Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.     

Ultra-fast microwave aided synthesis of gold nanocages and structural maneuver studies

Gold nanocages (AuNcgs) are well-studied,hollow,metallic nanostructures that have fascinated researchers in the fields of nanotechnology,materials science,photoelectronics,biotechnology,and medical science for the last decade.However,the time-consuming synthesis of AuNcgs has limited their widespread use in materials science and nano-biotechnology.A novel,ultra-fast,simple,and highly convenient method for the production of AuNcgs using microwave heating is demonstrated herein.This quick method of AuNcg synthesis requires mild laboratory conditions for large-scale production of AuNcgs.The microwave heating technique offers the advantage of precise mechanical control over the temperature and heating power,even for the shortest reaction period (i.e.,seconds).Microwave-synthesized AuNcgs were compared with conventionally synthesized AuNcgs.Structural maneuver studies employing the conventionally produced AuNcgs revealed the formation of screw dislocations and a shift in the lattice plane.Detailed characterization of the microwave-generated AuNcgs was performed using high resolution transmission electron microscopy (HRTEM),scanning electron microscopy (SEM),X-ray powder diffraction (XRD),and spectroscopic techniques.     

Fuzzy ARTMAP classification of invariant features derived using angle of rotation from a neural network

Conventional regular moment functions have been proposed as pattern sensitive features in image classification and recognition applications. But conventional regular moments are only invariant to translation, rotation and equal scaling. It is shown that the conventional regular moment invariants remain no longer invariant when the image is scaled unequally in the x- and y-axis directions. We address this problem by presenting a technique to make the regular moment functions invariant to unequal scaling. However, the technique produces a set of features that are only invariant to translation, unequal/equal scaling and reflection. They are not invariant to rotation. To make them invariant to rotation, moments are calculated with respect to the principal axis of the image. To perform this, the exact angle of rotation must be known. But the method of using the second-order moments to determine this angle will also be inclusive of an undesired tilt angle. Therefore, in order to correctly determine the amount of rotation, the tilt angle which differs for different scaling factors in the x- and y-axis directions for the particular image must be obtained. In order to solve this problem, a neural network using the back-propagation learning algorithm is trained to estimate the tilt angle of the image and from this the amount of rotation for the image can be determined. Next, the new moments are derived and a Fuzzy ARTMAP network is used to classify these images into their respective classes. Sets of experiments involving images rotated and scaled unequally in the x- and y-axis directions are carried out to demonstrate the validity of the proposed technique.     

Heating value of biomass and biomass pyrolysis products

Studies conducted on the heating value of various types of biomass components and their pyrolysis products such as char, liquids and gases are presented. Heating values of chars are comparable with those of lignite and coke; heating values of liquids are comparable with those of oxygenated fuels such as methanol and ethanol, which are much lower than those of petroleum fuels. Heating values of gases are comparable with those of producer gas or coal gas and are much lower than that of natural gas. It is also found that the heating values of products are functions of the initial composition of biomass; correlations are developed to express these. Also, correlations are developed which explain the influence of ash elements on heating values of the pyrolysis products and on percentage distribution of energy in the products.     

Content-based image quality metric using similarity measure of moment vectors

In this paper, the similarity of moment vectors between the test and the reference image blocks together with the result from the block classification are used in the formulation of an image quality metric (IQM). First, the reference and the test images are divided into non-overlapping 8×8 blocks and transformed into moment domain using Discrete Tchebichef Transform. The moment features are then used in two operations: the local quality index calculation and the image content (block) classification. The local quality index is obtained from the similarity measure of moment vectors between the reference and the test image blocks. Next, the content of each reference image block is classified into three types: “plain”, “edge” and “texture”, based on its moment energy level and moment energy distribution. The local quality indices obtained from all the image blocks are then averaged based on the block types to obtain three mean quality scores for each test image. The performance of these three mean quality scores and their combinations are studied using the LIVE database. The results show that the performance of the metric is significantly improved by combining the mean quality scores from the edge and texture image region. The best combination (the proposed metric) is then compared with five other IQMs using the LIVE database and four other independent databases. The results show that the proposed metric performs comparatively well for all the databases.     

Characterization and Low Temperature Study of Iron Telluride Thin Films Upon Ageing and Oxygen Ion Irradiation

The occurrence of superconductivity in iron telluride thin films has been observed upon ageing. The superconducting transition is found to be very robust under an application of magnetic fields up to 10 T. Infrared and Raman spectroscopic characterization of the aged films reveal the formation of Fe_1+δTeO _x upon ageing. As an alternative method of introducing oxygen in FeTe thin film, oxygen ion irradiation has also been carried out It is found that the irradiated film becomes increasingly disordered and ultimately transforms to an amorphous phase upon increasing the irradiation dose. Investigation of electrical resistivity and optical reflectivity of the irradiated FeTe films indicates an interesting possibility of an ion irradiationinduced phase change memory material in analogy to the phase change characteristics of laserirradiated FeTe films.     

The effect of Nb or Ta substitution into the M1 phase of the MoV(Nb,Ta)TeO selective oxidation catalyst

We use aberration corrected high-angle annular dark field (HAADF) imaging to systematically study, atomic column by atomic column, the effects of substituting Nb or Ta into the M1 phase of the MoV(Nb,Ta)TeO propane (amm)oxidation catalyst. The HAADF results indicate that the x,y coordinates of the metal sites within the M1 framework are unaffected by the substitution of either Nb or Ta for Mo. The HAADF analysis of the Ta-substituted catalyst demonstrated that the Ta preferentially substitutes into the pentagonal bipyramidal site, and by analogy, we anticipate that Nb substitutes similarly. Compositional analysis of the entire framework suggests that Ta/Nb behaves as a director of V among the octahedra that link the pentagonal rings, and the variable V occupancy may be correlated with variations in catalytic activities and selectivities. Finally, HAADF imaging provided evidence of coexistence of Ta-rich and Ta-poor domains. Similar phase segregation behavior may be present in Nb-substituted specimens, but would be very difficult to detect.     

Growth features of ammonium hydrogen<Emphasis Type="Italic">d</Emphasis>-tartrate single crystals

Ammonium hydrogend-tartrate (d-AHT) single crystals were grown in silica gel. The growth features of these crystals with variation of parameters like specific gravity of the gel, gel pH, acid concentrations, concentration of the feed solution and gel age were studied in detail.