The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform

Joseph Lauer - One of the best experts on this subject based on the ideXlab platform.

  • The Level-Set Flow of the Topologist’s Sine Curve is Smooth
    The Journal of Geometric Analysis, 2019
    Co-Authors: Joseph Lauer
    Abstract:

    In this note we prove that the level-set flow of the topologist’s Sine Curve is a smooth closed Curve. In Lauer (Geom Funct Anal 23(6): 1934–1961, 2013 ) it was shown by the second author that under the level-set flow, a locally connected set in the plane evolves to be smooth, either as a Curve or as a positive area region bounded by smooth Curves. Here we give the first example of a domain whose boundary is not locally connected for which the level-set flow is instantaneously smooth. Our methods also produce an example of a nonpath-connected set that instantly evolves into a smooth closed Curve.

  • the level set flow of the topologist s Sine Curve is smooth
    Journal of Geometric Analysis, 2019
    Co-Authors: Joseph Lauer
    Abstract:

    In this note we prove that the level-set flow of the topologist’s Sine Curve is a smooth closed Curve. In Lauer (Geom Funct Anal 23(6): 1934–1961, 2013) it was shown by the second author that under the level-set flow, a locally connected set in the plane evolves to be smooth, either as a Curve or as a positive area region bounded by smooth Curves. Here we give the first example of a domain whose boundary is not locally connected for which the level-set flow is instantaneously smooth. Our methods also produce an example of a nonpath-connected set that instantly evolves into a smooth closed Curve.

  • the level set flow of the topologist s Sine Curve is smooth
    arXiv: Differential Geometry, 2016
    Co-Authors: Joseph Lauer
    Abstract:

    In this note we prove that the level-set flow of the topologist's Sine Curve is a smooth closed Curve. In previous work it was shown by the second author that under level-set flow, a locally-connected set in the plane evolves to be smooth, either as a Curve or as a positive area region bounded by smooth Curves. Here we give the first example of a domain whose boundary is not locally-connected for which the level-set flow is instantaneously smooth. Our methods also produce an example of a non path-connected set that instantly evolves into a smooth closed Curve.

Yoon Kah Leow - One of the best experts on this subject based on the ideXlab platform.

  • a novel Sine Curve mobility model for intrusion detection in wireless sensor networks
    Wireless Communications and Mobile Computing, 2013
    Co-Authors: Yun Wang, Yoon Kah Leow
    Abstract:

    Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight-line intrusion path and a Boolean sensing model. However, a straight-line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real-world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel Sine-Curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

  • A novel Sine‐Curve mobility model for intrusion detection in wireless sensor networks
    Wireless Communications and Mobile Computing, 2011
    Co-Authors: Yun Wang, Yoon Kah Leow
    Abstract:

    Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight-line intrusion path and a Boolean sensing model. However, a straight-line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real-world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel Sine-Curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

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

  • a novel Sine Curve mobility model for intrusion detection in wireless sensor networks
    Wireless Communications and Mobile Computing, 2013
    Co-Authors: Yun Wang, Yoon Kah Leow
    Abstract:

    Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight-line intrusion path and a Boolean sensing model. However, a straight-line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real-world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel Sine-Curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

  • A novel Sine‐Curve mobility model for intrusion detection in wireless sensor networks
    Wireless Communications and Mobile Computing, 2011
    Co-Authors: Yun Wang, Yoon Kah Leow
    Abstract:

    Intrusion detection is prominently important for civil and military applications in wireless sensor networks (WSNs). To date, related works address the problem by assuming a straight-line intrusion path and a Boolean sensing model. However, a straight-line intrusion path is often not the case in reality, and the Boolean sensing model cannot resemble a real-world sensor precisely. Results based on these assumptions are therefore not applicable with desirable accuracy in practice. In view of this, we propose a novel Sine-Curve mobility model that can simulate different intrusion paths by adjusting its features (amplitude, frequency, and phase) and can be integrated into the random WSN model for intrusion detection analysis. It can also be applied to different sensor models and makes influencing factors tractable. With the model, we examine the effects of different intrusion paths on the intrusion detection probability in a random WSN, considering both Boolean and realistic Elfes sensing models. Further, we investigate the interplays between network settings and intruder's mobility patterns and identify the benefits and side effects of the model theoretically and experimentally. Simulation outcomes are shown to match well with the theoretical results, validating the modeling, analysis, and conclusions. Copyright © 2011 John Wiley & Sons, Ltd.

Sean Andersson - One of the best experts on this subject based on the ideXlab platform.

