Dynamics of pattern writing and erasure in photorefractive lithium niobate for data storage applications

The present rate of technological advance continues to place significant demands on data storage devices. The sheer amount of digital data being generated each year along with consumer expectations, fuels these demands. At present, most digital data is stored magnetically, in the form of hard disk drives or on magnetic tape. The increase in areal density (AD) of magnetic hard disk drives over the past 50 years has been of the order of 100 million times, and current devices are storing data at ADs of the order of hundreds of gigabits per square inch. However, it has been known for some time that the progress in this form of data storage is approaching fundamental limits. The main limitation relates to the lower size limit that an individual bit can have for stable storage. Various techniques for overcoming these fundamental limits are currently the focus of considerable research effort. Most attempt to improve current data storage methods, or modify these slightly for higher density storage. Alternatively, three dimensional optical data storage is a promising field for the information storage needs of the future, offering very high density, high speed memory. There are two ways in which data may be recorded in a three dimensional optical medium; either bit-by-bit (similar in principle to an optical disc medium such as CD or DVD) or by using pages of bit data. Bit-by-bit techniques for three dimensional storage offer high density but are inherently slow due to the serial nature of data access. Page-based techniques, where a two-dimensional page of data bits is written in one write operation, can offer significantly higher data rates, due to their parallel nature. Holographic Data Storage (HDS) is one such page-oriented optical memory technique. This field of research has been active for several decades, but with few commercial products presently available. Another page-oriented optical memory technique involves recording pages of data as phase masks in a photorefractive medium. A photorefractive material is one by which the refractive index can be modified by light of the appropriate wavelength and intensity, and this property can be used to store information in these materials. In phase mask storage, two dimensional pages of data are recorded into a photorefractive crystal, as refractive index changes in the medium. A low-intensity readout beam propagating through the medium will have its intensity profile modified by these refractive index changes and a CCD camera can be used to monitor the readout beam, and thus read the stored data. The main aim of this research was to investigate data storage using phase masks in the photorefractive crystal, lithium niobate (LiNbO3). Firstly the experimental methods for storing the two dimensional pages of data (a set of vertical stripes of varying lengths) in the medium are presented. The laser beam used for writing, whose intensity profile is modified by an amplitudemask which contains a pattern of the information to be stored, illuminates the lithium niobate crystal and the photorefractive effect causes the patterns to be stored as refractive index changes in the medium. These patterns are read out non-destructively using a low intensity probe beam and a CCD camera. A common complication of information storage in photorefractive crystals is the issue of destructive readout. This is a problem particularly for holographic data storage, where the readout beam should be at the same wavelength as the beam used for writing. Since the charge carriers in the medium are still sensitive to the read light field, the readout beam erases the stored information. A method to avoid this is by using thermal fixing. Here the photorefractive medium is heated to temperatures above 150�C; this process forms an ionic grating in the medium. This ionic grating is insensitive to the readout beam and therefore the information is not erased during readout. A non-contact method for determining temperature change in a lithium niobate crystal is presented in this thesis. The temperature-dependent birefringent properties of the medium cause intensity oscillations to be observed for a beam propagating through the medium during a change in temperature. It is shown that each oscillation corresponds to a particular temperature change, and by counting the number of oscillations observed, the temperature change of the medium can be deduced. The presented technique for measuring temperature change could easily be applied to a situation where thermal fixing of data in a photorefractive medium is required. Furthermore, by using an expanded beam and monitoring the intensity oscillations over a wide region, it is shown that the temperature in various locations of the crystal can be monitored simultaneously. This technique could be used to deduce temperature gradients in the medium. It is shown that the three dimensional nature of the recording medium causes interesting degradation effects to occur when the patterns are written for a longer-than-optimal time. This degradation results in the splitting of the vertical stripes in the data pattern, and for long writing exposure times this process can result in the complete deterioration of the information in the medium. It is shown in that simply by using incoherent illumination, the original pattern can be recovered from the degraded state. The reason for the recovery is that the refractive index changes causing the degradation are of a smaller magnitude since they are induced by the write field components scattered from the written structures. During incoherent erasure, the lower magnitude refractive index changes are neutralised first, allowing the original pattern to be recovered. The degradation process is shown to be reversed during the recovery process, and a simple relationship is found relating the time at which particular features appear during degradation and recovery. A further outcome of this work is that the minimum stripe width of 30 ìm is required for accurate storage and recovery of the information in the medium, any size smaller than this results in incomplete recovery. The degradation and recovery process could be applied to an application in image scrambling or cryptography for optical information storage. A two dimensional numerical model based on the finite-difference beam propagation method (FD-BPM) is presented and used to gain insight into the pattern storage process. The model shows that the degradation of the patterns is due to the complicated path taken by the write beam as it propagates through the crystal, and in particular the scattering of this beam from the induced refractive index structures in the medium. The model indicates that the highest quality pattern storage would be achieved with a thin 0.5 mm medium; however this type of medium would also remove the degradation property of the patterns and the subsequent recovery process. To overcome the simplistic treatment of the refractive index change in the FD-BPM model, a fully three dimensional photorefractive model developed by Devaux is presented. This model shows significant insight into the pattern storage, particularly for the degradation and recovery process, and confirms the theory that the recovery of the degraded patterns is possible since the refractive index changes responsible for the degradation are of a smaller magnitude. Finally, detailed analysis of the pattern formation and degradation dynamics for periodic patterns of various periodicities is presented. It is shown that stripe widths in the write beam of greater than 150 ìm result in the formation of different types of refractive index changes, compared with the stripes of smaller widths. As a result, it is shown that the pattern storage method discussed in this thesis has an upper feature size limit of 150 ìm, for accurate and reliable pattern storage.

Outsourcing data storage without outsourcing trust in cloud computing

The main theme of this thesis is to allow the users of cloud services to outsource their data without the need to trust the cloud provider. The method is based on combining existing proof-of-storage schemes with distance-bounding protocols. Specifically, cloud customers will be able to verify the confidentiality, integrity, availability, fairness (or mutual non-repudiation), data freshness, geographic assurance and replication of their stored data directly, without having to rely on the word of the cloud provider.

Nonvolatile multilevel data storage memory device from controlled ambipolar charge trapping mechanism

The capability of storing multi-bit information is one of the most important challenges in memory technologies. An ambipolar polymer which intrinsically has the ability to transport electrons and holes as a semiconducting layer provides an opportunity for the charge trapping layer to trap both electrons and holes efficiently. Here, we achieved large memory window and distinct multilevel data storage by utilizing the phenomena of ambipolar charge trapping mechanism. As fabricated flexible memory devices display five well-defined data levels with good endurance and retention properties showing potential application in printed electronics.

QUT’s research data storage solutions: A launchpad for research journeys

This poster presents key features of how QUT’s integrated research data storage and management services work with researchers through their own individual or team research life cycle. By understanding the characteristics of research data, and the long-term need to store this data, QUT has provided resources and tools that support QUT’s goal of being a research intensive institute. Key to successful delivery and operation has been the focus upon researchers’ individual needs and the collaboration between providers, in particular, Information Technology Services, High Performance Computing and Research Support, and QUT Library. QUT’s Research Data Storage service provides all QUT researchers (staff and Higher Degree Research students (HDRs)) with a secure data repository throughout the research data lifecycle. Three distinct storage areas provide for raw research data to be acquired, project data to be worked on, and published data to be archived. Since the service was launched in late 2014, it has provided research project teams from all QUT faculties with acquisition, working or archival data space. Feedback indicates that the storage suits the unique needs of researchers and their data. As part of the workflow to establish storage space for researchers, Research Support Specialists and Research Data Librarians consult with researchers and HDRs to identify data storage requirements for projects and individual researchers, and to select and implement the most suitable data storage services and facilities. While research can be a journey into the unknown[1], a plan can help navigate through the uncertainty. Intertwined in the storage provision is QUT’s Research Data Management Planning tool. Launched in March 2015, it has already attracted 273 QUT staff and 352 HDR student registrations, and over 620 plans have been created (2/10/2015). Developed in collaboration with Office of Research Ethics and Integrity (OREI), uptake of the plan has exceeded expectations.

Optical data encryption using time-dependent dynamics of refractive index changes in LiNbO3

We present a method for optical encryption of information, based on the time-dependent dynamics of writing and erasure of refractive index changes in a bulk lithium niobate medium. Information is written into the photorefractive crystal with a spatially amplitude modulated laser beam which when overexposed significantly degrades the stored data making it unrecognizable. We show that the degradation can be reversed and that a one-to-one relationship exists between the degradation and recovery rates. It is shown that this simple relationship can be used to determine the erasure time required for decrypting the scrambled index patterns. In addition, this method could be used as a straightforward general technique for determining characteristic writing and erasure rates in photorefractive media.

