Future Wei Technologies, Inc.

International Business Machines Corporation

Ge Precision Healthcare LLC

Thomson Licensing

Baidu USA LLC

Via Technologies, Inc.

Les Laboratoires Servier

Shanghai Huali Microelectronics Corporation

Chia Tai Tianqing Pharmaceutical Group Co., Ltd.

Alliance Fiber Optic Products, Inc.

Other

Intel Corporation

Feitian Technologies Co., Ltd.

Vid Scale, Inc.

Everdisplay Optronics (shanghai) Limited

Yu Huang

Hong Heather Yu

Jun Tian

Zhedong Yuan

Hanbin Shan

Xiong Yu

Taoran Lu

Dapeng Oliver Wu

Yanran Xu

Jun Zhu

Jian Ding

Xin Zhang

Zhi Hu Wang

Ning Duan

Zheng Yuan

Joan Llach

Hongbing Li

Yuan Jin

Weide Zhang

Hsien-Ting Cheng

Peng Zhang

Peng Gao

Jian Jun Wang

Jizhang Shan

Yao Li

Dejian Li

Yin Qian

Xiao Cao Cao

Xueyan Zhu

Pengkai Xu

Fulong Qiao

Xiao Rui Shao

Xingdong Cheng

Fanbo Meng

Hubo Wang

Ruihui Di

Xudong Jiang

Xufeng Cao

Shiping Yan

Chengyu Peng

Ye-Kui Wang

Su Liu

Zhou Lu

Chao Zhang

Joel Sole

Dong-Qing Zhang

Andy Fenglei Zhou

Hongjiang Xu

Sitaram Bhagavathy

Dong Gui

Qijun Xiao

Fang Frank Chen

Shan Jiang

Xiao Yang

Jie Ling

Zheng Qi

Prasad Modali

Tiegong Zheng

Xinzhong Wang

Fan Chen

Yinsheng Zhang

Raghu Prasad

Xi Xi Liu

Xiaojin Wang

Yan Ma

Meng Guo

Fan Xiao Xin

Xushi Liu

Mingtong Hu

Min Li

Hongjuan Pan

Zheng Ping Ac Chu

Ling Tong

Baomin Liu

Kiran Panchal

Yajun Guo

Duzheng Wang

Limin Zhou

Dongjing Shan

Qi Wang

Shu Wang

Jizhou Gao

Zhifeng Chen

Wenyan Sun

Zhengbang Chen

Kuo Gai

Ching Che Yang

Wei-Liang Wu

Hong Heather Wu

A joint training network including a multi-heard module comprising a network input, a feature network coupled to the network input and including a feature detector decoder outputting interest points and a descriptor generator decoder outputting descriptors, the feature detector decoder and the descriptor generator decoder coupled in parallel, a depth network including a monocular depth prediction decoder and outputting a depth map, a flow network including an image segmentation decoder and outputting a segmented image, a segmentation network including a warping module outputting a rotation and translation and an input warp signal to a segmentation decoder outputting a residual flow and a pose network including a fully connected pose estimator coupled to an adder that receives input from the pose estimator and the residual flow from the segmentation decoder, the adder outputting an optical flow.

Deep learning based visual simultaneous localization and mapping

A method of determining a vehicle position and a vehicle velocity, including receiving a camera image sequence based on a camera borne by a vehicle and determining a camera pose based on the camera image sequence. The method includes determining a global position system location based on a global position system receiver borne by the vehicle, determining an inertial movement signal based on an inertial movement unit borne by the vehicle and receiving a wheel encoder signal from a wheel of the vehicle. The method additionally includes determining at least one of the vehicle positions and the vehicle velocity based on at least two of the camera pose, the global position system location, the inertial movement signal and the wheel encoder signal in temporal synchronization.

Vehicle localization precision enhancement via multi-sensor fusion

Techniques facilitating resolution-based spatial computing are provided. In one example, a computer-implemented method comprises traversing, by a device operatively coupled to a processor, a data structure corresponding to a land area for a location having an index; and determining, by the device, whether the location is at least partially within the land area based on a result of the traversing. In some embodiments, the traversing comprises: obtaining a threshold number of levels based at least in part on a resolution parameter; scanning a first level of the data structure for a node having an index corresponding to the index of the location; and iterating the scanning for respective subsequent levels of the data structure based on the scanning returning a node having subordinate nodes and a number of levels for which the scanning and iterating have been performed being less than the threshold number of levels.

Spatial computing for location-based services

Selecting tables for compression by threshold statistical values. Identified tables are reordered according to fields having the lowest cardinality to increase the size of character strings replaced by keys during compression. Field locations are mapped between the original table and the reordered table. Dictionary-based compression is performed on reordered tables.

Reordering datasets in a table for increased compression ratio

The present disclosure provides a method for manufacturing a semiconductor device, including: providing a substrate having a plurality of stacked gates with silicon nitride mask layer and silicon oxide mask layer formed on top of the surface; depositing a first carbon-containing silicon oxide thin layer; depositing a second non-carbon-containing silicon oxide layer to fill the gaps between adjacent stacked gates; and planarizing the first silicon oxide thin layer and the second silicon oxide layer by applying the silicon nitride mask layer as a stop layer, removing the second silicon oxide layer, and forming the first sidewalls with the first silicon oxide thin layer on the sides of the stacked gates. The present disclosure further provides a semiconductor device made with the method thereof. The present disclosure can remove the silicon oxide mask layer above the stacked gates through a simple process flow.

