Nano-Gradient Materials Prepared by Rotary Swaging

Gradient nanostructured metallic materials with a nanostructured surface layer show immense potential for various industrial applications because of their outstanding mechanical, fatigue, corrosion, tribological properties, etc. In the past several decades, various methods for fabricating gradient nanostructure have been developed. Nevertheless, the thickness of gradient microstructure is still in the micrometer scale due to the limitation of preparation techniques. As a traditional but potential technology, rotary swaging (RS) allows gradient stress and strain to be distributed across the radial direction of a bulk cylindrical workpiece. Therefore, in this review paper, we have systematically summarized gradient and even nano-gradient materials prepared by RS. We found that metals processed by RS usually possess inverse nano-gradient, i.e., nano-grains appear in the sample center, texture-gradient and dislocation density-gradient along the radial direction. Moreover, a broad gradient structure is distributed from center to edge of the whole processed rods. In addition, properties including micro-hardness, conductivity, corrosion, etc., of RS processed metals are also reviewed and discussed. Finally, we look forward to the future prospects and further research work for the RS processed materials.

Soil Physico Chemical Properties and Macronutrients Evaluation during Sowing and after Harvesting of Crop at High Altitude Leh Ladakh India

The present study was aimed to investigate the effect of physico-chemical parameters and soil macro-nutrients to know the nutrient uptake status during sowing time (ST) and after the harvesting (AH) of crops of Leh-Ladakh. In this context, total 55 no. of soil samples were collected from the eleven villages. Thereafter, soil texture, pH, electrical conductivity (EC), total dissolved solids (TDS), organic carbon (OC), nitrogen (N), phosphorus (P), and potassium (K) were analyzed as per the standard methods. The results exhibited variation in different studied parameters at ST and AH, are OC (ST- 1.70 ± 0.11; AH-2.31±0.08), N (ST- 171.54±11.40; AH- 212.03±13.18), P (ST- 75.62±8.16; AH- 96.32±11.56), pH (ST- 8.12±0.05; AH- 8.16±0.06), EC (ST- 0.48±0.04; AH- 0.58±17), TDS (ST-309±22.41; AH-189±16.42) and soil texture gradient (Sand: ST-75.16±1.27 & AH-71.75±1.26, Silt: ST- 18.55±1.09 & AH- 20.66±1.02 and clay: ST- 6.33±0.53 & AH- 7.76±0.63). The comparison of physico-chemical parameters, macronutrients, soil texture, and organic carbon at sowing time (ST) and after harvesting (AH) revealed significant difference in some macronutrients, EC, and organic carbon, whereas no changes were observed in soil texture, pH and phosphorus. Hence, this study highlights the need of physico-chemical parameters management during crops sowing for enhancing macronutrients availability to crops in trans-Himalayan high altitude region.

Triple-Type Feature Extraction for Palmprint Recognition

Palmprint recognition has received tremendous research interests due to its outstanding user-friendliness such as non-invasive and good hygiene properties. Most recent palmprint recognition studies such as deep-learning methods usually learn discriminative features from palmprint images, which usually require a large number of labeled samples to achieve a reasonable good recognition performance. However, palmprint images are usually limited because it is relative difficult to collect enough palmprint samples, making most existing deep-learning-based methods ineffective. In this paper, we propose a heuristic palmprint recognition method by extracting triple types of palmprint features without requiring any training samples. We first extract the most important inherent features of a palmprint, including the texture, gradient and direction features, and encode them into triple-type feature codes. Then, we use the block-wise histograms of the triple-type feature codes to form the triple feature descriptors for palmprint representation. Finally, we employ a weighted matching-score level fusion to calculate the similarity between two compared palmprint images of triple-type feature descriptors for palmprint recognition. Extensive experimental results on the three widely used palmprint databases clearly show the promising effectiveness of the proposed method.

Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors

Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist, a pre-operative method for determining tumor malignancy state still remains as an active research area. In this regard, the paper presents a new method for the assessment of tumor grades using conventional MR sequences namely, T1, T1 with contrast enhancement, T2 and FLAIR. The proposed method for tumor gradation is mainly based on feature extraction using multiresolution image analysis and classification using support vector machine. Since the wavelet features of different tumor subregions, obtained from single MR sequence, do not carry equally important information, a wavelet fusion technique is proposed based on the texture information content of each voxel. The concept of texture gradient, used in the proposed algorithm, fuses the wavelet coefficients of the given MR sequences. The feature vector is then derived from the co-occurrence of fused wavelet coefficients. As each wavelet subband contains distinct detail information, a novel concept of multispectral co-occurrence of wavelet coefficients is introduced to capture the spatial correlation among different subbands. It enables to convey more informative features to characterize the tumor type. The effectiveness of the proposed method is analyzed, with respect to six classification performance indices, on BRATS 2012 and BRATS 2014 data sets. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under curve assessed by the ten-fold cross-validation are 91.3%, 96.8%, 66.7%, 92.4%, 88.4%, and 92.0%, respectively, on real brain MR data.

Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI

To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (~3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (~275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.

The Effect of Deformation Strain on Texture Uniformity in Tantalum Sheet during Asymmetric Cross Rolling

Microstructure and crystallographic texture play an important role in the sputtering target properties. The effect of asymmetric cross rolling (ACR) and deformation strain during ACR on texture homogeneity is not clear. Thus, high-purity tantalum (Ta) plates were ACR to 60% and 87% reduction in thickness. Texture of the rolled Ta sheets in the surface and center layer are characterized via X-ray diffraction (XRD). The XRD results indicate that ACR is effective to weaken the texture gradient existing in the as-received Ta plate. Besides, more homogeneous texture distribution along the thickness can be obtained with the increasing strain during ACR process.