The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
David W Latham - One of the best experts on this subject based on the ideXlab platform.
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photometric variability in kepler target stars ii an overview of amplitude Periodicity and rotation in first quarter data
The Astronomical Journal, 2011Co-Authors: Gibor Basri, Lucianne M Walkowicz, Natalie M Batalha, Ronald L Gilliland, Jon M Jenkins, William J Borucki, David G Koch, D A Caldwell, Andrea K Dupree, David W LathamAbstract:We provide an overview of stellar variability in the first quarter data from the Kepler mission. The intent of this paper is to examine the entire sample of over 150,000 target stars for Periodic Behavior in their light curves and relate this to stellar characteristics. This data set constitutes an unprecedented study of stellar variability given its great precision and complete time coverage (with a half hour cadence). Because the full Kepler pipeline is not currently suitable for a study of stellar variability of this sort, we describe our procedures for treating the raw pipeline data. About half of the total sample exhibits convincing Periodic variability up to two weeks, with amplitudes ranging from differential intensity changes of less than 10?4 up to more than 10%. K and M dwarfs have a greater fraction of period Behavior than G dwarfs. The giants in the sample have distinctive quasi-Periodic Behavior, but are not Periodic in the way we define it. Not all Periodicities are due to rotation, and the most significant period is not necessarily the rotation period. We discuss properties of the light curves, and in particular look at a sample of very clearly Periodic G dwarfs. It is clear that a large number of them do vary because of rotation and starspots, but it will take further analysis to fully exploit this.
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photometric variability in kepler target stars ii an overview of amplitude Periodicity and rotation in first quarter data
arXiv: Solar and Stellar Astrophysics, 2010Co-Authors: Gibor Basri, Lucianne M Walkowicz, Natalie M Batalha, Ronald L Gilliland, Jon M Jenkins, William J Borucki, David G Koch, D A Caldwell, Andrea K Dupree, David W LathamAbstract:We provide an overview of stellar variability in the first quarter of data from the Kepler mission. The intent of this paper is to examine the entire sample of over 150,000 target stars for Periodic Behavior in their lightcurves, and relate this to stellar characteristics. These data constitute an unprecedented study of stellar variability given its great precision and complete time coverage (with a half hour cadence). Because the full Kepler pipeline is not currently suitable for a study of stellar variability of this sort, we describe our procedures for treating the "raw" pipeline data. About half of the total sample exhibits convincing Periodic variability up to two weeks, with amplitudes ranging from differential intensity changes less than 10^{-4} up to more than 10 percent. K and M dwarfs have a greater fraction of period Behavior than G dwarfs. The giants in the sample have distinctive quasi-Periodic Behavior, but are not Periodic in the way we define it. Not all Periodicities are due to rotation, and the most significant period is not necessarily the rotation period. We discuss properties of the lightcurves, and in particular look at a sample of very clearly Periodic G dwarfs. It is clear that a large number of them do vary because of rotation and starspots, but it will take further analysis to fully exploit this.
Heni Ben Amor - One of the best experts on this subject based on the ideXlab platform.
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predictive modeling of Periodic Behavior for human robot symbiotic walking
International Conference on Robotics and Automation, 2020Co-Authors: Geoffrey A Clark, Joseph Campbell, Seyed Mostafa Rezayat Sorkhabadi, Wenlong Zhang, Heni Ben AmorAbstract:We propose in this paper Periodic Interaction Primitives – a probabilistic framework that can be used to learn compact models of Periodic Behavior. Our approach extends existing formulations of Interaction Primitives to Periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21◦ in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
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predictive modeling of Periodic Behavior for human robot symbiotic walking
arXiv: Robotics, 2020Co-Authors: Geoffrey A Clark, Joseph Campbell, Seyed Mostafa Rezayat Sorkhabadi, Wenlong Zhang, Heni Ben AmorAbstract:We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of Periodic Behavior. Our approach extends existing formulations of Interaction Primitives to Periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21 degrees in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
A E Cetin - One of the best experts on this subject based on the ideXlab platform.
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real time fire and flame detection in video
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: N Dedeoglu, B U Toreyin, Ugur Gudukbay, A E CetinAbstract:The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic Behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.
Enis A Cetin - One of the best experts on this subject based on the ideXlab platform.
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computer vision based method for real time fire and flame detection
Pattern Recognition Letters, 2006Co-Authors: Ugur B Toreyin, Ugur Gudukbay, Yigithan Dedeoglu, Enis A CetinAbstract:This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-Periodic Behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues.
Ugur Gudukbay - One of the best experts on this subject based on the ideXlab platform.
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computer vision based method for real time fire and flame detection
Pattern Recognition Letters, 2006Co-Authors: Ugur B Toreyin, Ugur Gudukbay, Yigithan Dedeoglu, Enis A CetinAbstract:This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-Periodic Behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues.
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real time fire and flame detection in video
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: N Dedeoglu, B U Toreyin, Ugur Gudukbay, A E CetinAbstract:The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic Behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.
