Milton Arulappan

Optical trapping is a technology that uses a laser beam to hold and move nanoparticles. Optical trapping finds its application in medicine, quantum science, and manipulating atoms and dielectric particles. Adjustable focal patterns are highly advantageous for optical trapping and manipulation of nanoparticles. We investigate the ability of a uniaxial crystal system to propagate a circularly polarized Gaussian beam. Numerical calculations are used to demonstrate the 3D intensity distribution in the r–z plane and the related axial intensity distribution. Simulation results demonstrate that by appropriately adjusting the position of a pair of vortices, focal patterns can be modified from a highly confined focal hole to a highly confined focal spot in the absence of axial birefringence. The focal patterns shift and split when axial birefringence is present. It is found that by properly manipulating the axial birefringence and vortex locations, focal patterns are tuned from a highly confined focal spot to an axially split spots, which is useful in optical trapping and manipulation of nanoparticles.

Earthquakes and tsunamis are among the most destructive natural disasters. Early detection is essential to mitigate loss of life and property damage. This research work mainly focuses on the ways to improve the accuracy of early detection of the Tsunamigenic potential of earthquakes by analyzing the earthquake seismogram signal. This research could lead to more effective and timely warnings, potentially saving lives and reducing damage caused by tsunamis. The seismic features are extracted from the primary wave (P-wave) of the signal and fed into the classifier. The characteristics of each feature are studied to know whether the individual selected features can differentiate the tsunami signals from the non-tsunami signals. The ensemble bagging random forest classifier has been used to classify the P-wave into tsunami and non-tsunami. The accuracy of the prediction of non-tsunami waves is about 92%, but the overall accuracy of the classifier is 96.62%. The classifier predicts the tsunami waves with better accuracy at about 99.6%. Moreover, it accurately forecasts the tsunami waves within 2.75[ ]s after the earthquake occurs. This suggests that the proposed technique is suitable for forecasting a tsunami soon after an earthquake occurs.

Using new technologies to know the nutrition contents of food is the new emerging area of research. Predicting protein content from the image of food is one such area that will be most useful to the human beings because monitoring the nutrition intake has many health benefits. Patients with rare diseases like maple syrup urine disease need to be in good diet practices in order to survive. Protein intake has to be monitored for those individuals. In this paper, protein measurement of health drink powder is performed using image processing techniques. In this work food images are captured and a new database with 990 images of 9 health drink powders is created. Protein content is predicted using deep learning convolutional neural network and also using image features with linear regression. Image features like first order statistics, histogram-oriented gradient, gray level co-occurrence matrix, local binary pattern and gradient magnitude and gradient direction features obtained by applying Prewitt, Sobel and Kirsch are used to estimate the protein content. Training and testing are done using linear regression model which uses support vector machine to obtain the optimal hyper plane. A tenfold cross validation is used to improve the statistical significance of the results. Experimental results show that the protein contents are predicted with an average error of ± 2.71. Deep learning improves the prediction with an average error of ± 1.96.