Enhanced geoproof : improved geographic assurance for data in the cloud

The geographic location of cloud data storage centres is an important issue for many organisations and individuals due to various regulations that require data and operations to reside in specific geographic locations. Thus, cloud users may want to be sure that their stored data have not been relocated into unknown geographic regions that may compromise the security of their stored data. Albeshri et al. (2012) combined proof of storage (POS) protocols with distance-bounding protocols to address this problem. However, their scheme involves unnecessary delay when utilising typical POS schemes due to computational overhead at the server side. The aim of this paper is to improve the basic GeoProof protocol by reducing the computation overhead at the server side. We show how this can maintain the same level of security while achieving more accurate geographic assurance.

Straightforward data management at QUT

QUT Library Research Support has simplified and streamlined the process of research data management planning, storage, discovery and reuse through collaboration and the use of integrated and tailored online tools, and a simplification of the metadata schema. This poster presents the integrated data management services a QUT, including QUT’s Data Management Planning Tool, Research Data Finder, Spatial Data Finder and Software Finder, and information on the simplified Registry Interchange Format – Collections and Services (RIF-CS) Schema. The QUT Data Management Planning (DMP) Tool was built using the Digital Curation Centre’s DMP Online Tool and modified to QUT’s needs and policies. The tool allows researchers and Higher Degree Research students to plan how to handle research data throughout the active phase of their research. The plan is promoted as a ‘live’ document’ and researchers are encouraged to update it as required. The information entered into the plan can be made private or shared with supervisors, project members and external examiners. A plan is mandatory when requesting storage space on the QUT Research Data Storage Service. QUT’s Research Data Finder is integrated with QUT’s Academic Profiles and the Data Management Planning Tool to create a seamless data management process. This process aims to encourage the creation of high quality rich records which facilitate discovery and reuse of quality data. The Registry Interchange Format – Collections and Services (RIF-CS) Schema that is used in the QUT Research Data Finder was simplified to “RIF-CS lite” to reflect mandatory and optional metadata requirements. RIF-CS lite removed schema fields that were underused or extra to the needs of the users and system. This has reduced the amount of metadata fields required from users and made integration of systems a far more simple process where field content is easily shared across services making the process of collecting metadata as transparent as possible.

Adopting healthcare big data in Sri Lankan healthcare sector

The concept of big data has already outperformed traditional data management efforts in almost all industries. Other instances it has succeeded in obtaining promising results that provide value from large-scale integration and analysis of heterogeneous data sources for example Genomic and proteomic information. Big data analytics have become increasingly important in describing the data sets and analytical techniques in software applications that are so large and complex due to its significant advantages including better business decisions, cost reduction and delivery of new product and services [1]. In a similar context, the health community has experienced not only more complex and large data content, but also information systems that contain a large number of data sources with interrelated and interconnected data attributes. That have resulted in challenging, and highly dynamic environments leading to creation of big data with its enumerate complexities, for instant sharing of information with the expected security requirements of stakeholders. When comparing big data analysis with other sectors, the health sector is still in its early stages. Key challenges include accommodating the volume, velocity and variety of healthcare data with the current deluge of exponential growth. Given the complexity of big data, it is understood that while data storage and accessibility are technically manageable, the implementation of Information Accountability measures to healthcare big data might be a practical solution in support of information security, privacy and traceability measures. Transparency is one important measure that can demonstrate integrity which is a vital factor in the healthcare service. Clarity about performance expectations is considered to be another Information Accountability measure which is necessary to avoid data ambiguity and controversy about interpretation and finally, liability [2]. According to current studies [3] Electronic Health Records (EHR) are key information resources for big data analysis and is also composed of varied co-created values [3]. Common healthcare information originates from and is used by different actors and groups that facilitate understanding of the relationship for other data sources. Consequently, healthcare services often serve as an integrated service bundle. Although a critical requirement in healthcare services and analytics, it is difficult to find a comprehensive set of guidelines to adopt EHR to fulfil the big data analysis requirements. Therefore as a remedy, this research work focus on a systematic approach containing comprehensive guidelines with the accurate data that must be provided to apply and evaluate big data analysis until the necessary decision making requirements are fulfilled to improve quality of healthcare services. Hence, we believe that this approach would subsequently improve quality of life.

