A cost-effective high-throughput digital system for observation and acquisition of animal behavioral data

We have designed and implemented a low-cost digital system using closed-circuit television cameras coupled to a digital acquisition system for the recording of in vivo behavioral data in rodents and for allowing observation and recording of more than 10 animals simultaneously at a reduced cost, as compared with commercially available solutions. This system has been validated using two experimental rodent models: one involving chemically induced seizures and one assessing appetite and feeding. We present observational results showing comparable or improved levels of accuracy and observer consistency between this new system and traditional methods in these experimental models, discuss advantages of the presented system over conventional analog systems and commercially available digital systems, and propose possible extensions to the system and applications to non-rodent studies.

Workflow data exchange semantics, object discovery & integration architecture for cross media production & distribution

There has been a clear lack of common data exchange semantics for inter-organisational workflow management systems where the research has mainly focused on technical issues rather than language constructs. This paper presents the neutral data exchanges semantics required for the workflow integration within the AXAEDIS framework and presents the mechanism for object discovery from the object repository where little or no knowledge about the object is available. The paper also presents workflow independent integration architecture with the AXAEDIS Framework.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Data forest: a collaborative version

As we increase our ability to produce and store ever larger amounts of data, it is becoming increasingly difficult to understand what the data is trying to tell us. Not all the data we are currently producing can easily fit into traditional visualization methods. This paper presents a new and novel visualization technique based on the concept of a Data Forest. Our Data Forest has been developed to be utilised by virtual reality (VR) systems. VR is a natural information medium. This approach can easily be adapted to be used in collaborative environments. A test application has been developed to demonstrate the concepts involved and a collaborative version tested.

Managing groups of files in a rule oriented data management system (iRODS)

The iRODS system, created by the San Diego Supercomputing Centre, is a rule oriented data management system that allows the user to create sets of rules to define how the data is to be managed. Each rule corresponds to a particular action or operation (such as checksumming a file) and the system is flexible enough to allow the user to create new rules for new types of operations. The iRODS system can interface to any storage system (provided an iRODS driver is built for that system) and relies on its’ metadata catalogue to provide a virtual file-system that can handle files of any size and type. However, some storage systems (such as tape systems) do not handle small files efficiently and prefer small files to be packaged up (or “bundled”) into larger units. We have developed a system that can bundle small data files of any type into larger units - mounted collections. The system can create collection families and contains its’ own extensible metadata, including metadata on which family the collection belongs to. The mounted collection system can work standalone and is being incorporated into the iRODS system to enhance the systems flexibility to handle small files. In this paper we describe the motivation for creating a mounted collection system, its’ architecture and how it has been incorporated into the iRODS system. We describe different technologies used to create the mounted collection system and provide some performance numbers.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

A block coprocessor for user data rate improvements to GPRS coding scheme 4

The general packet radio service (GPRS) has been developed to allow packet data to be transported efficiently over an existing circuit-switched radio network, such as GSM. The main application of GPRS are in transporting Internet protocol (IP) datagrams from web servers (for telemetry or for mobile Internet browsers). Four GPRS baseband coding schemes are defined to offer a trade-off in requested data rates versus propagation channel conditions. However, data rates in the order of > 100 kbits/s are only achievable if the simplest coding scheme is used (CS-4) which offers little error detection and correction (EDC) (requiring excellent SNR) and the receiver hardware is capable of full duplex which is not currently available in the consumer market. A simple EDC scheme to improve the GPRS block error rate (BLER) performance is presented, particularly for CS-4, however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel and improving the user's application data rate. As GPRS requires intensive processing in the baseband, a viable field programmable gate array (FPGA) solution is presented in this paper.

A comparison-based diagnosis algorithm tailored for crossed cube multiprocessor systems

Comparison-based diagnosis is an effective approach to system-level fault diagnosis. Under the Maeng-Malek comparison model (NM* model), Sengupta and Dahbura proposed an O(N-5) diagnosis algorithm for general diagnosable systems with N nodes. Thanks to lower diameter and better graph embedding capability as compared with a hypercube of the same size, the crossed cube has been a promising candidate for interconnection networks. In this paper, we propose a fault diagnosis algorithm tailored for crossed cube connected multicomputer systems under the MM* model. By introducing appropriate data structures, this algorithm runs in O(Nlog(2)(2) N) time, which is linear in the size of the input. As a result, this algorithm is significantly superior to the Sengupta-Dahbura's algorithm when applied to crossed cube systems. (C) 2004 Elsevier B.V. All rights reserved.

