The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 25, 2023

Filed:

Aug. 28, 2018
Applicant:

University of Florida Research Foundation, Inc., Gainesville, FL (US);

Inventors:

Kan Li, Gainesville, FL (US);

Jose C. Principe, Gainesville, FL (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06K 9/62 (2022.01); G06K 9/00 (2022.01); G06N 3/063 (2006.01); G06V 10/94 (2022.01); G06V 20/40 (2022.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06K 9/00503 (2013.01); G06K 9/00536 (2013.01); G06K 9/622 (2013.01); G06K 9/6273 (2013.01); G06N 3/0445 (2013.01); G06N 3/063 (2013.01); G06N 3/084 (2013.01); G06V 10/955 (2022.01); G06V 20/40 (2022.01); G06N 3/0454 (2013.01); G06N 3/0481 (2013.01); G06N 3/088 (2013.01);
Abstract

Various examples related to real time detection with recurrent networks are presented. These can be utilized in automatic insect recognition to provide accurate and rapid in situ identification. In one example, among others, a method includes training parameters of a kernel adaptive autoregressive-moving average (KAARMA) using a signal of an input space. The signal can include source information in its time varying structure. A surrogate embodiment of the trained KAARMA can be determined based upon clustering or digitizing of the input space, binarization of the trained KAARMA state and a transition table using the outputs of the trained KAARMA for each input in the training set. A recurrent network detector can then be implemented in processing circuitry (e.g., flip-flops, FPGA, ASIC, or dedicated VLSI) based upon the surrogate embodiment of the KAARMA The recurrent network detector can be configured to identify a signal class.


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