Design of electronic sections for nano-displacement measuring system

Noncontact displacement measurement is generally based on the interferometry method.In the semiconductor industry,a technique for measuring small features is required as circuit integration becomes denser and the wafer size becomes larger.An interferometric system known as a three-longitudinal-mode heterodyne interferometer (TLMI) is made of two main parts:optical setup and electronic sections.In the optical part,the base and measurement signals having 500-MHz frequency are produced,resulting from interfering three longitudinal modes.The secondary beat frequency to measure the displacement in the TLMI is about 300kHz.To extract the secondary beat frequency,wide-band amplifiers,double-balanced mixers (DBMs),band-pass filters (BPFs),and low-pass filters (LPFs) are used.In this paper,we design the integrated circuit of a super-heterodyne interferometer with total gain of 56.9dB in size of 1030μm×1030μm.     

Influence of Ultrasonic Waves on the Processing of Cotton with Cationic Softener

In recent years, the textile industry has been forced to develop new technologies to reduce energy and water consumption. The use of ultrasound in textile wet processing is one solution to this problem. The aim of this work was to investigate the effects of ultrasonic energy on the processing of cotton with a cationic softener. For this purpose, cotton fabric was treated with a fatty acid amide derivative cationic softener in water using ultrasonic energy during treatment. The physical properties of the fabrics treated under different conditions are discussed. The results show that the treatment of fabrics with softeners in an ultrasound bath is more effective compared to conventional methods and that it enhances the physical properties of the cotton.     

Rail RCF damage quantification and comparison for different damage models

There are several fatigue-based approaches that estimate the evolution of rolling contact fatigue(RCF)on rails over time and built to be used in tandem with mul...     

Weldability of titanium and nickel with alloy filler addition and different electrodes tip shapes by using micro spot brazing method

The weldability of titanium and nickel using micro spot brazing technology was successfully investigated. Brazing was performed using a 4-kA welding current, 2-V voltage, 60-N load, and a 25–50-ms welding time. An alloy filler metal, specifically a 0.1-mm thick foil with a composition of 71Ag-28Cu-1Mg, was sandwiched between Ti and Ni. The three electrode copper tip shapes that were investigated in this study were circular-, rectangular-, and ring-shaped tips. The weldability of the joint was evaluated in terms of the fracture load, weld size, shear strength, and micro hardness. A high joint strength was obtained in the case of the 71Ag-28Cu-1Mg filler when a rectangular electrode tip face was used.     

Wheat gluten: high molecular weight glutenin subunits--structure, genetics, and relation to dough elasticity

ABSTRACT:  Gluten proteins, representing the major protein fraction of the starchy endosperm, are predominantly responsible for the unique position of wheat amongst cereals. These form a continuous proteinaceous matrix in the cells of the mature dry grain and form a continuous viscoelastic network during the mixing process of dough development. These viscoelastic properties underline the utilization of wheat to prepare bread and other wheat flour based foodstuffs. One group of gluten proteins is glutenin, which consists of high molecular weight (HMW) and low molecular weight (LMW) subunits. The HMW glutenin subunits (HMW-GS) are particularly important for determining dough elasticity. The common wheat possesses 3 to 5 HMW subunits encoded at the Glu-1 loci on the long arms of group 1 chromosomes (1A, 1B, and 1D). The presence of certain HMW subunits is positively correlated with good bread-making quality. Glutamine-rich repetitive sequences that comprise the central part of the HMW subunits are actually responsible for the elastic properties due to extensive arrays of interchain hydrogen bonds. Genetic engineering can be used to manipulate the amount and composition of the HMW subunits, leading to either increased dough strength or more drastic changes in gluten structure and properties.     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

Multifractal analysis of Mg-doped ZnO thin films deposited by sol–gel spin coating method

A multifractal analysis has been performed on the 3D (three-dimensional) surface microtexture of magnesium-doped zinc oxide (ZnO:Mg) thin films with doping concentration of 0, 2, 4, and 5%. Thin films were deposited onto the glass substrates via the sol–gel spin coating method. The effect of magnesium doping, on the crystal structure, morphology, and band gap for ZnO:Mg thin films has been analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and UV–Vis spectroscopy. It has been observed that the surface of ZnO thin films is multifractal in nature. However, multifractality and complexity observed to decrease with increasing content of Mg in ZnO thin films due to formation of islands on the surface in accordance with Volmer–Weber growth mechanism. The investigations revealed that crystallinity, microtexture, morphology, and optical properties of the thin films can be tuned by controlling the Mg content within the ZnO lattice. In particular, their optical band gap energies were 3.27, 3.31, 3.34, and 3.33 eV at 0, 2, 4, and 5%, respectively. The prepared thin films of ZnO:Mg with tuned characteristics would have promising applications in optoelectronic devices.     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.