Mode coupling contribution to radiation losses in curvatures for high and low numerical aperture plastic optical fibers

We have studied the optical power losses due to multiple curvatures in polymethylmetacrylate (PMMA) plastic optical fibers (POFs) of different numerical apertures (NAs) and attenuation. The fibers were tested for several configurations in order to assess the influence of different types of curved-to-straight fiber transitions in the amount of power radiation. We found that losses are below the standards for all tested fiber types, and thus, they are a suitable choice for local area network (LAN) applications. In addition, our results revealed the presence of modal interactions as confirmed using an experimental procedure to estimate the mode coupling strength for the same fibers.     

Direct synthesis of copper faujasite

Copper containing faujasite has been successfully prepared for the first time using a direct synthesis method. Faujasite type zeolite can be prepared in the presence of copper species by tuning the synthesis conditions. Ammonium hydroxide was used to form a copper complex that was later mixed with the reacting gel. Sodium is required to obtain copper faujasite. The complete elimination of sodium ions from the starting gel produces amorphous material. Crystallization took place at 358 K for 11 days. Crystallization temperature of 373 K produces ANA type zeolite as an impurity. Increasing by two times the amount of copper complex added to the reacting gel increases the crystallization time of Cu-FAU from 11 to 20 days (the crystallization rate decreases). The copper containing faujasite obtained was characterized by XRD, FESEM, EDX, EPR, FT-IR, TPR, and BET. According to the XRD pattern only FAU type zeolite was obtained. According to TPR experiments, the reduction temperature for Cu²⁺ ions present in Cu-FAU prepared by direct synthesis was 70 K more than for Cu-FAU prepared by ion-exchange. This difference can be due to the different location of the copper ions in the supercages or in the sodalite cages of the faujasite.     

Selective oxidation of alcohols using octahedral molecular sieves: influence of synthesis method and property–activity relations

Manganese oxides having a tunnel structure (OMS-2) have been utilized as selective catalysts for alcohol oxidation. In this study manganese oxide catalysts were synthesized in different media and modified by exchanging the tunnel cation by H⁺, using acid treatment or exchanging with NH₄⁺ followed by thermolysis. Various alcohol oxidations were performed using these catalysts to ascertain the influence of synthesis method on their activity. A correlation is made between lattice oxygen instability and activity of the catalysts, which indicates involvement of the lattice oxygen in the mechanism. The exchange of the tunnel cation with the smaller H⁺ ions leads to weakening of the Mn–O bond, as verified by temperature programmed desorption (TPD) results. Only the chemisorbed oxygen on the surface (O⁻) and the lattice oxygen in the layers close to the surface is involved in the oxygen transfer during the reaction.     

Polyethylene-layered silicate nanocomposites prepared by the polymerization-filling technique: synthesis and mechanical properties

Polyethylene-layered silicate nanocomposites were prepared by the in situ intercalative polymerization of ethylene by the so-called polymerization-filling technique and analyzed by transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), differential scanning calorimetry, dynamic mechanical analysis and tensile testing. Non-modified montmorillonite and hectorite were first treated by trimethylaluminum-depleted methylaluminoxane before being contacted by a Ti-based constrained geometry catalyst. The nanocomposite was formed by addition and polymerization of ethylene. In the absence of a chain transfer agent, ultra high molecular weight polyethylene was produced. The tensile properties of these nanocomposites were poor and essentially independent of the nature and content of the silicate. Upon hydrogen addition, the molecular weight of the polyethylene was decreased with parallel improvement of the tensile and shear moduli, in relation to the filler content. The exfoliation of the layered silicates was confirmed by XRD analysis and TEM observation. The mechanical kneading of the molten nanocomposites resulted in the partial collapse of the exfoliated structure driven by the thermodynamic stability of the layered filler.     

Outliers detection in environmental monitoring databases

Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.     

Design and optimization of buck and double buck converters by means of geometric programming

This paper describes a new method for determining the optimal components values and switching frequencies of buck DC–DC converters. First, we revisit some concepts of the optimization technique named geometric programming. Then, we observe that the problem of converter designing can be modeled by means of an objective function and certain constraints which can be written in a specific form known as the posynomial form. The constraints involve expressions that depend on magnitudes such as efficiency, bandwidth, and current and voltage ripples.     

Intrinsic Images by Clustering

Decomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.     

Narrow-FSK optical packet labeling scheme for optical ethernet networks

In this work, an intensity modulation/frequency-shift keying (IM/FSK) orthogonal modulation scheme is achieved by direct modulation of a distributed feedback laser source using its adiabatic chirp characteristics. Optical frequencies for "1" and "0" bits are separated only 0.7 GHz, obtaining a narrow-FSK modulation and, accordingly, a low residual intensity modulation. Ethernet frames at 1.25 Gb/s (GbE) are transmitted with a label inserted using coded mark inversion codification at a 155-Mb/s rate. Error rates for the Ethernet payload and for the label have been measured for different payload extinction ratios, showing the viability of this scheme for distances up to 25 km.     

An efficient parameterization of dynamic neural networks for nonlinear system identification

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.