Solubilization of Phenanthrene and Fluorene in Equimolar Binary Mixtures of Gemini/Conventional Surfactants

abstract This study deals with the enhanced solubilization of polycyclic aromatic hydrocarbons (PAHs) such as phenan-threne (PHE) and fluorene (FLR) in a pure cationic gemini (G6) and three conventiona...     

Biodegradable poly(L‐lactic acid)/TiO2 nanocomposites: Thermal properties and degradation

Poly(L ‐lactic acid)‐titanium dioxide nanocomposites (with various loadings of TiO₂: 0.5, 1, 2, 5, and 10 wt %) were produced by solution casting method. The influence of TiO₂ on thermal properties and crystallinity of PLA was investigated by DSC and FTIR spectroscopy. The TiO₂ nano filler has no significant influence on the characteristic temperatures (T_g, T_c, and T_m), but has high impact on the crystallinity of these systems. The degree of crystallinity X_c significantly increases for PLA nanocomposites loaded with up to 5 wt % of TiO₂, while for 10 wt % load of TiO₂ it drops below X_c of the pure resin. The degradation of the prepared composites was evaluated hydrolytically in 1N NaOH, enzymatically in α‐amylase solutions, and under UV irradiation. The catalytic effect of TiO₂ nano particles on the degradation processes under UV light exposure (λ = 365 nm) and hydrolytic degradation was confirmed with the increase of the filler content. The opposite effect was identified in enzymatic degradation experiments. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Design of a power efficient,high slew rate and gain boosted improved recycling folded cascode amplifier with adaptive biasing technique

This paper presents an adaptive Improved Recycling Folded Cascode (IRFC) amplifier with improved gain, high slew rate, high phase margin and reduced power consumption. The proposed design is implemented using 180 nm technology with a supply voltage of 1.8 V and a capacitive load of 1 pF. The proposed design is compared with basic two stage op-amp, cascode amplifier and conventional recycling folded cascode amplifier (RFC). Analysis demonstrates that the flexible structure of IRFC with adaptive biasing shows an improvement in gain to 87.74 dB, approximately three times enhancement in slew rate to 53.8 V/µs when compared with the design specifications. The phase margin was observed to be 64.86°. The design also reports an increase in output swing. The gain increases to 109 dB when a cascode stage is added to the IRFC structure.

     

Development and performance evaluation of an improved complex valued radar pulse compressor

Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg–Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods.     

IIR system identification using cat swarm optimization

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.     

Designing communication strategies for heterogeneous parallel systems

This paper explores the suitability of the emerging passive star-coupled optical interconnection using wavelength division multiplexing as the system interconnect to provide high bandwidth (Gbits/sec) communication demanded by heterogeneous systems. Several different communication strategies (combinations of communication topologies and protocols) are investigated under a representative master-slave computational model. The interplay between system speed, network speed, task granularity, and degree of parallelism is studied using both analytical modeling and simulations. It is shown that a hierarchical ALOHA-based communication strategy between the master and the slaves, implemented on top of the passive star-coupled network, leads to a considerable reduction in channel contention and provides 50–80% reduction in task completion time for applications with medium to high degrees of coarse grain parallelism. Comparable reduction in channel contention is also shown to be achieved by using tunable acoustooptic filters at master nodes.     

Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.     

Haven or hell? Telework,flexibility and family in the e-society: a Marxist analysis

The reconfiguration of the home-work boundary that at-home telework entails has significant implications for gender issues and the use of ubiquitous information and communications technologies (ICTs). By presenting a Marx-inspired dialectical analysis of the family and home as both ‘haven and hell’, we develop a critique of proposed advantages for women home workers. Not only do we question the ability of ICTs to deliver the promises made on their behalf – we show how this socio-technical innovation may in fact contribute to compounding the double-burden of work associated with gender roles within the home. Contemporary critical understanding of the e-society should incorporate the influence of at-home teleworking because of its implications for the use of ubiquitous ICTs in the home environment, the shaping of work relations and its impact on gender issues. This increasing use of ICTs outside of the workplace is matched by the growing consensus within the European Union on the desirability of flexible working coupled with family friendly policies. This paper explores some of the rhetoric and research surrounding the proposed benefits of at home ‘telework’ and the likely cost–benefits, from an employee's perspective, in terms of increased freedom, reduced burden and ‘flexibility’.     

Twin-image suppression in digital in-line holography based on wave-front filtering

Pattern Analysis and Applications - Digital holography is an imaging process able to recreate three-dimensional representations of objects from recording pattern interference among distinct waves....