Xerox Corporation

Conduent Business Services, LLC

Mks Instruments, Inc.

Peter Paul

Aaron Michael Burry

Beilei Xu

Palghat Srinivas Ramesh

Thomas F Wade

Wencheng Wu

Howard A Mizes

Robert P Loce

Lalit Keshav Mestha

Orhan Bulan

Edgar A Bernal

Michael F Zona

Dennis Lee Venable

Michael D Borton

Stanley John Wallace

Claude Fillion

Steven Robert Moore

Bruce Earl Thayer

Vladimir Kozitsky

Aaron T Radomski

Kenneth Russell Ossman

Abu Saeed Islam

Eric Michael Gross

Eric Scott Hamby

Martin Edward Hoover

Daniel S Hann

Yusuf Oguzhan Artan

Yao Rong Wang

Florent C Perronnin

R Enrique Viturro

Joseph C Sheflin

Jack Thomas Lestrange

Charles Donahue Rizzolo

Brian Robert Conrow

Jess Robert Gentner

Safwan R Wshah

Adrien Pierre Côté

Mark Cantelli

Raja Bala

Gerald M Fletcher

Matthew Adam Shreve

William J Nowak

Jack T LeStrange

Jorge Antonio Alvarez

David Clark Craig

Jack Gaynor Elliot

Jayant Kumar

Christine Ann Steurrys

Richard Leighton Howe

Rakesh Suresh Kulkarni

Nancy Beth Goodman

Ethan Shen

Ross Reinhardt

Mariusz Oldziej

Julianna Elizabeth Lin

Pramod Sankar Kompalli

Valerie J Raburn

Yusuf Artan

Sahand Rabbani

Zhigang Fan

Reiner Eschbach

Stuart Alan Schweid

Edul N Dalal

Eliud Robles Flores

Peter John Knausdorf

Augusto E Barton

Grace T Brewington

Adrien Pierre Cote

Nancy Y Jia

Paul C Julien

David G Anderson

Daniel M Bray

Martin Krucinski

Bruce J Parks

Qun Li

Helen HaeKyung Shin

Tonya L Love

William Joseph Hannaway

Jonathan W Smyka

Douglas Raymond Taylor

Craig John Saunders

Mark Sennett Jackson

Jose Antonio Rodriguez-Serrano

Sandra Skaff

Christopher A Dirubio

William C Dean

Fei Xiao

Eldridge M Mount, Iv

Lee Anne Williams

Robert J Mead

David Martin Todd Jackson

James D Shoots

Linnell Martinez

Stanely J Wallace

Lee Anne S Williams

David Chuang

David Miller

Robert De Beukelaer

Steve Beer

William Lee Kennedy

A radio frequency (RF) power generation system includes a RF power source that generates a RF output signal delivered to a load. A RF power controller is configured to generate a control signal to vary the RF output signal. The controller adjusts a parameter associated with the RF output signal, and the parameter is controlled in accordance with a trigger signal. The parameter is adjusted over a predetermined number of bins. The parameter is adjusted in accordance with a cost function, and the cost function is determined by introducing a perturbation for each bin into an actuator that affects the cost function. The actuator may control an external RF output signal, and the trigger signal may vary in accordance with the external RF output signal. The perturbation is determined in accordance with a basis set having fewer dimensions than the number of bins.

Enhanced tuning methods for mitigating RF load impedance variations due to periodic disturbances

A radio frequency (RF) power generation system includes a RF power source that generates a RF output signal delivered to a load. A RF power controller is configured to generate a control signal to vary the RF output signal. The controller adjusts a parameter associated with the RF output signal, and the parameter is controlled in accordance with a trigger signal. The parameter is adjusted in accordance with a cost function, and the cost function is determined by intruding a perturbation into an actuator that affects the cost function. The actuator may control an external RF output signal, and the trigger signal may vary in accordance with the external RF output signal.

Apparatus and tuning method for mitigating RF load impedance variations due to periodic disturbances

A controller for a plasma generation system includes a model evaluation module receives a sensed value that varies in accordance with a state of a plasma controlled by a RF power generator. The model evaluation module generates a plasma parameter that varies in accordance with the sensed value. A model integration module receives the plasma parameter, integrates the plasma parameter, and outputs an integrated model parameter. An IEDF evaluation module receives the integrated model parameter and generates an ion energy distribution function (IEDF) in accordance with the integrated model parameter. An IEDF controller module receives the IEDF and generates a signal for controlling a RF generator. A RF generator control module receives the signal and generates an RF generator control signal to control at least one of power, frequency, or phase of the RF power generator.

