Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.     

Design method of the layered active magnetic regenerator (AMR) for hydrogen liquefaction by numerical simulation

The design procedure of an active magnetic regenerator (AMR) operating between liquid nitrogen temperature and liquid hydrogen temperature is discussed with the selected magnetic refrigerants. Selected magnetic refrigerants (GdNi₂, Dy_0.85Er_0.15Al₂, Dy_0.5Er_0.5Al₂, and Gd_0.1Dy_0.9Ni₂) that have different transition temperatures are layered in an AMR to widen the temperature span. The optimum volume fraction of the layered refrigerants for the maximum COP with minimum volume is designed in a two-stage active magnetic regenerative refrigerator (AMRR) using one dimensional numerical simulation. The entropy generation in each stage of the AMR is calculated by the numerical simulation to optimize the proposed design. The main sources of the entropy generation in the AMR are pressure drop, convection and conduction heat transfers in the AMR. However, the entropy generation by the convective heat transfer is mostly dominant in the optimized cases. In this paper, the design parameters and the operating conditions such as the distribution of the selected refrigerants in the layered AMR, the intermediate temperature between two stages and the mass flow rate of heat transfer fluid are specifically determined to maximize the performance of the AMR. The proposed design method will facilitate the construction of AMR systems with various magnetic refrigerants and conditions such as AMR size, operating temperature range, and magnetic field variation.     

Development of low-noise drag-type vertical wind turbines

In this study, the aerodynamic noise characteristics of Savonius wind turbines were investigated using hybrid computational aero-acoustics techniques, and low-noise designs were proposed based on the understanding of the noise generation mechanism. First, the flow field around the turbine was analyzed in detail by solving three-dimensional unsteady incompressible Reynolds-averaged Navier–Stokes equations using computational fluid dynamics techniques. Then, the aerodynamic noise radiating from the wind turbine was predicted using the Ffowcs Williams and Hawkings equation with the obtained flow field information. Two distinct harmonic noise components—the blade passing frequency (BPF) and harmonics with a fundamental frequency that is much higher than the BPF—were identified in the predicted noise spectrum. The origin of the higher harmonic components was found to be related to vortex shedding from the rotating turbine. Based on this finding, the proposed low-noise design for Savonius wind turbines uses S-shaped blades. S-shaped blades were found to reduce the noise levels of Savonius wind turbines by up to 2.7 dB.     

Random-valued impulse noise removal using adaptive dual threshold median filter

Noise detection and its removal is very important in the image processing. Detection of noise is very crucial and significant in random valued impulse noise because it does not hamper the image pixels uniformly. This paper presents a novel and unique concept of adaptive dual threshold for the detection of random valued impulse noise along with simple median filter at noise removal stage. Simulation results shows that an efficient noise detection leads to a superior quality of de-noised image as compared to existing adaptive threshold based image de-noising techniques. Proposed threshold computation is based on averaging of pixel values of window which enhances the PSNR of our system as compared to existing median filter based image de-noising methods.     

Voltage Assisted Control of Spin-Transfer Nano-Oscillators

The spin-transfer nano-oscillator(STNO) has recently acquired a huge amount of research interest, due to its promising easy tunability along with the miniature size. The output frequency control of an STNO through magnetic field and current has been examined almost to its full extent; however, there are issues that still need to be addressed. Here, we propose a novel way of voltage control of the output frequency of an STNO, and alongside reducing its power requirement.     

Effect of thermal noise on random lasers in diffusion regime

In this paper, we study the effects of thermal noise on the time evolution of a weak light pulse (probe) in the presence of a strong light pulse (pump) within a gain medium which includes random scatterer particles. Suitable thermal noise term is added to a set of four coupled equations including three diffusion equations for energy densities and a rate equation for the upper level population in a four-level gain medium. These equations have been solved simultaneously by Crank–Nicholson numerical method. The main result is that the back-scattered output probe light is increased as the thermal noise strength is increased and simultaneously, with the same rate, the amplified spontaneous emission is decreased. Therefore, the amplified response of the random laser in diffusion regime for the input probe pulse is enhanced due to effect of the thermal noise.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Effect of TiB2 addition on grain orientation of porous Si3N4-TiB2 composites by magnetic field alignment technology

Textured porous Si₃N₄-TiB₂ composites were prepared by gel-casting–assisted magnetic field alignment technology and subsequent pressureless sintering in the N₂ atmosphere. The effects of TiB₂ content on grain orientation and properties of composites were studied. Results showed that using the magnetic field of 6T, Si₃N₄ grains exhibited a high a- or b-axis orientation and TiB₂ grains exhibited a high c-axis orientation. In textured green bodies, the increase in TiB₂ content from 0 to 20 wt%, the degree of texture of Si₃N₄ grains showed a slight decrease from 0.78 to 0.71. However, the degree of texture of TiB₂ grains was maintained at about 0.98 regardless of TiB₂ content. After sintering, except for the Si₃N₄ and TiB₂ phases, new TiN and BN phases also appeared in the samples. Additionally, the sintering process promotes grain orientation of Si₃N₄ and TiB₂. Herein, the degree of texture of TiB₂ reached to about 1 regardless of TiB₂ content, meaning that the complete grain orientation formed for TiB₂. Owing to the reconstruction reaction between TiB₂ and N₂, new TiN and BN phases did not form the orientation. The increase in TiB₂ contents gradually increased the apparent porosity of the textured samples from 27.9% to 39.4%, thereby leading to the decrease in bending strength from 126.8 ± 18.8 MPa to 60.3 ± 11.2 MPa and from 105.3 ± 19.8 MPa to 53.6 ± 13.0 MPa in two directions, respectively.