Effect of external electric field on the charge density waves in one dimensional Hubbard superlattices

We have studied the ground state of the one dimensional Hubbard superlattice structures with different unit cell sizes in the presence of electric field. Self consistent Hartree-Fock approximation calculation is done in the weak to intermediate interaction regime. Studying the charge gap at the Fermi level and the charge density structure factor, we get an idea how the charge modulation on the superlattice is governed by the competition between the electronic correlation and the external electric field.Comment: 6 pages, 8 figures. accepted in Journal of Physics: Condensed Matte

Flow properties of driven-diffusive lattice gases: theory and computer simulation

We develop n-cluster mean-field theories (0 < n < 5) for calculating the flow properties of the non-equilibrium steady-states of the Katz-Lebowitz-Spohn model of the driven diffusive lattice gas, with attractive and repulsive inter-particle interactions, in both one and two dimensions for arbitrary particle densities, temperature as well as the driving field. We compare our theoretical results with the corresponding numerical data we have obtained from the computer simulations to demonstrate the level of accuracy of our theoretical predictions. We also compare our results with those for some other prototype models, notably particle-hopping models of vehicular traffic, to demonstrate the novel qualitative features we have observed in the Katz-Lebowitz-Spohn model, emphasizing, in particular, the consequences of repulsive inter-particle interactions.Comment: 12 RevTex page

CFD Modeling of Globe Valves for Oxygen Application

Components used in high-pressure, high-temperature, flowing oxygen are susceptible to ignition and combustion in presence of restriction or when particles impact these restriction. The valves in any systems are the common flow restrictors, hence, the design and analyses of valves are most critical tasks. The flow of oxygen through valves distinguishes itself by accentuating auto-ignition and consequent flame propagation in metals and non-metals, apart from other usual characteristics present with gases/liquids. The combination of ignition resistance, proper and reliable performance and fabrication economy marks the specification of material and design of valves in oxygen-enriched environment. The analyses have been performed by applying the commercial computational fluid dynamics (CFD) code, FLUENT, to obtain the solution of the two-dimensional turbulent flow field through a globe valve for its different openings in the GOX environment. The flow control valves in high velocity oxygen systems for different openings are simulated for turbulence and eddy dissipation. The influence of pressure, flow rate and opening of the valve on the rise in temperature and eddy dissipation rate is also obtained for compressible flow range. The simulation for turbulence is done by k- and k- turbulence models and the results have been compared

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets