Vasileios Anagnostopoulos

A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments

Unlike any previous effort, the Workflow Recognition (WR) large-scale dataset is a collection of video sequences from the real industrial manufacturing environment of a major automobile manufacturer.

Operational evapotranspiration estimates from SEVIRI in support of sustainable water management

This study aimed at evaluating the accuracy of the evapotranspiration (ET) operational estimates from the Meteosat Second Generation (MSG) Spinning Enhanced Visible Infra-Red Imager (SEVIRI) at a range of selected ecosystems in Europe. For this purpose in-situ eddy covariance measurements were used, acquired from 7 selected experimental sites belonging to the CarboEurope ground observational network over 2 full years of observations (2010–2011). Appraisal of ET accuracy was also investigated with respect to land cover, season and each site(s) degree of heterogeneity, the latter being expressed by the fractional vegetation cover (FVC) operational product of SEVIRI. Results indicated a close agreement between the operational product’s ET estimates and the tower based in-situ ET measurements for all days of comparison, showing a satisfactory correlation (r of 0.709) with accuracies often comparable to previous analogous studies. For all land cover types, the grassland and cropland sites exhibited the closest agreement (r from 0.705 to 0.759). In terms of seasons the strongest correlations were observed during the summer and autumn (r of 0.714 & 0.685 respectively), and with FVC the highest correlation of 0.735 was observed for the class FVC 0.75-1 when compared against the observed values for the complete monitoring period. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and corroborate its credibility for usage in many practical applications. The latter is of particular importance for water limiting environments, such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making in those areas.

A modernized version of a 1D soil vegetation atmosphere transfer model for improving its future use in land surface interactions studies

SimSphere is a land biosphere model that provides a mathematical representation of vertical views of the physical mechanisms controlling Earths energy and mass transfers in the soil/vegetation/atmosphere continuum. Herein, we present recent advancements introduced to SimSphere code, aiming at making its use more integrated to the automation of processes within High Performance Computing (HPC) that allows using the model at large scale. In particular, a new interface to the model is presented, so-called SimSphere-SOA which forms a command line land biosphere tool, a Web Service interface and a parameters verification facade that offers a standardised environment for specification execution and result retrieval of a typical model simulation based on Service Oriented Architecture (SOA). SimSphere-SOA library can now execute various simulations in parallel. This allows exploitation of the tool in a simple and efficient way in comparison to the currently distributed approach. In SimSphere-SOA, an Application Programming Interface (API) is also provided to execute simulations that can be publicly consumed. Finally this API is exported as a Web Service for remotely executing simulations through web based tools. This way a simulation by the model can be executed efficiently and subsequently the model simulation outputs may be used in any kind of relevant analysis required.The use of these new functionalities offered by SimSphere-SOA is also demonstrated using a real world simulation configuration file. The inclusion of those new functions in SimSphere are of considerable importance in the light of the models expanding use worldwide as an educational and research tool. Application Programming Interface (API) is now provided to execute SimSphere.A Service Oriented Architecture with Web Service interface added to SimSphere.SimSphere becomes now suitable for HPC use.Developments vital in its future use as standalone tool and for its synergy with EO data.

High performance fault-tolerance for clouds

Cloud computing and virtualized infrastructures are currently the baseline environments for the provision of services in different application domains. While the number of service consumers increasingly grows, service providers aim at exploiting infrastructures that enable non-disruptive service provisioning, thus minimizing or even eliminating downtime. Nonetheless, to achieve the latter current approaches are either application-specific or cost inefficient, requiring the use of dedicated hardware. In this paper we present the reference architecture of a fault-tolerance scheme, which not only enhances cloud environments with the aforementioned capabilities but also achieves high-performance as required by mission critical every day applications. To realize the proposed approach, a new paradigm for memory and I/O externalization and consolidation is introduced, while current implementation references are also provided.

Tools for semi-automatic monitoring of industrial workflows

This paper describes a tool chain for monitoring complex workflows. Statistics obtained from automatic workflow monitoring in a car assembly environment assist in improving industrial safety and process quality. To this end, we propose automatic detection and tracking of humans and their activity in multiple networked cameras. The described tools offer human operators retrospective analysis of a huge amount of pre-recorded and analyzed footage from multiple cameras in order to get a comprehensive overview of the workflows. Furthermore, the tools help technical administrators in adjusting algorithms by letting the user correct detections (for relevance feedback) and ground truth for evaluation. Another important feature of the tool chain is the capability to inform the employees about potentially risky conditions using the tool for automatic detection of unusual scenes.

