Limits to List Decoding Reed–Solomon Codes

In this paper, we prove the following two results that expose some combinatorial limitations to list decoding Reed-Solomon codes. 1) Given n distinct elements alpha₁,...,alpha_n from a field F, and n subsets S₁,...,S_n of F, each of size at most l, the list decoding algorithm of Guruswami and Sudan can in polynomial time output all polynomials p of degree at most k that satisfy p(alpha_i)isinS_i for every i, as long as ldelta for small enough delta, we exhibit an explicit received word with a superpolynomial number of Reed-Solomon codewords that agree with it on (2-epsi)k locations, for any desired epsi>0 (agreement of k is trivial to achieve). Such a bound was known earlier only for a nonexplicit center. Finding explicit bad list decoding configurations is of significant interest-for example, the best known rate versus distance tradeoff, due to Xing, is based on a bad list decoding configuration for algebraic-geometric codes, which is unfortunately not explicitly known     

Sensitivity analysis of an in-plane MEMS vibratory gyroscope

Microsystem Technologies - This paper aims to put forward a detailed sensitivity analysis of an in-plane MEMS gyroscope with respect to various performance criteria that are very critical for use...     

Metabonomic study of host-phage interaction by nuclear magnetic resonance- and statistical total correlation spectroscopy-based analysis

We present a method for the qualitative and quantitative study of transient metabolic flux of phage infection at the molecular level. The method is based on statistical total correlation spectroscopy (STOCSY) and partial least squares discriminant analysis (PLS-DA) applied to nuclear magnetic resonance (NMR) metabonomic data sets. An algorithm for this type of study is developed and demonstrated. The method has been implemented on (1)H NMR data sets of growth media in planktonic cultures of Pseudomonas aeruginosa infected with bacteriophage pf1. Transient metabolic flux of various important metabolites, identified by STOCSY and PLS-DA analysis applied to the NMR data set, are estimated at various stages of growth. The opportunistic and nosocomial pathogen P. aeruginosa is one of the best-studied model organism for bacterial biofilms. Complete information regarding metabolic connectivity of this system is not possible by conventional spectroscopic approach. Our study presents temporal comparative (1)H NMR metabonomic analyses of filamentous phage pf1 infection in planktonic cultures of P. aeruginosa K strain (PAK). We exemplify here the potential of STOCSY and PLS-DA tools to gain mechanistic insight into subtle changes and to determine the transient flux associated with metabolites following metabolic perturbations resulting from phage infection. Our study has given new avenues in correlating existing postgenomic data with current metabonomic results in P. aeruginosa biofilms research.     

Novel Cu–carbon nanofiber composites for the counter electrodes of dye‐sensitized solar cells

Copper (Cu)‐catalyzed carbon nanofibers (CNFs) were used as an alternative of the conventional platinum‐noble‐metal‐based catalyst at the counter electrode (CE) of a dye‐sensitized solar cell (DSSC). The CNFs were grown on activated carbon microfiber powder (PACF) using chemical vapor deposition (CVD) and the Cu nanoparticles (NPs). The Cu NPs served simultaneous roles: (i) as the CVD catalyst for the growth of the CNFs; (ii) as an enhancer of the electrode conductivity; and (iii) as a catalyst for the reduction reaction. The Cu‐CNF composite was applied as a thin layer on the fluorine‐doped tin oxide glass using the simple doctor blade method. The prepared Cu‐NP‐dispersed PACF/CNF composite was characterized using various spectroscopic techniques, including scanning electron microscopy, Fourier transform infrared ray, X‐ray diffraction, Raman spectroscopy, and transmission electron microscopy. The electrochemical tests showed that the Cu‐PACF/CNFs had a high electrocatalytic activity and low charge transfer resistance (1.26 Ω cm²), using the cyclic voltammetry and electrochemical impedance spectroscopy measurements. The DSSC fabricated with Cu‐PACFs/CNFs exhibited a power conversion efficiency value of 4.36%, open circuit voltage of 0.75 V, short circuit current density of 11.12 mA cm^?2, and fill factor of 54%. The prepared transition metal–CNF composite was simple to develop and can potentially be used as an efficient catalyst at the CE of DSSCs. Copyright © 2014 John Wiley & Sons, Ltd.     

Tuning of structural,optical, and magnetic properties of ultrathin and thin ZnO nanowire arrays for nano device applications

One-dimensional (1-D) ultrathin (15 nm) and thin (100 nm) aligned 1-D (0001) and (0001¯) oriented zinc oxide (ZnO) nanowire (NW) arrays were fabricated on copper substrates by one-step electrochemical deposition inside the pores of polycarbonate membranes. The aspect ratio dependence of the compressive stress because of the lattice mismatch between NW array/substrate interface and crystallite size variations is investigated. X-ray diffraction results show that the polycrystalline ZnO NWs have a wurtzite structure with a = 3.24 Å, c = 5.20 Å, and [002] elongation. HRTEM and SAED pattern confirmed the polycrystalline nature of ultrathin ZnO NWs and lattice spacing of 0.58 nm. The crystallite size and compressive stress in as-grown 15- and 100-nm wires are 12.8 nm and 0.2248 GPa and 22.8 nm and 0.1359 GPa, which changed to 16.1 nm and 1.0307 GPa and 47.5 nm and 1.1677 GPa after annealing at 873 K in ultrahigh vacuum (UHV), respectively. Micro-Raman spectroscopy showed that the increase in E₂ (high) phonon frequency corresponds to much higher compressive stresses in ultrathin NW arrays. The minimum-maximum magnetization magnitude for the as-grown ultrathin and thin NW arrays are approximately 8.45 × 10⁻³ to 8.10 × 10⁻³ emu/g and approximately 2.22 × 10⁻⁷ to 2.190 × 10⁻⁷ emu/g, respectively. The magnetization in 15-nm NW arrays is about 4 orders of magnitude higher than that in the 100 nm arrays but can be reduced greatly by the UHV annealing. The origin of ultrathin and thin NW array ferromagnetism may be the exchange interactions between localized electron spin moments resulting from oxygen vacancies at the surfaces of ZnO NWs. The n-type conductivity of 15-nm NW array is higher by about a factor of 2 compared to that of the 100-nm ZnO NWs, and both can be greatly enhanced by UHV annealing. The ability to tune the stresses and the structural and relative occupancies of ZnO NWs in a wide range by annealing has important implications for the design of advanced photonic, electronic, and magneto-optic nano devices.     

Phenomenological model-based study on electron beam welding process,and input-output modeling using neural networks trained by back-propagation algorithm,genetic algorithms,particle swarm optimization algorithm and bat algorithm

High power density welding technologies are widely used nowadays in various fields of engineering. However, a computationally efficient and quick predictive tool to select the operating parameters in order to achieve the specified weld attribute is conspicuously missing in the literature. In the present study, a computationally efficient inverse model has been developed using artificial neural networks (ANNs). These ANNs have been trained with the outputs of physics-based phenomenological model using back-propagation (BP) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm and bat algorithm (BA) separately to develop both the forward and reverse models. Unlike data driven ANN model, such approach is unique and yet based on science. Power, welding speed, beam radius and power distribution factor have been considered as input process parameters, and four weld attributes, such as length of the pool, depth of penetration of the pool, half-width of the pool and cooling time are treated as the responses. The predicted outputs of both the forward and reverse models are found to be in good agreement with the experimental results. The novelty of this study lies with the development and testing of five neural network-based approaches for carrying out both forward and reverse mappings of the electron beam welding process.