An Innovative Tree Gradient Boosting Method Based Image Object detection from a Uniform Background Scene

Object detection is a wide area problem domain in the field of computer and machine vision. Complex background adds challenge and error margin as well to the problem significantly lot algorithms for object detection are hard to comply with occlusion and pixel bending moment affect. In this paper a highly robust algorithm ORBTRIAN for a less resolution image has been proposed and implemented using ORB detection with gradient boosting machine learning algorithm. The work has been compared with Adaboost and Surf based technology. The analysis shows 3.8% increase in performance of earlier model. The feature points extracted from ORB method are further processed to reduce the processing further. Only those points are selected which are triangularly farthest from centroid of it and only 1 point of feature selected. Thus the result is around 28%, much faster than earlier computation. The tree based GB has been implemented in this algorithm. With more number of feature points more classes need to be recognized and hence the computations performed is required an unreasonable amount of effort and time. So some nearby classes are assigned at same level using our algorithm to reduce the number of tree nodes. Overall performance of the proposed algorithm shows a significant increase in efficiency in computation time.     

Localized CO2 laser annealing induced dehydrogenation/ablation and optical refinement of silicon-rich silicon dioxide film with embedded si nanocrystals

CO2 laser annealing induced effects of dehydrogenation, Si nanocrystal precipitation, ablation, and optical refinement in PECVD grown SiO1.25 film are investigated. Dehydrogenation shrinks SiO1.25 thickness by 40 nm after annealing at laser intensity (Plaser) of 4 kW/cm(2) for 1.4 ms. As Plaser increases to 6 kW/cm(2), the photoluminescence (PL) red-shifts to 806 nm due to the size enlargement of Si nanocrystals, while a reduced optical bandgap energy from 3.3 to 2.43 eV and an enlarged refractive index from 1.57 to 1.87 are also observed. Transmission electron microscopy analysis reveals that the randomly oriented Si nanocrystals exhibit an average diameter of 5.3 nm and a volume density of 1.9 x 10(18) cm(-3). CO2 Laser ablation initiates at intensity higher than 7 kW/cm(2), which introduces numerous structural defects with a strong PL at 410 nm. Such an ablation inevitably leads to a blue-shifted optical bandgap energy from 2.43 to 2.76 eV as Plaser enlarges from 6 to 12 kW/cm(2) are concluded.     

Prediction of protein structural classes by support vector machines

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino and composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.     

Rippled and Helical MoS<Subscript>2</Subscript> Nanowire Catalysts: An Aberration Corrected STEM Study

Abstract  

Aberration corrected (C_s) scanning transmission electron microscopy (STEM) has been used for the first time to characterize MoS₂ catalysts (supported on Al₂O₃ substrates) to provide detailed information of its shape and structure. The high-resolution imaging reveals unprecedented morphologies present in the MoS₂ catalyst that have never been observed before with other experimental techniques because of the insufficient image contrast and/or resolution. High angle annular dark field (HAADF)-STEM images shows very clearly that the catalyst is formed by elongated chains with a twisted and helical structure. Based on the HAADF-STEM images, we built three atomic models to illustrate the different morphologies found in the MoS₂ catalyst. The existence of these nanostructures opens the posibility for novel catalyticaly active edge morphologies in MoS₂-based nanocatalysts.     

Synthesis of Sulfonated Carbon Nanocage and Its Performance as Solid Acid Catalyst

Abstract  

A carbon nanocage material (CKT) was first successfully sulfonated by introducing sulfophenyl groups on the surface of pore channels through benzenesulfonic acid-containing aryl radical in situ generated from the reaction of 4-aminobenzenesulfonic acid and isoamyl nitrite in water. The sulfonated carbon nanocage material (S-CKT) was characterized by Fourier transform infrared spectroscopy, transmission electron microscopy, powder small-angle X-ray diffraction and nitrogen sorption measurements. The results showed that the S-CKT still possess the high specific surface area (787 m²/g) and uniform mesoporous (pore diameter 4.7 nm) structures, although the structure of S-CKT is slightly disorder, compared with its unsulfonated precursor. S-CKT, as a carbon-based solid acid catalyst, showed good catalytic performance and reusability in the cross-Aldol condensation of ketones with aromatic aldehydes under solvent-free condition.     

Nanosized Mn(acac)<Subscript>3</Subscript> Anchored on Amino Functionalized Silica for the Selective Oxidative Synthesis of 2-arylbenzimidazoles, 2-arylbenzothiazoles and Aerobic Oxidation of Benzoins in Water

Abstract  

Silica functionalized Mn(acac)₃ was prepared and employed for the one-pot oxidative synthesis of 2-arylbenzimidazoles, 2-arylbenzothiazoles; and oxidation of benzoins to benzils under air atmosphere using water as the reaction medium. Environmentally friendly procedure, chemoselectivity and excellent yields are main advantages of this procedure. In all the cases, the catalyst was found to be highly active and selective; passes hot filtration test successfully; and could be recycled several times with a slight loss of activity.     

Selective Oxidation of Glycerol Catalyzed by Rh/Activated Carbon: Importance of Support Surface Chemistry

Abstract  

Noble metal catalysts (Pt, Ir, Pd, Rh, Au) supported on activated carbon were assessed for glycerol oxidation. Rhodium is a highly efficient catalyst when the support has neutral or basic properties. The surface chemistry of activated carbon plays a key role in the performance.