A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.     

Managing Global Software Engineering: A Comparative Analysis of Offshore Outsourcing and the Internal Offshoring of Software Development

In this article, the authors compare offshore outsourcing and the internal offshoring of software development. Empirical evidence is presented from a case study conducted in five companies. Based on a detailed literature review, a framework was developed that guided the authors' analysis of the differences in the challenges faced by companies and the patterns of evolution in the practice of software development in each business model.     

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.     

Experience with model-based performance, reliability, and adaptability assessment of a complex industrial architecture

In this paper, we report on our experience with the application of validated models to assess performance, reliability, and adaptability of a complex mission critical system that is being developed to dynamically monitor and control the position of an oil-drilling platform. We present real-time modeling results that show that all tasks are schedulable. We performed stochastic analysis of the distribution of task execution time as a function of the number of system interfaces. We report on the variability of task execution times for the expected system configurations. In addition, we have executed a system library for an important task inside the performance model simulator. We report on the measured algorithm convergence as a function of the number of vessel thrusters. We have also studied the system architecture adaptability by comparing the documented system architecture and the implemented source code. We report on the adaptability findings and the recommendations we were able to provide to the system’s architect. Finally, we have developed models of hardware and software reliability. We report on hardware and software reliability results based on the evaluation of the system architecture.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

Flatness-Based Control of a Single Qubit Gate

This work considers the open-loop control problem of steering a two-level quantum system from any initial to any final condition. The model of this system evolves on the state space , having two inputs that correspond to the complex amplitude of a resonant laser field. A symmetry preserving flat output is constructed using a fully geometric construction and quaternion computations. Simulation results of this flatness-based open-loop control are provided.     

Algorithm based on simulated annealing for land-use allocation

This article describes the use of simulated annealing for allocation of land units to a set of possible uses on, the basis of their suitability for those uses, and the compactness of the total areas allotted to the same use or kind of use, which are fixed a priori. The results obtained for the Terra Chá district of Galicia (N.W. Spain) using different objective weighting schemes are compared with each other and with those obtained for this district under the same area constraints, using hierarchical optimization, ideal point analysis, and multi-objective land allocation (MOLA) to maximize average use suitability. Inclusion of compactness in the simulated annealing objective function avoids the highly disperse allocations typical of optimizations that ignore this sub-objective.     

A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding

This report describes the design of a modular, massive-parallel, neural-network (NN)-based vector quantizer for real-time video coding. The NN is a self-organizing map (SOM) that works only in the training phase for codebook generation, only at the recall phase for real-time image coding, or in both phases for adaptive applications. The neural net can be learned using batch or adaptive training and is controlled by an inside circuit, finite-state machine-based hard controller. The SOM is described in VHDL and implemented on electrically (FPGA) and mask (standard-cell) programmable devices.     

Performance bounds in linear control of unstable MIMO systems with pole location constraint

This paper proposes a methodology to compute quadratic performance bounds when the closed loop poles of a discrete-time multivariable control loop are confined to a disk, centred at the origin, and with radius less than one. The underlying philosophy in this constraint is to avoid certain undesirable dynamic features which arise in quadratic optimal designs. An expression for the performance loss due to the pole location constraint is also provided. Using numerical examples, we show that the performance loss is compensated by an improved transient performance, specially visible in the control signals.     

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Burr XII regression models.