  • A stepped-Sine Curve-fit algorithm for finding cantilever resonance shifts in AFM
    2019 American Control Conference (ACC), 2019
    Co-Authors: Zhixin Kang, Verda Saygin, Keith A. Brown, Sean Andersson
    Abstract:

    Atomic force microscopes (AFMs) are used not only to image with nanometer-scale resolution, but also to nanofabricate structures on a surface using methods such as dip-pen nanolithography (DPN). DPN involves using the tip of the AFM to deposit a small amount of material on the surface. Typically, this process is done in open loop, leading to large variations in the amount of material transferred. One of the first steps to closing this loop is to be able to accurately and rapidly measure the amount of deposition. This can be done by measuring the change in the resonance frequency of the cantilever before and after a write as that shift is directly related to the change in mass on the cantilever. Currently, this is done using a thermal-based system identification, a technique which uses the natural Brownian excitation of the cantilever as a white noise excitation combined with a fast Fourier transform to extract a Bode plot. However, thermal-based techniques do not have a good signal to noise ratio at typical cantilever resonance frequencies and thus do not provide the needed resolution in the DPN application. Here we develop a scheme that iteratively uses a stepped-Sine approach. At each step of the iteration, three frequencies close to the approximate location of the resonance are injected and used to fit a model of the magnitude of the transfer function. The identified peak is used to select three new frequencies in a smaller range in a binary search to reduce the uncertainty of the measured resonance peak location. The scheme is demonstrated through simulation and shown to produce an accuracy of better than 0.5 Hz on a cantilever with a 14 kHz resonance in a physically realistic noise scenario.

Zhixin Kang - One of the best experts on this subject based on the ideXlab platform.

  • A stepped-Sine Curve-fit algorithm for finding cantilever resonance shifts in AFM
    2019 American Control Conference (ACC), 2019
    Co-Authors: Zhixin Kang, Verda Saygin, Keith A. Brown, Sean Andersson
    Abstract:

    Atomic force microscopes (AFMs) are used not only to image with nanometer-scale resolution, but also to nanofabricate structures on a surface using methods such as dip-pen nanolithography (DPN). DPN involves using the tip of the AFM to deposit a small amount of material on the surface. Typically, this process is done in open loop, leading to large variations in the amount of material transferred. One of the first steps to closing this loop is to be able to accurately and rapidly measure the amount of deposition. This can be done by measuring the change in the resonance frequency of the cantilever before and after a write as that shift is directly related to the change in mass on the cantilever. Currently, this is done using a thermal-based system identification, a technique which uses the natural Brownian excitation of the cantilever as a white noise excitation combined with a fast Fourier transform to extract a Bode plot. However, thermal-based techniques do not have a good signal to noise ratio at typical cantilever resonance frequencies and thus do not provide the needed resolution in the DPN application. Here we develop a scheme that iteratively uses a stepped-Sine approach. At each step of the iteration, three frequencies close to the approximate location of the resonance are injected and used to fit a model of the magnitude of the transfer function. The identified peak is used to select three new frequencies in a smaller range in a binary search to reduce the uncertainty of the measured resonance peak location. The scheme is demonstrated through simulation and shown to produce an accuracy of better than 0.5 Hz on a cantilever with a 14 kHz resonance in a physically realistic noise scenario.

  • ACC - A stepped-Sine Curve-fit algorithm for finding cantilever resonance shifts in AFM
    2019 American Control Conference (ACC), 2019
    Co-Authors: Zhixin Kang, Verda Saygin, Keith A. Brown, Sean B. Andersson
    Abstract:

    Atomic force microscopes (AFMs) are used not only to image with nanometer-scale resolution, but also to nanofabricate structures on a surface using methods such as dip-pen nanolithography (DPN). DPN involves using the tip of the AFM to deposit a small amount of material on the surface. Typically, this process is done in open loop, leading to large variations in the amount of material transferred. One of the first steps to closing this loop is to be able to accurately and rapidly measure the amount of deposition. This can be done by measuring the change in the resonance frequency of the cantilever before and after a write as that shift is directly related to the change in mass on the cantilever. Currently, this is done using a thermal-based system identification, a technique which uses the natural Brownian excitation of the cantilever as a white noise excitation combined with a fast Fourier transform to extract a Bode plot. However, thermal-based techniques do not have a good signal to noise ratio at typical cantilever resonance frequencies and thus do not provide the needed resolution in the DPN application. Here we develop a scheme that iteratively uses a stepped-Sine approach. At each step of the iteration, three frequencies close to the approximate location of the resonance are injected and used to fit a model of the magnitude of the transfer function. The identified peak is used to select three new frequencies in a smaller range in a binary search to reduce the uncertainty of the measured resonance peak location. The scheme is demonstrated through simulation and shown to produce an accuracy of better than 0.5 Hz on a cantilever with a 14 $\mathbf{kHz}$ resonance in a physically realistic noise scenario.