University of Florida Research Foundation, Incorporated

Convergent Engineering, Inc.

Hewlett-packard Development Company, L.p.

Etectrx, Inc.

Other

Euliano, Neil R.

Jose Carlos Principe

Richard Joel Melker

Neil Russell Euliano, Ii

Neil R Euliano

Tammy Y Euliano

John Gregory Harris

Brent Arnold Myers

Choudur K Lakshminarayan

Michael Joseph Banner

Paul Bradford Blanch

Glen Flores

Dorothee Marossero

Venkata V Meka

Gabriel Nallathambi

Evan Kriminger

Kan Li

Dorothee Maroserro

Alexander Singh Alvarado

Puskal Prasad Pokharel

Justin C Sanchez

Abhishek Singh

Weifeng Liu

Du Chen

Deniz Erdogmus

Toshikazu Nishida

Christy Leigh She

Rizwan Bashirullah

Vikas V Meka

Michael W Stahl, Jr

Bryan Davis

Goktug Tankut Cinar

Jose Fortes

Dazhi Wei

Marcelino Lazaro

Ignacio Santamaria

Vitor Manual Grade Tavares

Pedro Guedes De Oliveira

John F Digiovanna

Bert De Vries

Babak Mahmoudi

Jeremiah D Mitzelfelt

Rati Agrawal

Austin J Brockmeier

Rakesh Chalasani

Yadunandana Nagaraja Rao

Neil Russell Ii Euliano

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.

Real time implementation of recurrent network detectors

Systems and methods for monitoring uterus contraction activity and progress of labor. The system of the subject invention can comprises (1) a plurality of sensors; (2) an amplifying/filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, progress of labor, prediction and monitoring of preterm labor, and intrauterine pressure prediction. In a preferred embodiment, the system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis as well as delivery strategy.

System and method for analyzing progress of labor and preterm labor

Various methods and systems are related to pulse-based automatic gain control. In one example, pulse-based automatic gain control (AGC) includes a variable gain amplifier (VGA) configured to amplify an analog input signal to generate an analog output signal based upon an amplification control signal; an integrate-and-fire (IF) sampler configured to generate a pulse train corresponding to the analog output signal; and a gain adjustment configured to generate the amplification control signal based upon a comparison of time between pulses of the pulse train to a reference time. In another example, a method includes determining time between pulses of a pulse train corresponding to an analog output signal from a VGA; generating an amplification control signal based upon a comparison of the time between pulses of the pulse train to a reference time; and adjusting amplification of the VGA in response to the amplification control signal.

Pulse based automatic gain control for analog and pulse domain regulation

Various examples related to automatically composing universal filters are presented. In one example, among others, a system includes processing circuitry that can organize data received by the system into clusters or quasi-orthogonal regions, which are organized based upon a centroid threshold distance. The data can be organized by applying a cluster and retain operation, a cluster and merge operation or a split and retain operation. The system can then determine filter weights based at least in part upon centers of the clusters; update a content addressable filter bank (CAFB) based upon the filter weights; and filter subsequently received data based upon the CAFB. In another example, a method includes receiving and organizing initial data into clusters or quasi-orthogonal regions; determining filter weights based at least in part upon centers of the clusters; updating a CAFB based upon the filter weights; and receiving and filtering subsequent data based upon the CAFB.

Automatic composition of universal filters

A system and method for monitoring a patient's compliance with a medication regimen includes an electronic tag integral with or attached to a medicine delivery device such as a capsule, the tag having an antenna and a receiver/transmitter, the system also including a reader positioned externally for detecting the presence and location of the delivery device in the patient.

Electronic medication compliance monitoring system and associated methods

Various examples are provided related to speech recognition. In one example, a method includes converting an auditory signal into a pulse train, segmenting the pulse train into a series of frames having a predefined duration, and identifying a portion of the auditory signal by applying at least a portion of the series of frames segmented from the pulse train to a kernel adaptive autoregressive-moving-average (KAARMA) network. In another example, a speech recognition system includes processing circuitry configured to convert an auditory signal into a pulse train, segment the pulse train into a secured of frames, and identifying a portion of the auditory signal by applying at least a portion of the series of frames segmented from the pulse train to a KAARMA network. The series of frames segmented from the pulse train can be applied to a KAARMA chain including a plurality of KAARMA networks for identification.

