Parabolic similaritons in optical fibres

Recent developments in nonlinear optics have brought to the fore of intensive research an interesting class of pulses with a parabolic intensity profile and a linear instantaneous frequency shift or chirp. Parabolic pulses propagate in optical fibres with normal group-velocity dispersion in a self-similar manner, holding certain relations (scaling) between pulse power, duration and chirp parameter, and can tolerate strong nonlinearity without distortion or wave breaking. These solutions, which have been dubbed similaritons, were demonstrated theoretically and experimentally in fibre amplifiers in 2000. Similaritons in fibre amplifiers are, along with solitons in passive fibres, the most well-known classes of nonlinear attractors for pulse propagation in optical fibre, so they take on major fundamental importance. The unique properties of parabolic similaritons have stimulated numerous applications in nonlinear optics, ranging from ultrashort high-power pulse generation to highly coherent continuum sources and to optical nonlinear processing of telecommunication signals. In this work, we review the physics underlying the generation of parabolic similaritons as well as recent results obtained in a wide range of experimental configurations.

Data partitioning and replication management in distributed geographic information system (GIS) database

This research presents several components encompassing the scope of the objective of Data Partitioning and Replication Management in Distributed GIS Database. Modern Geographic Information Systems (GIS) databases are often large and complicated. Therefore data partitioning and replication management problems need to be addresses in development of an efficient and scalable solution. ^ Part of the research is to study the patterns of geographical raster data processing and to propose the algorithms to improve availability of such data. These algorithms and approaches are targeting granularity of geographic data objects as well as data partitioning in geographic databases to achieve high data availability and Quality of Service(QoS) considering distributed data delivery and processing. To achieve this goal a dynamic, real-time approach for mosaicking digital images of different temporal and spatial characteristics into tiles is proposed. This dynamic approach reuses digital images upon demand and generates mosaicked tiles only for the required region according to user's requirements such as resolution, temporal range, and target bands to reduce redundancy in storage and to utilize available computing and storage resources more efficiently. ^ Another part of the research pursued methods for efficient acquiring of GIS data from external heterogeneous databases and Web services as well as end-user GIS data delivery enhancements, automation and 3D virtual reality presentation. ^ There are vast numbers of computing, network, and storage resources idling or not fully utilized available on the Internet. Proposed "Crawling Distributed Operating System "(CDOS) approach employs such resources and creates benefits for the hosts that lend their CPU, network, and storage resources to be used in GIS database context. ^ The results of this dissertation demonstrate effective ways to develop a highly scalable GIS database. The approach developed in this dissertation has resulted in creation of TerraFly GIS database that is used by US government, researchers, and general public to facilitate Web access to remotely-sensed imagery and GIS vector information. ^

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

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.

In vivo characterization of myocardial tissue post myocardial infarction using combined fluorescence and diffuse reflectance spectroscopy

Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region—one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees/severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.

In Vivo Characterization of Myocardial Tissue Post Myocardial Infarction Using Combined Fluorescence and Diffuse Reflectance Spectroscopy

Accurately assessing the extent of myocardial tissue injury induced by Myocardial infarction (MI) is critical to the planning and optimization of MI patient management. With this in mind, this study investigated the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at the different stages of its development. An animal study was conducted using twenty male Sprague-Dawley rats with MI. In vivo fluorescence spectra at 337 nm excitation and diffuse reflectance between 400 nm and 900 nm were measured from the heart using a portable fiber-optic spectroscopic system. Spectral acquisition was performed on - (1) the normal heart region; (2) the region immediately surrounding the infarct; and (3) the infarcted region - one, two, three and four weeks into MI development. The spectral data were divided into six subgroups according to the histopathological features associated with various degrees / severities of myocardial tissue injury as well as various stages of myocardial tissue remodeling, post infarction. Various data processing and analysis techniques were employed to recognize the representative spectral features corresponding to various histopathological features associated with myocardial infarction. The identified spectral features were utilized in discriminant analysis to further evaluate their effectiveness in classifying tissue injuries induced by MI. In this study, it was observed that MI induced significant alterations (p < 0.05) in the diffuse reflectance spectra, especially between 450 nm and 600 nm, from myocardial tissue within the infarcted and surrounding regions. In addition, MI induced a significant elevation in fluorescence intensities at 400 and 460 nm from the myocardial tissue from the same regions. The extent of these spectral alterations was related to the duration of the infarction. Using the spectral features identified, an effective tissue injury classification algorithm was developed which produced a satisfactory overall classification result (87.8%). The findings of this research support the concept that optical spectroscopy represents a useful tool to non-invasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing valuable real-time feedback to surgeons during various surgical interventions for MI.

56 Gb/s multi-band CAP for data center interconnects up to an 80 km SMF

We present the first (to the best of our knowledge) experimental demonstration of a 56 Gb/s multi-band carrierless amplitude and phase modulation (CAP) signal transmission over an 80-km single-mode fiber link with zero overhead pre-FEC signal recovery and enhanced timing jitter tolerance for optical data center interconnects.

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.