Semiconductor device and method for manufacturing the same

The present invention relates to a system and a method for calibration between coordinate systems of a 3D camera and a medical imaging apparatus and an application thereof. The calibration system comprises: a calibration tool arranged on a scanning table, wherein the calibration tool is provided with markers and a reference point, the reference point is aligned with a center of the medical imaging apparatus to serve as an origin of the coordinate system of the medical imaging apparatus, and positions of the markers in the coordinate system of the medical imaging apparatus are calculated according to relative positions of the markers with respect to the reference point; a 3D camera for capturing images of the markers and determining positions of the markers in the coordinate system of the 3D camera based on the captured images; and a calculation device for calculating a calibration matrix using the positions of the markers in the coordinate system of the 3D camera and the positions of the markers in the coordinate system of the medical imaging apparatus, and performing calibration between the coordinate system of the 3D camera and the coordinate system of the medical imaging apparatus using the calibration matrix. The method corresponds to the aforementioned system. The present invention further relates to an application of the calibration and a computer-readable storage medium capable of implementing the method and the application.

System and method for calibration between coordinate systems of 3D camera and medical imaging apparatus and application thereof

A method of wheel encoder to camera calibration, including receiving a LiDAR (Light Detection and Ranging) signal, receiving a camera signal, receiving a wheel encoder signal, calibrating the camera signal to the LiDAR signal, calibrating the wheel encoder signal to the LiDAR signal and calibrating the camera signal to the wheel encoder signal based on the calibration of the camera signal to the LiDAR signal and the wheel encoder signal to the LiDAR signal.

LiDAR assisted wheel encoder to camera calibration

In various embodiments, free-space optical collimator and multi-channel wavelength division multiplexers including free-space optical collimators are provided. In one embodiment, for example, a free-space optical collimator includes a base having a length, a generally flat bottom surface and a top surface. A groove is disposed along the top surface of the base extending through the length of the base. A lens is disposed within the groove of the base and a fiber optic pigtail is disposed generally adjacent to a focal point of the lens. The lens and fiber optic pigtail are aligned within the groove to reduce an off-angle offset of an optical light signal propagating through the free-space optical collimator. In other embodiments, a process of producing a free-space optical collimator is also provided.

Free-space optical collimator

The present invention relates to a method and system for automatically setting a scan range. The method comprises: receiving an RGB image and a depth image of an object positioned on a scan table, respectively, by an RGB image prediction model and a depth image prediction model; generating an RGB prediction result based on the RGB image and a depth prediction result based on the depth image with respect to predetermined key points of the object, respectively, by the RGB image prediction model and the depth image prediction model; selecting a prediction result for setting the scan range from the RGB prediction result and the depth prediction result; and automatically setting the scan range based on the selected prediction result.

Method and system for automatically setting scan range

The present invention relates to a 3D perspective indicator and a generation method therefor and an application thereof. The present invention specifically provides a 3D perspective indicator for a CT system and a generation method therefor as well as a method and an apparatus for controlling a scanning range in a CT system. The method for generating a 3D perspective indicator comprises: generating an upper boundary plane and a lower boundary plane perpendicular to a scanning direction and an auxiliary plane perpendicular to the upper boundary plane and the lower boundary plane in a coordinate system space of the CT system, the auxiliary plane being located above a to-be-scanned part of a patient; and identifying an upper contour line intersecting the upper boundary plane and a lower contour line intersecting the lower boundary plane in a point cloud generated based on a depth image of the patient, wherein the 3D perspective indicator comprises the upper contour line and the lower contour line, the upper boundary plane and the lower boundary plane, and the auxiliary plane. The present invention further provides a CT system using the apparatus and a computer-readable storage medium capable of implementing the method.

3D perspective indicator and generation method therefor and application thereof

Methods and systems are provided for estimating patient structure prior to a scan by a medical imaging system. As one example, a method may include acquiring depth images of a patient positioned on a table of the medical imaging system, correcting the depth images based on histogram data from the depth images, and extracting a three-dimensional structure of the patient based on the corrected depth images.

Systems and methods for patient structure estimation during medical imaging

The present invention provides a semiconductor structure and method of manufacturing the same. The semiconductor structure includes a substrate and a gate formed on the substrate. The above manufacturing method is used to form a gate on the substrate. The above manufacturing method specifically includes: providing a substrate; forming a trench in an upper portion of the substrate; depositing a gate layer on the substrate, the gate layer including two step portions extending from the outside of the trench to the inside of the trench; etching the gate layer from two ends of the trench along the two step portions toward the center of the trench to form the gate in the trench, wherein the width of the gate is smaller than the width of the trench. The manufacturing method of the present invention can easily and efficiently form a gate having a small critical dimension and precisely controllable on a semiconductor substrate, thereby meeting increasingly stringent gate size requirements.

Semiconductor structure and method of manufacturing the same

Various embodiments of a deep learning (DL)-based face perception engine for constructing, providing, and applying a highly-personalized face perception model for an individual through a deep learning process are disclosed. In some embodiments, a disclosed face perception engine includes a deep neural network configured for training a personalized face perception model for a unique individual based on a standard set of training images and a corresponding set of decisions on the set of training images provided by the unique individual. When sufficiently trained using the standard set of training images and the corresponding set of decisions, the personalized face perception model for the unique individual perceives a new face photo/image as if through the eyes of that unique individual. Hence, the trained face perception model can be used an 'agent' or 'representative' of the associated person in making very personal decisions, such as to decide if a given face photo/image includes a desirable face in the eyes of that person.

Method and system for providing a highly-personalized recommendation engine

Approaches presented herein enable displaying a barrage message. Specifically, one or more objects and location information for each object in a frame of a video are identified. A barrage message to be displayed in the frame of the video is obtained. The barrage message is displayed without covering any object in the frame.

Barrage message processing

Growing community of inventors

Sunnyvale, CA, United States of America

Yu Huang