Ultra-compact with improved data rate optical encoder based on 2D linear photonic crystal ring resonator

Photonic Network Communications - In the present work, a high-speed optical encoder is proposed based on two-dimensional photonic crystal ring resonator using coupled mode theory and resonance...     

Nonsimilar solution of a boundary layer flow of a Reiner–Philippoff fluid with nonlinear thermal convection

The thermodynamics modeling of a Reiner–Philippoff-type fluid is essential because it is a complex fluid with three distinct probable modifications. This fluid model can be modified to describe a shear-thinning, Newtonian, or shear-thickening fluid under varied viscoelastic conditions. This study constructs a mathematical model that describes a boundary layer flow of a Reiner–Philippoff fluid with nonlinear radiative heat flux and temperature- and concentration-induced buoyancy force. The dynamical model follows the usual conservation laws and is reduced through a nonsimilar group of transformations. The resulting equations are solved using a spectral-based local linearization method, and the accuracy of the numerical results is validated through the grid dependence and convergence tests. Detailed analyses of the effects of specific thermophysical parameters are presented through tables and graphs. The study reveals, among other results, that the buoyancy force, solute and thermal expansion coefficients, and thermal radiation increase the overall wall drag, heat, and mass fluxes. Furthermore, the study shows that amplifying the space and temperature-dependent heat source parameters allows fluid particles to lose their cohesive force and, consequently, maximize flow and heat transfer.     

An Efficient Near Lossless Image Compression Algorithm Using Dissemination of Spatial Correlation for Remote Sensing Color Images

Wireless Personal Communications - Image transmission from space borne equipment is a challenging task owing to its restrictions on bandwidth and memory requirement. Many of the state of art...     

Mobile data offloading based on minority game theoretic framework

Wireless Networks - Mobile data offloading is a current-day networking paradigm to channelize certain fraction of the cellular data traffic over unlicensed spectrum of WiFi. In this paper, we...     

GaN Quantum-Dot Formation by a Temperature Increase in an Ammonia Flow

Semiconductors - The transformation of a two-dimensional GaN layer into three-dimensional islands (2D–3D transition) under increasing temperature in a flow of ammonia is investigated...     

Polysorbate identity and quantity dictate the extensional flow properties of protein-excipient solutions

While protein medications are promising for treatment of cancer and autoimmune diseases, challenges persist in terms of development and injection stability of high-concentration formulations. Here, the extensional flow properties of protein-excipient solutions are examined via dripping-onto-substrate extensional rheology, using a model ovalbumin (OVA) protein and biocompatible excipients polysorbate 20 (PS20) and 80 (PS80). Despite similar PS structures, differences in extensional flow are observed based on PS identity in two regimes: at moderate total concentrations where surface tension differences drive changes in extensional flow behavior, and at small PS:OVA ratios, which impact the onset of weakly elastic flow behavior. Undesirable elasticity is observed in ultra-concentrated formulations, independent of PS identity; higher PS contents are required to observe these effects than in analogous polymeric excipient solutions. These studies reveal novel extensional flow behaviors in protein-excipient solutions, and provide a straightforward methodology for assessing the extensional flow stability of new protein-excipient formulations.     

Effect of Heat Treatment of Molten Bi2O3 Containing 22 mol % SiO2 on the State of the Metastable δ*-Phase Forming during Solidification

Inorganic Materials - We have studied the effect of isothermal holding of a melt containing 78 mol % Bi2O3 and 22 mol % SiO2 and various melt cooling schedules on the morphology, microstructure,...     

Gamma ray-irradiated induced effects on SCN ligand-based MMTC single crystals for optoelectronic applications synthesized by SR method

Journal of Materials Science: Materials in Electronics - Nonlinear optical organometallic single crystal of manganese mercury thiocyanate (MMTC) has been grown by SR method in aqueous solution. The...     

Analytical Synthesis of an Amplitude-Constrained Controller

Automation and Remote Control - We consider a time-invariant optimal control problem of a new linear-quadratic type on the positive time axis with an amplitude constraint on the control. Using...     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).