Intel Corporation

Beijing Baidu Netcom Science and Technology Co., Ltd.

Tfi Digital Media Limited

Beijing Institute of Control Engineering

Yuming Li

Xiaodong Wang

Zhen Zhou

Quan Yin

Li Wang

Lin Li

Chen Wang

Bin Zhu

Hon Wah Wong

Xiaoyan Wang

Yiu Fai Yuen

Xuyuan Xu

Hok Kwan Cheung

Hon Tung Luk

Yunlong Wang

Jun Zhong

Miaomiao Wang

Li Yuan

Yanpeng Wu

Zuo Wang

Mengyang Liu

Chang Zhou

Kin Wai Lau

He Lin

Ran Zheng

Jie Sui

Zhouyi Xie

Huiyan Cheng

Wenxiang Lu

Lai Man Po

An embodiment of a semiconductor package apparatus may include technology to pre-process an image to simplify a background of the image, and perform object detection on the pre-processed image with the simplified background. For example, an embodiment of a semiconductor package may include technology to pre-process an image to subtract the background from the image and perform object detection on the pre-processed image with the background subtracted. Other embodiments are disclosed and claimed.

Object detection with modified image background

A device and a method for determining a microvibration effect on a millisecond-level space optical sensor are provided. The device includes: a light source, a star simulator, an air flotation vibration isolation platform, a suspension system/air flotation system, a zero stiffness system, a supporting system, a six-degree-of-freedom microvibration simulator, a signal driving apparatus, and a data acquisition and processing system. In the device for determining a microvibration effect on a space pointing measurement apparatus, a free boundary condition and a zero gravity environment are simulated by using a suspension system and a zero stiffness system. A light source and a star simulator simulate a star at infinity. A six-degree-of-freedom microvibration simulator simulates an on-orbit microvibration mechanical environment which is used as an input of a test. Extremely high-precision sensors collect system response data.

Apparatus and method for measuring micro-vibration influence of spatial orientation measuring instrument

Embodiments of the present disclosure relate to a method and apparatus for assigning a computing task. The method includes: segmenting, based on a computing resource required for a computing task of a graph, the graph into a subgraph set; determining, based on a computing resource required for a computing task of a subgraph in the subgraph set, whether a node in a node cluster is adapted to the subgraph in the subgraph set; assigning, in response to determining the node in the node cluster being adapted to the subgraph in the subgraph set, the computing task of the subgraph in the subgraph set to the node adapted to the subgraph in the subgraph set, to compute the subgraph; and synthesizing a computing result of the graph based on a computing result of each subgraph in the subgraph set.

Method and apparatus for assigning computing task

Apparatuses, methods and storage medium associated with cloud computing for mobile client devices are disclosed herein. In embodiments, a cloud server may include one or more processors, and a mobile computing operating system to host execution of an application. The cloud server may also include a cloud application server to interact with a cloud application client of a mobile client device to provide audio and video streams to the mobile client device, and to receive touch, key and sensor events from the mobile client device, to enable the application to be executed for the mobile client device. Other embodiments may be described or claimed.

Cloud computing for mobile client devices

The present disclosure relates to a method for image patch selection for training a neural network for image quality assessment. The method includes receiving an input image and extracting one or more image patches from the input image. The moment of the extracted image patches is measured. There is a decision to accept or decline the extracted image patches according to the measured moment. Additional image patches are extracted until a minimum number, N, of extracted image patches are accepted. Alternatively, selection criteria are adjusted until the minimum number of extracted image patches are accepted. The selected image patches are input into a neural network with a corresponding image quality value of the input image, and the neural network is trained with the image patches and image quality value. Also provided is a method for image quality assessment using a neural network trained as set forth above.

Patch selection for neural network based no-reference image quality assessment

Growing community of inventors

Beijing, China

Yuming Li