Collocation method based on shifted Chebyshev and radial basis functions with symmetric variable shape parameter for solving the parabolic inverse problem

This work introduces a new numerical solution to the inverse parabolic problem with source control parameter that has important applications in large fields of applied science. We expand the approximate solution of the inverse problem in terms of shifted Chebyshev polynomials in time and radial basis functions with symmetric variable shape parameter in space, with unknown coefficients. Unknown coefficient matrix determined using the collocation technique. Sample results show that the proposed method is very accurate. Moreover, the proposed method is compared with two other methods, fourth-order compact difference scheme and method of lines. Finally, we examine the stability of our method for the case where there is additive noise in input data.     

An exact method for scheduling of the alternative technologies in R&D projects

A fundamental challenge associated with research or new product development projects is identifying that innovative activity that will deliver success. In such projects, it is typically the case that innovative breakthroughs can be achieved by any of several possible alternative technologies, some of which may fail due to the technological risks involved. In some cases, the project payoff is obtained as soon as any single technology is completed successfully. We refer to such a project as alternative-technologies project and in this paper we consider the alternative-technologies project scheduling problem. We examine how to schedule alternative R&D activities in order to maximize the expected net present value, when each technology has a cost and a probability of failure. Although a branch-and-bound algorithm has been presented for this problem in the literature, we reformulate the problem and develop a new and improved branch-and-bound algorithm. We show using computational results that the new algorithm is much more efficient and outperforms the previous one.     

Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification

Previous studies have shown that there are altered areas that do not show anomalies in geophysical data but anomalies can be seen in processed Thematic Mapper images and vice versa. In order to map the altered areas associated with porphyry type copper deposits, the airborne geophysical data, i.e. radiometry and magnetometry and Landsat Enhanced Thematic Mapper (ETM+) data (hydroxyle and iron oxide images) of an area located in the Central Iranian Volcanic Belt have been integrated and analysed using fuzzy classification. Sar Cheshmeh and Darrehzar areas with known mineralization and alteration were chosen as training areas. After defining the training areas (Sar Cheshmeh and Darrehzar), the entire region was classified into altered and unaltered areas. Field observation and sampling confirmed a high classification accuracy. This analysis was found to be useful for exploration of porphyry type deposits in the Central Iranian Volcanic-Sedimentary Belt, where most parts of this belt are surveyed using airborne geophysics.     

Synthesis and characterization of an anticratering agent based on APTES for cathodic electrocoatings

The present study developed an improved aqueous electrocoating containing an anticratering additive that is a reaction product of silane based on amino-propyl-triethoxy-silane and diglycidyl ether of bisphenol A. Structural characterization was performed using Fourier Transform Infrared Spectroscopy (FTIR), epoxy equivalent weight measurement, particle size analysis and SEM. Number of craters on the electrocoated sample was evaluated according to GM9532p. The final electrocoats containing the new anticratering agent showed far fewer craters.     

Spatial error concealment: A novel exemplar-based approach using segmentation

In this paper, the problem of spatial error concealment for real-time applications is addressed. The proposed method can be categorized in exemplar-based error concealment approaches. In this category, a patch of corrupted pixels are replaced by another patch of the image that contains correct pixels. For splitting the erroneous block to different patches, a novel context-dependent exemplar-based algorithm based on a previously proposed segmentation method is proposed. The capability of the proposed method for concealment in diverse image regions is depicted. Our detailed conducted experiments show that the proposed method outperforms the state-of-the-art spatial error concealment methods in terms of output quality.     

An adaptive sliding mode controller design to cope with unmatched uncertainties and disturbance in a MEMS voltage reference source

Tunable micro-electro-mechanical systems (MEMS) capacitors as the fundamental parts are embedded in MEMS AC voltage reference sources (VRS). Being concerned with the accuracy of the output voltage in the reference sources, it gets important to address uncertainties in the physical parameters of the MEMS capacitor. The uncertainties have the great inevitable potentiality of bringing about output voltage perturbation. The output deterioration is more remarkable when the uncertainties are accompanied by disturbance and noise. Manufacturers have been making great attempts to make the MEMS adjustable capacitor with desired rigorous physical characteristics. They have also tried to mitigate physical parameter veracity. However, ambiguity in the values of the parameters inescapably occurs in fabrication procedures since the micro-machining process might itself suffer from uncertainties. Employing a proportional integral (PI) adaptive sliding mode controller (ASMC), both terms of matched and unmatched uncertainties as well as the disturbance, are addressed in this work for the MEMS AC VRS so that a strict voltage is stabilized while the system is simultaneously subjected into uncertainties and exogenous disturbance. Cross-talk, some inertial forces, and electrostatic coercions may appear as matched and unmatched disturbances. Alteration in stiffness and damping coefficients might also take place as matched uncertainties due to variations in the fabrication process or even working environment. The simulation results in the paper are persuasive and the controller design has shown a satisfactory tracking performance.     

Synthesis and Characterization of Lanthanum Oxide Nanoparticles from Thermolysis of Nano-sized Lanthanum(III) Supramolecule as a Novel Precursor

A new nano-sized La(III) supramolecular compound, [pyda.H]₂[La₂(pydc)₄(H₂O)₄]·2H₂O (1), where [pyda.H]⁺ = 2,6-diaminopyridinium, and [pydc]^2? = 2,6-pyridinedicarboxylate, was synthesized by the sonochemical method and characterized by field emission scanning electron microscope (FESEM), X-ray powder diffraction (XRD) and elemental analyses. Lanthanum oxide nanoparticles were prepared by direct thermal decomposition of 1 nanostructures in a programmable furnace up to 800 °C in ambient atmosphere. The structural characterization was performed by powder XRD; and, morphological observations by FESEM revealed that the quasi-spherical La₂O₃ nanoparticles obtained are well crystallized and uniform in both morphology and particle size. The study demonstrates that supramolecular compounds may be suitable precursors for the simple one-pot preparation of nanoscale metal oxide materials with different morphologies.     

Single trial estimation of event-related potential components using spatiotemporal filtering and artificial bee colony optimized Gaussian kernel mixture model

Single trial estimation of event-related potential (ERP) components is an open research topic in neuroscience. In this article, we have proposed a method to improve the performance of spatiotemporal filtering by decreasing its dependency to prior estimates of ERP components. For this purpose, we have used a mixture of Gaussian kernels instead of a raw prior signal, and the parameters of the Gaussian kernel are estimated using artificial bee colony algorithm. The algorithm starts with one Gaussian kernel, and after optimizing its parameters, another Gaussian kernel is added. This procedure goes on until the stopping criterion is reached. The efficiency of the algorithm is tested for one single uncorrelated component and two correlated components for synthesized electroencephalogram (EEG) signal. Also, the efficiency of the proposed method is presented on real data for extraction of N170 component in real EEG data.     

Microstructural evaluation of zirconia dispersed alumina composites

Powder Metallurgy and Metal Ceramics - An extensive scanning electron microscopy and image analysis is carried out on Alumina and Zirconia Dispersed Alumina (ZDA) ceramic composites containing...     

Speech and music separation approaches - a survey

Multimedia Tools and Applications - With the growth of acoustic data in the development of multimedia tools, mobile phones and the Internet of Multimedia Things (IoMT), recent studies exploit...