Earticle

The problem of estimating a signal that is corrupted by additive noise has been of interest to many researchers for practical as well as theoretical reasons. Many of the traditional denoising methods have been using methods such as the Wiener filtering. Recently, nonlinear methods, especially those based on wavelets have become increasingly popular, due to a number of advantages over the linear methods. It has been shown that wavelet and multiwavelet thresholding guarantees better rate of convergence, despite its simplicity. This paper demonstrates the work of combining Parametric multiwavelet and Sureshrink to remove noise from the signal. Experimental results shows that the proposed work is 4% efficient in terms of SNR values and image quality when compared to other wavelet families

The properties and performance of DDR p+pnn+ Wurtzite Gallium Nitride (Wz-GaN) Impatt diodes at Terahertz (THz) frequencies has been investigated for optimum bias current density through modeling and simulation technique. A double iterative computer method based on drift-diffusion model has been used to study the DC and small signal admittance properties of the device. The bias current density is optimized with respect to maximum conversion efficiency and device negative resistance at both 0.3 and 0.5 THz frequencies. The simulation studies show that these devices are potential sources for generating high RF power. The DC-to-RF conversion efficiency of the device is found to be 12.3% and 11.4% at 0.3 THz and 0.5 THz at the optimum bias current density of 2 x 107 A/m2 and 1.8 x 108 A/m2 respectively. The design results presented in this paper will be useful to realize experimentally Wz-GaN IMPATTs for Terahertz frequencies.

Effect of fractional gas hold-up εG on volumetric mass transfer coefficient KLa were studied for two different type of airlift contactors conventional and modified. KLa was directly proportional to gas hold-up within bubbly flow regime (UG < 0.075 m/s ) in conventional one but reverse trend ( inversely proportional) was reported in modified airlift contactor. Mathematical relation between KLa and gas hold-up were developed for both the reactors with the help of experimental data.

In this project, we have studied the growth, morphology and surface characterization of metallized Carbon Nanotubes and conducted a comprehensive study on the application of this technology. Carbon Nanotubes are a tubular shaped isotope of carbon with unique physical, chemical and mechanical properties. In particular, we synthesized Carbon Nanotubes, using a RF-Powered Plasma Enhanced Chemical Vapor Deposition System. In this process, we used methane as the carbon source and high purity argon gas to create the plasma. The Silicon (Si)/Glass (SiO2) substrate was coated with a transition metal as a catalyst using both wet chemical means and Electron beam Evaporation. After successfully growing Carbon Nanotubes, they were metallized with Iron (Fe) Nanoparticles using Physical Vapor Deposition System with an E Beam Evaporator. A comparative study between the metal coated and non coated Carbon Nanotubes was conducted. Possible applications such as use of this technology in synthesis of FET’s, use as Scanning Probe Microscope tips and other electronic applications are also discussed. A successful development in this field would have enormous applications in various fields and could lead to fabrication of miniature electronic components (in the order of nanometers).

This paper presents algorithms based on image processing for detecting internal blood spots and eggshell dirt by processing acquired images from eggs under different illuminations. The algorithm can also detect the severity of dirt on eggshell. In order to carry out image processing and extract useful features of captured images of eggs by machine vision we developed an algorithm in HSI color space. The hue histogram was used for blood spots detection, and maximum values of two ends of histogram were selected as criterions of defect detection. Eggshell dirt was detected using connected areas detection technique. The results of experiments showed that accuracy of differentiation of blood spots algorithm was 90.66% of defected eggs and 91.33% of intact eggs and total average of this algorithm was 91%. Accuracy of differentiation of dirt detect algorithm was 86% of clean eggs, 83% of low dirt eggs and 88% of high dirt eggs. Then total average of this algorithm was 85.66%.