Abstract: Methods, systems and methods for designing a system that provides adaptive control and adaptive predictive filtering using nonlinear components. A system design is described that provides an engineered architecture. This architecture defines a core set of network dynamics that carry out specific functions related to control or prediction. The adaptation systems and methods can be applied to limited areas of the system to allow the system to learn to compensate for unmodeled system dynamics and kinematics. Two types of adaptive modules are described which are configured to account for the unmodeled system dynamics and kinematics.

Abstract: Methods, systems and methods for designing a system that provides adaptive control and adaptive predictive filtering using nonlinear components. A system design is described that provides an engineered architecture. This architecture defines a core set of network dynamics that carry out specific functions related to control or prediction. The adaptation systems and methods can be applied to limited areas of he system to allow the system to learn to compensate for unmodeled system dynamics and kinematics. Two types of adaptive modules are described which are configured to account for the unmodeled system dynamics and kinematics.

Abstract: Techniques for determining a feature in an image or soundtrack of one or more dimensions include receiving a subject image. A sparse transformed subject image is determined, which represents the subject image with a few significant coefficients compared to a number of values in the subject image. Multiple patch functions are received, which are based on a portion of a sparse transformed image for each of a training set of images and which represent learned features in the training set. A feature is determined to be in the subject image based on the transformed subject image and the plurality of patch functions. In various embodiments, a wavelet transformation or audio spectrogram is performed to produce the sparse transformed images. In some embodiments, the feature in the subject is determined regardless of feature location or size or orientation in the subject image.

Abstract: Techniques for determining a feature in an image or soundtrack of one or more dimensions include receiving a subject image. A sparse transformed subject image is determined, which represents the subject image with a few significant coefficients compared to a number of values in the subject image. Multiple patch functions are received, which are based on a portion of a sparse transformed image for each of a training set of images and which represent learned features in the training set. A feature is determined to be in the subject image based on the transformed subject image and the plurality of patch functions. In various embodiments, a wavelet transformation or audio spectrogram is performed to produce the sparse transformed images. In some embodiments, the feature in the subject is determined regardless of feature location or size or orientation in the subject image.