Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Support vector machine & Transconductance. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Enhancement of mixing operation through new movement strategies in digital microfluidic biochips

Abstract: Microfluidic biochip is a lab-on-a-chip system that replaces conventional laboratory experiments. Digital Microfluidic Biochip (DMFB) handles liquids as discrete droplets, and offers highly reconfigurable and scalable technology. DMFB combines electronics with biology opening the new application areas of Microelectronics, Biochemistry, and Biomedical sciences. A new application area of DMFB using equilateral triangular electrodes instead of square electrodes has been proposed, considering the design issues and the fluidic constraints while performing all the modular operations. The improvement of sample-reagent mixing procedure is a key challenge issue in bioassay implementation as mixing is the most dominating operation in DMFB; hence, the Triangular DMFB (TEDMB) system leads over the existing DMFB system. In this paper, we have presented a study of TEDMB mixers and developed mixing library for TEDMB synthesis.

Deletion, Bell's inequality, teleportation

Abstract: In this letter we analyze the efficacy of the entangled output of Pati-Braunstein (arxiv:quant-ph/0007121v1, 2000) deletion machine as a teleportation channel. We analyze the possibility of it violating the Bell's inequality. Interestingly we find that for all values of the input parameter ? the state does not violate the Bell's inequality but when used as a teleportation channel can give a fidelity higher than the classical optimum $${({\rm i.e.}\,\frac{2}{3})}$$ .

A new approach for development of kinetics of wastewater treatment in aerobic biofilm reactor

Abstract: Biofilm process is widely used for the treatment of a variety of wastewater especially containing slowly biodegradable substances. It provides resistance against toxic environment and is capable of retaining biomass under continuous operation. Development of kinetics is very much pertinent for rational design of a biofilm process for the treatment of wastewater with or without inhibitory substances. A simple approach for development of such kinetics for an aerobic biofilm reactor has been presented using a novel biofilm model. The said biofilm model is formulated from the correlations between substrate concentrations in the influent/effluent and at biofilm liquid interface along with substrate flux and biofilm thickness complying Monod’s growth kinetics. The methodology for determining the kinetic coefficients for substrate removal and biomass growth has been demonstrated stepwise along with graphical representations. Kinetic coefficients like K, k, Y, b t, b s, and b d are determined either from the intercepts of X- and Y-axis or from the slope of the graphical plots.

Feature Selection Using Multi-Objective Optimization Technique for Supervised Cancer Classification

Abstract: In this chapter, a new multi-objective blended particle swarm optimization (MOBPSO) technique is proposed for the selection of significant and informative genes from the cancer datasets. As the basic optimization algorithm suffers from the local trapping, a blended Laplacian operator is integrated with it to overcome the drawback. The concept is also implemented for differential evolution, artificial bee colony, genetic algorithm and subsequently multi-objective blended differential evolution (MOBDE), multi-objective blended artificial bee colony (MOBABC) and multi-objective blended genetic algorithm (MOBGA) are proposed to extract the relevant genes from the cancer datasets. Proposed methodology utilizes two objective functions to sort out the genes which are differentially expressed from class to class as well as provides good results for the classification of disease. Experimental result reveals that the proposed methodology very efficiently selects differential and biologically relevant genes which are effective for the classification of disease which in turn offers more useful information about the gene–disease association.