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.

Workflow Modelling in Grid System for Satellite Data Processing

This paper focuses on a problem of Grid system decomposition by developing its object model. Unified Modelling Language (UML) is used as a formalization tool. This approach is motivated by the complexity of the system being analysed and the need for simulation model design.

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.

Grid Infrastructure for Satellite Data Processing in Ukraine

In this paper conceptual foundations for the development of Grid systems that aimed for satellite data processing are discussed. The state of the art of development of such Grid systems is analyzed, and a model of Grid system for satellite data processing is proposed. An experience obtained within the development of the Grid system for satellite data processing in the Space Research Institute of NASU-NSAU is discussed.

Distributed Visualization Systems in Remote Sensing Data Processing Grid

Implementation of GEOSS/GMES initiative requires creation and integration of service providers, most of which provide geospatial data output from Grid system to interactive user. In this paper approaches of DOS- centers (service providers) integration used in Ukrainian segment of GEOSS/GMES will be considered and template solutions for geospatial data visualization subsystems will be suggested. Developed patterns are implemented in DOS center of Space Research Institute of National Academy of Science of Ukraine and National Space Agency of Ukraine (NASU-NSAU).

Advances in characterisation, calibration and data processing speed of optical coherence tomography systems

This thesis describes advances in the characterisation, calibration and data processing of optical coherence tomography (OCT) systems. Femtosecond (fs) laser inscription was used for producing OCT-phantoms. Transparent materials are generally inert to infra-red radiations, but with fs lasers material modification occurs via non-linear processes when the highly focused light source interacts with the materials. This modification is confined to the focal volume and is highly reproducible. In order to select the best inscription parameters, combination of different inscription parameters were tested, using three fs laser systems, with different operating properties, on a variety of materials. This facilitated the understanding of the key characteristics of the produced structures with the aim of producing viable OCT-phantoms. Finally, OCT-phantoms were successfully designed and fabricated in fused silica. The use of these phantoms to characterise many properties (resolution, distortion, sensitivity decay, scan linearity) of an OCT system was demonstrated. Quantitative methods were developed to support the characterisation of an OCT system collecting images from phantoms and also to improve the quality of the OCT images. Characterisation methods include the measurement of the spatially variant resolution (point spread function (PSF) and modulation transfer function (MTF)), sensitivity and distortion. Processing of OCT data is a computer intensive process. Standard central processing unit (CPU) based processing might take several minutes to a few hours to process acquired data, thus data processing is a significant bottleneck. An alternative choice is to use expensive hardware-based processing such as field programmable gate arrays (FPGAs). However, recently graphics processing unit (GPU) based data processing methods have been developed to minimize this data processing and rendering time. These processing techniques include standard-processing methods which includes a set of algorithms to process the raw data (interference) obtained by the detector and generate A-scans. The work presented here describes accelerated data processing and post processing techniques for OCT systems. The GPU based processing developed, during the PhD, was later implemented into a custom built Fourier domain optical coherence tomography (FD-OCT) system. This system currently processes and renders data in real time. Processing throughput of this system is currently limited by the camera capture rate. OCTphantoms have been heavily used for the qualitative characterization and adjustment/ fine tuning of the operating conditions of OCT system. Currently, investigations are under way to characterize OCT systems using our phantoms. The work presented in this thesis demonstrate several novel techniques of fabricating OCT-phantoms and accelerating OCT data processing using GPUs. In the process of developing phantoms and quantitative methods, a thorough understanding and practical knowledge of OCT and fs laser processing systems was developed. This understanding leads to several novel pieces of research that are not only relevant to OCT but have broader importance. For example, extensive understanding of the properties of fs inscribed structures will be useful in other photonic application such as making of phase mask, wave guides and microfluidic channels. Acceleration of data processing with GPUs is also useful in other fields.

