New Alternatives to the Estimation Problem in Hardware-Software Codesign of Complex Embedded Systems: The H.261 Video Co-dec Case Study

As Codesign problems become larger and more realistic, the required time to estimate their solutions turns into an important bottleneck. This paper presents a new approach to improve the traditional estimation techniques, in order to avoid this drawback. The presented method has been successfully tested on a large experimental benchmark, attaining quality levels close to those provided by the Synopsys Behavioral Compiler. Finally, a case study based on the standard H.261 video co-dec is described, proving the convenience of the technique on real-life situations. The obtained results show a significant improvement in the process time, while keeping the good precision and fidelity levels that the traditional estimation models usually offer.     

Effect of outliers on forecasting temporally aggregated flow variables

Economic time series are of two types: stock and flows, and may be available at different levels of aggregation (for instance, monthly or quarterly). The economist, in many situations, is interested in forecasting the aggregated observations. The forecast function, in this case, can be based either on the disaggregated series or the aggregated series. The forecasts based on the disaggregated data are at least as efficient, in terms of mean squared forecast errors, as the forecasts based on temporally aggregated observations when the data generating process (DGP) is a known ARIMA process. However, the effect of outliers on both forecast functions is not known. In this paper, we consider the effect of additive and innovation outliers on forecasting aggregated values based on aggregated and disaggregated models when the DGP is a known ARIMA process and the presence of the outliers is ignored. Results when the model is not known and tests applied for the detection of outliers are derived through simulation.     

Learning to Match the Schemas of Data Sources: A Multistrategy Approach

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are element location maps to address and price maps to listed-price. We propose a multistrategy learning approach to automatically find such mappings. The approach applies multiple learner modules, where each module exploits a different type of information either in the schemas of the sources or in their data, then combines the predictions of the modules using a meta-learner. Learner modules employ a variety of techniques, ranging from Naive Bayes and nearest-neighbor classification to entity recognition and information retrieval. We describe the LSD system, which employs this approach to find semantic mappings. To further improve matching accuracy, LSD exploits domain integrity constraints, user feedback, and nested structures in XML data. We test LSD experimentally on several real-world domains. The experiments validate the utility of multistrategy learning for data integration and show that LSD proposes semantic mappings with a high degree of accuracy.     

A note on pure and <Emphasis Type="Italic">p</Emphasis>-pure languages

A variation of first order logic with variables for exponents is developed to solve some problems in the setting of rational languages on the free monoid, implying in particular algorithms for purity and p-purity. This same problem is addressed for the case of rational free group languages, and characterizations of the rational subsets of involved are also obtained.Received: 5 December 2002, Pedro V. Silva: http://www.fc.up.pt/cmup     

Influence of the starting condition on the kinetics of sensitization and loss of toughness in an AISI 304 steel

This work describes the investigation of the embrittlement of AISI 304 steel sensitized at 650°C by Charpy impact test, comparing two starting conditions: (1) mill annealed and machined (MA-M); and (2) solution treated at 1050°C by 1 h followed by oil quenching (ST). The degree of sensitization for both samples was assessed by Optical Microscopy (OM), Scanning Electron Microscopy (SEM) and by Double Loop Electrochemical Potentiodynamic Reactivation test. The results showed that MA-M samples undergo more severe and rapid embrittlement than ST ones and a higher kinetics of sensitization due to small strains concentrated in grain boundaries and martensite phase produced during the machining operations. The martensite phase is found to be quite stable at the sensitization treatment at 650°C. The increase of microvoids nucleation at the grain boundaries seems to be the mechanism of embrittlement in the sensitized 304 steel.     

Treatment of brines by combined Fenton's reagent-aerobic biodegradation. II. Process modelling

Process modelling of the integrated Fenton's reagent-aerobic biodegradation system has been carried out by considering a detailed reaction mechanism for the chemical oxidation step and the generalised Monod equation for the biological treatment. Chemical oxygen demand has been contemplated as a pseudo-component for simulation purposes. The proposed mechanism takes into consideration different features experimentally found. Thus, the inefficient hydrogen peroxide decomposition into oxygen and water, the influence of temperature and other operating variables and the role of oxygen have been considered. The aerobic biodegradation of the effluent after the chemical oxidation has taken place has been well simulated by Monod equation with no inhibitory terms. Dependency on temperature has been correlated by Arrhenius expression.     

An adaptive sharing elitist evolution strategy for multiobjective optimization

Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.     

Towards useful and usable interaction design tools: CanonSketch

Despite all the effort dedicated to bringing better User-Centered Design (UCD) tools to market, current studies show that the industry is still dominated by tools that do not support the activities and workstyles of designers. Also, there is a growing need for interaction design tools aimed at software engineers, a problem related to bringing usability into the software engineering processes.

We propose a new workstyle model that can be effectively used to envision, design and evaluate a new generation of innovative interaction and software design tools, aimed at integrating usability and software engineering.

We illustrate the effectiveness of our model by describing a new tool, called CanonSketch, that was built in order to support UCD in terms of the dimensions in our workstyle model. We also describe an evaluation study aimed at contrasting paper prototyping with our tool as well as the level of workstyle support.     

Enhanced second order algorithm applied to the capacitated minimum spanning tree problem

Given a centralized undirected graph with costs associated with its edges, the capacitated minimum spanning tree problem is to find a minimum cost spanning tree of the given graph, subject to a capacity constraint in all subtrees incident in the central node. As the problem is NP-hard, we propose an enhanced version of the well-known second order algorithm, described in [Karnaugh M. A new class of algorithms for multipoint network optimization. IEEE Transactions on Communications 1976;COM-24:500–5.]. The original version of this algorithm is based on a look-ahead strategy, used for a tentative inclusion of a constraint to the problem, performed in each iteration. In the new enhanced version, we propose the inclusion of look-behind steps, which can be seen as the reverse of the look-ahead procedure. Therefore and using some memory features, the method can continue even when facing the traditional stopping criterion of the original algorithm. Computational experiments showing the effectiveness of the new method on benchmark instances are reported.     

Mobile Learning

Works in progress from selected papers presented at the 2006 IADIS International Conference on Mobile Learning provide an overview of the diverse research being conducted in this field.