Generation of self-similar Gaussian time series by means of the DWT and DWPT variance maps

Due to the several kinds of services that use the Internet and data networks infra-structures, the present networks are characterized by the diversity of types of traffic that have statistical properties as complex temporal correlation and non-gaussian distribution. The networks complex temporal correlation may be characterized by the Short Range Dependence (SRD) and the Long Range Dependence - (LRD). Models as the fGN (Fractional Gaussian Noise) may capture the LRD but not the SRD. This work presents two methods for traffic generation that synthesize approximate realizations of the self-similar fGN with SRD random process. The first one employs the IDWT (Inverse Discrete Wavelet Transform) and the second the IDWPT (Inverse Discrete Wavelet Packet Transform). It has been developed the variance map concept that allows to associate the LRD and SRD behaviors directly to the wavelet transform coefficients. The developed methods are extremely flexible and allow the generation of Gaussian time series with complex statistical behaviors.

An RFID Bulk Cargo Supervising System

In this work, a system using active RFID tags to supervise truck bulk cargo is described. The tags are attached to the bodies of the trucks and readers are distributed in the cargo buildings and attached to weighs and the discharge platforms. PDAs with camera and support to a WiFi network are provided to the inspectors and access points are installed throughout the discharge area to allow effective confirmations of unload actions and the acquisition of pictures for future audit. Broadband radio equipments are used to establish efficient communication links between the weighs and cargo buildings which are usually located very far from each other in the field. A web application software was especially developed to enable robust communication between the equipments for efficient device management, data processing and reports generation to the operating personal. The system was deployed in a cargo station of a Brazilian seashore port. The obtained results prove the effectiveness of the proposed system.

Providing Integrity and Authenticity in DICOM Images: A Novel Approach

The increasing adoption of information systems in healthcare has led to a scenario where patient information security is more and more being regarded as a critical issue. Allowing patient information to be in jeopardy may lead to irreparable damage, physically, morally, and socially to the patient, potentially shaking the credibility of the healthcare institution. Medical images play a crucial role in such context, given their importance in diagnosis, treatment, and research. Therefore, it is vital to take measures in order to prevent tampering and determine their provenance. This demands adoption of security mechanisms to assure information integrity and authenticity. There are a number of works done in this field, based on two major approaches: use of metadata and use of watermarking. However, there still are limitations for both approaches that must be properly addressed. This paper presents a new method using cryptographic means to improve trustworthiness of medical images, providing a stronger link between the image and the information on its integrity and authenticity, without compromising image quality to the end user. Use of Digital Imaging and Communications in Medicine structures is also an advantage for ease of development and deployment.

Optimization of the Topology of Electric Energy Distribution Networks by Using Algorithm Inspired on Ant Behaviors

An algorithm inspired on ant behavior is developed in order to find out the topology of an electric energy distribution network with minimum power loss. The algorithm performance is investigated in hypothetical and actual circuits. When applied in an actual distribution system of a region of the State of Sao Paulo (Brazil), the solution found by the algorithm presents loss lower than the topology built by the concessionary company.

Jointly multi-user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE-GAMuD scheme can be regarded as a promising alternative for implementing third-generation (3G) and fourth-generation (4G) wireless systems in the near future. Copyright (C) 2010 John Wiley & Sons, Ltd.

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques

SKAN: Skin Scanner - System for Skin Cancer Detection Using Adaptive Techniques - combines computer engineering concepts with areas like dermatology and oncology. Its objective is to discern images of skin cancer, specifically melanoma, from others that show only common spots or other types of skin diseases, using image recognition. This work makes use of the ABCDE visual rule, which is often used by dermatologists for melanoma identification, to define which characteristics are analyzed by the software. It then applies various algorithms and techniques, including an ellipse-fitting algorithm, to extract and measure these characteristics and decide whether the spot is a melanoma or not. The achieved results are presented with special focus on the adaptive decision-making and its effect on the diagnosis. Finally, other applications of the software and its algorithms are presented.

The AdapLib Library for Execution of Adaptive Devices: Architecture and Use

In this paper it is presented the theoretical background, the architecture (using the ""4+1"" model), and the use of the library for execution of adaptive devices, AdapLib. This library was created seeking to be accurate to the adaptive devices theory, and to allow its easy extension considering the specific details of solutions that employ this kind of device. As an example, it is presented a case study in which the library was used to create a proof of concept to monitor and diagnose problems in an online news portal.

A Specialized Processor for Emulating Peripherals of the PIC Microcontroller

This paper proposes and describes an architecture that allows the both engineer and programmer for defining and quantifying which peripheral of a microcontroller will be important to the particular project. For each application, it is necessary to use different types of peripherals. In this study, we have verified the possibility for emulating the behavior of peripheral in specifically CPUs. These CPUs hold a RAM memory, where code spaces specifically written for them could represent the behavior of some target peripheral, which are loaded and executed on it. We believed that the proposed architecture will provide larger flexibility in the use of the microcontrolles since this ""dedicated hardware components"" don`t execute to a special function, but it is a hardware capable to self adapt to the needs of each project. This research had as fundament a comparative study of four current microcontrollers. Preliminary tests using VHDL and FPGAs were done.

