Modeling and Evaluation of a Policy Provisioning Architecture for Mobile Ad-Hoc Networks

The ability to dynamically configure mobile ad-hoc network (MANET) devices is critical for supporting complex services such as quality of service (QoS), security and access control in these networks. In our work, we address the problem of policy distribution and provisioning in MANETs. Previously, we have proposed a policy provisioning architecture that enables the operator, e.g., a military command and control center, to maintain a logically centralized control of the network as a whole, while allowing a physically decentralized and self-managing implementation essential for a MANET environment. In this paper, we present an analytical model of the availability of the policy distribution service in a MANET and analyze the performance using stochastic Petri nets (SPNs). We develop the model at two levels of complexity: as a simple Markovian model with Poisson assumptions and as a more accurate non-Markovian model with general distributions obtained using statistical parameterization. We compare and cross-validate the analytical results with simulation and experimental results. Finally, we illustrate the effectiveness of the architecture for managing QoS for soft real-time applications, using an emulated mobile ad-hoc network testbed.     

Modeling the leadership – project performance relation: radial basis function,Gaussian and Kriging methods as alternatives to linear regression

The purpose of this paper is to analyze alternative forecasting methods that produce results at least similar to or better than linear regression (MLR) that can be used in the modeling of social systems. While organizations may be considered as typically non-linear systems, the common feature of most models found in literature continues to be the use of linear regression techniques. From a case study, advanced statistical methods of Gaussian and Kriging are evaluated, as well as an artificial intelligence (AI) tool, the radial basis function (RBF). The results show the best performance of the suggested methods compared to MLR, especially RBF, because of its uniform prediction behavior throughout all ranges of evaluation. These techniques, although somewhat unconventional in social systems modeling, present a potential contribution in increasing the accuracy and precision of the predictions allowing a more accurate assessment of the impact of certain strategies on the project performance to be made before the allocation of material, human and financial resources.     

Reconstruction of 3D Object Meshes from Silhouette Images

In this work we propose a method for computing mesh representations of 3D objects reconstructed from a set of silhouette images. Our method is based on the polygonization of volumetric reconstructions by using a modified version of the dual contouring method. In order to apply dual contouring on volumetric reconstruction from silhouettes we devised a method that is able to determine the discrete topology of the surface in relation to the octree cells. We also developed a new scheme for computing hermitian data representing the intersections of conic volumes with the octree cells and their corresponding normals with subpixel accuracy. Due to the discrete and extremely noisy nature of the data used in the reconstruction we had to devise a different criterion for mesh simplification that applies topological consistency tests only when the geometric error measure is beyond a given tolerance. We present results of the application of the proposed method in the extraction of a mesh corresponding to the surface of objects of a real scene.     

A neural network-based preset generation tool for a steel tandem cold mill

This paper traces the development of a software tool, based on a combination of artificial neural networks (ANN) and a few process equations, aiming to serve as a backup operation instrument in the reference generation for real-time controllers of a steel tandem cold mill. By emulating the mathematical model responsible for generating presets under normal operational conditions, the system works as an option to maintain plant operation in the event of a failure in the processing unit that executes the mathematical model. The system, built from the production data collected over six years of plant operation, steered to the replacement of the former backup operation mode (based on a lookup table), which degraded both product quality and plant productivity. The study showed that ANN are appropriated tools for the intended purpose and that by this instrument it is possible to achieve nearly the totality of the presets needed by this kind of process. The text characterizes the problem, relates the investigated options to solve it, justifies the choice of the ANN approach, describes the methodology and system implementation and, finally, shows and discusses the attained results.     

Automatically running experiments on checking multi-party contracts

Contracts play an important role in business management where relationships among different parties are dictated by legal rules. Electronic contracts have emerged mostly due to technological advances and electronic trading between companies and customers. New challenges have then arisen to guarantee reliability among the stakeholders in electronic negotiations. In this scenario, automatic verification of electronic contracts appeared as an imperative support, specially the conflict detection task of multi-party contracts. The problem of checking contracts has been largely addressed in the literature, but there are few, if any, methods and practical tools that can deal with multi-party contracts using a contract language with deontic and dynamic aspects as well as relativizations, over the same formalism. In this work we present an automatic checker for finding conflicts on multi-party contracts modeled by an extended contract language with deontic operators and relativizations. Moreover a well-known case study of sales contract is modeled and automatically verified by our tool. Further, we performed practical experiments in order to evaluate the efficiency of our method and the practical tool.

     

Facial expression synthesis based on similar faces

Multimedia Tools and Applications - Facial expression synthesis has several applications involving animation, human-computer interaction, entertainment, and training people with mental disorders....     

Line patterning of graphite and the fabrication of cheap, inexpensive, “throw-away” sensors

A new simple method (“line patterning technique”) using only standard office equipment is described whereby clearly defined, electrically conducting patterns of graphite can be deposited on polymer (plastic) or paper substrates. The properties of the conductive patterns have been characterized by electrical conductivity and SEM measurements. Sensors were constructed by using interdigitated patterns of graphite deposited on plastic and paper, and coated with a thin film of conducting electronic polymer, e.g. polyaniline emeraldine salt.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.