Fusion of multi-focus images with registration inaccuracies

Image fusion combines complementary information for several input images. To obtain useful information from two misaligned images, registration is required. A hybrid textural registration-based multi-focus image fusion scheme is proposed. The Gabor filtering with specific frequency and orientation is used to extract different texture features from the image. The resulting Gabor-filtered images are then aligned using existing affine transformation. The proposed registration scheme yields superior performance as compared to affine registration, as Gabor transform extracts all the features. The fusion is performed using undecimated dual-tree complex wavelet transform. The quantitative and qualitative analysis of the proposed scheme outperforms existing image fusion schemes.     

Nucleation and formation of oxide film with the magnetic field on dry sliding contact of ferromagnetic steel

The nucleation and formation of oxide film in unidirectional dry sliding contact has been studied in a vacuum chamber with and without the application of a magnetic field in the intention of identifying the role of magnetic intensity on the oxidation wear. The wear tests of the steel AISI 1045/steel AISI 1045 couple are investigated on a pin‐disc configuration under three various gas environments: in ambient air, under oxygen at 10⁵ Pa and in vacuum at 5.10⁻⁵ Pa. The formation of oxide layer strongly depends on oxygen partial pressure and magnetic field intensity. These took the form of protective raised ‘islands’ of compacted debris which is developed gradually but rapidly increased as the oxygen partial pressure is increased and which could persist for extended periods during subsequent evacuation. Evidence from various experimental techniques indicates that the compacted debris is a mixture of iron oxides in the form of oxide‐covered particles, although the depth of oxide film has not yet been fully elucidated. Copyright © 2010 John Wiley & Sons, Ltd.     

Microbial safety and probiotic potential of packaged yogurt products in Pakistan

Yogurt is a health food with notable market production and demand. Because of this, we conducted a study on prominent commercial brands of yogurts in Pakistan for microbial content and the probiotic potential of the contained lactic acid bacteria (LAB), in the context of their label claims. All contained viable LAB, but the numbers (cfu g⁻¹) varied considerably. Three of the products made explicit probiotic claims, but LAB from these displayed no probiotic attributes per WHO-FAO guidelines. The yogurt starter and nonstarter Lactobacillus strains had no gelatinase or hemolytic activity and exhibited significant antibacterial activity against some human pathogens. One brand with a probiotic claim contained an L. acidophilus strain that showed cholesterol assimilation activity in vitro. Some potential human pathogens that were hemolytic and resistance to β-lactam antibiotics were also detected in the products. The findings demonstrate a need for better quality control and regulation to ensure safety and efficacy of yogurt products.     

Effect of thermal aging on electrical conductivity of the 2‐acrylamido‐2‐methyl‐1‐propanesulfonic acid‐doped polyaniline fiber

The thermal characteristics of inherently conductive polyaniline (PANi) fiber have been studied using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Fibers show five major weight losses at ∼100°C, 165°C, 215°C, 315°C, and 465°C, which are associated with the removal of moisture, residual solvent, decompositions of the sulfonic acid and degradation of PANi fiber, respectively. The 2‐acrylamido‐2‐methyl‐1‐propanesulfonic acid (AMPSA) that dopes the PANi (in fiber form) performs two‐stage decompositions. The conductivity of the drawn fibers aged at 50°C, 100°C, 150°C, and 190°C under vacuum for various periods of time decreases, particularly at temperatures higher than 100°C. The reduction in conductivity of the fiber aged at temperatures lower than 100°C is mainly due to the evaporation of the residual solvent (15–20% in the as‐spun fiber). Further decrease in conductivity of the fiber aged at temperatures higher than 100°C is caused by the decomposition of the dopant AMPSA. The temperature‐dependent conductivity of the fiber was measured at 15 K (−258.5°C) to 295 K (21.5°C). The conductivity of both aged and un‐aged fibers is all temperature activated, however, the conductivity of the un‐aged fibers is higher than that of the aged fibers. Although a negative temperature coefficient was observed in the temperature range from 240 K (–24.5°C) to 270 K (–3.5°C) for the un‐aged fibers, it was disappeared when the fibers were thermal aged at 100°C for 24 h in vacuum oven. These results indicate that the residual solvent trapped inside the fiber enhanced the electrical conductivity of the fibers and its “metallic” electrical conductivity at temperatures ∼263 K (–10°C). © 2001 John Wiley & Sons, Inc. † J Appl Polym Sci 79: 2503–2508, 2001     

Interpretable tabular data generation

Generative adversarial network (GAN) models have been successfully utilized in a wide range of machine learning applications, and tabular data generation domain is not an exception. Notably, some state-of-the-art models of tabular data generation, such as CTGAN,  TableGan, MedGAN, etc. are based on GAN models. Even though these models have resulted in superior performance in generating artificial data when trained on a range of datasets, there is a lot of room (and desire) for improvement. Not to mention that existing methods do have some weaknesses other than performance. For example, the current methods focus only on the performance of the model, and limited emphasis is given on the interpretation of the model. Secondly, the current models operate on raw features only, and hence they fail to exploit any prior knowledge on explicit feature interactions that can be utilized during data generation process. To alleviate the two above-mentioned limitations, in this work, we propose a novel tabular data generation model—Generative Adversarial Network modelling inspired from Naive Bayes and Logistic Regression’s relationship (\({ { \texttt {GANBLR} } }\)), which not only address the interpretation limitation of existing tabular GAN-based models but provides capability to handle explicit feature interactions as well. Through extensive evaluations on wide range of datasets, we demonstrate \({ { \texttt {GANBLR} } }\)’s superior performance as well as better interpretable capability (explanation of feature importance in the synthetic generation process) as compared to existing state-of-the-art tabular data generation models.

     

Electric Properties of AlGaN/GaN/Si High electron Mobility Transistors

Silicon - This work investigated the electrical properties in AlGaN/GaN/Si HEMTsgrown by molecular beam epitaxy. The electrical behavior have been investigated using by electric permittivity,...     

Improved Water Retention and Positive Behavior of Silica Based Geopolymer Utilizing Granite Powder

Silicon - This research aims to study the behavior of silica based geopolymeric material (22–28%Si) from granitic waste. Granitic waste in powder form was used as main precursor in...