A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

In last year’s, the expert target recognition has been become very important topic in radar literature. In this study, a target recognition system is introduced for expert target recognition (ATR) using radar target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert target recognition. The GAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar target recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahern et al., 1989, Al-Otum and Al-Sowayan, 2011, Avci et al., 2005a, Avci et al., 2005b, Biswal et al., 2009, Frigui et al., in press, Cao et al., 2010, Guo et al., 2011, Famili et al., 1997, Han and Huang, 2006, Huang et al., 2011, Huang et al., 2006, Huang and Siew, 2005, Huang et al., 2009, Jiang et al., 2011, Kubrusly and Levan, 2009, Le et al., 2011, Lhermitte et al., in press, Martínez-Martínez et al., 2011, Matlab, 2011, Nelson et al., 2002, Nejad and Zakeri, 2011, Tabib et al., 2009, Tang et al., 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert target recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform–short-time Fourier transform (DWT–STFT), discrete wavelet transform–Born–Jordan time–frequency transform (DWT–BJTFT), and discrete wavelet transform–Choi–Williams time–frequency transform (DWT–CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time–frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar targets. The performance of the developed GAWELM expert radar target recognition system is examined by using noisy real radar target echo signals. The applications results of the developed GAWELM expert radar target recognition system show that this GAWELM system is effective in rating real radar target echo signals. The correct classification rate of this GAWELM system is about 90% for radar target types used in this study.     

Selective and Sustainable Production of Sub-terminal Hydroxy Fatty Acids by a Self-Sufficient CYP102 Enzyme from Bacillus Amyloliquefaciens

Enzymatic hydroxylation of fatty acids by Cytochrome P450s (CYPs) offers an eco-friendly route to hydroxy fatty acids (HFAs), high-value oleochemicals with various applications in materials industry and with potential as bioactive compounds. However, instability and poor regioselectivity of CYPs are their main drawbacks. A newly discovered self-sufficient CYP102 enzyme, BAMF0695 from Bacillus amyloliquefaciens DSM 7, exhibits preference for hydroxylation of sub-terminal positions (ω-1, ω-2, and ω-3) of fatty acids. Our studies show that BAMF0695 has a broad temperature optimum (over 70 % of maximal enzymatic activity retained between 20 to 50 °C) and is highly thermostable (T₅₀ >50 °C), affording excellent adaptive compatibility for bioprocesses. We further demonstrate that BAMF0695 can utilize renewable microalgae lipid as a substrate feedstock for HFA production. Moreover, through extensive site-directed and site-saturation mutagenesis, we isolated variants with high regioselectivity, a rare property for CYPs that usually generate complex regioisomer mixtures. BAMF0695 mutants were able to generate a single HFA regiosiomer (ω-1 or ω-2) with selectivities from 75 % up to 91 %, using C12 to C18 fatty acids. Overall, our results demonstrate the potential of a recent CYP and its variants for sustainable and green production of high-value HFAs.     

Synthesis and Properties of Polyimides from 1,3-di(p- dimethylamino benzyl)-imidazolidine-2-thion

Summary A synthetic strategy is presented that allows the preparation of dimethylamino functionalized heterocyclic system 1,3-di (p-dimethylaminobenzy1)-imidazolidine-2-thion that was then used to prepare polyimides by the two-stage polycondensation method. The key to this success was the development of an efficient procedure leading highly pure dimethylamino monomer. Polyimides derived from 1,3-di(p-dimethylaminobenzyl)-imidazolidine-2-thion and commercial dianhydrides exhibited excellent solubility in various polar solvents. These polymers showed glass transition temperatures between 290–360°C, and initial decomposition temperature 490–485 °C and 10% mass loss ranging from 505–75°C in air. Received: 17 April 2002/Revised version: 25 January 2003/ Accepted: 14 March 2003 Correspondence to Turgay Seckin     

Synthesis of New Polyimides from Ru (II) Complex of 2,6-bis [1-(p-dimethylaminophenylimino) Ethyl] Pyridine

Pyridine-based tridentate ligand containing pendant NMe₂ unit was used to prepare novel polyimides via one-stage solution polycondensation due to their stability under a variety of oxidative and reductive conditions. Ru (II) complex of the pydim ligand was synthesized starting from [RuCl₂ (p-cymene)]₂ and 2,6-bis [1-(p-dimethylaminophenylimino) ethyl] pyridine. A series of stable polyimides were synthesized from Ru (II) complex of 2,6-bis [1-(p-dimethylaminophenylimino) ethyl] pyridine (2) and various aromatic dianhyrides had inherent viscosities ranging from 1.31 to 1.55 dL/g and were soluble in polar solvents. The glass transition temperatures were 245–308°C, and the 10% weight loss temperatures were above 482–548°C.     

Oil Content,Oil Yield and Fatty Acid Profile of Groundnut Germplasm in Mediterranean Climates

A high amount of good-quality vegetable oil in seeds has an overwhelming contribution to the groundnut (Arachis hypogaea L.) cultivation throughout the world. In order to take into account great variation in oil characteristics in Arachis subspecies and botanical varieties, 256 groundnut genotypes including ICRISAT’s mini core collection were investigated. Significant variability in oil content (31.7–57.0%) was detected among groundnut genotypes. Oil yield varied from 9.5 to 179.3 kg da^?1 with the average being 67.7 kg da^?1. Significant genotypic differences were also observed for all the fatty acids studied. Oleic and linoleic acids accounted for the major fraction with mean values of 45.3 and 32.1% in the ranges of 35.3–60.9% and 16.1–43.6%, respectively. Significant negative correlation was observed between oleic and linoleic acid. In the present investigation, desirable values were obtained for oil traits which would be useful to develop nutritional and health-beneficial cultivars.     