Real-time, non-invasive IEDF plasma sensor

A RF generator includes a RF power source and a RF control module coupled to the RF power source. The RF control module is configured to generate at least one control signal to vary a respective at least one of an RF output signal from the RF power source or an impedance between the RF power source and a load. The RF output signal includes a RF signal modulated by a pulse signal, and the RF control module is further configured to adjust the at least one control signal to vary at least one of an amplitude or a frequency of the RF output signal or the impedance between the RF power source and the load to control a shape of the pulse signal. The at least one of the amplitude, the frequency, or the impedance is adjusted in accordance with respective feedforward adjustments that vary in accordance with a respective sensed pulse parameter detected between a matching network and the load.

Adaptive pulse shaping with post match sensor

A store profile generation method includes moving a mobile base around a product facility which includes an arrangement of product display units for displaying products. Product labels are associated with the display units. The product labels display product-related data for the displayed products. With an image capture assembly mounted on the mobile base, images are acquired of the product display units at a sequence of locations of the mobile base. With a computer processor, the product-related data is extracted from the acquired images and a store profile is constructed, based on the extracted product-related data, a spatial characterization of the image capture assembly, and information on the locations of the mobile base when the images were acquired, which indicates locations of the product labels throughout the product facility.

Store shelf imaging system

A method, non-transitory computer readable medium and apparatus for calculating a by spot occupancy of a parking lot are disclosed. For example, the method includes receiving an indication of a triggering event, sending a query to receive a first image and a second image in response to the triggering event, receiving the first image and the second image, analyzing the first image and the second image to determine a change in an occupancy status of a parking spot within the parking lot and calculating the by spot occupancy of the parking lot based on the change in the occupancy status of the parking spot.

Method and apparatus for calculating parking occupancy

According to exemplary methods, automated image processing of images from order locations of a multi-lane drive-thru system is continually performed using an automated ordering system. It is automatically determined if a vehicle is present at any of the order locations based on the automated image processing. It is automatically determined if an order has been initiated into the automated ordering system. When the order has been initiated while the vehicle is present at any of the order locations, an automated sequencing processing is automatically begun. The automated sequencing processing determines an order pick-up sequence for picking up orders at pickup windows of the multi-lane drive-thru system. When the order has been initiated while the vehicle is not present at any of the order locations, a potential out-of-sequence warning is automatically associated with the order and output from the automated ordering system.

System and method for lane merge sequencing in drive-thru restaurant applications

According to exemplary methods of training a convolutional neural network, input images are received into a computerized device having an image processor. The image processor evaluates the input images using first convolutional layers. The number of first convolutional layers is based on a first size for the input images. Each layer of the first convolutional layers receives layer input signals comprising features of the input images and generates layer output signals that include signals from the input images and ones of the layer output signals from previous layers within the first convolutional layers. Responsive to an input image being a second size larger than the first size, additional convolutional layers are added to the convolutional neural network. The number of additional convolutional layers is based on the second size in relation to the first size. The additional convolutional layers are initialized using weights from the first convolutional layers. Feature maps comprising the layer output signals are created.

System and method for expanding and training convolutional neural networks for large size input images

A system for assessing a property damage claim uses an imaging device to capture images of a property that has reportedly been damaged by an incident. The system receives a property damage incident type, along with image processing criteria for processing images that are associated with the incident type. The system automatically processes the images according to the image processing criteria to identify one or more characteristics in the images and, based on the identified characteristics, determines whether certain claim processing criteria are satisfied. The claim processing criteria may help the system determine whether the property actually was damaged by the reported incident. The system will generate a command to process the property damage claim only if the system determines that one or more images show that the claim processing criteria are satisfied.

Image analysis system for property damage assessment and verification

A store profile generation system includes a mobile base and an image capture assembly mounted on the base. The assembly includes at least one image capture device for acquiring images of product display units in a product facility, product labels being associated with the product display units which include product-related data. A control unit acquires the images captured by the at least one image capture device at a sequence of locations of the mobile base in the product facility. The control unit extracts the product-related data from the acquired images and constructs a store profile indicating locations of the product labels throughout the product facility, based on the extracted product-related data. The store profile can be used for generating new product labels for a sale in an appropriate order for a person to match to the appropriate locations in a single pass through the store.

A dynamic digital signage system based on measured customer behaviors through video analytics.

Systems and methods for dynamic digital signage based on measured customer behaviors through video analytics

A store profile generation system includes a mobile base and an image capture assembly mounted on the base. The assembly includes at least one image capture device for acquiring images of product display units in a retail environment. A control unit acquires the images captured by the at least one image capture device at a sequence of locations of the mobile base in the retail environment. The control unit extracts product-related data from the acquired images and generates a store profile indicating locations of products and their associated tags throughout the retail environment, based on the extracted product-related data. The store profile can be used for generating new product labels for a sale in an appropriate order for a person to match to the appropriate locations in a single pass through the store.

Store shelf imaging system and method

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Webster, NY, United States of America

Peter Paul