Multiple-scattering in radar systems: a review

Although extensively studied within the lidar community, the multiple scattering phenomenon has always been considered a rare curiosity by radar meteorologists. Up to few years ago its appearance has only been associated with two- or three-body-scattering features (e.g. hail flares and mirror images) involving highly reflective surfaces. Recent atmospheric research aimed at better understanding of the water cycle and the role played by clouds and precipitation in affecting the Earth's climate has driven the deployment of high frequency radars in space. Examples are the TRMM 13.5 GHz, the CloudSat 94 GHz, the upcoming EarthCARE 94 GHz, and the GPM dual 13-35 GHz radars. These systems are able to detect the vertical distribution of hydrometeors and thus provide crucial feedbacks for radiation and climate studies. The shift towards higher frequencies increases the sensitivity to hydrometeors, improves the spatial resolution and reduces the size and weight of the radar systems. On the other hand, higher frequency radars are affected by stronger extinction, especially in the presence of large precipitating particles (e.g. raindrops or hail particles), which may eventually drive the signal below the minimum detection threshold. In such circumstances the interpretation of the radar equation via the single scattering approximation may be problematic. Errors will be large when the radiation emitted from the radar after interacting more than once with the medium still contributes substantially to the received power. This is the case if the transport mean-free-path becomes comparable with the instrument footprint (determined by the antenna beam-width and the platform altitude). This situation resembles to what has already been experienced in lidar observations, but with a predominance of wide- versus small-angle scattering events. At millimeter wavelengths, hydrometeors diffuse radiation rather isotropically compared to the visible or near infrared region where scattering is predominantly in the forward direction. A complete understanding of radiation transport modeling and data analysis methods under wide-angle multiple scattering conditions is mandatory for a correct interpretation of echoes observed by space-borne millimeter radars. This paper reviews the status of research in this field. Different numerical techniques currently implemented to account for higher order scattering are reviewed and their weaknesses and strengths highlighted. Examples of simulated radar backscattering profiles are provided with particular emphasis given to situations in which the multiple scattering contributions become comparable or overwhelm the single scattering signal. We show evidences of multiple scattering effects from air-borne and from CloudSat observations, i.e. unique signatures which cannot be explained by single scattering theory. Ideas how to identify and tackle the multiple scattering effects are discussed. Finally perspectives and suggestions for future work are outlined. This work represents a reference-guide for studies focused at modeling the radiation transport and at interpreting data from high frequency space-borne radar systems that probe highly opaque scattering media such as thick ice clouds or precipitating clouds.

The exclusive use of integer-order spots for LEED-IV structure analysis of adsorption systems: p(2×2)-O on Ni{111}

Two different ways of performing low-energy electron diffraction (LEED) structure determinations for the p(2 x 2) structure of oxygen on Ni {111} are compared: a conventional LEED-IV structure analysis using integer and fractional-order IV-curves collected at normal incidence and an analysis using only integer-order IV-curves collected at three different angles of incidence. A clear discrimination between different adsorption sites can be achieved by the latter approach as well as the first and the best fit structures of both analyses are within each other's error bars (all less than 0.1 angstrom). The conventional analysis is more sensitive to the adsorbate coordinates and lateral parameters of the substrate atoms whereas the integer-order-based analysis is more sensitive to the vertical coordinates of substrate atoms. Adsorbate-related contributions to the intensities of integer-order diffraction spots are independent of the state of long-range order in the adsorbate layer. These results show, therefore, that for lattice-gas disordered adsorbate layers, for which only integer-order spots are observed, similar accuracy and reliability can be achieved as for ordered adsorbate layers, provided the data set is large enough.

Ocean data dissemination: new challenges for data integration

In the decade since OceanObs `99, great advances have been made in the field of ocean data dissemination. The use of Internet technologies has transformed the landscape: users can now find, evaluate and access data rapidly and securely using only a web browser. This paper describes the current state of the art in dissemination methods for ocean data, focussing particularly on ocean observations from in situ and remote sensing platforms. We discuss current efforts being made to improve the consistency of delivered data and to increase the potential for automated integration of diverse datasets. An important recent development is the adoption of open standards from the Geographic Information Systems community; we discuss the current impact of these new technologies and their future potential. We conclude that new approaches will indeed be necessary to exchange data more effectively and forge links between communities, but these approaches must be evaluated critically through practical tests, and existing ocean data exchange technologies must be used to their best advantage. Investment in key technology components, cross-community pilot projects and the enhancement of end-user software tools will be required in order to assess and demonstrate the value of any new technology.

Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

Data management for the ocean sciences - perspectives for the next decade

There is remarkable agreement in expectations today for vastly improved ocean data management a decade from now -- capabilities that will help to bring significant benefits to ocean research and to society. Advancing data management to such a degree, however, will require cultural and policy changes that are slow to effect. The technological foundations upon which data management systems are built are certain to continue advancing rapidly in parallel. These considerations argue for adopting attitudes of pragmatism and realism when planning data management strategies. In this paper we adopt those attitudes as we outline opportunities for progress in ocean data management. We begin with a synopsis of expectations for integrated ocean data management a decade from now. We discuss factors that should be considered by those evaluating candidate “standards”. We highlight challenges and opportunities in a number of technical areas, including “Web 2.0” applications, data modeling, data discovery and metadata, real-time operational data, archival of data, biological data management and satellite data management. We discuss the importance of investments in the development of software toolkits to accelerate progress. We conclude the paper by recommending a few specific, short term targets for implementation, that we believe to be both significant and achievable, and calling for action by community leadership to effect these advancements.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.