Services for Satellite Data Processing

Data processing services for Meteosat geostationary satellite are presented. Implemented services correspond to the different levels of remote-sensing data processing, including noise reduction at preprocessing level, cloud mask extraction at low-level and fractal dimension estimation at high-level. Cloud mask obtained as a result of Markovian segmentation of infrared data. To overcome high computation complexity of Markovian segmentation parallel algorithm is developed. Fractal dimension of Meteosat data estimated using fractional Brownian motion models.

Large scale data processing using MapReduce

As massive data sets become increasingly available, people are facing the problem of how to effectively process and understand these data. Traditional sequential computing models are giving way to parallel and distributed computing models, such as MapReduce, both due to the large size of the data sets and their high dimensionality. This dissertation, as in the same direction of other researches that are based on MapReduce, tries to develop effective techniques and applications using MapReduce that can help people solve large-scale problems. Three different problems are tackled in the dissertation. The first one deals with processing terabytes of raster data in a spatial data management system. Aerial imagery files are broken into tiles to enable data parallel computation. The second and third problems deal with dimension reduction techniques that can be used to handle data sets of high dimensionality. Three variants of the nonnegative matrix factorization technique are scaled up to factorize matrices of dimensions in the order of millions in MapReduce based on different matrix multiplication implementations. Two algorithms, which compute CANDECOMP/PARAFAC and Tucker tensor decompositions respectively, are parallelized in MapReduce based on carefully partitioning the data and arranging the computation to maximize data locality and parallelism.

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.

Data Processing Systems Manual: Highway Noise Prediction, 1972

The Data Processing Department of ISHC has developed coding forms to be used for the data to be entered into the program. The Highway Planning and Programming and the Design Departments are responsible for coding and submitting the necessary data forms to Data Processing for the noise prediction on the highway sections.

Ruminal methane emission by dairy cattle in southeast Brazil.

ABSTRACT: Ruminal gases, particularly methane, generated during the fermentative process in rumen, represent a partial loss of feed energy and are also pointed to as an important factors in greenhouse effect. This study aimed at quantifying methane (CH 4) emission rates from lactating and dry cows and heifers, 24 month-old in average, on pasture under Southeast Brazil tropical conditions, using the tracer gas technique, sulphur hexafluoride (SF 6), four animals per category, distributed in four blocks. Measurements were performed in February and June, 2002, with Holstein and Brazilian Dairy Crossbred (Holstein ¾ x Gir (Zebu) ¼), maintained on fertilized Tanzania-grass (Panicum maximum Jacq. cv. Tanzania) and fertilized Brachiaria-grass (Brachiaria decumbens cv. Basilisk) pastures. Heifers of both breeds were maintained on unfertilized Brachiaria-grass to simulate conditions of extensive cattle farming systems. CH 4 and SF 6 levels were measured with gas chromatography. Differences in CH4 emissions were measured (p < 0.05) for genetical groups. Holstein produced more methane (299.3g day?1) than the Crossbred (264.2 g day?1). Lactating cows produced more methane (353.8 g day?1) than dry cows (268.8 g day?1) and heifers (222.6 g day?1). Holstein, with greater milk production potential, produced less CH4 (p < 0.05) per unit of dry matter intake (19.1 g kg?1) than the Crossbred (22.0 g kg?1). Methane emission by heifers grazing fertilized pasture (intensive system) was 222.6 g day?1, greater (p < 0.05) than that of heifers on unfertilized pasture (179.2 g day?1). Methane emission varied as function of animal category and management intensity of production system.

