Simulation Engine

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The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

Séverin Lemaignan - One of the best experts on this subject based on the ideXlab platform.

  • Modular open robots Simulation Engine: MORSE
    2011 IEEE International Conference on Robotics and Automation, 2011
    Co-Authors: Gilberto Echeverria, Nicolas Lassabe, Arnaud Degroote, Séverin Lemaignan
    Abstract:

    This paper presents MORSE, a new open-source robotics simulator. MORSE provides several features of interest to robotics projects: it relies on a component-based architecture to simulate sensors, actuators and robots; it is flexible, able to specify Simulations at variable levels of abstraction according to the systems being tested; it is capable of representing a large variety of heterogeneous robots and full 3D environments (aerial, ground, maritime); and it is designed to allow Simulations of multiple robots systems. MORSE uses a “Software-in-the-Loop” philosophy, i.e. it gives the possibility to evaluate the algorithms embedded in the software architecture of the robot within which they are to be integrated. Still, MORSE is independent of any robot architecture or communication framework (middleware). MORSE is built on top of Blender, using its powerful features and extending its functionality through Python scripts. Simulations are executed on Blender's Game Engine mode, which provides a realistic graphical display of the simulated environments and allows exploiting the reputed Bullet physics Engine. This paper presents the conception principles of the simulator and some use-case illustrations.

Khaled Benkrid - One of the best experts on this subject based on the ideXlab platform.

  • FPT - Design and implementation of a high performance financial Monte-Carlo Simulation Engine on an FPGA supercomputer
    2008 International Conference on Field-Programmable Technology, 2008
    Co-Authors: Xiang Tian, Khaled Benkrid
    Abstract:

    Monte-Carlo Simulation is a very widely used technique in scientific computations in general with huge computation benefits in solving problems where closed form solutions are impossible to derive. This technique is also characterized by a high degree of parallelism as a large number of different Simulation paths need to be calculated, which makes it ideal for a parallel hardware implementation. This paper illustrates the benefits of such implementation in the context of financial computing as it implements a financial Monte-Carlo Simulation Engine on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our Engine can implement various Monte-Carlo Simulations on the Maxwell machine with speed-ups in the 3-order magnitude compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the Black-Scholes option pricing model. Real hardware implementation shows that our FPGA-based implementation of the Black-Scholes model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 750, which is the fastest ever reported FPGA implementation of this model.

  • Design and Implementation of a High Performance Financial Monte-Carlo Simulation Engine on an FPGA Supercomputer
    2008 International Conference on Field-Programmable Technology, 2008
    Co-Authors: Xiang Tian, Khaled Benkrid
    Abstract:

    Monte-Carlo Simulation is a very widely used technique in scientific computations in general with huge computation benefits in solving problems where closed form solutions are impossible to derive. This technique is also characterized by a high degree of parallelism as a large number of different Simulation paths need to be calculated, which makes it ideal for a parallel hardware implementation. This paper illustrates the benefits of such implementation in the context of financial computing as it implements a financial Monte-Carlo Simulation Engine on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our Engine can implement various Monte-Carlo Simulations on the Maxwell machine with speed-ups in the 3-order magnitude compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the Black-Scholes option pricing model. Real hardware implementation shows that our FPGA-based implementation of the Black-Scholes model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 750, which is the fastest ever reported FPGA implementation of this model.

Christian Floerkemeier - One of the best experts on this subject based on the ideXlab platform.

  • rfidsim a physical and logical layer Simulation Engine for passive rfid
    IEEE Transactions on Automation Science and Engineering, 2009
    Co-Authors: Christian Floerkemeier, Sanjay E Sarma
    Abstract:

    Radio-frequency identification (RFID) poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. A number of new approaches to address these issues have been proposed recently, but due to the highly integrated nature of passive RFID tags, it is difficult to evaluate them in real-world scenarios. In this paper, we present an RFID Simulation Engine, RFIDSim, which implements the ISO 18000-6C communication protocol and supports pathloss, fading, backscatter, capture, and tag mobility models. This paper also shows that our implementation of RFIDSim that relies on a discrete event simulator can be used to simulate large populations featuring thousands of RFID tags. RFIDSim also simulates the deep fades that lead to frequent power losses of the battery-less RFID tags by modeling the multipath effects statistically.

