Quasi-lossless optical links for broad-band transmission and data processing

We present the first experimental implementation of a recently designed quasi-lossless fiber span with strongly reduced signal power excursion. The resulting fiber waveguide medium can be advantageously used both in lightwave communications and in all-optical nonlinear data processing.

Quasi-lossless spans for broadband transmission and data processing

We present the first experimental implementation of a recently designed quasi-lossless fibre span with strongly reduced signal power excursion. The resulting fibre waveguide medium can be advantageously used both in lightwave communications and in all-optical nonlinear data processing.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

Quasi-lossless optical links for broad-band transmission and data processing

We present the first experimental implementation of a recently designed quasi-lossless fiber span with strongly reduced signal power excursion. The resulting fiber waveguide medium can be advantageously used both in lightwave communications and in all-optical nonlinear data processing. © 2005 IEEE.

Quasi-lossless spans for broadband transmission and data processing

We present the first experimental implementation of a recently designed quasi-lossless fibre span with strongly reduced signal power excursion. The resulting fibre waveguide medium can be advantageously used both in lightwave communications and in all-optical nonlinear data processing.

Metadata and Geospatial Data Processing on the Base of XML and Grid

The software architecture and development consideration for open metadata extraction and processing framework are outlined. Special attention is paid to the aspects of reliability and fault tolerance. Grid infrastructure is shown as useful backend for general-purpose task.

Measurement of intervertebral cervical motion by means of dynamic X-ray image processing and data interpolation

Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements. © 2013 Paolo Bifulco et al.

Signal processing and information fusing algorithms for the synthesis of an alternative electromyogram/eye gaze tracking computer cursor control system

This research pursued the conceptualization and real-time verification of a system that allows a computer user to control the cursor of a computer interface without using his/her hands. The target user groups for this system are individuals who are unable to use their hands due to spinal dysfunction or other afflictions, and individuals who must use their hands for higher priority tasks while still requiring interaction with a computer. ^ The system receives two forms of input from the user: Electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an Eye Gaze Tracking (EGT) system. In order to produce reliable cursor control from the two forms of user input, the development of this EMG/EGT system addressed three key requirements: an algorithm was created to accurately translate EMG signals due to facial movements into cursor actions, a separate algorithm was created that recognized an eye gaze fixation and provided an estimate of the associated eye gaze position, and an information fusion protocol was devised to efficiently integrate the outputs of these algorithms. ^ Experiments were conducted to compare the performance of EMG/EGT cursor control to EGT-only control and mouse control. These experiments took the form of two different types of point-and-click trials. The data produced by these experiments were evaluated using statistical analysis, Fitts' Law analysis and target re-entry (TRE) analysis. ^ The experimental results revealed that though EMG/EGT control was slower than EGT-only and mouse control, it provided effective hands-free control of the cursor without a spatial accuracy limitation, and it also facilitated a reliable click operation. This combination of qualities is not possessed by either EGT-only or mouse control, making EMG/EGT cursor control a unique and practical alternative for a user's cursor control needs. ^

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

Options to advance co-ordination and analysis of climate data in Europe

Systematic, high-quality observations of the atmosphere, oceans and terrestrial environments are required to improve understanding of climate characteristics and the consequences of climate change. The overall aim of this report is to carry out a comparative assessment of approaches taken to addressing the state of European observations systems and related data analysis by some leading actors in the field. This research reports on approaches to climate observations and analyses in Ireland, Switzerland, Germany, The Netherlands and Austria and explores options for a more coordinated approach to national responses to climate observations in Europe. The key aspects addressed are: an assessment of approaches to develop GCOS and provision of analysis of GCOS data; an evaluation of how these countries are reporting development of GCOS; highlighting best practice in advancing GCOS implementation including analysis of Essential Climate Variables (ECVs); a comparative summary of the differences and synergies in terms of the reporting of climate observations; an overview of relevant European initiatives and recommendations on how identified gaps might be addressed in the short to medium term.

A Framework for Citizen-Centred Social Statistics and Analysis (Working Paper 48)

Economic policy-making has long been more integrated than social policy-making in part because the statistics and much of the analysis that supports economic policy are based on a common conceptual framework – the system of national accounts. People interested in economic analysis and economic policy share a common language of communication, one that includes both concepts and numbers. This paper examines early attempts to develop a system of social statistics that would mirror the system of national accounts, particular the work on the development of social accounts that took place mainly in the 60s and 70s. It explores the reasons why these early initiatives failed but argues that the preconditions now exist to develop a new conceptual framework to support integrated social statistics – and hence a more coherent, effective social policy. Optimism is warranted for two reasons. First, we can make use of the radical transformation that has taken place in information technology both in processing data and in providing wide access to the knowledge that can flow from the data. Second, the conditions exist to begin to shift away from the straight jacket of government-centric social statistics, with its implicit assumption that governments must be the primary actors in finding solutions to social problems. By supporting the decision-making of all the players (particularly individual citizens) who affect social trends and outcomes, we can start to move beyond the sterile, ideological discussions that have dominated much social discourse in the past and begin to build social systems and structures that evolve, almost automatically, based on empirical evidence of ‘what works best for whom’. The paper describes a Canadian approach to developing a framework, or common language, to support the evolution of an integrated, citizen-centric system of social statistics and social analysis. This language supports the traditional social policy that we have today; nothing is lost. However, it also supports a quite different social policy world, one where individual citizens and families (not governments) are seen as the central players – a more empirically-driven world that we have referred to as the ‘enabling society’.

