Development of Solar Drying

Importance of Solar Drying is increasing worldwide, especially in areas where the use of the abundant, renewable and clean solar energy is essentially advantageous. In the developing countries and in rural areas the traditional open-air drying methods should be substituted by the more effective and more economic solar drying technologies.

R&D needs should be considered in the basic research and experimental fields; in performance measurement; in the modelling-simulation-design and testing. The international co-operation of experts should be improved and more efforts would be needed in the policy and in the public information.     

Optic flow field segmentation and motion estimation using a robustgenetic partitioning algorithm

Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, the authors present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, the authors propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation     

Polyaniline‐supported acid catalyst: Esterification of cinnamic acid with alcohols

Polyaniline‐supported acid salts such as polyaniline‐hydrochloride, polyaniline‐sulfate, and polyaniline‐nitrate were prepared by oxidation of aniline using benzoyl peroxide and ammonium persulfate as oxidizing agents. Polyaniline salts were used as catalysts in the esterification of cinnamic acid with alcohols. Polyaniline‐sulfate salt was found to be the best catalyst for the esterification of cinnamic acid. The reusability, handling, and recovery of the catalyst were found to be good. The yield of the ester depended on the type of the polyaniline salt, amount of the catalyst, amount of alcohol, and both the time and the temperature of the reaction. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 1584–1590, 2005     

Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance

The presentation order of training patterns to a simplified fuzzy ARTMAP (SFAM) neural network affects the classification performance. The common method to solve this problem is to use several simulations with training patterns presented in random order, where voting strategy is used to compute the final performance. Recently, an ordering method based on min–max clustering was introduced to select the presentation order of training patterns based on a single simulation. In this paper, another single simulation method based on genetic algorithm is proposed to obtain the presentation order of training patterns for improving the performance of SFAM. The proposed method is applied to a 40-class individual classification problem using visual evoked potential signals and three other datasets from UCI repository. The proposed method has the advantages of improved classification performance, smaller network size and lower training time compared to the random ordering and min–max methods. When compared to the random ordering method, the new ordering scheme has the additional advantage of requiring only a single simulation. As the proposed method is general, it can also be applied to a fuzzy ARTMAP neural network when it is used as a classifier.     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network

Conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications. But conventional regular moments are only invariant to translation, rotation and equal scaling. It is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x- and y-axis directions. We address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling. However, the technique produces a set of features that are only invariant to translation, unequal/equal scaling and reflection. They are not invariant to rotation. To make them invariant to rotation, moments are calculated with respect to the principal axis of the image. To perform this, the exact angle of rotation must be known. But the method of using the second-order moments to determine this angle will also be inclusive of an undesired tilt angle. Therefore, in order to correctly determine the amount of rotation, the tilt angle which differs for different scaling factors in the x- and y-axis directions for the particular image must be obtained. In order to solve this problem, a neural network using the back-propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined. Next, the new moments are derived and a Fuzzy ARTMAP network is used to classify these images into their respective classes. Sets of experiments involving images rotated and scaled unequally in the x- and y-axis directions are carried out to demonstrate the validity of the proposed technique.     

Utilizing gamma band to improve mental task based brain-computer interface design.

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.     

Preparation of a functional nanofibrous polymer membrane incorporated with poly(2-aminothio phenol) stabilized gold nanoparticles

Functional nanofibrous polymer membranes were prepared by incorporating poly(2-aminothio phenol) (P2AT) stabilized Au NPs onto electrospun polyvinylidene fluoride (PVdF) nanofibers (designated as P2AT-Au NPs@PVdF-NFM). The preparation of P2AT-Au NPs@PVdF-NFM involves two steps: loading of 2AT (monomer) into electrospun PVdF nanofibrous membrane and polymerization of 2AT by gold chloride. P2AT and Au NPs were simultaneously formed into the electrospun PVdF-NFM. Transmission electron microscope image of P2AT-Au NPs@PVdF-NFM informs the presence of Au NPs (with sizes ~10 nm) onto PVdF-NFM.     

Molecular Sensing Using Hyperpolarized Xenon NMR Spectroscopy

Molecular imaging is the determination of the spatial location and concentration of specific molecules in a sample of interest. Sophisticated modern magnetic resonance imaging machines can collect NMR spectra from small-volume elements within a sample, enabling local chemical analysis. However, abundant water and fat signals limit detection of metabolites to near mM concentrations. Alternatively, targeted relaxation contrast agents enhance the relaxation of the strong water signal where they bind. A comparison of images with and without a contrast agent shows the target distribution, but high µM concentrations are needed. We have developed an approach that exploits the strong signals of hyperpolarized ¹²⁹Xe (an inert reporter introduced for imaging). The imaging contrast agents are composed of a biological recognition motif to localize the agent (antibodies or aptamers) and covalently tethered cryptophane cages. Xenon binds to the cryptophane and though chemical exchange saturation transfer creates contrast in a xenon image. Imaging agents can deliver many cages per target, giving detection limits in the pM concentration range. The evolution and principles of this approach are described herein.     

Pd-Decorated Tungsten as Pt-Free Bimetallic Catalysts for Hydrogen Oxidation Reaction in Alkaline Electrolyte

Developing Pt-free catalysts for hydrogen oxidation reaction (HOR) in alkaline solution is becoming a key challenge in the development of anion exchange membrane fuel cells and electrochemical reactors. Herein, we present the preparation, HOR activity, and stability of Pd-decorated tungsten (Pd-d-W) catalysts. The Pd-d-W catalysts were prepared by the chemically activated surface of tungsten nanoparticles by Pd ions. The resultant bimetallic catalysts consisted of crystalline phases of both Pd and W nanoparticles. The CO stripping voltammograms and H-desorption (H_des) peak potential of hydrogen desorption in Pd suggests that the enhancement of HOR catalytic activity observed in Pd-d-W catalyst can be ascribed to the modification of electronic property of Pd and availability of OH_ad near-surface Pd atoms.