Medical Data Access Accountability in EHR Systems, A Practical Perspective

The world has experienced a large increase in the amount of available data. Therefore, it requires better and more specialized tools for data storage and retrieval and information privacy. Recently Electronic Health Record (EHR) Systems have emerged to fulfill this need in health systems. They play an important role in medicine by granting access to information that can be used in medical diagnosis. Traditional systems have a focus on the storage and retrieval of this information, usually leaving issues related to privacy in the background. Doctors and patients may have different objectives when using an EHR system: patients try to restrict sensible information in their medical records to avoid misuse information while doctors want to see as much information as possible to ensure a correct diagnosis. One solution to this dilemma is the Accountable e-Health model, an access protocol model based in the Information Accountability Protocol. In this model patients are warned when doctors access their restricted data. They also enable a non-restrictive access for authenticated doctors. In this work we use FluxMED, an EHR system, and augment it with aspects of the Information Accountability Protocol to address these issues. The Implementation of the Information Accountability Framework (IAF) in FluxMED provides ways for both patients and physicians to have their privacy and access needs achieved. Issues related to storage and data security are secured by FluxMED, which contains mechanisms to ensure security and data integrity. The effort required to develop a platform for the management of medical information is mitigated by the FluxMED's workflow-based architecture: the system is flexible enough to allow the type and amount of information being altered without the need to change in your source code.

Data distribution and task scheduling for distributed computing of all-to-all comparison problems

This research studied distributed computing of all-to-all comparison problems with big data sets. The thesis formalised the problem, and developed a high-performance and scalable computing framework with a programming model, data distribution strategies and task scheduling policies to solve the problem. The study considered storage usage, data locality and load balancing for performance improvement in solving the problem. The research outcomes can be applied in bioinformatics, biometrics and data mining and other domains in which all-to-all comparisons are a typical computing pattern.

Data-aware task scheduling for all-to-all comparison problems in heterogeneous distributed systems

Solving large-scale all-to-all comparison problems using distributed computing is increasingly significant for various applications. Previous efforts to implement distributed all-to-all comparison frameworks have treated the two phases of data distribution and comparison task scheduling separately. This leads to high storage demands as well as poor data locality for the comparison tasks, thus creating a need to redistribute the data at runtime. Furthermore, most previous methods have been developed for homogeneous computing environments, so their overall performance is degraded even further when they are used in heterogeneous distributed systems. To tackle these challenges, this paper presents a data-aware task scheduling approach for solving all-to-all comparison problems in heterogeneous distributed systems. The approach formulates the requirements for data distribution and comparison task scheduling simultaneously as a constrained optimization problem. Then, metaheuristic data pre-scheduling and dynamic task scheduling strategies are developed along with an algorithmic implementation to solve the problem. The approach provides perfect data locality for all comparison tasks, avoiding rearrangement of data at runtime. It achieves load balancing among heterogeneous computing nodes, thus enhancing the overall computation time. It also reduces data storage requirements across the network. The effectiveness of the approach is demonstrated through experimental studies.

Coordinated research support services at Queensland University of Technology, Australia

Queensland University of Technology (QUT) is a large multidisciplinary university located in Brisbane, Queensland, Australia. QUT is increasing its research focus and is developing its research support services. It has adopted a model of collaboration between the Library, High Performance Computing and Research Support (HPC) and more broadly with Information Technology Services (ITS). Research support services provided by the Library include the provision of information resources and discovery services, bibliographic management software, assistance with publishing (publishing strategies, identifying high impact journals, dealing with publishers and the peer review process), citation analysis and calculating authors’ H Index. Research data management services are being developed by the Library and HPC working in collaboration. The HPC group within ITS supports research computing infrastructure, research development and engagement activities, researcher consultation, high speed computation and data storage systems , 2D/ 3D (immersive) visualisation tools, parallelisation and optimization of research codes, statistics/ data modeling training and support (both qualitative and quantitative) and support for the university’s central Access Grid collaboration facility. Development and engagement activities include participation in research grants and papers, student supervision and internships and the sponsorship, incubation and adoption of new computing technologies for research. ITS also provides other services that support research including ICT training, research infrastructure (networking, data storage, federated access and authorization, virtualization) and corporate systems for research administration. Seminars and workshops are offered to increase awareness and uptake of new and existing services. A series of online surveys on eResearch practices and skills and a number of focus groups was conducted to better inform the development of research support services. Progress towards the provision of research support is described within the context organizational frameworks; resourcing; infrastructure; integration; collaboration; change management; engagement; awareness and skills; new services; and leadership. Challenges to be addressed include the need to redeploy existing operational resources toward new research support services, supporting a rapidly growing research profile across the university, the growing need for the use and support of IT in research programs, finding capacity to address the diverse research support needs across the disciplines, operationalising new research support services following their implementation in project mode, embedding new specialist staff roles, cross-skilling Liaison Librarians, and ensuring continued collaboration between stakeholders.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.