Ecological distribution of stream macroalgal communities from a drainage basin in the Serra da Canastra National Park, Minas Gerais, Southeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

Joint Computing and Network Resource Scheduling in a Lambda Grid Network

Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

New Studies Initiated by the U.S. Geological Survey— Effects of Nutrient Enrichment on Stream Ecosystems

In 2001, the U.S. Geological Survey’s National Water-Quality Assessment (NAWQA) Program began an intensive study of nutrient enrichment—elevated concentrations of nitrogen and phosphorus— in streams in five agricultural basins across the Nation (see map, p. 2). This study is providing nationally consistent and comparable data and analyses of nutrient conditions, including how these conditions vary as a result of natural and human-related factors, and how nutrient conditions affect algae and other biological communities. This information will benefit stakeholders, including the U.S. Environmental Protection Agency (USEPA) and its partners, who are developing nutrient criteria to protect the aquatic health of streams in different geographic regions.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Analysis of the environmental impact on a stream: is only tannery to blame?

The contribution of wastewater from a tannery industry to the pollution of a stream was investigated. The main parameters studied were biochemical oxygen demand, chemical oxygen demand, chromium, dissolved oxygen, fecal and total conforms, nitrogen, oils and greases, pH, phosphorous, sulfides, suspended solids, turbidity, and volatile solids. Three sampling points were located: (I) at the discharge point of tannery wastewater, (2) 50 m upstream, and (3) 80 m downstream of discharge point. Also was investigated the pollution at the stream source.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

A quantitative analysis of current security concerns and solutions for cloud computing

The development of cloud computing services is speeding up the rate in which the organizations outsource their computational services or sell their idle computational resources. Even though migrating to the cloud remains a tempting trend from a financial perspective, there are several other aspects that must be taken into account by companies before they decide to do so. One of the most important aspect refers to security: while some cloud computing security issues are inherited from the solutions adopted to create such services, many new security questions that are particular to these solutions also arise, including those related to how the services are organized and which kind of service/data can be placed in the cloud. Aiming to give a better understanding of this complex scenario, in this article we identify and classify the main security concerns and solutions in cloud computing, and propose a taxonomy of security in cloud computing, giving an overview of the current status of security in this emerging technology.

Computing inverse optical flow

[EN] We propose four algorithms for computing the inverse optical flow between two images. We assume that the forward optical flow has already been obtained and we need to estimate the flow in the backward direction. The forward and backward flows can be related through a warping formula, which allows us to propose very efficient algorithms. These are presented in increasing order of complexity. The proposed methods provide high accuracy with low memory requirements and low running times.In general, the processing reduces to one or two image passes. Typically, when objects move in a sequence, some regions may appear or disappear. Finding the inverse flows in these situations is difficult and, in some cases, it is not possible to obtain a correct solution. Our algorithms deal with occlusions very easy and reliably. On the other hand, disocclusions have to be overcome as a post-processing step. We propose three approaches for filling disocclusions. In the experimental results, we use standard synthetic sequences to study the performance of the proposed methods, and show that they yield very accurate solutions. We also analyze the performance of the filling strategies. 

Detección, seguimiento y análisis de personas en streams de vídeo

[ES] El presente TFG consiste en una aplicación para la detección de personas de cuerpo entero. La idea es aplicar este detector a las continuas imágenes recogidas en tiempo real a través de una web-cam, o de un archivo con formato de vídeo que se encuentre ubicado en el propio sistema. El código está escrito en C++. Para conseguir este objetivo nos basamos en el uso conjunto de dos sistemas de detección ya existentes: primero, OpenCV, mediante un método de histograma de gradientes orientados, el cual ya proporciona propiamente un detector de personas que será aplicado a cada una de las imágenes del stream de vídeo; por otro lado, el detector facial de la librería Encara que se aplica a cada una de las detecciones de supuestas personas obtenidas en el método de OpenCV, para comprobar si hay una cara en la supuesta persona detectada. En caso de ser así, y de haber una cara más o menos correctamente situada, determinamos que es realmente una persona. Para cada persona detectada se guardan sus datos de situación en la imagen, en una lista, para posteriormente compararlos con los datos obtenidos en frames anteriores, e intentar hacer un  seguimiento de todas las personas. Visualmente se observaría como se va recuadrando cada persona con un color determinado aleatorio asignado a cada una, mientras se visualiza el vídeo. También se registra la hora y frame de aparición, y la hora y frame de salida, de cada persona detectada, quedando estos datos guardados tanto en un fichero de log, como en una base de datos. Los resultados son, bastante satisfactorios, aunque con posibilidades de mejora, ya que es un trabajo que permite combinar otras técnicas diferentes a las descritas. Debido a la complejidad de los métodos empleados se destaca la necesidad de alta capacidad de computación para poder ejecutar la aplicación en tiempo real sin ralentizaciones. 

A PDE model for computing the optical flow

[EN] In this paper we present a new model for optical flow calculation using a variational formulation which preserves discontinuities of the flow much better than classical methods. We study the Euler-Lagrange equations asociated to the variational problem. In the case of quadratic energy, we show the existence and uniqueness of the corresponding evolution problem. Since our method avoid linearization in the optical flow constraint, it can recover large displacement in the scene. We avoid convergence to irrelevant local minima by embedding our method into a linear scale-space framework and using a focusing strategy from coarse to fine scales.