Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

New Method of Image Compression Using Multiwavelets and Set Partitioning Algorithm

Abstract: Advances in wavelet transforms and quantization methods have produced algorithms capable of surpassing the existing image compression standards like the joint photographic experts group (JPEG) algorithm. The existing compression methods for JPEG standards are using DCT with arithmetic coding and DWT with Huffman coding. The DCT uses a single kernel where as wavelet offers more number of filters depends on the applications. The wavelet based set partitioning in hierarchical trees (SPIHT) algorithm gives better compression. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry, but they cannot simultaneously possess all of these properties. The relatively new field of multiwavelets offer more design options and can combine all desirable transform features. But there are some limitations in using the SPIHT algorithm for multiwavelet coefficients. This paper presents a new method for encoding the multiwavelet decomposed images by defining coefficients suitable for SPIHT algorithm which gives better compression performance over the existing methods in many cases.

MHD convection of nanofluid in porous medium influenced by slanted Lorentz force

Abstract: This paper presents a numerical investigation on magneto-hydrodynamics combined convection in a nanofluid-saturated porous 2-D cavity with different tilting angles of applied magnetic field and aspect ratios. The moving upper horizontal wall is heated uniformly. The temperature along the bottom wall is a constant cold temperature while the adiabatic condition is maintained at the vertical sidewalls. The finite volume method is applied to solve the system of non-dimensional equations. The pertinent parameters of the current study are Hartmann number (Ha), solid volume fraction (χ), Richardson number (Ri), the aspect ratio (Ar), Darcy number (Da), and the inclination angle of the magnetic field (γ). The slope of applied magnetic field affects the magnetic field intensity and the overall rate of heat transfer is augmented in the forced convection regime than the mixed convection regime. The mean Nusselt number raises on increasing of Ar for all considered Richardson numbers. In the presence of magnetic field, the rate of heat transfer is almost equal to the amplification of solid volume concentration when Ar = 0.25, whereas, it increases for Ar > 0.25 with the raise in the solid volume concentration. An increase in Hartmann number and Darcy number is insignificant on mean rate of heat transfer in the mixed convection regime at Ar ≤ 0.5.

Assessment of the Influence of Graphene Nanoparticles on Thermal Conductivity of Graphene/Water Nanofluids Using Factorial Design of Experiments

Abstract: Assessment of the Influence of Graphene Nanoparticles ... 2018 62 3 Abstract In this study, 23 factorial design of experiment was employed to evaluate the effect of parameters of hot fluid inlet temperature, graphene nanofluid concentration and hot fluid flow rate on thermal conductivity of graphene/water nanofluid. The levels of hot fluid inlet temperature are kept at 35°C and 85°C, nanofluid concentration is kept at 0.1 and 1.0 volume% (vol.%) and the hot fluid flow rate are kept at 2 lpm and 10 lpm. Experiments were conducted with 16 runs as per MINITAB design software using graphene/water nanofluids in the corrugated plate type heat exchanger. The nanofluid thermal conductivity was determined using the mixing rule for different nanofluid concentrations ranging from 0.1 to 1.0%. Normal, Pareto, Residual, Main and Interaction effects, Contour Plots were drawn. The Analysis of Variance (ANOVA) of test results depict that the hot fluid temperature and nanofluid concentration have significant effect on the thermal conductivity of graphene/water nanofluid (response variable).

EOG based wheelchair control for quadriplegics

Abstract: Quadriplegics is paralysis caused by illness or injury to a human that results in the partial or total loss of all their limbs and torso. Quadriplegics is highly dependent on an assistant for wheelchair movement. It is not always the case where the helper is with the patient all the time, therefore independence is encouraged among the wheelchair users. The eye movement can be considered as a significant communication tool for tetraplegia. The signal from the eye muscles, called electrooculogram is generated due to different eye movements, directions and levels. The signal strength for various eye movements are obtained using EOG electrodes. So it has been proposed to control the wheelchair of physically handicapped people with their eye movements.

A Metacognitive Fully Complex Valued Functional Link Network for solving real valued classification problems

Abstract: In this paper, a sequential learning based meta-cognitive fully complex valued functional link network (Mc-FCFLN) is developed for solving complex real world problems. Mc-FCFLN network consists of two components: a cognitive component and a meta-cognitive one. A fully complex-valued functional link network (FCFLN) is a cognitive component and the self-regulatory learning method is its meta-cognitive component. As the network does not possess hidden layers, the multi-variable polynomials are represented in the input layer for capturing the nonlinear relationship between the input and the output sample. Moreover, when the sample is presented to the Mc-FCFLN network, the meta-cognitive component decides what to learn, when to learn, and how to learn depending on the knowledge gained by the FCFLN network and the novel information present in the sample. The network can learn sample one after the other and thus the drawback existing with the batch learning strategy can be eliminated while orthogonal least square principle is used for selecting the best polynomial and the recursive least square update is used for tuning the network. Multi-category and binary datasets chosen from the UCI machine learning repository is used for the validation of the proposed classifier. Lastly, a performance comparison of the Mc-FCFLN applied for classification problems shows better classification ability when compared with the other existing classifiers in the literature.