Ni-Cr-Ti protective coating

Ni-Cr-Ti protective films were prepared by a plasma beam sputter deposition process at 250 °C. Ni-Cr-Ti thin films (50 nm) have been used to study the microstructure by transmission electron microscopy and the grain growth during heating in the heating stage of an electron microscope and after baking in an oxygen flow in an oven. Energy-dispersive spectroscopy analysis in the transmission electron microscope was used to study compositional variations in the films. Ni-Cr-Ti films 300 nm thick deposited on polished hot-worked tool steel (AISI H11) substrates have been studied by Auger electron spectroscopy and depth profiling before and after heat treatment in oxygen in an oven at temperatures from 600 to 800 °C. Basic microstructural, morphological, compositional and mechanical properties have been studied on Ni-Cr-Ti protective coatings 3 μm thick. Oxidation and electrochemical properties have also been studied.     

Concentration effect of photostabilizers on poly(1-butene) film

Effect of addition of various concentrations of stabilizers on poly (1-butene) [IPB] film has been studied, by measuring rate of protection and protective effectiveness through light scattering technique and UV spectral measurements. It has been observed that after a certain percentage of stabilizer addition there was not any further change.     

Role of Cenosphere Addition on Dielectric Properties of Sisal Fiber-Polypropylene Composites

This research investigates the influence of addition of porous additives on dielectric constant of polypropylene. Composite composed of PP matrix with sisal fiber having cylindrical pores and cenospheres having spherical pore, presents low dielectric constant. A new relation concerning porosity is proposed by modifying the usual mixing rule to predict the dielectric constant of PP composite. This research presents the dielectric properties of sisal fiber-reinforced PP composites with and with out cenospheres. Treated and untreated cenospheres with different concentration were loaded in chopped sisal fiber-reinforced polypropylene. The loading of the polypropylene with the sisal fiber, increases the dielectric constant ε′ and improves the ac conductivity σ_ac. The effect of temperature on the dielectric spectrum of polypropylene composites was investigated in the frequency range ranging from 1–10 kHz. Sisal fiber-reinforced polypropylene composites having 20% sisal fiber with and without cenospheres were developed and electrical properties such as dielectric constant (?′), dissipation factor (tanδ) and ac conductivity (σ_ac) of these composites were determined. Dielectric constant, tan δ, and a.c. conductivity increases with increase in temperature at different frequencies.     

Image cryptosystem based on plain image correlation rate and selective chaotic maps

In this paper, a new image encryption algorithm is introduced for encrypting grayscale digital images of any size. To improve the encryption evaluation parameters, we suggested that the value of the plain image correlation coefficient be effective in the cryptographic process, so plain images with different properties and correlation coefficient rates are encrypted in different ways. According to the average absolute value of correlation coefficient of plain image, Logistic or Tent maps is selected to generate chaotic sequences to expand plain image matrix. As the first step of the diffusion phase, the plain image matrix is developed with larger size by proper selected chaotic sequences, and simultaneously a chaotic matrix with the same size is generated by chaotic Sine map sequences. In confusion phase, the modified Lorenz map changes pixel locations of new developed matrix by means of certain equations. Then bitwise XOR is applied between developed matrix include plain image and Sine map chaotic matrix, as second step of diffusion phase. Finally, encrypted image is generated after applying exchange operations on the content of pixels, as third step of diffusion phase. Experimental results and comparisons with some of the existing methods, show that the proposed image cryptosystem is able to resist common cryptanalytic attacks and can be used as a secure method for encrypting digital images.

     

Multifold stiffness and fracture toughness enhancement in W-doped VO2 microcrystals

Vanadium dioxide is popular for the metal-insulator phase transition at 68°C. Chemical doping is one of the effective ways adopted to tune the phase transition temperature, where tungsten is known to reduce the transition temperature of VO₂. This work investigates the effect of tungsten doping on the mechanical properties of VO₂ microcrystals and their polymer composites. Doping of VO₂ with W shows a systematic reduction in phase transition temperature up to 33°C for 4 wt% W-doped VO₂. For 3 wt% W-doped VO₂, the elastic modulus values enhance by 50%. The fracture toughness of 3 wt% W-doped VO₂ shows an enhancement of fourfold compared to the undoped VO₂. The dynamic compressive strength of 3 wt% W-doped VO₂–UHMWPE polymer composite at room temperature is found to be 7% higher than the undoped VO₂—composite.     

Video Frame Prediction by Joint Optimization of Direct Frame Synthesis and Optical-Flow Estimation

Video prediction is the problem of generating future frames by exploiting the spatiotemporal correlation from the past frame sequence. It is one of the crucial issues in computer vision and has many real-world applications, mainly focused on predicting future scenarios to avoid undesirable outcomes. However, modeling future image content and object is challenging due to the dynamic evolution and complexity of the scene, such as occlusions, camera movements, delay and illumination. Direct frame synthesis or optical-flow estimation are common approaches used by researchers. However, researchers mainly focused on video prediction using one of the approaches. Both methods have limitations, such as direct frame synthesis, usually face blurry prediction due to complex pixel distributions in the scene, and optical-flow estimation, usually produce artifacts due to large object displacements or obstructions in the clip. In this paper, we constructed a deep neural network Frame Prediction Network (FPNet-OF) with multiple-branch inputs (optical flow and original frame) to predict the future video frame by adaptively fusing the future object-motion with the future frame generator. The key idea is to jointly optimize direct RGB frame synthesis and dense optical flow estimation to generate a superior video prediction network. Using various real-world datasets, we experimentally verify that our proposed framework can produce high-level video frame compared to other state-of-the-art framework.