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In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems. (c) 2007 Elsevier Ltd. All rights reserved.

This paper presents a new fuzzy classifier design, which constructs one classifier for each fuzzy partition of a given system. The new approach, namely Fuzzy Classifier Functions (FCF), is an adaptation of our generic design on Fuzzy Functions to classification problems. This approach couples any fuzzy clustering algorithm with any classification method, in a unique way. The presented model derives fuzzy functions (rules) from data to classify patterns into number of classes. Fuzzy c-means clustering is used to capture hidden fuzzy patterns and a linear or a non-linear classifier function is used to build one classifier model for each pattern identified. The performance of each classifier is enhanced by using corresponding membership values of the data vectors as additional input variables. FCF is proposed as an alternate representation and reasoning schema to fuzzy rule base classifiers. The proposed method is evaluated by the comparison of experiments with the standard classifier methods using cross validation on test patterns., Infobright Inc, MaRS Discovery Dist, York Univ, Int Rough Set Soc, Int Fuzzy Syst Assoc, Chinese Assoc Artificial Intelligence, Rough Sets ; Soft Computat Soc, Natl Res Council Canada

In building an approximate fuzzy classifier system, significant effort is laid oil estimation and fine tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy Clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based oil it dual optimization method, which yields simultaneous estimates of the parameters of (c-classification functions together with fuzzy c partitioning of dataset based oil a distance measure. The merit of novel IFCF is that the information oil natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results Of the new modeling approach indicate that the new IFCF is it promising method for two-class pattern recognition problems. (c) 2007 Elsevier Ltd. All rights reserved.

The neutron rich carbon isotope 15C is the only known case of an almost "pure" 2s1/2 single-neutron halo ground state configuration. At collision energies around the Coulomb barrier the reaction dynamics is expected to be dominated by single neutron transfer and breakup. To investigate these effects, we have measured the scattering of 15C with a 208Pb target at 65 MeV at the HIE-ISOLDE facility in CERN (Geneva, Switzerland). The preliminary data demonstrates the presence of a strong long-range absorption pattern in the angular distribution of the elastic cross section. The results are discussed in the framework of Optical Model calculations.

The neutron-rich carbon isotope C was postulated to be a halo nucleus (S = 1215 keV, S = 9395 keV) according to different high-energy experiments. If so, it would be the only halo nucleus exhibiting a "pure" s-wave structure of the ground state. At low collision energies, the effect of this halo structure should manifest as a strong absorption pattern in the angular distribution of the elastic cross section, with a total suppression of the nuclear rainbow due to the large neutron transfer and breakup probabilities, enhanced by the halo configuration. The IS619 experiment, carried out at the HIE-ISOLDE facility at CERN (Switzerland), is the first dynamical study of this nucleus at energies around the Coulomb barrier. It aims to probe the halo structure via the measurement of the elastic cross section on a high-Z Pb target. Preliminary results of the elastic cross section are discussed and compared to Optical Model calculations.

The new generation mobile communication systems using spectrum efficient linear modulation schemes (QPSK,8PSK,QAM)need linear power amplifiers in the transmission path to have good ACPR and EVM values. Linearization methods can be used to increase the linearity of the power amplifiers (PA).However,it is not reasonable o use complicated,power consuming and high cost systems. This paper presents a digital predistortion implementation for WCDMA signals using an FPGA (Field Programmable Gate Array)as DSP and investigates the application of this system in handsets.The method applied requires minimum change in the conventional transmitter path configuration but considerable PAE improvement can be achieved.

The new generation mobile communication systems using spectrum efficient linear modulation schemes (QPSK,8PSK,QAM)need linear power amplifiers in the transmission path to have good ACPR and EVM values. Linearization methods can be used to increase the linearity of the power amplifiers (PA).However,it is not reasonable o use complicated,power consuming and high cost systems. This paper presents a digital predistortion implementation for WCDMA signals using an FPGA (Field Programmable Gate Array)as DSP and investigates the application of this system in handsets.The method applied requires minimum change in the conventional transmitter path configuration but considerable PAE improvement can be achieved.