Surfactant Assisted Autocombustion Synthesis Of Bismuth Ferrite

In this work, bismuth ferrite powders has been synthesized by glycine nitrate auto-combustion method with addition of ammonium lauryl sulfate (anionic) and triton-X (non-ionic) surfactant. The precursor solutions were prepared from ferric and nickel nitrates. The effects of the surfactant on crystallite size has been investigated by XRD techniques. The results showed that addition of surfactant to the starting solution affected the crystallite size in the final produc

Feature Level Fusion of Face and Fingerprint Biometrics

The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the problem of curse of dimensionality, the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc

Strengthening of High-Alloy Steel through Innovative Heat Treatment Routes

Heat treatment route is an important route for the development of high-strength alloy steel. Many heat treatment processes are applied depending on alloy compositions and desired mechanical properties. There are various high-strength alloy steels, namely, austenitic stainless steel (16–26 wt%Cr, 0.07–0.15 wt%C, 8–10 wt%Ni, rest Fe), where the heat treatment adopted is the low-temperature plasma nitriding so as to achieve a strength in a range of 800–1000 MPa. In twinning-induced plasticity (TWIP) steel (>20 wt%Mn, <1 wt%C, <3 wt%Si, <3 wt%Al, rest Fe), high-temperature thermomechanical heat treatment provides a strength greater than 1000 MPa. High-speed steel (18 wt%W, 4 wt%Cr, 1 wt%V, 0.7 wt%C, 5–8 wt%Co, rest Fe) suits best for high-speed machining purpose, owing to secondary hardening. Besides, high-temperature annealing is performed with majorly ferritic structure to achieve a maximum bending strength of 4700 MPa. Furthermore, in Hadfield steel (11–14 wt%Mn, 1–1.4 wt%C), a fully austenitic phase is obtained with a strength level of 1000 MPa. High-alloy tool steel (5 wt%Mo, 6 wt%W, 4 wt%Cr, 0.3 wt%Si, 1 wt%V, rest Fe) is provided with austenitizing, quenching, and tempering treatment to achieve a maximum hardness of 1200–1400 HV

Ear Identification by Fusion of Segmented Slice Regions using Invariant Features: An Experimental Manifold with Dual Fusion Approach

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. The proposed ear identification method makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are then detected and extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors which are used for identification of individuals separately. The proposed identification technique is tested on IIT Kanpur ear database of 400 individuals and is found to achieve 98.25% accuracy for identification while top 5 matched criteria is set for each subject.Comment: 12 pages, 3 figure