Estimation of nonlinear systems via a Chebyshev approximation approach

This paper proposes to decompose the nonlinear dynamic of a chaotic system with Chebyshev polynomials to improve performances of its estimator. More widely than synchronization of chaotic systems, this algorithm is compared to other nonlinear stochastic estimator such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Chebyshev polynomials orthogonality properties is used to fit a polynomial to a nonlinear function. This polynomial is then used in an Exact Polynomial Kalman Filter (ExPKF) to run real time state estimation. The ExPKF offers mean square error optimality because it can estimate exact statistics of transformed variables through the polynomial function. Analytical expressions of those statistics are derived so as to lower ExPKF algorithm computation complexity and allow real time applications. Simulations under the Additive White Gaussian Noise (AWGN) hypothesis, show relevant performances of this algorithm compared to classical nonlinear estimators.     

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.     

Tuning catalytic performances of cobalt catalysts for clean hydrogen generation via variation of the type of carbon support and catalyst post-treatment temperature

Multi-walled carbon nanotubes, three types of activated carbons, single wall carbon nanotube and reduced graphene oxides were used to synthesize nano-sized Co catalysts for H₂ preparation via NH₃ decomposition. Catalyst samples were characterized by number of techniques such as N₂ physisorption, X-ray diffraction (XRD), X-ray photoelectron spectroscopes (XPS), Transmission electron microscopy (TEM), CO chemisorption, temperature-programmed reduction (H₂-TPR) and temperature-programmed desorption (N₂-TPD). The catalytic activities of the studied catalysts for H₂ production via NH₃ decomposition were measured in a fixed-bed micro-reactor. Co catalyst supported on multi-wall carbon nanotubes has shown the highest catalytic activity. The Co particles size was significantly affected by the variation of the post-treatment temperature. The Co particles size in the range of 4.7–64.8 nm can be effectively controlled by varying post-treatment temperature between 230 and 700 °C. The maximum TOF of NH₃ decomposition was registered on cobalt catalyst post-treated at 600 °C.     

Impedance spectroscopy properties of polypyrrole doped with boric acid

The electrical conductivity and dielectric properties of polypyrrole doped with boric acid have been investigated. The direct current conductivity (dc) of the polymer increases with increasing temperature. The alternating current (ac) conductivity of the polymer obeys the power law, i.e., σ_ac(ω) = Aω^s. The alternating conductivity of polypyrrole doped with boric acid is controlled by the correlated barrier hopping model. The activation energy for alternating current mechanism decreases with increasing frequency which confirms the hopping conduction to the dominant mechanism as compared with the dc activation energy. The density of localized states N(E_F) for polypyrrole doped with boric acid was in the range of 2.5–9.2 × 10²² cm^?3 for various temperatures. The dielectric relaxation mechanism was explained on the basis of complex dielectric modulus. The imaginary modulus plot at different temperatures shows a dielectric mechanism with non-Debye relaxation. Boric acid can be a good candidate for controlling the electrical conductivity of the conducting polymer.     

Composite right/left handed artificial transmission line structures in CMOS for controlled insertion phase at 30 GHz

Two CMOS integrated circuits are presented that utilize metamaterial composite right/left handed (CRLH) transmission lines (TLs) for zero insertion phase at 30 GHz. Specifically, 2 and 3 unit cell structures are presented with controlled insertion phase that is achieved by cascading lumped element capacitors and spiral inductors in an LC network configuration defining the TL unit cells. Furthermore, the fixed TL structures suggest the possibility of zero, advanced or delayed insertion phases by element variation, or by the use of simple active components. Simulation and measured results are in good agreement with CRLH TL theory, and display a linear insertion phase and flat group delay values that are dependent on the number of unit cells with an insertion loss of ～0.8 dB per cell. These findings suggest that such high speed CRLH TLs structures can be implemented for linear array feeding networks and compact antenna designs in CMOS at millimeter wave frequencies. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

An improved algorithm for the detection of plumes caused by natural or technological hazards using AVHRR imagery

Fires caused by natural or technological disasters emit large amounts of smoke which, once formed into plumes, may affect the human health and the environment. Satellite remote sensing data provide an effective tool to achieve detection and monitoring of these plumes over large areas on a routine basis. Discrimination of plumes on satellite images is a prerequisite to study and retrieve physical, chemical and optical properties of emitted smoke. An improved algorithm for the detection of plumes caused by natural or technological hazards using AVHRR imagery is presented in this study. The method is based on a multi-temporal and multi-spectral change detection algorithm. It is performed in two main steps: a) appropriate spectral and spatial filters are applied on the images acquired before and after a fire event in visible and near-infrared ranges in order to extract the core of the plume; b) a criterion on spectral information is defined as an homogeneity measure that enables, through a modified version of the region-growing method, the spatial expansion of the detected core to include the complete area covered by the plume. Through this approach, a pixel is identified as a plume pixel if it is “close” to the core plume pixels in both spatial and spectral spaces. The algorithm was developed and calibrated using AVHRR images acquired over Spain before and during a major forest fire event on July 16, 2005. It was applied using past events of natural and technological hazards in several locations to ensure its global applicability and robustness. The algorithm produced accurate results in all cases of plumes, either in natural or in technological fire events. Three application cases are presented in this study: A major fire in an industrial installation in London (December 11, 2005), a major fire in Baghdad during the recent war in Iraq (April 1, 2003) and a forest fire in California (September 29, 2005).     

Accelerating filtering techniques for numeric CSPs

Search algorithms for solving Numeric CSPs (Constraint Satisfaction Problems) make an extensive use of filtering techniques. In this paper¹ we show how those filtering techniques can be accelerated by discovering and exploiting some regularities during the filtering process. Two kinds of regularities are discussed, cyclic phenomena in the propagation queue and numeric regularities of the domains of the variables. We also present in this paper an attempt to unify numeric CSPs solving methods from two distinct communities, that of CSP in artificial intelligence, and that of interval analysis.     

N-3 Polyunsaturated Fatty Acids Modulate In-Vitro T Cell Function in Type I Diabetic Patients

In this work, we assessed the in-vitro effects of eicosapentaenoic acid (EPA; C20:5n-3) and docosahexaenoic acid (DHA; C22:6n-3) (final concentration, 15 microM) on T cell blastogenesis, interleukin-2 and -4 (IL-2, IL-4) secretion, fatty acid composition and intracellular oxidative status in type I diabetic patients with or without complications. Con A stimulated lymphocyte proliferation, glucose uptake, intracellular reduced glutathione levels and catalase activity were lower in diabetics as compared to controls, regardless to the presence of complications. EPA and DHA diminished T-lymphocyte proliferation and IL-2 production but enhanced IL-4 secretion in both diabetic and control groups. No changes in the levels of reduced glutathione and in the activities of catalase and SOD were observed in control T cells cultured in the presence of EPA and DHA. However, in diabetic patients, addition of n-3 PUFA to culture induced an increase in T cell levels of reduced glutathione and hydroperoxide, and in activities of catalase and SOD. Low levels of arachidonic acid (C20:4n-6) were found in plasma membrane phospholipids of lymphocytes from diabetic patients compared to controls. Incubation of lymphocytes with EPA and DHA was associated with an incorporation of these fatty acids in membrane phospholipids. In conclusion, the beneficial effects of n-3 PUFA on T cell functions in type I diabetes could be attributed to their suppressive action and modulation of cytokine secretion, and to the improvement of intracellular oxidative status.