  • RFIDSim—A Physical and Logical Layer Simulation Engine for Passive RFID
    IEEE Transactions on Automation Science and Engineering, 2009
    Co-Authors: Christian Floerkemeier, Sanjay Sarma
    Abstract:

    Radio-frequency identification (RFID) poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. A number of new approaches to address these issues have been proposed recently, but due to the highly integrated nature of passive RFID tags, it is difficult to evaluate them in real-world scenarios. In this paper, we present an RFID Simulation Engine, RFIDSim, which implements the ISO 18000-6C communication protocol and supports pathloss, fading, backscatter, capture, and tag mobility models. This paper also shows that our implementation of RFIDSim that relies on a discrete event simulator can be used to simulate large populations featuring thousands of RFID tags. RFIDSim also simulates the deep fades that lead to frequent power losses of the battery-less RFID tags by modeling the multipath effects statistically.

  • Evaluation of RFIDSim - a Physical and Logical Layer RFID Simulation Engine
    2008 IEEE International Conference on RFID, 2008
    Co-Authors: Christian Floerkemeier, Ravikanth Pappu
    Abstract:

    Radio frequency identification poses a number of research challenges, such as interference mitigation, throughput optimization and security over the RF channel. In this paper we investigate to what extent the physical and logical layer Simulation Engine RFIDSim can be used to evaluate RFID system performance. Our analysis compares Simulation results against measurements with RFID equipment and theoretical predictions. Our analysis focusses on timing and medium access behaviour and signal strength modeling in RFIDSim.

Gilberto Echeverria - One of the best experts on this subject based on the ideXlab platform.

  • Modular open robots Simulation Engine: MORSE
    2011 IEEE International Conference on Robotics and Automation, 2011
    Co-Authors: Gilberto Echeverria, Nicolas Lassabe, Arnaud Degroote, Séverin Lemaignan
    Abstract:

    This paper presents MORSE, a new open-source robotics simulator. MORSE provides several features of interest to robotics projects: it relies on a component-based architecture to simulate sensors, actuators and robots; it is flexible, able to specify Simulations at variable levels of abstraction according to the systems being tested; it is capable of representing a large variety of heterogeneous robots and full 3D environments (aerial, ground, maritime); and it is designed to allow Simulations of multiple robots systems. MORSE uses a “Software-in-the-Loop” philosophy, i.e. it gives the possibility to evaluate the algorithms embedded in the software architecture of the robot within which they are to be integrated. Still, MORSE is independent of any robot architecture or communication framework (middleware). MORSE is built on top of Blender, using its powerful features and extending its functionality through Python scripts. Simulations are executed on Blender's Game Engine mode, which provides a realistic graphical display of the simulated environments and allows exploiting the reputed Bullet physics Engine. This paper presents the conception principles of the simulator and some use-case illustrations.

Xiang Tian - One of the best experts on this subject based on the ideXlab platform.

  • FPT - Design and implementation of a high performance financial Monte-Carlo Simulation Engine on an FPGA supercomputer
    2008 International Conference on Field-Programmable Technology, 2008
    Co-Authors: Xiang Tian, Khaled Benkrid
    Abstract:

    Monte-Carlo Simulation is a very widely used technique in scientific computations in general with huge computation benefits in solving problems where closed form solutions are impossible to derive. This technique is also characterized by a high degree of parallelism as a large number of different Simulation paths need to be calculated, which makes it ideal for a parallel hardware implementation. This paper illustrates the benefits of such implementation in the context of financial computing as it implements a financial Monte-Carlo Simulation Engine on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our Engine can implement various Monte-Carlo Simulations on the Maxwell machine with speed-ups in the 3-order magnitude compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the Black-Scholes option pricing model. Real hardware implementation shows that our FPGA-based implementation of the Black-Scholes model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 750, which is the fastest ever reported FPGA implementation of this model.

  • Design and Implementation of a High Performance Financial Monte-Carlo Simulation Engine on an FPGA Supercomputer
    2008 International Conference on Field-Programmable Technology, 2008
    Co-Authors: Xiang Tian, Khaled Benkrid
    Abstract:

    Monte-Carlo Simulation is a very widely used technique in scientific computations in general with huge computation benefits in solving problems where closed form solutions are impossible to derive. This technique is also characterized by a high degree of parallelism as a large number of different Simulation paths need to be calculated, which makes it ideal for a parallel hardware implementation. This paper illustrates the benefits of such implementation in the context of financial computing as it implements a financial Monte-Carlo Simulation Engine on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our Engine can implement various Monte-Carlo Simulations on the Maxwell machine with speed-ups in the 3-order magnitude compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the Black-Scholes option pricing model. Real hardware implementation shows that our FPGA-based implementation of the Black-Scholes model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 750, which is the fastest ever reported FPGA implementation of this model.