公开(公告)号：US09674406B2

公开(公告)日：2017-06-06

申请号：US14828396

申请日：2015-08-17

Applicant: University of Washington

Inventor： Jose Nathan Kutz ,  Jacob Grosek ,  Steven Brunton ,  Xing Fu ,  Seth Pendergrass

IPC: H04N5/262 ,  H04N5/222 ,  H04N5/14

CPC classification number: H04N5/2226 ,  H04N5/144

Abstract: The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

公开(公告)号：US20160043526A1

公开(公告)日：2016-02-11

申请号：US14823944

申请日：2015-08-11

Applicant: UNIVERSITY OF WASHINGTON

Inventor： Jose Nathan Kutz ,  Steven Brunton ,  Xing Fu

IPC: H01S3/11 ,  H01S3/00 ,  H01S3/067 ,  G05B13/04 ,  G05B13/02

CPC classification number: H01S3/1115 ,  H01S3/0014 ,  H01S3/06791 ,  H01S3/10069 ,  H01S3/1305 ,  H01S3/139

Abstract: Systems and methods for tuning multi-input complex dynamic systems in order to automatically obtain optimal performance are provided. Training is performed by measuring performance of the complex system using an objective function for a sparse sampling of input values over a variety of dynamic regimes. A sparse representation of the performance for each dynamic regime is stored in a library. At run-time, performance is measured and matched to a sparse representation in the library, and the complex system is configured with the optimal input values associated with the matching sparse representation from the library. Performance may then be optimized using an extremum-seeking controller. In some embodiments, the disclosed techniques are applied to a self-tuning mode-locked laser. In some embodiments, the disclosed techniques are applied to other complex systems such as phased array antennas and neurostimulation systems.

Abstract translation: 提供了用于调整多输入复杂动态系统以自动获得最佳性能的系统和方法。 通过使用目标函数测量复杂系统的性能来执行训练，用于在各种动态方案中进行输入值的稀疏采样。 每个动态体系的表现的稀疏表示存储在库中。 在运行时，测量性能并与库中的稀疏表示匹配，并且复杂系统配置有与库中匹配的稀疏表示相关联的最佳输入值。 然后可以使用极值寻求控制器优化性能。 在一些实施例中，所公开的技术被应用于自调谐锁模激光器。 在一些实施例中，所公开的技术被应用于诸如相控阵天线和神经刺激系统之类的其它复杂系统。

公开(公告)号：US09972962B2

公开(公告)日：2018-05-15

申请号：US14823944

申请日：2015-08-11

Applicant: University of Washington

Inventor： Jose Nathan Kutz ,  Steven Brunton ,  Xing Fu

IPC: G06F17/00 ,  G06F19/00 ,  H01S3/098 ,  H01S3/11 ,  H01S3/067 ,  H01S3/13 ,  H01S3/139 ,  H01S3/10 ,  H01S3/00

CPC classification number: H01S3/1115 ,  H01S3/0014 ,  H01S3/06791 ,  H01S3/10069 ,  H01S3/1305 ,  H01S3/139

Abstract: Systems and methods for tuning multi-input complex dynamic systems in order to automatically obtain optimal performance are provided. Training is performed by measuring performance of the complex system using an objective function for a sparse sampling of input values over a variety of dynamic regimes. A sparse representation of the performance for each dynamic regime is stored in a library. At run-time, performance is measured and matched to a sparse representation in the library, and the complex system is configured with the optimal input values associated with the matching sparse representation from the library. Performance may then be optimized using an extremum-seeking controller. In some embodiments, the disclosed techniques are applied to a self-tuning mode-locked laser. In some embodiments, the disclosed techniques are applied to other complex systems such as phased array antennas and neurostimulation systems.

公开(公告)号：US20160050343A1

公开(公告)日：2016-02-18

申请号：US14828396

申请日：2015-08-17

Applicant: University of Washington

Inventor： Jose Nathan Kutz ,  Jacob Grosek ,  Steven Brunton ,  Xing Fu ,  Seth Pendergrass

IPC: H04N5/222 ,  H04N5/14

CPC classification number: H04N5/2226 ,  H04N5/144

Abstract: The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

Abstract translation: 本文公开了动态模式分解（DMD）的技术，用于将视频帧实时强制地分离为背景（低级）和前景（稀疏）分量。 在仅一个奇异值分解（SVD）和一个线性方程的计算成本下实现前景/背景分离，从而产生比鲁棒主成分分析（RPCA）更快的结果数量级。 本文还描述了附加技术，包括用于分析用于多分辨率时间尺度组件的视频的技术以及用于重用计算以允许实时流式视频处理的技术。