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Investigation of multisensory processing and structural brain differences in Autism Spectrum Disorder

This thesis is an investigation of structural brain abnormalities, as well as multisensory and unisensory processing deficits in autistic traits and Autism Spectrum Disorder (ASD). To achieve this, structural and functional magnetic resonance imaging (fMRI) and psychophysical techniques were employed. ASD is a neurodevelopmental condition which is characterised by the social communication and interaction deficits, as well as repetitive patterns of behaviour, interests and activities. These traits are thought to be present in a typical population. The Autism Spectrum Quotient questionnaire (AQ) was developed to assess the prevalence of autistic traits in the general population. Von dem Hagen et al. (2011) revealed a link between AQ with white matter (WM) and grey matter (GM) volume (using voxel-based-morphometry). However, their findings revealed no difference in GM in areas associated with social cognition. Cortical thickness (CT) measurements are known to be a more direct measure of cortical morphology than GM volume. Therefore, Chapter 2 investigated the relationship between AQ scores and CT in the same sample of participants. This study showed that AQ scores correlated with CT in the left temporo-occipital junction, left posterior cingulate, right precentral gyrus and bilateral precentral sulcus, in a typical population. These areas were previously associated with structural and functional differences in ASD. Thus the findings suggest, to some extent, autistic traits are reflected in brain structure - in the general population. The ability to integrate auditory and visual information is crucial to everyday life, and results are mixed regarding how ASD influences audiovisual integration. To investigate this question, Chapter 3 examined the Temporal Integration Window (TIW), which indicates how precisely sight and sound need to be temporally aligned so that a unitary audiovisual event can be perceived. 26 adult males with ASD and 26 age and IQ-matched typically developed males were presented with flash-beep (BF), point-light drummer, and face-voice (FV) displays with varying degrees of asynchrony and asked to make Synchrony Judgements (SJ) and Temporal Order Judgements (TOJ). Analysis of the data included fitting Gaussian functions as well as using an Independent Channels Model (ICM) to fit the data (Garcia-Perez & Alcala-Quintana, 2012). Gaussian curve fitting for SJs showed that the ASD group had a wider TIW, but for TOJ no group effect was found. The ICM supported these results and model parameters indicated that the wider TIW for SJs in the ASD group was not due to sensory processing at the unisensory level, but rather due to decreased temporal resolution at a decisional level of combining sensory information. Furthermore, when performing TOJ, the ICM revealed a smaller Point of Subjective Simultaneity (PSS; closer to physical synchrony) in the ASD group than in the TD group. Finding that audiovisual temporal processing is different in ASD encouraged us to investigate the neural correlates of multisensory as well as unisensory processing using functional magnetic resonance imaging fMRI. Therefore, Chapter 4 investigated audiovisual, auditory and visual processing in ASD of simple BF displays and complex, social FV displays. During a block design experiment, we measured the BOLD signal when 13 adults with ASD and 13 typically developed (TD) age-sex- and IQ- matched adults were presented with audiovisual, audio and visual information of BF and FV displays. Our analyses revealed that processing of audiovisual as well as unisensory auditory and visual stimulus conditions in both the BF and FV displays was associated with reduced activation in ASD. Audiovisual, auditory and visual conditions of FV stimuli revealed reduced activation in ASD in regions of the frontal cortex, while BF stimuli revealed reduced activation the lingual gyri. The inferior parietal gyrus revealed an interaction between stimulus sensory condition of BF stimuli and group. Conjunction analyses revealed smaller regions of the superior temporal cortex (STC) in ASD to be audiovisual sensitive. Against our predictions, the STC did not reveal any activation differences, per se, between the two groups. However, a superior frontal area was shown to be sensitive to audiovisual face-voice stimuli in the TD group, but not in the ASD group. Overall this study indicated differences in brain activity for audiovisual, auditory and visual processing of social and non-social stimuli in individuals with ASD compared to TD individuals. These results contrast previous behavioural findings, suggesting different audiovisual integration, yet intact auditory and visual processing in ASD. Our behavioural findings revealed audiovisual temporal processing deficits in ASD during SJ tasks, therefore we investigated the neural correlates of SJ in ASD and TD controls. Similar to Chapter 4, we used fMRI in Chapter 5 to investigate audiovisual temporal processing in ASD in the same participants as recruited in Chapter 4. BOLD signals were measured while the ASD and TD participants were asked to make SJ on audiovisual displays of different levels of asynchrony: the participants’ PSS, audio leading visual information (audio first), visual leading audio information (visual first). Whereas no effect of group was found with BF displays, increased putamen activation was observed in ASD participants compared to TD participants when making SJs on FV displays. Investigating SJ on audiovisual displays in the bilateral superior temporal gyrus (STG), an area involved in audiovisual integration (see Chapter 4), we found no group differences or interaction between group and levels of audiovisual asynchrony. The investigation of different levels of asynchrony revealed a complex pattern of results indicating a network of areas more involved in processing PSS than audio first and visual first, as well as areas responding differently to audio first compared to video first. These activation differences between audio first and video first in different brain areas are constant with the view that audio leading and visual leading stimuli are processed differently.