PROPOSAL OF A PERFORMANCE MEASURING SYSTEM APPLIED TO AGRICULTURAL MECHANIZATION

The leaders` organizations of several different sectors have as characteristic to measure their own performance in a systematic way. However, this concept is still unusual in agricultural enterprises, including the mechanization sector. Mechanization has an important role on the production costs and to know its performance is a key factor for the agricultural enterprise success. This work was generated by the importance that measurement of performance has for the management and the mechanization impact on the production costs. Its aim is to propose an integrated performance measurement system to give support to agricultural management. The methodology was divided in two steps: adjustment of a conceptual model based on Balanced Score Card - BSC; application of the model in a study case at sugar cane mill. The adjustment and the application of the conceptual model allowed to obtain the performance index in a systematic way, that are associated to: costs and deadline ( traditionally used); control and improvement on the quality of operations and support process; environmental preservation; safety; health; employees satisfaction; development of information systems. The adjusted model helped the development of the performance measurement system for the mechanized management systems and the index allows an integrated view of the enterprise, related to its strategic objectives.

Definition of priority areas for forest conservation through the ordered weighted averaging method

The general objective of this study was to evaluate the ordered weighted averaging (OWA) method, integrated to a geographic information systems (GIS), in the definition of priority areas for forest conservation in a Brazilian river basin, aiming at to increase the regional biodiversity. We demonstrated how one could obtain a range of alternatives by applying OWA, including the one obtained by the weighted linear combination method and, also the use of the analytic hierarchy process (AHP) to structure the decision problem and to assign the importance to each criterion. The criteria considered important to this study were: proximity to forest patches; proximity among forest patches with larger core area; proximity to surface water; distance from roads: distance from urban areas; and vulnerability to erosion. OWA requires two sets of criteria weights: the weights of relative criterion importance and the order weights. Thus, Participatory Technique was used to define the criteria set and the criterion importance (based in AHP). In order to obtain the second set of weights we considered the influence of each criterion, as well as the importance of each one, on this decision-making process. The sensitivity analysis indicated coherence among the criterion importance weights, the order weights, and the solution. According to this analysis, only the proximity to surface water criterion is not important to identify priority areas for forest conservation. Finally, we can highlight that the OWA method is flexible, easy to be implemented and, mainly, it facilitates a better understanding of the alternative land-use suitability patterns. (C) 2008 Elsevier B.V. All rights reserved.

Rational Design and 3D-Pharmacophore Mapping of 5 `-Thiourea-Substituted alpha-Thymidine Analogues as Mycobacterial TMPK Inhibitors

Thymidine monophosphate kinase (TMPK) has emerged as an attractive target for developing inhibitors of Mycobacterium tuberculosis growth. In this study the receptor-independent (RI) 4D-QSAR formalism has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 5`-thiourea-substituted alpha-thymidine inhibitors. Models were developed for the entire training set and for a subset of the training set consisting of the most potent inhibitors. The optimized (RI) 4D-QSAR models are statistically significant (r(2) = 0.90, q(2) = 0.83 entire set, r(2) = 0.86, q(2) = 0.80 high potency subset) and also possess good predictivity based on test set predictions. The most and least potent inhibitors, in their respective postulated active conformations derived from the models, were docked in the active site of the TMPK crystallographic structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. This model identifies new regions of the inhibitors that contain pharmacophore sites, such as the sugar-pyrimidine ring structure and the region of the 5`-arylthiourea moiety. These new regions of the ligands can be further explored and possibly exploited to identify new, novel, and, perhaps, better antituberculosis inhibitors of TMPKmt. Furthermore, the 3D-pharmacophores defined by these models can be used as a starting point for future receptor-dependent antituberculosis drug design as well as to elucidate candidate sites for substituent addition to optimize ADMET properties of analog inhibitors.

Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

A Study of the Corporative Portals as a Tool of Explicit Knowledge Externalization in Universities

The corporative portals, enabled by Information Technology and Communication tools, provide the integration of heterogeneous data proceeding from internal information systems, which are available for access and sharing of the interested community. They can be considered an important instrument of explicit knowledge evaluation in the. organization, once they allow faster and,safer, information exchanges, enabling a healthful collaborative environment. In the specific case of major Brazilian universities, the corporate portals assume a basic aspect; therefore they offer an enormous variety and amount of information and knowledge, due to the multiplicity of their activities This. study aims to point out important aspects of the explicit knowledge expressed by the searched universities; by the analysis, of the content offered in their corporative portals` This is an exploratory study made through, direct observation of the existing contents in the corporative portals of two public universities as. Well as three private ones. A. comparative analysis of the existing contents in these portals was carried through;. it can be useful to evaluate its use as factor of optimization of the generated explicit knowledge in the university. As results, the existence of important differences, could be verified in the composition and in the content of the corporative portals of the public universities compared to the private institutions. The main differences are about the kind of services and the destination-of the,information that have as focus different public-target. It-could also be concluded that the searched private universities, focus, on the processes related to the attendance of the students, the support for the courses as well as the spreading of information to the public interested in joining the institution; whereas the anal public universities prioritize more specific information, directed to,the dissemination-of the research, developed internally or with institutional objectives.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.