Recent Developments in Organic Pigments

Azo, vat and phthalocyanine pigments are discussed briefly. The demand for high-grade pigments has led to the development of new classes of organic pigments, viz. quinacridone, dioxazine, perylene—perinone, fluorubine, pyrrocoline, and isoindolinone pigments. The preparation and properties of these pigments are discussed, particular attention being paid to the range of colours and the fastness properties obtained and to the prospects of commercial development.     

Accurate direct illumination using iterative adaptive sampling

This paper introduces a new multipass algorithm for efficiently computing direct illumination in scenes with many lights and complex occlusion. Images are first divided into 8/spl times/8 pixel blocks and for each point to be shaded within a block, a probability density function (PDF) is constructed over the lights and sampled to estimate illumination using a small number of shadow rays. Information from these samples is then aggregated at both the pixel and block level and used to optimize the PDFs for the next pass. Over multiple passes the PDFs and pixel estimates are updated until convergence. Using aggregation and feedback progressively improves the sampling and automatically exploits both visibility and spatial coherence. We also use novel extensions for efficient antialiasing. Our adaptive multipass approach computes accurate direct illumination eight times faster than prior approaches in tests on several complex scenes.     

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled–controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the “Luck-Choose” mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.     

Improving the Local Search Ability of Spider Monkey Optimization Algorithm Using Quadratic Approximation for Unconstrained Optimization

Spider monkey optimization (SMO) algorithm, which simulates the food searching behavior of a swarm of spider monkeys, is a new addition to the class of swarm intelligent techniques for solving unconstrained optimization problems. The purpose of this article is to study the performance of SMO after incorporating quadratic approximation (QA) operator in it. The proposed version is named as QA‐based spider monkey optimization (QASMO). An experimental study has been carried out to check the validity and applicability of QASMO. For validation purpose, the performance of QASMO is tested over a benchmark set of 46 scalable and nonscalable problems, and results are compared with the original SMO algorithm. In order to test the applicability of the proposed algorithm in solving real‐life optimization problems, one of the most challenging optimization problems, namely, Lennard–Jones (LJ) problem is considered. LJ clusters containing atoms from three to ten have been taken into consideration, and results are presented. To the best of our knowledge, this is the first attempt to apply SMO and its proposed variant on a real‐life problem. The results demonstrate that incorporation of QA in SMO has positive effects on its performance in terms of reliability, efficiency, and accuracy.     

Euler-Lagrange conditions and estimation of states for a discrete-time linear system with time-delay†

In this paper, the Euler-Lagrange conditions for the extremization of a performance functional for a discrete time linear system with delay fire derived and this is then used for the estimation of states for the same system. The estimation problem is represented as a Two Point Boundary Value (TPBV) problem which is then solved numerically by using the steepest descent technique.     

Abundance of antennal chemosensilla in two parasitoid wasps with different degree of host specificity may explain sexual and species differences in their response to host-related volatiles

The endoparasitic wasps (Hymenoptera: Braconidae), Microplitis croceipes (specialist), and Cotesia marginiventris (generalist) are parasitoids of lepidopteran larvae that differ in their degree of host specificity. Previous studies by our group have reported key differences in the response of both parasitoids to host-related volatiles. To possibly understand the basis for our results, we compared the abundance of antennal sensilla in both sexes of both parasitoid species using scanning electron microscopy and silver-staining techniques. Males of both species had relatively longer antennae than conspecific females. Five major sensilla types were recorded in both species: sensilla chaetica (nonporous), s. trichodea (nonporous), s. placodea (multiporous), s. basiconica (two types, type 1 with terminal opening and type 2 with wall pores), and s. coeloconica (nonporous). Silver staining confirmed multiple pores on the walls of s. placodea and s. basiconica type 2 and terminal openings on s. basiconica type 1, suggesting chemosensory function. In general, both putative chemosensilla types, s. placodea and s. basiconica, were more abundant in M. croceipes (specialist) than in C. marginiventris (generalist), and this was true for both sexes. Comparing the sexes, s. placodea and s. trichodea were significantly more abundant in M. croceipes males compared to females. In contrast, s. placodea was relatively more abundant in female C. marginiventris than in males. These results may explain the reported differences in the responses of both parasitoid species to host-related volatiles and are discussed in relation to the possible roles of the sensilla types in the behavioral ecology of the parasitoids.     

A new analytical model for the response of AlGaN/GaN HEMT-based pH sensors

Journal of Computational Electronics - pH sensors are monitoring devices with wide applications in biology, chemistry, medicine, and agriculture. To enhance their sensitivity and long-term...     

Intrinsic Decompositions for Image Editing

Intrinsic images are a mid‐level representation of an image that decompose the image into reflectance and illumination layers. The reflectance layer captures the color/texture of surfaces in the scene, while the illumination layer captures shading effects caused by interactions between scene illumination and surface geometry. Intrinsic images have a long history in computer vision and recently in computer graphics, and have been shown to be a useful representation for tasks ranging from scene understanding and reconstruction to image editing. In this report, we review and evaluate past work on this problem. Specifically, we discuss each work in terms of the priors they impose on the intrinsic image problem. We introduce a new synthetic ground‐truth dataset that we use to evaluate the validity of these priors and the performance of the methods. Finally, we evaluate the performance of the different methods in the context of image‐editing applications.     

Mitigation of black hole attacks using firefly and artificial neural network

Neural Computing and Applications - In Mobile Ad hoc Network (MANET), network topology changes as devices/users/nodes move and nodes can serve as a source, destination, or router for the...     

Plant Disease Diagnosis and Image Classification Using Deep Learning

Indian agriculture is striving to achieve sustainable intensification, the system aiming to increase agricultural yield per unit area without harming natural resources and the ecosystem. Modern farming employs technology to improve productivity. Early and accurate analysis and diagnosis of plant disease is very helpful in reducing plant diseases and improving plant health and food crop productivity. Plant disease experts are not available in remote areas thus there is a requirement of automatic low-cost, approachable and reliable solutions to identify the plant diseases without the laboratory inspection and expert's opinion. Deep learning-based computer vision techniques like Convolutional Neural Network (CNN) and traditional machine learning-based image classification approaches are being applied to identify plant diseases. In this paper, the CNN model is proposed for the classification of rice and potato plant leaf diseases. Rice leaves are diagnosed with bacterial blight, blast, brown spot and tungro diseases. Potato leaf images are classified into three classes: healthy leaves, early blight and late blight diseases. Rice leaf dataset with 5932 images and 1500 potato leaf images are used in the study. The proposed CNN model was able to learn hidden patterns from the raw images and classify rice images with 99.58% accuracy and potato leaves with 97.66% accuracy. The results demonstrate that the proposed CNN model performed better when compared with other machine learning image classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree and Random Forest.