Pulse-based automatic speech recognition

Various examples of devices, methods and systems related to pulse based arithmetic units. In one example, a pulse domain device includes an augend area calculator to provide an augend area output for an augend pulse train; an addend area calculator to provide an addend area output for an addend pulse train; a resultant sum area (RSA) decoder to provide a RSA output using the augend and addend area outputs; and a pulse timing calculator to provide RSA output pulse timing. In another example, a pulse domain device includes a multiplicand area calculator to provide an multiplicand area output for a multiplicand pulse train; a multiplier area calculator to provide a multiplier area output for a multiplier pulse train; a resultant product area (RPA) decoder to provide a RPA output using the multiplicand and multiplier area outputs; and a pulse timing calculator to provide RPA output pulse timing.

Pulsed based arithmetic units

Various examples are provided for brain state advisory systems using calibrated metrics and optimal time-series decompositions. In one embodiment, a method includes monitoring brainwave activity of a subject and classifying a brain state of the subject based upon the brainwave activity and a model of the brainwave activity. In another embodiment, brain states of the subject are modeled based upon the brainwave activity of the subject.

Brain state advisory system using calibrated metrics and optimal time-series decomposition

A system and method for monitoring a patient's compliance with a medication regimen includes an electronic tag having an antenna and a receiver/transmitter positioned with a medication capsule, and a reader positioned externally for detecting the presence and location of the tag in the patient.

Various examples are provided for automation of QRS detection. In one example, among others, a system includes an integrate and fire (IF) sampler that can generate an IF pulse train from an analog input signal, and decision logic circuitry that can determine whether a QRS complex waveform is present in a pulse segment of the IF pulse train. In another example, a method includes generating an integrate and fire (IF) pulse train from an analog input signal, identifying a pulse segment of the IF pulse train, and determining whether a QRS waveform is present in the pulse segment based at least in part upon attributes associated with the pulse segment.

Integrate and fire pulse train automation for QRS detection

Various examples are provided for object recognition in video. In one example, among others, a system includes processing circuitry including a processor. The processing circuitry is configured to process a sequence of images to recognize an object in the images, the recognition of the object based upon a hierarchical model. In another example, a method includes determining input data from a plurality of overlapping pixel patches of a video image; determining a plurality of corresponding states based at least in part upon the input data and an over-complete dictionary of filters; and determining a cause based at least in part upon the plurality of corresponding states. The cause may be used to identify an object in the video image.

Distributive hierarchical model for object recognition in video

Various methods and systems are provided for adaptive background estimation. In one embodiment, among others, a signal processing device includes at least one processing unit and a memory storing a background estimation system executable in the at least one processing unit. The background estimation system, when executed, causes the signal processing device to separate foreground objects from at least one image frame in a sequence of image frames based at least in part upon a correntropy cost function. In another embodiment, a method includes obtaining an image frame from a sequence of image frames; performing correntropy filtering on the image frame to separate foreground objects from the image frame; and providing the foreground objects for rendering. The correntropy filtering may adapt to compensate for diffusion effects in the image frame.

Adaptive background estimation

Various methods and systems are provided for related to adaptive systems using correntropy. In one embodiment, a signal processing device includes a processing unit and a memory storing an adaptive system executable in the at least one processing unit. The adaptive system includes modules that, when executed by the processing unit, cause the signal processing device to adaptively filter a desired signal using a correntropy cost function. In another embodiment, a method includes adjusting a coefficient of an adaptive filter based at least in part on a correntropy cost function signal, providing an adaptive filter output signal based at least in part on the adjusted coefficient and a reference signal, and determining an error signal based at least in part on a received signal and the adaptive filter output signal.

Adaptive systems using correntropy

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Gainesville, FL, United States of America

Jose Carlos Principe