DESIGN OF EFFICIENT IN-NETWORK DATA PROCESSING AND DISSEMINATION FOR VANETS

By providing vehicle-to-vehicle and vehicle-to-infrastructure wireless communications, vehicular ad hoc networks (VANETs), also known as the “networks on wheels”, can greatly enhance traffic safety, traffic efficiency and driving experience for intelligent transportation system (ITS). However, the unique features of VANETs, such as high mobility and uneven distribution of vehicular nodes, impose critical challenges of high efficiency and reliability for the implementation of VANETs. This dissertation is motivated by the great application potentials of VANETs in the design of efficient in-network data processing and dissemination. Considering the significance of message aggregation, data dissemination and data collection, this dissertation research targets at enhancing the traffic safety and traffic efficiency, as well as developing novel commercial applications, based on VANETs, following four aspects: 1) accurate and efficient message aggregation to detect on-road safety relevant events, 2) reliable data dissemination to reliably notify remote vehicles, 3) efficient and reliable spatial data collection from vehicular sensors, and 4) novel promising applications to exploit the commercial potentials of VANETs. Specifically, to enable cooperative detection of safety relevant events on the roads, the structure-less message aggregation (SLMA) scheme is proposed to improve communication efficiency and message accuracy. The scheme of relative position based message dissemination (RPB-MD) is proposed to reliably and efficiently disseminate messages to all intended vehicles in the zone-of-relevance in varying traffic density. Due to numerous vehicular sensor data available based on VANETs, the scheme of compressive sampling based data collection (CS-DC) is proposed to efficiently collect the spatial relevance data in a large scale, especially in the dense traffic. In addition, with novel and efficient solutions proposed for the application specific issues of data dissemination and data collection, several appealing value-added applications for VANETs are developed to exploit the commercial potentials of VANETs, namely general purpose automatic survey (GPAS), VANET-based ambient ad dissemination (VAAD) and VANET based vehicle performance monitoring and analysis (VehicleView). Thus, by improving the efficiency and reliability in in-network data processing and dissemination, including message aggregation, data dissemination and data collection, together with the development of novel promising applications, this dissertation will help push VANETs further to the stage of massive deployment.

A Livestock-Associated, Multidrug-Resistant, Methicillin-Resistant Staphylococcus aureus Clonal Complex 97 Lineage Spreading in Dairy Cattle and Pigs in Italy

Pandemic methicillin-resistant Staphylococcus aureus (MRSA) clonal complex 97 (CC97) lineages originated from livestock-to-human host jumps. In recent years, CC97 has become one of the major MRSA lineages detected in Italian farmed animals. The aim of this study was to characterize and analyze differences in MRSA and methicillin-susceptible S. aureus (MSSA) mainly of swine and bovine origins. Forty-seven CC97 isolates, 35 MRSA isolates, and 6 MSSA isolates from different Italian pig and cattle holdings; 5 pig MRSA isolates from Germany; and 1 human MSSA isolate from Spain were characterized by macrorestriction pulsed-field gel electrophoresis (PFGE) analysis, multilocus sequence typing (MLST), spa typing, staphylococcal cassette chromosome mec (SCCmec) typing, and antimicrobial resistance pattern analysis. Virulence and resistance genes were investigated by PCR and microarray analysis. Most of the isolates were of SCCmec type V (SCCmec V), except for two German MRSA isolates (SCCmec III). Five main clusters were identified by PFGE, with the German isolates (clusters I and II) showing 60.5% similarity with the Italian isolates, most of which (68.1%) grouped into cluster V. All CC97 isolates were Panton-Valentine leukocidin (PVL) negative, and a few (n = 7) tested positive for sak or scn. All MRSA isolates were multidrug resistant (MDR), and the main features were erm(B)- or erm(C)-mediated (n = 18) macrolide-lincosamide-streptogramin B resistance, vga(A)-mediated (n = 37) pleuromutilin resistance, fluoroquinolone resistance (n = 33), tet(K) in 32/37 tet(M)-positive isolates, and blaZ in almost all MRSA isolates. Few host-associated differences were detected among CC97 MRSA isolates: their extensive MDR nature in both pigs and dairy cattle may be a consequence of a spillback from pigs of a MRSA lineage that originated in cattle as MSSA and needs further investigation. Measures should be implemented at the farm level to prevent spillover to humans in intensive farming areas.