MKSSS's Cummins College of Engineering for Women

About: MKSSS's Cummins College of Engineering for Women is a based out in . It is known for research contribution in the topics: Support vector machine & Discrete wavelet transform. The organization has 432 authors who have published 424 publications receiving 1919 citations.

Investigation of the Leggett-Garg inequality for precessing nuclear spins.

Abstract: We report experimental implementation of a protocol for testing the Leggett-Garg inequality (LGI) for nuclear spins precessing in an external magnetic field. The implementation involves certain controlled operations, performed in parallel on pairs of spin-$1/2$ nuclei (target and probe) from molecules of a nuclear magnetic resonance ensemble, which enable evaluation of temporal correlations from an LG string. Our experiment demonstrates violation of the LGI for time intervals between successive measurements, over which the effects of relaxation on the quantum state of target spin are negligible. Further, it is observed that the temporal correlations decay, and the same target spin appears to display macrorealistic behavior consistent with LGI.

Adsorption characterization of Pb(II) ions onto iodate doped chitosan composite: equilibrium and kinetic studies

Abstract: Iodate doped chitosan (I-CS) composite was synthesized, characterized and used as an adsorbent for adsorption of Pb(II) ions from aqueous solution. The I-CS sorbent was extensively characterized by nitrogen adsorption/desorption to determine the BET surface areas and BJH pore size distribution, elemental analysis, TGA/DSC, FTIR, XRD and SEM. The influences of various chemical parameters viz. pH, contact time, dose of adsorbent and initial metal ion concentration on adsorption performance of Pb(II) ions were analyzed. Equilibrium adsorption isotherm and kinetics of adsorption has also been studied. The BET results exhibited decreased porosity and specific surface area of I-CS composite due to the blockage of internal porous cavities by incorporated iodate. The maximum removal of the Pb(II) ions using I-CS adsorbent was observed at optimum pH 6. The dose of adsorbent on the percentage removal of Pb(II) ions also has a prominent effect and maximum Pb(II) ions mitigation found at 0.5 g L−1 adsorbent dose with 99% efficiency achieved in 4 h. The adsorption of Pb(II) ions shown applicability of the Langmuir and Freundlich adsorption isotherm suggests the existence of both heterogeneous surface and monolayer coverage of adsorbed molecules. The adsorption process follows pseudo-second-order kinetics. This doped composite adsorbent proved to be an effective for adsorption of Pb(II) ions from aqueous solution.

Continuous cuffless blood pressure monitoring based on PTT

Abstract: Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. This paper describes the method developed by the authors to measure systolic blood pressure from pulse transit time (PTT) Pulse transit time is the time taken for the arterial pulse pressure wave to travel from the aortic valve to a peripheral site. It is usually measured from the R wave on the electrocardiogram to a photoplethysmography signal. PTT is inversely proportional to blood pressure. This method does not require an air cuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. Twenty three healthy subjects (age 18–60 yrs) were studied. Blood pressure measurement is carried out using pulse transit time and is compared with sphygmomanometry, the reference standard and the oscillometric based automatic BP measuring machine. The results show that the standard deviation of their differences was around 3 mmHg. The developed pulse transit time based method can be used as a noninvasive and cuffless alternative to the conventional occluding-cuff approaches for long-term and continuous monitoring of blood pressure.

Moving Object Detection and Tracking Using Convolutional Neural Networks

Abstract: The object detection and tracking is the important steps of computer vision algorithm. The robust object detection is the challenge due to variations in the scenes. Another biggest challenge is to track the object in the occlusion conditions. Hence in this approach, the moving objects detection using TensorFlow object detection API. Further the location of the detected object is pass to the object tracking algorithm. A novel CNN based object tracking algorithm is used for robust object detection. The proposed approach is able to detect the object in different illumination and occlusion. The proposed approach achieved the accuracy of 90.88% on self generated image sequences.