Canon Kabushiki Kaisha

General Electric Company

Eastman-kodak Company

Exxonmobil Upstream Research Company

Intellectual Ventures Fund 83 LLC

Applied Materials, Inc.

Emc Corporation

Jie Yu

Jiebo Luo

Francisco H Imai

Dhiraj Joshi

Sandra Skaff

Ali Can

Kartik Ramaswamy

Yang Yang

Jianchao Yang

Yue Guo

Ser Nam Lim

Jens Rittscher

Clifford Bueno

Arun Karthi Subramaniyan

Sen Wang

Ya Xue

Ronald Brown

Leslie A Wahrmund

John Duncan Bankier

Wei Hao

John Robert Hare

Erhan Bas

Srinivas Aji

Khan Mohamed Khirullah Genghis Khan

Dylan Simon

Dongrui Wu

Adrian Gabriel Loghin

Junjie Cai

Iro Armeni

A wideband variable impedance load for high volume manufacturing qualification and diagnostic testing of a radio frequency power source, an impedance matching network and RF sensors for generating plasma in a semiconductor plasma chamber for semiconductor fabrication processes. The wideband variable impedance load may comprise a fixed value resistance operable at a plurality of frequencies and coupled with a variable impedance network capable of transforming the fixed value resistance into a wide range of complex impedances at the plurality of frequencies. Response times and match tuning element position repeatability may be verified. Automatic testing, verification and qualification of production and field installed radio frequency power sources for plasma generation are easily performed.

Wideband variable impedance load for high volume manufacturing qualification and on-site diagnostics

Devices, systems, and methods obtain data in a first modality; propagate the data in the first modality through a neural network, thereby generating network outputs, wherein the neural network includes a first-stage neural network and a second-stage neural network, wherein the first-stage neural network includes two or more layers, wherein each layer of the two or more layers of the first-stage neural network includes a plurality of respective nodes, wherein the second-stage neural network includes two or more layers, one of which is an input layer and one of which is an output layer, and wherein each node in each layer of the first-stage neural network is connected to the input layer of the second-stage neural network; calculate a gradient of a loss function based on the network outputs; backpropagate the gradient through the neural network; and update the neural network based on the backpropagation of the gradient.

Devices, systems, and methods for feature encoding

Devices, systems, and methods obtain a region of an image; generate known-object scores for the region using known-object detectors, wherein each known-object detector of the known-object detectors detects objects in a respective object class; determine a likelihood that the region includes a complete object; and determine a likelihood that the region includes an unknown object based on the likelihood that the region includes a complete object and on the known-object scores.

Devices, systems, and methods for detecting unknown objects

Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.

Devices, systems, and methods for generating multi-modal images of a synthetic scene

Devices, systems, and methods for computer recognition of action in video obtain frame-level feature sets of visual features that were extracted from respective frames of a video, wherein the respective frame-level feature set of a frame includes the respective visual features that were extracted from the frame; generate first-level feature sets, wherein each first-level feature set is generated by pooling the visual features from two or more frame-level feature sets, and wherein each first-level feature set includes pooled features; and generate second-level feature sets, wherein each second-level feature set is generated by pooling the pooled features in two or more first-level feature sets, wherein each second-level feature set includes pooled features.

Devices, systems, and methods for generating a temporal-adaptive representation for video-event classification

A method includes retrieving a seismic data set, receiving training data that includes one or more seed points of an identified geobody, determining a geobody trajectory of the identified geobody, based on the one or more seed points of the identified geobody, displaying the geobody trajectory, receiving inputs expanding the geobody trajectory, shrinking the geobody trajectory, confirming the geobody trajectory, or a combination thereof, training a classification algorithm using the geobody trajectory, running the classification algorithm on the seismic data set, receiving an output of one or more sets of voxels from the classification algorithm, skeletonizing the one or more sets of voxels to present the one or more sets of voxels as a set of possible geobody trajectories, and retraining the classification algorithm based on feedback received from a reviewer.

Expert guided knowledge acquisition system for analyzing seismic data

A monitoring system for determining component wear is provided. The monitoring system includes a memory device configured to store a reference model of a component and a component wear monitoring (CWM) device configured to receive a component image of a first component being inspected, detect a plurality of manmade structural features in the received component image, adjust the component image to mask out at least some of the plurality of manmade structural features from the received component image, compare the adjusted component image with the reference model to determine one or more potential defect areas in the first component, analyze each of the one or more defect areas to determine a state of the potential defect areas, and output the state of the one or more potential defect areas to a user.

Methods and systems for detecting component wear

Systems, devices, and methods for material recognition extract one or more features from a patch in an image of a scene; generate an initial prediction of a material category of the patch from candidate material categories based on the one or more features; identify a scene in the image; identify one or more objects in the image; obtain a first relationship model of two or more of the one or more features, the candidate material categories, the one or more objects, and the scene; and make a refined prediction of the material category of the patch based on the initial prediction of the material category and on the first relationship model.

Devices, systems, and methods for knowledge-based inference for material recognition

An approach for seismic data analysis is provided. In accordance with various embodiments, the active learning approaches are employed in conjunction with an analysis algorithm that is used to process the seismic data. Algorithms that may employ such active learning include, but are not limited to, ranking algorithms and classification algorithms.

Seismic data analysis

A method of computing at least one photogenic route from a starting location to a destination location, including; computing photogenic values for images in a large collection representing a geographic region that includes the starting location and the destination location; computing a photogenic index for each route segment based on computed photogenic values of images taken along the route segment; computing at least one photogenic route from the starting location to the destination location and presenting the route(s) to a user.

Generating photogenic routes from starting to destination locations

A method of recognizing an event depicted in an image from the image and a location information associated with the image is disclosed. The method includes acquiring the image and its associated location information; using the location information to acquire an aerial image(s) correlated to the location information; identifying the event using both the image and the acquired aerial image(s); and storing the event in association with the image for subsequent use.

Event recognition using image and location information

A method for organizing individual collections of images or videos captured for the same event by different cameras into a master collection, wherein each individual collection forms a media stream in chronological order. A processor for extracting image features for each image or video of the media stream of each collection; constructing a graph based on the extracted features to link the images or videos from the two media streams; finding on the graph at least a pair of images or videos, each from one of the two media streams, that corresponds to correlated captured content; aligning the remaining images or videos in response to the aligned pair so that the all images or videos from the two media streams are aligned over a common timeline; and producing a master stream in chronological order of the event over a common timeline by merging the aligned individual.

Method for aligning different photo streams

A method of identifying an image classification for an input digital image comprising receiving an input digital image for a captured scene; receiving a range map which represents range information associated with the input digital image, wherein the range information represents distances between the captured scene and a known reference location; identifying the image classification using both the range map and the input digital image; and storing the image classification in association with the input digital image in a processor-accessible memory system.

Image classification using range information

The present invention is a system and method for reconciling results returned from a multi-protocol scan of a NAS system. Certain NAS systems use different protocols. Some may use the NFS protocol, whereas others may use the CIFS protocol. A scan of the NAS may therefore produce two data object names for a single data object. When a storage virtualization system is tasked with acting as an intermediary between the client and the NAS, these two data object names will cause errors during data migration, synchronization or redirection. The present invention is a method for using the results of a multi-protocol scan of the NAS in order to reconcile data object names that may present differently depending on the protocol applied.

Growing community of inventors

Santa Clara, CA, United States of America

Jie Yu