The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform

A. Savcheva - One of the best experts on this subject based on the ideXlab platform.

  • Computer Vision for The Solar Dynamics Observatory
    Solar Physics, 2017
    Co-Authors: P C H Martens, G. D. R. Attrill, A. R. Davey, A. Engell, P. C. Grigis, K. Korreck, Ss Farid, James Kasper, S H Saar, A. Savcheva
    Abstract:

    In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded Software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic Elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new Software Element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control Software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).

  • Computer Vision for the Solar Dynamics Observatory (SDO)
    Solar Physics, 2011
    Co-Authors: P C H Martens, G. D. R. Attrill, A. R. Davey, A. Engell, P. C. Grigis, K. Korreck, Ss Farid, S H Saar, Justin C. Kasper, A. Savcheva
    Abstract:

    In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded Software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic Elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new Software Element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control Software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).

Ramon Puigjaner - One of the best experts on this subject based on the ideXlab platform.

  • RTCSA - Improved performance model of a real-time Software Element: the producer-consumer
    Proceedings Second International Workshop on Real-Time Computing Systems and Applications, 1
    Co-Authors: Carlos Juiz, Ramon Puigjaner
    Abstract:

    Design of real-time systems needs to take into account their performance behaviour before their implementation. This goal can be attained by means of queueing network modelling. To process this kind of models normally simulation is used with its inherent costs of debugging and running times. This paper presents the analysis and comparison of several kinds of performance models of a basic Software Element used in the construction of real-time systems: the producer-consumer. These models go from the simple FIFO queue to the simulation of the complete Element passing through some decomposition-aggregation approximate model. This model can be considered as an Element of a library and the model of a real-time system can be built by putting together the models of the real-time components.

  • MASCOTS - Approximate performance models of real-time Software systems
    MASCOTS '95. Proceedings of the Third International Workshop on Modeling Analysis and Simulation of Computer and Telecommunication Systems, 1
    Co-Authors: Carlos Juiz, Ramon Puigjaner
    Abstract:

    Design of real-time systems needs to take into account their performance behaviour before their implementation. This goal can be attained by means of queueing network modelling. To process this kind of models normally simulation is used with its inherent costs of debugging and running times. This paper presents the analysis and comparison of several kinds of performance models of a basic Software Element used in the construction of real-time systems: the producer-consumer. These models go from the simple FIFO queue to the simulation of the complete system passing through some decomposition-aggregation approximate models. >

P C H Martens - One of the best experts on this subject based on the ideXlab platform.

  • Computer Vision for The Solar Dynamics Observatory
    Solar Physics, 2017
    Co-Authors: P C H Martens, G. D. R. Attrill, A. R. Davey, A. Engell, P. C. Grigis, K. Korreck, Ss Farid, James Kasper, S H Saar, A. Savcheva
    Abstract:

    In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded Software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic Elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new Software Element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control Software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).

  • Computer Vision for the Solar Dynamics Observatory (SDO)
    Solar Physics, 2011
    Co-Authors: P C H Martens, G. D. R. Attrill, A. R. Davey, A. Engell, P. C. Grigis, K. Korreck, Ss Farid, S H Saar, Justin C. Kasper, A. Savcheva
    Abstract:

    In Fall 2008 NASA selected a large international consortium to produce a comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of the Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field images from the Helioseismic and Magnetic Imager (HMI). We produce robust, very efficient, professionally coded Software modules that can keep up with the SDO data stream and detect, trace, and analyze numerous phenomena, including flares, sigmoids, filaments, coronal dimmings, polarity inversion lines, sunspots, X-ray bright points, active regions, coronal holes, EIT waves, coronal mass ejections (CMEs), coronal oscillations, and jets. We also track the emergence and evolution of magnetic Elements down to the smallest detectable features and will provide at least four full-disk, nonlinear, force-free magnetic field extrapolations per day. The detection of CMEs and filaments is accomplished with Solar and Heliospheric Observatory (SOHO)/Large Angle and Spectrometric Coronagraph (LASCO) and ground-based Hα data, respectively. A completely new Software Element is a trainable feature-detection module based on a generalized image-classification algorithm. Such a trainable module can be used to find features that have not yet been discovered (as, for example, sigmoids were in the pre-Yohkoh era). Our codes will produce entries in the Heliophysics Events Knowledgebase (HEK) as well as produce complete catalogs for results that are too numerous for inclusion in the HEK, such as the X-ray bright-point metadata. This will permit users to locate data on individual events as well as carry out statistical studies on large numbers of events, using the interface provided by the Virtual Solar Observatory. The operations concept for our computer vision system is that the data will be analyzed in near real time as soon as they arrive at the SDO Joint Science Operations Center and have undergone basic processing. This will allow the system to produce timely space-weather alerts and to guide the selection and production of quicklook images and movies, in addition to its prime mission of enabling solar science. We briefly describe the complex and unique data-processing pipeline, consisting of the hardware and control Software required to handle the SDO data stream and accommodate the computer-vision modules, which has been set up at the Lockheed-Martin Space Astrophysics Laboratory (LMSAL), with an identical copy at the Smithsonian Astrophysical Observatory (SAO).

Dimitra Simeonidou - One of the best experts on this subject based on the ideXlab platform.

  • WCNC - Building SDN Agent for Wireless Local Area Networks
    2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019
    Co-Authors: Mark A Beach, Reza Nejabati, Dimitra Simeonidou
    Abstract:

    Recently, a lot of research is focused on applying Software-Defined Networking (SDN) concepts on wireless networks. However, the current wireless systems are lack of SDN support and remain closed and (mostly) proprietary, which makes it difficult to integrate with SDN systems. The SDN agent is introduced to solve the above issues. An SDN agent is a Software Element bridging the SDN controller and any legacy wireless network Elements (NEs) by providing the abstraction of these Elements. The main advantage of this approach is that there is no modification to either the existing SDN Elements or the 802.11 protocols, while the SDN controller is enabled to control and manage legacy wireless NEs. In this paper, we present an SDN agent framework for WLANs and describe an implementation of the proposed agent on the Wireless Open-Access Research Platform (WARP), an FPGA based open source Software-Defined Radio (SDR) platform. With the support from such an agent, innovative applications and functionalities can be developed for the WLAN access points (APs), such as dynamically slicing AP bandwidth among users and adapting the transmit power in flexible granularity, i.e. at per frame/per flow level.

  • ICC - An SDN Agent-Enabled Rate Adaptation Framework for WLAN
    ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019
    Co-Authors: George Oikonomou, Mark A Beach, Reza Nejabati, Dimitra Simeonidou
    Abstract:

    Rate or link adaptation is the determination of the optimal modulation and coding scheme (MCS) that will maximize the performance under the current wireless channel conditions. A Software-Defined Networking (SDN) agent is a Software Element bridging an SDN controller and any legacy wireless network Elements by providing the abstraction of these Elements. In this paper, we present the work of an SDN approach for designing and implementing a Rate/Link Adaptation (RA) framework for wireless local area networks (WLAN). The framework provides support for real-time RA applications and flexibility to satisfy various degrees of Quality of Service (QoS) or Quality of Experience (QoE) requirements. We implement the proposed framework as an extension to the Wireless Open-Access Research Platform (WARP), an FPGA based Software-Defined Radio (SDR) platform, with evaluation results indicating the feasibility of using SDN-RA under the stringent time constraints posed by the WLAN. To demonstrate the effectiveness of decoupling rate decision functions from the underlying wireless interface card and to highlight its applicability for a diverse set of scenarios, we present a use case deployed over the framework focusing on rate adaptation for individual traffic, and display optimization in different aspects, such as the reduction transmission errors.

He Ke-qing - One of the best experts on this subject based on the ideXlab platform.

  • Soft component of image processing based on strategy model
    Computer Engineering, 2005
    Co-Authors: Li Li-juan, He Ke-qing
    Abstract:

    Based on the point of Software reuse, a form of soft component construction was introduced to image process system, and an application of Software components in the research of digital watermarking was given. A test platform for embedding watermarking and picking up watermarking was implemented. The Software component can also be used as an independent Software Element in other correlative Software constructions.It was argued that the component-based method could be of great help for building Software of image recognizing, with increased efficiency and degree of maturation in Software design.