Disaster Recovery Layer for Distributed OpenStack Deployments

We present the Disaster Recovery Layer (DRL) that enables OpenStack-managed datacenter workloads, Virtual Machines (VMs) and Volumes, to be protected and recovered in another datacenter, in case of a disaster. This work has been carried out in the context of the EU FP7 ORBIT project that develops technologies for enabling business continuity as a service. The DRL framework is based on a number of autonomous components and extensions of OpenStack modules, while its functionalities are available through OpenStacks Horizon UI and command line interface. Also, the DRLs architecture is extensible, allowing for the easy and dynamic integration of protection, restoration and orchestration plug-ins that adopt new approaches. A distributed disaster detection mechanism was also developed for identifying datacenter disasters and alerting the DRL. For the evaluation of the DRL, a two (active and backup) datacenters testbed has been setup in respective sites in Umeå and Luleå, 265km apart and connected through the Swedish national research and education network. In case of a disaster, traffic is redirected between the datacenters utilizing the BGP anycast scheme. The experiments performed, show that DRL can efficiently protect VMs and Volumes, with minimum service disruption in case of failures and low overhead, even when the available bandwidth is limited.

Chapter 5 – Spatiotemporal Estimates of Surface Soil Moisture from Space Using the Ts/VI Feature Space

Abstract Earth Observation (EO) has played an imperative role in extending our abilities for obtaining information on the spatio-temporal distribution of surface soil moisture (SSM). A wide range of techniques have been proposed for this purpose. Some of those techniques have based on the integration of satellite-derived estimates of Fractional Vegetation Cover (Fr) and Land Surface Temperature (Ts) in the form of a scatterplot domain, often combining land surface process model simulations. These techniques aim at combining the horizontal coverage and spectral resolution of EO imagery with the vertical coverage and fine temporal continuity of the process models. Herein one such technique - named the “triangle” - has been implemented with EO datasets from both the AATSR and ASTER sensors together with SimSphere land surface model. Validation of the derived SMC maps was undertaken in different sites in Europe representing a variety of climatic, topographic and environmental conditions, for which validated in-situ observations from diverse operational ground observational networks were available. Results indicated a good agreement between the in-situ and both “triangle” schemes for the estimation of SMC (ASTER R – 0.561/AATSR R – 0.844), with the AATSR results again outperforming the ASTER, comparable to previous studies implemented using different types of EO data. Comparisons of the derived SMC maps regionally against other satellite-derived products also showed largely an explainable distribution of SMC in relation to surface heterogeneity. Our results provide strong supportive evidence for the potential value of the “triangle” inversion modelling technique to accurately derive estimates of SMC, and are important steps as well towards efforts focusing on operational implementation of this approach.

Intelligent Clouds: A Middleware Architecture Supporting Business Elasticity

Cloud computing aims to realize the vision of computing as a utility provided in an economically viable manner. A necessary condition for its success as a business solution is the existence of proper software tools for the development and provision of applications for this highly dynamic ecosystem. In this paper we focus on the tools providing PaaS (Platform-as-a-Service) functionalities and more specific we propose a new middleware architecture that could allow the creation of elastic applications with the scalability of the application transparently embedded in the middleware. Departing from legacy considerations, we embrace the latest trends in scalable distributed software design as well as concepts from SOAs (Service Oriented Architectures) and agent-based computing with the goal to provide a future-proof platform in technical and business sense. Central to our approach is the tackling of the scalability and pricing problems in order to reliably further the adoption of the pay as you go model. Moreover we put special emphasis on a formal definition of scalability rules and on structuring our middleware along these lines.

ESTIMATION OF FRAME SEQUENCE NOISE WITH REMOVAL OF JPEG ARTIFACTS

This paper proposes a method to remove JPEG noise artifacts from frame sequences. Using extensive experimental results we show how an online system with periodic noise estimation functionality can estimate the real frame noise even if the images are in JPEG format. We present the mathematical basis of the methodology and show in real content that we can have reliable measurements. We also present the results obtained on a real network camera and show that our method can provide a much better estimation of the noise standard deviation compared to common practice but comparable interchannel and spatial intra-channel correlation estimates. We also provide some guidelines for capturing datasets necessary to apply computer vision tasks. Our approach exploits the well known stochastic linearization phenomenon which we prove that is present in our case.