The role of satellite image-processing for national-scale estimates of gene flow from genetically modified crops: rapeseed in the UK as a model

1.There is concern over the possibility of unwanted environmental change following transgene movement from genetically modified (GM) rapeseed Brassica napus to its wild and weedy relatives. 2. The aim of this research was to develop a remote sensing-assisted methodology to help quantify gene flow from crops to their wild relatives over wide areas. Emphasis was placed on locating sites of sympatry, where the frequency of gene flow is likely to be highest, and on measuring the size of rapeseed fields to allow spatially explicit modelling of wind-mediated pollen-dispersal patterns. 3. Remote sensing was used as a tool to locate rapeseed fields, and a variety of image-processing techniques was adopted to facilitate the compilation of a spatially explicit profile of sympatry between the crop and Brassica rapa. 4. Classified satellite images containing rapeseed fields were first used to infer the spatial relationship between donor rapeseed fields and recipient riverside B. rapa populations. Such images also have utility for improving the efficiency of ground surveys by identifying probable sites of sympatry. The same data were then also used for the calculation of mean field size. 5. This paper forms a companion paper to Wilkinson et al. (2003), in which these elements were combined to produce a spatially explicit profile of hybrid formation over the UK. The current paper demonstrates the value of remote sensing and image processing for large-scale studies of gene flow, and describes a generic method that could be applied to a variety of crops in many countries. 6.Synthesis and applications. The decision to approve or prevent the release of a GM cultivar is made at a national rather than regional level. It is highly desirable that data relating to the decision-making process are collected at the same scale, rather than relying on extrapolation from smaller experiments designed at the plot, field or even regional scale. It would be extremely difficult and labour intensive to attempt to carry out such large-scale investigations without the use of remote-sensing technology. This study used rapeseed in the UK as a model to demonstrate the value of remote sensing in assembling empirical information at a national level.

State-of-the-art for entity-centric repository and authoring environment for multimedia

Many projects, e.g. VIKEF [13] and KIM [7], present grounded approaches for the use of entities as a means of indexing and retrieval of multimedia resources from heterogeneous sources. In this paper, we discuss the state-of-the-art of entity-centric approaches for multimedia indexing and retrieval. A summary of projects employing entity-centric repositories are portrayed. This paper also looks at the current state-of-the-art authoring environment, Macromedia Authorware, and the possibility of potential extension of this environment for entity-based multimedia authoring.

Landscape of intelligent cores: An autonomic multi-agent approach for space application

Space applications are challenged by the reliability of parallel computing systems (FPGAs) employed in space crafts due to Single-Event Upsets. The work reported in this paper aims to achieve self-managing systems which are reliable for space applications by applying autonomic computing constructs to parallel computing systems. A novel technique, 'Swarm-Array Computing' inspired by swarm robotics, and built on the foundations of autonomic and parallel computing is proposed as a path to achieve autonomy. The constitution of swarm-array computing comprising for constituents, namely the computing system, the problem / task, the swarm and the landscape is considered. Three approaches that bind these constituents together are proposed. The feasibility of one among the three proposed approaches is validated on the SeSAm multi-agent simulator and landscapes representing the computing space and problem are generated using the MATLAB.

Intelligent agents: are they feasible in swarm-array computing?

The work reported in this paper proposes 'Intelligent Agents', a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

The Namib Sand Sea digital database of aeolian dunes and key forcing variables

A new digital atlas of the geomorphology of the Namib Sand Sea in southern Africa has been developed. This atlas incorporates a number of databases including a digital elevation model (ASTER and SRTM) and other remote sensing databases that cover climate (ERA-40) and vegetation (PAL and GIMMS). A map of dune types in the Namib Sand Sea has been derived from Landsat and CNES/SPOT imagery. The atlas also includes a collation of geochronometric dates, largely derived from luminescence techniques, and a bibliographic survey of the research literature on the geomorphology of the Namib dune system. Together these databases provide valuable information that can be used as a starting point for tackling important questions about the development of the Namib and other sand seas in the past, present and future.

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.

Speed sensorless vector control of induction machines

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, ωr, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.