Monthly Archives: April 2014

OpenCL Implementation of Unsharp Filtering on GPU and FPGA

OpenCL Implementation of Unsharp Filtering on GPU and FPGA

  • Özge Ünel and Toygar Akgün. OpenCL Implementation of Unsharp Filtering on GPU and FPGA. In Proceedings of the 22nd IEEE Signal Processing and Communications Applications Conference (SIU 2014), pages 212-215, Karadeniz Technical University, Trabzon, Turkey, 2014. doi:10.1109/SIU.2014.6830203
    [BibTeX] [Abstract]

    The purpose of this study is to evaluate the performance of two dimensional multi-threaded linear filtering process on the GPU and FPGA platforms. To obtain the implementation on varying platforms, OpenCL API is used. OpenCL provides platform independent programming advantage. The results on three different platforms are compared to each other within this scope. These platforms are CPU, GPU, and FPGA. With changing filter and video frame sizes, varying processing times on these platforms are observed, and platform dependent advantages/disadvantages are studied.

    @InProceedings{2014-04-UNEL,
    author = {{\"O}zge {\"U}nel and Toygar Akg{\"u}n},
    title = {{OpenCL Implementation of Unsharp Filtering on GPU and FPGA}},
    booktitle = {{Proceedings of the 22nd IEEE Signal Processing and Communications Applications Conference (SIU 2014)}},
    date = {2014-04-23/2014-04-25},
    address = {Karadeniz Technical University, Trabzon, Turkey},
    pages = {212-215},
    doi = {10.1109/SIU.2014.6830203},
    abstract = {The purpose of this study is to evaluate the performance of two dimensional multi-threaded linear filtering process on the GPU and FPGA platforms. To obtain the implementation on varying platforms, OpenCL API is used. OpenCL provides platform independent programming advantage. The results on three different platforms are compared to each other within this scope. These platforms are CPU, GPU, and FPGA. With changing filter and video frame sizes, varying processing times on these platforms are observed, and platform dependent advantages/disadvantages are studied.},
    year = {2014}
    }

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Distributed Coordination of Sub-systems Power-modes and Software-modes

Distributed Coordination of Sub-systems Power-modes and Software-modes

  • Tiana Rakotovao, Maxime Louvel, Anca Molnos, Julien Mottin, and François Pacull. Distributed Coordination of Sub-systems Power-modes and Software-modes. Poster presented at the 5th International Conference on Cyber-Physical Systems (ICCPS) in Berlin, Germany, 2014.
    [BibTeX] [Abstract]

    Energy management is essential for cyber-physical systems. Such systems typically consist of several sub-systems that may communicate. Hardware used in these sub-systems often has several power-modes that can be controlled to consume less energy. However, to-date, the decision of power-mode does not consider neither the external context of the system, nor the software-modes which involve on the QoS of the system. To address this problem, we propose a loosely coupled and distributed framework that selects the appropriate sub-systems power-modes, regarding both external context (e.g. GPS location, ambient temperature, information from external applications) and software-modes. The framework is based on the LINC coordination middleware. It is evaluated in a vehicle obstacle perception application running on the STHORM platform, a manycore SoC. Depending on the vehicle location and speed, the application has different software-mode and processing requirements. This information is used to scale the power-mode of STHORM in order to reduce energy consumption. The results have shown that significant power-saving can be realized by considering external context. The loosely coupled approach of LINC simplifies the integration of any sub-systems into the framework. In addition, through its goal-driven production rules, LINC allows to easily coordinate the whole system and to adapt it to the external context.

    @Misc{2014-04-RAKOTOVAO,
    author = {Tiana Rakotovao and Maxime Louvel and Anca Molnos and Julien Mottin and Fran\c{c}ois Pacull},
    title = {{Distributed Coordination of Sub-systems Power-modes and Software-modes}},
    date = {2014-04-14/2014-04-17},
    howpublished = {Poster presented at the 5th International Conference on Cyber-Physical Systems (ICCPS) in Berlin, Germany},
    abstract = {Energy management is essential for cyber-physical systems. Such systems typically consist of several sub-systems that may communicate. Hardware used in these sub-systems often has several power-modes that can be controlled to consume less energy. However, to-date, the decision of power-mode does not consider neither the external context of the system, nor the software-modes which involve on the QoS of the system. To address this problem, we propose a loosely coupled and distributed framework that selects the appropriate sub-systems power-modes, regarding both external context (e.g. GPS location, ambient temperature, information from external applications) and software-modes. The framework is based on the LINC coordination middleware. It is evaluated in a vehicle obstacle perception application running on the STHORM platform, a manycore SoC. Depending on the vehicle location and speed, the application has different software-mode and processing requirements. This information is used to scale the power-mode of STHORM in order to reduce energy consumption. The results have shown that significant power-saving can be realized by considering external context. The loosely coupled approach of LINC simplifies the integration of any sub-systems into the framework. In addition, through its goal-driven production rules, LINC allows to easily coordinate the whole system and to adapt it to the external context.},
    year = {2014}
    }

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Camera localization using trajectories and maps

Camera localization using trajectories and maps

  • Raul Mohedano, Andrea Cavallaro, and Narciso Garcia. Camera localization using trajectories and maps. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(4):684-697, 2014. doi:10.1109/TPAMI.2013.243
    [BibTeX] [Abstract]

    We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.

    @Article{2014-04-MOHEDANO,
    title={{Camera localization using trajectories and maps}},
    author={Raul Mohedano and Andrea Cavallaro and Narciso Garcia},
    journal={{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
    volume={36},
    number={4},
    pages={684-697},
    date={2014-04-01},
    year={2014},
    doi={10.1109/TPAMI.2013.243},
    abstract={We propose a new Bayesian framework for automatically determining the position (location and orientation) of an uncalibrated camera using the observations of moving objects and a schematic map of the passable areas of the environment. Our approach takes advantage of static and dynamic information on the scene structures through prior probability distributions for object dynamics. The proposed approach restricts plausible positions where the sensor can be located while taking into account the inherent ambiguity of the given setting. The proposed framework samples from the posterior probability distribution for the camera position via data driven MCMC, guided by an initial geometric analysis that restricts the search space. A Kullback-Leibler divergence analysis is then used that yields the final camera position estimate, while explicitly isolating ambiguous settings. The proposed approach is evaluated in synthetic and real environments, showing its satisfactory performance in both ambiguous and unambiguous settings.}
    }

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