of our protocol we describe a constructive proof of the well-known theorem that every graph of even parity can be decomposed into a list of disjoint cycles. Given such a graph as input, the extracted program produces a list of the (non-trivial) disjoint cycles as promised.

Especificação de uma rede MPLS fim-a-fim com diferenciação de serviços

New multimedia applications that use the Internet as a communication media are pressing for the development of new technologies, such as: MPLS (Multiprotocol Label Switching) and DiffServ. These technologies introduce new and powerful features to the Internet backbone, as the provision of QoS (Quality of Service) capabilities. However, to obtain a true end-to-end QoS, it is not enough to implement such technologies in the network core, it becomes indispensable to extend such improvements to the access networks, what is the aim of the several works presently under development. To contribute to this process, this Thesis presents the RSVP-SVC (Resource Reservation Protocol Switched Virtual Connection) that consists in an extension of RSVP-TE. The RSVP-SVC is presented herein as a mean to support a true end-to-end QoS, through the extension of MPLS scope. Thus, it is specified a Switched Virtual Connection (SVC) service to be used in the context of a MPLS User-to-Network Interface (MPLS UNI), that is able to efficiently establish and activate Label Switched Paths (LSP), starting from the access routers that satisfy the QoS requirements demanded by the applications. The RSVP-SVC was specified in Estelle, a Formal Description Technique (FDT) standardized by ISO. The edition, compilation, verification and simulation of RSVP-SVC were made by the EDT (Estelle Development Toolset) software. The benefits and most important issues to be considered when using the proposed protocol are also included

Proposta de integração das redes de comunicação da COSERN como uma solução de contigêncvia em caso de falhas nos enlaces do backbone de automação

T'his dissertation proposes alternative models to allow the interconnectioin of the data communication networks of COSERN Companhia Energética do Rio Grande do Norte. These networks comprise the oorporative data network, based on TCP/IP architecture, and the automation system linking remote electric energy distribution substations to the main Operatin Centre, based on digital radio links and using the IEC 60870-5-101 protoco1s. The envisaged interconnection aims to provide automation data originated from substations with a contingent route to the Operation Center, in moments of failure or maintenance of the digital radio links. Among the presented models, the one chosen for development consists of a computational prototype based on a standard personal computer, working under LINUX operational system and running na application, developesd in C language, wich functions as a Gateway between the protocols of the TCP/IP stack and the IEC 60870-5-101 suite. So, it is described this model analysis, implementation and tests of functionality and performance. During the test phase it was basically verified the delay introduced by the TCP/IP network when transporting automation data, in order to guarantee that it was cionsistent with the time periods present on the automation network. Besides , additional modules are suggested to the prototype, in order to handle other issues such as security and prioriz\ation of the automation system data, whenever they are travesing the TCP/IP network. Finally, a study hás been done aiming to integrate, in more complete way, the two considered networks. It uses IP platform as a solution of convergence to the communication subsystem of na unified network, as the most recente market tendencies for supervisory and other automation systems indicate

Design and Analysis of Optical Interconnection Networks for a Dataflow Parallel Computer

This work shows the design, simulation, and analysis of two optical interconnection networks for a Dataflow parallel computer architecture. To verify the optical interconnection network performance on the Dataflow architecture, we have analyzed the load balancing among the processors during the parallel programs executions. The load balancing is a very important parameter because it is directly associated to the dataflow parallelism degree. This article proves that optical interconnection networks designed with simple optical devices can provide efficiently the dataflow requirements of a high performance communication system.

Alterações da default mode network provocadas pela ingestão de Ayahuasca investigadas por Ressonância Magnética Funcional

Ayahuasca is psychotropic beverage that has been used for ages by indigenous populations in South America, notably in the Amazon region, for religious and medicinal purposes. The tea is obtained by the decoction of leaves from the Psychotria viridis with the bark and stalk of a shrub, the Banisteriopsis caapi. The first is rich in N-N-dimethyltryptamine (DMT), which has an important and well-known hallucinogenic effect due to its agonistic action in serotonin receptors, specifically 5-HT2A. On the other hand, β-carbolines present in B. caapi, particularly harmine and harmaline, are potent monoamine oxidase inhibitors (MAOi). In addition, the tetrahydroharmine (THH), also present in B. caapi, acts as mild selective serotonin reuptake inhibitor and a weak MAOi. This unique composition induces a number of affective, sensitive, perceptual and cognitive changes in individuals under the effect of Ayahuasca. On the other hand, there is growing interest in the Default Mode Network (DMN), which has been consistently observed in functional neuroimaging studies. The key components of this network include structures in the brain midline, as the anterior medial frontal cortex, ventral medial frontal cortex, posterior cingulate cortex, precuneus, and some regions within the inferior parietal lobe and middle temporal gyrus. It has been argued that DMN participate in tasks involving self-judgments, autobiographical memory retrieval, mental simulations, thinking in perspective, meditative states, and others. In general, these tasks require an internal focus of attention, hence the conclusion that the DMN is associated with introspective mental activity. Therefore, this study aimed to evaluate by functional magnetic resonance imaging (fMRI) changes in DMN caused via the ingestion of Ayahuasca by 10 healthy subjects while submitted to two fMRI protocols: a verbal fluency task and a resting state acquisition. In general, it was observed that Ayahuasca causes a reduction in the fMRI signal in central nodes of DMN, such as the anterior cingulate cortex, the medial prefrontal cortex, the posterior cingulate cortex, precuneus and inferior parietal lobe. Furthermore, changes in connectivity patterns of the DMN were observed, especially a decrease in the functional connectivity of the precuneus. Together, these findings indicate an association between the altered state of consciousness experienced by individuals under the effect of Ayahuasca, and changes in the stream of spontaneous thoughts leading to an increased introspective mental activity