Direct visualization of magnetic-field-induced magnetoelectric switching in multiferroic Aurivillius phase thin films

Multiferroic materials displaying coupled ferroelectric and ferromagnetic order parameters could provide a means for data storage whereby bits could be written electrically and read magnetically, or vice versa. Thin films of Aurivillius phase Bi6Ti2.8Fe1.52Mn0.68O18, previously prepared by a chemical solution deposition (CSD) technique, are multiferroics demonstrating magnetoelectric coupling at room temperature. Here, we demonstrate the growth of a similar composition, Bi6Ti2.99Fe1.46Mn0.55O18, via the liquid injection chemical vapor deposition technique. High-resolution magnetic measurements reveal a considerably higher in-plane ferromagnetic signature than CSD grown films (MS = 24.25 emu/g (215 emu/cm3), MR = 9.916 emu/g (81.5 emu/cm3), HC = 170 Oe). A statistical analysis of the results from a thorough microstructural examination of the samples, allows us to conclude that the ferromagnetic signature can be attributed to the Aurivillius phase, with a confidence level of 99.95%. In addition, we report the direct piezoresponse force microscopy visualization of ferroelectric switching while going through a full in-plane magnetic field cycle, where increased volumes (8.6 to 14% compared with 4 to 7% for the CSD-grown films) of the film engage in magnetoelectric coupling and demonstrate both irreversible and reversible magnetoelectric domain switching.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.