Abstract
The potential applications of luminescent semiconductor nanocrystals to optical oxygen sensing are explored. The suitability of quantum dots to provide a reference signal in luminescence-based chemical sensors is addressed. A CdSe-ZnS nanocrystal, with an emission peak at 520 nm, is used to provide a reference signal. Measurements of oxygen concentration, which are based on the dynamic quenching of the luminescence of a ruthenium complex, are performed. Both the dye and the nanocrystal are immobilized in a solgel matrix and are excited by a blue LED. Experimental results show that the ratio between the reference and the sensor signals is highly insensitive to fluctuations of the excitation optical power. The use of CdTe, near-infrared quantum dots with an emission wavelength of 680 run, in combination with a ruthenium complex to provide a new mechanism for oxygen sensing, is investigated. The possibility of creating oxygen sensitivity in different spectral regions is demonstrated. The results obtained clearly show that this technique can be applied to develop a wavelength division multiplexed system of oxygen sensors. (c) 2006 Optical Society of America.

Abstract

Abstract
This paper presents an application for next-day electricity prices forecasting based on neural networks. Good forecasting tools hedging against daily price volatility are becoming increasingly important in nowadays competitive electricity markets.. avowing misjudgement of future price movements and preventing considerable losses for consumers and producers. Next-day electricity price forecast is essential to consumers and to producers in planning the operations of their electric energy resources and for developing negotiation skills in order to achieve better profits. We evaluate the accuracy of the proposed application of neural networks for next-day electricity prices forecasting based on case studies for a real world electricity market and report our experience with this application.

Abstract
Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that the SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM? We have decided to investigate this issue and compared SVM to kNN and naive Bayes on binary classification tasks. An important issue is to compare optimized versions of these algorithms, which is what we have done. Our results show all the classifiers achieved comparable performance on most problems. One surprising result is that SVM was not a clear winner, despite quite good overall performance. If a suitable preprocessing is used with kNN, this algorithm continues to achieve very good results and scales up well with the number of documents, which is not the case for SVM. As for naive Bayes, it also achieved good performance.

Abstract
This paper provides a review and general backgrounds of research and developments in the field of thermal power systems operational planning, namely on economic and environmental policy issues. On the one hand, within the energy market, operational planning has evolved from a minimum-cost policy in state-owned monopolistic companies to a profit-based policy under market conditions. On the other hand, as a consequence of growing environmental concern, an unprecedented change points to a scenario where it is necessary to take into account the constraints related to the environment. Consequently, operational planning of thermal power systems needs to be not only considered within the energy market, but also within preserving healthy conditions and self recovery cycles in the environment.

Abstract
Configurable architectures offer the unique opportunity of customizing the storage allocation to meet specific applications' needs. In this paper we describe a compiler approach to map the arrays of a loop-based computation to internal memories of a configurable architecture with the objective of minimizing the overall execution time. We present an algorithm that considers the data access patterns of the arrays along the critical path of the computation as well as the available storage and memory bandwidth. We demonstrate experimental results of the application of this approach for a set of kernel codes when targeting a Field-Programmable Gate-Array (FPGA). The results reveal that our algorithm outperforms naive and custom data layouts for these kernels by an average of 33% and 15% in terms of execution time, while taking into account the available hardware resources. © 2006 IEEE.