Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

New sol–gel synthesis of NiO antibacterial nano-pigment and its application as healthcare coating

Abstract: This report deals with the fabrication of NiO nanoparticle (NiO Nps) dispersed alkyd coating and its antimicrobial activity. NiO Nps is prepared by novel sol–gel technique using pectic polysaccharides as a gelling agent. The gelling agent converts the hydroxo-metal network into a gel within 4 h of time. After calcination, the FESEM and TEM micrograph of the obtained NiO Nps shows cubic and irregular shaped structures with size ranging from 30 to 200 nm. The structural characterizations have been evaluated through XRD, FTIR, UV–vis and VSM studies. The antimicrobial activity of NiO Nps has been carried out against both gram negative and gram positive bacteria namely Escherichia coli, Klebsiella pneumonia and Proteus vulgaris, Streptococcus mutans, Bacillus subtilis, and Staphylococcus aureus. The findings of the present study indicate that the NiO Nps permeate the bacterial cell wall, accumulated inside thereby facilitating the synergistic impact against the growth of bacterial strains. The experimental outcomes suggest the potential use of NiO as pigments in the alkyd coating. Alkyd coating consisting of NiO Nps exhibits considerable reduction in bacterial viability when it comes in contact against microbial strains, proving the efficacy of the coating.

Texture classification using curvelet transform

Abstract: Texture classification has long been an important research topic in image processing. Nowadays classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of texture classification based on curvelet transform has been analyzed. Features are derived from the sub-bands of the curvelet decomposition and are used for classification for the four different datasets containing 20, 30, 112 and 129 texture images respectively. Experimental results show that this approach allows high degree of success rate in classification to be obtained.

Facile synthesis and characterization of Co 3 O 4 nanoparticles for high-performance supercapacitors using Camellia sinensis

Abstract: In this study, synthesis and characterization of cobalt oxide nanoparticles (Co3O4 NPs) using green tea leaf (Camellia sinensis) and its utilization as high-performance supercapacitors has been studied. Co3O4 NPs were fabricated utilizing aquatic extract of Camellia sinensis as a reduction agent, and it was annealed at various annealing temperatures, such as 200 °C, 400 °C, 600 °C, and 800 °C for 2 h. The synthesized Co3O4 NPs were analyzed using XRD, FESEM, EDX, HRTEM, PL, FTIR, UV–visible techniques. The higher concentration of phenolic compounds with antioxidant potential, amino acids, proteins and lipids in Camellia sinensis act as the reducing as well as the stabilizing agents for the growth of NPs. The XRD analysis confirms that the crystalline behavior of the annealed samples increases with increasing annealing temperature. The electrochemical performance of green synthesized nanostructured Co3O4 NPs shows an excellent supercapacitance performance with quick dispersing of electrolyte ions into the Co3O4 electrode owing to their rapid charging and discharging performances. This affirms that the bio-synthesized Co3O4 nanomaterial is the appropriate material for high performance supercapacitor applications.

Design of Optimal Discrete Wavelet for ECG Signal Using Orthogonal Filter Bank

Abstract: Biomedical signal processing is the process of extracting clinically useful information from biosignals for the aspect of medical procedures. Biomedical signals like electrocardiogram wave commonly change their statistical properties over time tending to be nonstationary. For analyzing this kind of signal wavelet transforms are a powerful tool. The design of good wavelet for cardiac signal is discussed from the perspective of orthogonal filter banks. In this paper two wavelets are designed and evaluated based on perfect reconstruction conditions and the filters are perfectly matched. ECG records from the MIT-BIH Arrhythmia database are chosen for processing. In the first step, the filters are designed by reparametrization of filter coefficients by thetas for the proposed wavelets W1 and W2. ECG signal is decomposed to three levels and then reconstructed. From the reconstructed signal the error signal is found and it is compared with other wavelets available in the literature such as db4, bior4.4 and bior6.8. The reconstructed results show the potential of the method. The wavelet W2 gives the maximum error of 1.8*10-11 to 2.73*10-11 that is better than all other wavelets already exists in the literature. Baseline wandering is one of the noise artifacts that affect ECG signals. Automatic detection of the QRS complex can be affected unless the baseline wander is removed Thus baseline wander removal and QRS detection algorithm are effectively done using proposed wavelets and db4, bior4.4 and bior6.8 for ECG records from the MIT-BIH Arrhythmia database and their performance are compared.