Investigation of bismuth doped bioglass/graphene oxide nanocomposites for bone tissue engineering

In this study, bismuth doped 45S5 nanobioactive bioglass (nBG) and graphene oxide (GO) nanocomposites were developed and characterized in terms of microstructural, mechanical, bioactivity and biological properties. Bismuth (Bi) - doped nBG was synthesized by sol-gel method and sintered at 600 °C for 2 h. Nanosized GO was homogeneously mixed with Bi doped bioglass at various ratios to prepare nanocomposites. Addition of Bi increased the density of nBG samples while a considerable decrease in density was observed for nanocomposites with GO incorporation. Bi improved the diametral tensile strength of nBG and addition of 2.5% GO to the composite also increased the diametral tensile strength of the nanocomposites. However, addition of more than 2.5% GO had negative effect on the diametral tensile strength of the composites. Bi doping to bioglass and its composite with GO increased the biocompatibility of 45S5 nBG in which 96.5BG1Bi2.5GO (containing 96.5% BG 1% Bi 2.5% GO in weight ratio) showed highest cell viability. Overall, it can be concluded that composites of Bi doped 45S5 nBG with GO hold promise as biomaterial for biomedical applications.     

Formation of dynamic virtual enterprises and enterprise networks

This paper addresses the preparation and set up of virtual enterprises and enterprise networks. A virtual enterprise (VE) can be perceived as a customer solution delivery system created by a temporary and re-configurable information and communications technology (ICT) enabled aggregation of competencies. The main achievements of the research include: (1) Clarification and definition of the concept for virtual enterprises and enterprise networks including preparation of these. (2) Development of a framework and a reference architecture for virtual enterprises named as Structured Methodology and ICT Reference Architecture respectively. Structured methodology structures the body of knowledge related to preparation, setup, and operation of virtual enterprises and enterprise networks. ICT reference architecture consists of three levels with seven layers to portray in a diagrammatic fashion how different enterprises may exchange and use information between their respective organizations’ specific proprietary systems and a central server. (3) Development of a methodology for virtual enterprise named as virtual enterprise methodology (VEM). VEM consists of a set of guidelines, which systematically describes activities that enterprises should consider in relation to set up and preparation of own enterprise networks with the aim to set up virtual enterprises. (4) Testing and validation of the developed VEM with the realization of a virtual case study. Virtual case study demonstrates and validates the application of the developed VE methodology with the illustration of the key activities related to setting up breeding environment, setting up and operating VE and dissolution of VE.     

Performance analysis of open cycle gas turbines

In this study, the performance of ideal open cycle gas turbine system was examined based on its thermodynamic analysis. The effects of some parameters, such as compressor inlet temperature (CIT), pressure ratio (PR) and the turbine inlet temperature (TIT), on the performance parameters of open cycle gas turbine were discussed. The turbine net power output, the thermal efficiency and the fuel consumption of the turbine were taken as the performance parameters. The values of these parameters were calculated using some basic cycle equations and variables values of thermodynamic properties. Other variables such as lower heating value, combustion efficiency and isentropic efficiencies of compressor and turbine were assumed to be constant. The result showed that the net power output and the thermal efficiency increased by a decrease in the CIT and increase in the TIT and PR values. If it is aimed to have a high net power output and the thermal efficiency for the turbine, the CIT should be chosen as low as possible and the TIT should be chosen as high as possible. Copyright © 2008 John Wiley & Sons, Ltd.     

Controlling fibroblast adhesion and proliferation by 1D Al2 O3 nanostructures

The fibrotic encapsulation, which is mainly accompanied by an excessive proliferation of fibroblasts, is an undesired phenomenon after the implantation of various medical devices. Beside the surface chemistry, the topography plays also a major role in the fibroblast–surface interaction. In the present study, one‐dimensional aluminium oxide (1D Al₂ O₃) nanostructures with different distribution densities were prepared to reveal the response of human fibroblasts to the surface topography. The cell size, the cell number and the ability to form well‐defined actin fibres and focal adhesions were significantly impaired with increasing distribution density of the 1D Al₂ O₃ nanostructures on the substratum.Inspec keywords: biomechanics, adhesion, surface chemistry, biomedical materials, cellular biophysics, surface topography, nanostructured materials, alumina, nanomedicineOther keywords: fibrotic encapsulation, medical devices, surface chemistry, human fibroblasts, surface topography, cell size, cell number, well‐defined actin fibres, focal adhesions, distribution density, fibroblast adhesion, 1D nanostructures, distribution densities, fibroblast proliferation, fibroblast‐surface interaction, one‐dimensional aluminium oxide nanostructures, Al₂ O₃      

Multimodal speaker identification using an adaptive classifier cascade based on modality reliability

We present a multimodal open-set speaker identification system that integrates information coming from audio, face and lip motion modalities. For fusion of multiple modalities, we propose a new adaptive cascade rule that favors reliable modality combinations through a cascade of classifiers. The order of the classifiers in the cascade is adaptively determined based on the reliability of each modality combination. A novel reliability measure, that genuinely fits to the open-set speaker identification problem, is also proposed to assess accept or reject decisions of a classifier. A formal framework is developed based on probability of correct decision for analytical comparison of the proposed adaptive rule with other classifier combination rules. The proposed adaptive rule is more robust in the presence of unreliable modalities, and outperforms the hard-level max rule and soft-level weighted summation rule, provided that the employed reliability measure is effective in assessment of classifier decisions. Experimental results that support this assertion are provided.