Abstract
In this paper, Artificial Neural Networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal. A strategy is used to complement the experimental data collected in laboratory during extraction procedures of useful compositions for the pharmaceutical industry using Black Agglomerate Residues (BAR) originating from of the cork production as raw material. The strategy involves fitting of data obtained during an operation of extraction. Two neural models are presented: the neural model trained using a Mean Square Error (MSE) minimization algorithm and the neural model which the learning was based on the error entropy minimization. A comparison of the performance of the two models is presented. © 2008 IEEE.

Abstract
Vowel recognition is frequently based on Linear Prediction (LP) analysis and formant estimation techniques. However, the performance of these techniques decreases in the case of female or child speech because at high pitch frequencies (F0) the magnitude spectrum is scarcely sampled making formant estimation unreliable. In this paper we describe the implementation of a perceptually motivated concept of vowel recognition that is based on Perceptual Spectral Clusters (PSC) of harmonic partials. PSC based features were evaluated in automatic recognition tests using the Mahalanobis distance and using a data base of five natural Portuguese vowel sounds uttered by 44 speakers, 27 of whom are child speakers. LP based features and Mel-Frequency Cepstral Coefficients (MFCC) were also included in the tests as a reference. Results show that while the recognition performance of PSC features falls between that of LP based features and that of MFCC coefficients, the normalization of PSC features by F0 increases the performance and approaches that of MFCC coefficients. PSC features are not only amenable to a psychophysical interpretation (as LP based features are) but have also the potential to compete with global shape features such as MFCCs.

Abstract
Wireless sensor network (WSN) is an emerging technology that targets multiple applications in the different environments. Its infrastructure is composed of a large number of sensor nodes with a limited physical capacity and low cost. The energy consumption must be as optimized as possible in order to extend its lifetime. The use of data compression techniques can be an advantage in the WSN context, once these techniques eliminate transmission of redundant information and consequently can be adopted to minimize the consumption of energy in the sensor nodes. WSN for monitoring applications can benefit from this technique as it may maximize the lifetime of batteries. The main motivation of this paper is to investigate the performance of a data compression algorithm for WSN in the context of monitoring applications. To validate the proposal, simulation experiments have been performed using the Network Simulator (NS-2) tool.

Abstract
A review of optical fiber sensing demonstrations based on photonic crystal fibers is presented. The text is organized in five main sections: the first three deal with sensing approaches relying on fiber Bragg gratings, long-period gratings and interferometric Structures; the fourth one reports applications or these fibers for gas and liquid sensing; finally, the last section focuses oil the exploitation of nonlinear effects in photonic crystal fibers for sensing. A brief review about splicing with photonic crystal fibers is also included.

Abstract
Wireless Sensor Networks (WSN) are being targeted for use in applications like security, resources monitoring and factory automation. However, the reduced available resources raise a lot of technical challenges. Self organization in WSN is a desirable characteristic that can be achieved by means of data fusion techniques when delivering reliable data to users. In this paper it is proposed a genetic machine learning algorithm (GMLA) approach that makes a trade-off between quality of information and communication efficiency. GMLA is based on genetic algorithms and it can adapt itself dynamically to environment modifications. The main target of the proposed approach is to achieve set(organization in a WSN application with data fusion. Simulations demonstrate that the proposed approach can optimize communication efficiency in a dense WSN.

Abstract
This article introduces the Service Experience Blueprint (SEB), a multidisciplinary method for designing multi-interface service experiences, and illustrates its application with two case examples of the redesign of the service experiences of a multichannel bank. The SEB method starts by studying the customer service experience to understand customer experience requirements for different service activities and how these requirements can be satisfied through alternative service interfaces. Based on this analysis, the multi-interface service is designed to allocate service activities to the interfaces best suited to provide the desired experience, defining channel specialization and integration. Finally, with the SEB method each service interface is designed to best leverage its unique capabilities and guide customers to other service interfaces whenever that interface better enhances the overall customer experience. By incorporating the contributions of service management, interaction design, and software engineering, the SEB method is a multidisciplinary tool and terminology for service design.