Georgia Tech Research Corporation

Sheng-Yu Peng

Paul Edward Hasler

Paul E Halser

The present invention describes systems and methods to provide programmable analog classifiers. An exemplary embodiment of the present invention provides an analog classifier circuit comprising a bump circuit enabled to store a template vector, wherein the template vector can model a probability distribution with exponential behavior. Furthermore, the bump circuit is enabled to generate an output corresponding to a comparison between an input vector received by the bump circuit and the template vector stored by the bump circuit. Additionally, the analog classifier circuit includes a variable gain amplifier in communication with the bump circuit, and the variable gain amplifier can be adjusted to modify the variance of the template vector.

Systems and methods for providing programmable analog classifiers

The present invention relates to systems and methods for sensing capacitance change of a capacitive sensor and for optimizing a capacitive sensing circuit. In an exemplary embodiment, a capacitive sensor may be coupled to an amplifier at floating node. A programming circuit is connected to the floating node for controlling a charge on the floating node. A method of controlling the charge of the floating node is also provided. The method includes applying a first predetermined voltage to a source of a programming transistor, applying a second predetermined voltage to a floating gate of the programming transistor, and applying a third predetermined voltage to a drain of the programming transistor until a charge on the floating gate of the programming transistor reaches a predetermined value. The charge on the floating gate of the programming transistor drives the charge on the floating node to the predetermined value, and thus is controlled.

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Atlanta, GA, United States of America

Sheng-Yu Peng