Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

A comparative study on the metaheuristic-based optimization of skew composite laminates

Engineering with Computers - Plate structures are the integral parts of any maritime engineering platform. With the recent focus on composite structures, the need for optimizing their design and...     

The synthesis of pixelated metamaterial cross polarizer using binary wind driven optimization algorithm

Microsystem Technologies - In this paper synthesis of two wideband Metamaterial Cross Polarizer (MCPs) is proposed. The synthesis of proposed MCPs is done by using Binary Wind Driven Optimization...     

An overview on microwave processing of material: A special emphasis on glass melting

Material processing adopting microwave heating has emerged as an alternative tool owing to faster processing, a cleaner environment, and several other advantages. This review provides a summary of recent reports of microwave synthesis of materials. This study reviews the use of microwave energy for application in several material processing technologies apart from food processing. A special emphasis has been made in the processing of glass adopting microwave energy. Melting of glass comprising SiO₂, P₂O₅, B₂O₃ as the main building block has been discussed. It has been revealed that silica, a microwave transparent material as reported earlier, can be heated under microwave heating directly. Microwave absorption of raw materials and different glass system has been discussed. Dielectric properties, particularly loss tangent or loss factor, are presented for some glass composition. Less evaporation of ingredient and low contamination from the crucible wall are noticed during glass melting using microwave heating. Enhanced iron redox ratio (Fe⁺²/∑Fe) in microwave processing may be considered an advantage in the preparation of heat absorbing filter glass. Small-scale glass melting using the microwave heating has a significant impact on energy and time saving. However, the challenges associated with the upscaling glass melting with microwave heating and future scope have been talked about.     

LDA + U Study of Induced Half Metallicity in Cr-Doped GaN

Half-metallic ferromagnetism in the Ga_{1 ? x}Cr _x N compound at different concentrations, x = 25, 12.5 and 6.25 %, have been investigated using density functional theory as implemented in code Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA) using LDA + U as exchange-correlation (XC) potential, to find out the possibility of new diluted DMSs. The outcomes reveal that transition metal atom (Cr) doping in GaN induces ferromagnetism. The 3d levels of the TM ion originate a half-metallic gap at the Fermi level in the majority spin channel for all concentrations. Moreover, diluted magnetic semiconductor compounds retain the half-metallic nature at all concentrations, i.e., x = 0.25, 0.125 and 0.0625, with 100 % spin polarization at the Fermi level (E _F). The total magnetic moment of these compounds is due to Cr-3d states, and the existence of a small magnetic moment on Ga and N, non-magnetic atoms, for all doping concentrations is a consequence of p-d hybridization of Cr-d and N-p states. The calculated values of s-d exchange constant N α and p-d exchange constant N βconfirm the ferromagnetic character of the Cr-doped GaN compound.     

Hydrostatic Pressure Effect on Ga<Subscript>0.75</Subscript>Cr<Subscript>0.25</Subscript>As DMS: DFT Study

This paper studies the first-principles calculations of the effect of hydrostatic pressure on the structural, electronic and magnetic properties of Ga_0.75Cr_0.25As dilute magnetic semiconductor in zb (B3) phase. High-pressure behaviour of Ga_0.75Cr_0.25As has been investigated between 0 and 100 GPa. The calculations have been performed using DFT as implemented in code SIESTA using LDA + U as an exchange-correlation (XC) potential. The study of band structures shows half-metallic ferromagnetic nature with 100% spin polarization. Under application of external pressure, the valence band and conduction band are shifted from their positions which lead to modification of electronic structure.     

Automatic text classification using BPLion-neural network and semantic word processing

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.     

Layered Approach Using Conditional Random Fields for Intrusion Detection

Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed system based on Layered Conditional Random Fields outperforms other well-known methods such as the decision trees and the naive Bayes. The improvement in attack detection accuracy is very high, particularly, for the U2R attacks (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.