The Experts below are selected from a list of 288012 Experts worldwide ranked by ideXlab platform

Ryan Kastner - One of the best experts on this subject based on the ideXlab platform.

  • riffa a reusable Integration Framework for fpga accelerators
    Field-Programmable Custom Computing Machines, 2012
    Co-Authors: Matthew Jacobsen, Yoav Freund, Ryan Kastner
    Abstract:

    We present RIFFA, a reusable Integration Framework for FPGA accelerators. RIFFA provides communication and synchronization for FPGA accelerated software using a standard interface. Our goal is to expand the use of FPGAs as an acceleration platform by releasing, as open source, a no cost Framework that easily integrates software on traditional CPUs with FPGA based IP cores, over PCIe, with minimal custom configuration. RIFFA requires no specialized hardware or fee licensed IP cores. It can be deployed on common Linux workstations with a PCIe bus and has been tested on two different Linux distributions using Xilinx FPGAs.

  • RIFFA: A reusable Integration Framework for FPGA accelerators
    Proceedings of the 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines FCCM 2012, 2012
    Co-Authors: Matthew Jacobsen, Yoav Freund, Ryan Kastner
    Abstract:

    We present RIFFA 2.0, a reusable Integration Framework for FPGA accelerators. RIFFA 2.0 provides communication and synchronization for FPGA accelerated applications using simple interfaces for hardware and software. Our goal is to expand the use of FPGAs as an acceleration platform by releasing, as open source, a Framework that easily integrates software running on commodity CPUs with FPGA cores. RIFFA 2.0 uses PCIe to connect FPGAs to a CPU's system bus. RIFFA 2.0 extends the original RIFFA project by supporting more classes of Xilinx FPGAs, multiple FPGAs in a system, more PCIe link configurations, higher bandwidth, and Linux and Windows operating systems. This release also supports C/C++, Java, and Python bindings. Tests show that data transfers between hardware and software can saturate the PCIe link to achieve the highest bandwidth possible.

Wang Jiye - One of the best experts on this subject based on the ideXlab platform.

  • heterogeneous information resource Integration Framework for power enterprise based on grid technology
    Power system technology, 2007
    Co-Authors: Wang Jiye
    Abstract:

    The authors propose a grid technology based heterogeneous information resource Integration Framework for power enterprise,in which according to the data resource in electric power enterprise a unified data model and the data service directed to the unified data model are established at first;then by means of grid encapsulation technology the development of business oriented service is implemented,thus the flexible combination of service modules can be ensured to quickly suit to the variation of business. The proposed heterogeneous resource Integration Framework is more flexible and expandable.

Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.

  • links learning based multi source Integration Framework for segmentation of infant brain images
    NeuroImage, 2015
    Co-Authors: Li Wang, Yaozong Gao, Feng Shi, John H Gilmore, Weili Lin, Dinggang Shen
    Abstract:

    Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source Integration Framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy.

  • links learning based multi source Integration Framework for segmentation of infant brain images
    Medical Image Computing and Computer-Assisted Intervention, 2014
    Co-Authors: Li Wang, Yaozong Gao, Feng Shi, John H Gilmore, Weili Lin, Dinggang Shen
    Abstract:

    Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and the ongoing maturation and myelination processes. In particular, the image contrast inverts around 6–8 months of age, and the white and gray matter tissues are isointense in both T1- and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses the significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the available multi-modality images and is often computationally expensive. In this paper, we propose a novel learning-based multi-source Integration Framework for infant brain image segmentation. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. The multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infant subjects and MICCAI challenges show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods, with significantly reduction of running time from hours to 5 minutes.

Matthew Jacobsen - One of the best experts on this subject based on the ideXlab platform.

  • riffa a reusable Integration Framework for fpga accelerators
    Field-Programmable Custom Computing Machines, 2012
    Co-Authors: Matthew Jacobsen, Yoav Freund, Ryan Kastner
    Abstract:

    We present RIFFA, a reusable Integration Framework for FPGA accelerators. RIFFA provides communication and synchronization for FPGA accelerated software using a standard interface. Our goal is to expand the use of FPGAs as an acceleration platform by releasing, as open source, a no cost Framework that easily integrates software on traditional CPUs with FPGA based IP cores, over PCIe, with minimal custom configuration. RIFFA requires no specialized hardware or fee licensed IP cores. It can be deployed on common Linux workstations with a PCIe bus and has been tested on two different Linux distributions using Xilinx FPGAs.

  • RIFFA: A reusable Integration Framework for FPGA accelerators
    Proceedings of the 2012 IEEE 20th International Symposium on Field-Programmable Custom Computing Machines FCCM 2012, 2012
    Co-Authors: Matthew Jacobsen, Yoav Freund, Ryan Kastner
    Abstract:

    We present RIFFA 2.0, a reusable Integration Framework for FPGA accelerators. RIFFA 2.0 provides communication and synchronization for FPGA accelerated applications using simple interfaces for hardware and software. Our goal is to expand the use of FPGAs as an acceleration platform by releasing, as open source, a Framework that easily integrates software running on commodity CPUs with FPGA cores. RIFFA 2.0 uses PCIe to connect FPGAs to a CPU's system bus. RIFFA 2.0 extends the original RIFFA project by supporting more classes of Xilinx FPGAs, multiple FPGAs in a system, more PCIe link configurations, higher bandwidth, and Linux and Windows operating systems. This release also supports C/C++, Java, and Python bindings. Tests show that data transfers between hardware and software can saturate the PCIe link to achieve the highest bandwidth possible.

Wang Chenguang - One of the best experts on this subject based on the ideXlab platform.

  • Integration Framework of enterprise information system based on service oriented architecture
    Computer Engineering, 2010
    Co-Authors: Wang Chenguang
    Abstract:

    In order to solve the poor information sharing capability and business adaptability,by integrating logistics information system based on Service-Oriented Architecture(SOA),a fast and flexible Integration method for enterprise information system is presented.Theory analysis and experiments show that it effectively reduces the cost of system adjustment,shortens the adjustment time,and improves the efficiency of execution and the quality of adjustment,so that the market competitiveness of enterprise is improved.