Planar photonic crystal structure with inherently single-mode waveguides

A planar photonic crystal that allows inherently gap-guided single-mode waveguides is proposed and discussed. This novel structure consists of a two-dimensional lattice of silicon rods embedded on a thin silica slab sandwiched between two silica claddings whose refractive indices are slightly lower than the index of the silica core. The physical parameters of the structure, i.e., rod radius and core thickness, are optimized to maximize the bandgap width for odd modes. Lossless guided modes inside the bandgap and below the claddings' light cone are obtained by reducing the radius of a row of rods. The waveguide bandwidth can be increased by inserting a thin silicon dielectric waveguide instead of the row of rods. The proposed approach may overcome many of the common drawbacks in conventional holes-on-dielectric planar photonic crystal waveguides.     

Decentralized adaptive recurrent neural control structure

This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.     

Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.     

Assessment of conductor thermal models for grid studies

Existing EMS applications resort to static network models built upon constant resistances whose values are specified beforehand. However, the steady-state thermal balance of overhead conductors requires that ohmic losses be dissipated by convection and radiation heat, leading to conductor resistances being a function both of environmental factors and transmitted current. Each term of the equation involved in the thermal balance of overhead conductors is analysed allowing the influence of conductor current on conductor temperature to be separated from that corresponding to environmental factors. As a result, three approximate thermal models are elaborated, which are compared on the Spanish transmission system. Test cases are included showing that the influence of the selected model on transmission losses may be significant enough, particularly when the transmission system spans areas with diverse loading levels and weather conditions.     

Band Selection in Multispectral Images by Minimization of Dependent Information

In this paper, a band selection technique for hyperspectral image data is proposed. Supervised feature extraction techniques allow a reduction of the dimensionality to extract relevant features through a labeled training set. This implies an analysis of the existing class distributions, which usually means, in the case of hyperspectral imaging, a large number of samples, making the labeling process difficult. A possible alternative could be the use of information measures, which are the basis of the proposed method. The present approach basically behaves as an unsupervised feature selection criterion, to obtain the relevant spectral bands from a set of sample images. The relations of information content between spectral bands are analyzed, leading to the proposed technique based on the minimization of the dependent information between spectral bands, while trying to maximize the conditional entropies of the selected bands     

Cover Picture: Magnetorheological Fluids Based on Ionic Liquids (Adv. Mater. 13/2007)

The use of ionic liquids as carriers of magnetorheological fluids is described by Schubert and co‐workers on p. 1740. Combining the properties of ionic liquids with magnetorheological technology may lead to new, “smart” fluids for application in medical therapies, engineering devices, or multiphase biological and chemical systems. In the presence of a magnetic field the fluids behave as solids owing to a stronger interaction between their suspended magnetic particles. In the absence of the magnetic field, they become liquids again in a fully reversible process.     

A Comparative Study and Evaluation of Parallel Programming Models for Shared-Memory Parallel Architectures

Nowadays, shared-memory parallel architectures have evolved and new programming frameworks have appeared that exploit these architectures: OpenMP, TBB, Cilk Plus, ArBB and OpenCL. This article focuses on the most extended of these frameworks in commercial and scientific areas. This paper shows a comparative study of these frameworks and an evaluation. The study covers several capacities, such as task deployment, scheduling techniques, or programming language abstractions. The evaluation measures three dimensions: code development complexity, performance and efficiency, measure as speedup per watt. For this evaluation, several parallel benchmarks have been implemented with each framework. These benchmarks are created to cover certain scenarios, like regular memory access or irregular computation. The conclusions show some highlights, like the fact that some frameworks (OpenMP, Cilk Plus) are better for transforming quickly a sequential code, others (TBB) have a small footprint which is ideal for small problems, and others (OpenCL) are suited for heterogeneous architectures but they require a very complex development process. The conclusions also show that the vectorization support is more critical than multitasking to achieve efficiency for those problems where this approach fits.