Raman spectroscopy of graphene on different substrates and influence of defects

We show the evolution of Raman spectra with number of graphene layers on different substrates, SiO$_{2}$/Si and conducting indium tin oxide (ITO) plate. The G mode peak position and the intensity ratio of G and 2D bands depend on the preparation of sample for the same number of graphene layers. The 2D Raman band has characteristic line shapes in single and bilayer graphene, capturing the differences in their electronic structure. The defects have a significant influence on the G band peak position for the single layer graphene: the frequency shows a blue shift upto 12 cm$^{-1}$ depending on the intensity of the D Raman band, which is a marker of the defect density. Most surprisingly, Raman spectra of graphene on the conducting ITO plates show a lowering of the G mode frequency by $\sim$ 6 cm$^{-1}$ and the 2D band frequency by $\sim$ 20 cm$^{-1}$. This red-shift of the G and 2D bands is observed for the first time in single layer graphene.Comment: 6 pages, 8 figure

Effect of realistic interatomic interactions and two-body correlation on the heat capacity of a trapped BEC

An approximate many-body theory has been used to calculate the heat capacity and the condensate fraction of a BEC with effective repulsive interaction. The effect of interactions has been analyzed and compared with the non-interacting case. It has been found that the repulsive interaction lowers the critical temperature from the value found in the non-interacting case. The difference between the critical temperatures increases with the increase in the total number of atoms in the trap.Comment: 15 pages, 5 figure

Boolean function monotonicity testing requires (almost) n1/2n^{1/2} non-adaptive queries

We prove a lower bound of $\Omega(n^{1/2 - c})$, for all $c>0$, on the query complexity of (two-sided error) non-adaptive algorithms for testing whether an $n$-variable Boolean function is monotone versus constant-far from monotone. This improves a $\tilde{\Omega}(n^{1/5})$ lower bound for the same problem that was recently given in [CST14] and is very close to $\Omega(n^{1/2})$, which we conjecture is the optimal lower bound for this model

Remote Sensing in Agriculture

Remote sensing is defined as the art and science of gathering information about objects or areas from a distance without having physical contact with objects/areas being investigated. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Remote sensing is a tool to monitor the earth's resources using space technology in addition to ground observations. Remote sensing is the science and technology of making inferences about material objects from measurement made at a distance without coming into physical contact with the object under study. Spectral signature of any object that detect by remote sensing is the main principle. Remote sensing technology uses the visible, infrared and microwave regions of radiation to collect information about the various objects on the earth surface. The responses of the objects of different regions of the electromagnetic spectrum are different. The typical responses are used to distinguish object such as vegetation, water, bare soil, concert and other similar features. Remote sensing is two types viz, active and passive remote sensing. Passive remote sensing: It makes use of seasons that detects the reflected/emitted electromagnetic radiation natural sources. Active remote sensing: It makes the use of seasons that detects reflected responses from object that are irradiated from artificially generated energy sources, such as radar. There are three types of platforms-air based, ground based and satellite based. The various applications of remote sensing in agriculture are- crop condition monitoring, detection of plant stress, vegetative indices, canopy transmission and crop stress, cropping system analysis, application on forestry, drought monitoring and its assessment, flood mapping and its assessment, ground water exploration, storm and flood warning, water availability and location of canals, wildlife inventory and fire surveillance etc