Intel Corporation

Samsung Electronics Co., Ltd.

Beijing Baidu Netcom Science and Technology Co., Ltd.

China University of Mining and Technology (CUMT)

Beijing Qihoo Technology Company Limited

Baidu Online Network Technology (beijing) Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Compo Advanced Technology Co., Ltd.

Baidu Online Network Technology Co., Ltd.

Zhigang Wang

Jian Wang

Yan Li

Hao Sun

Errui Ding

Ranran Zhang

Dawei Wang

Xiaohui Yang

Ganmei You

Ying Wang

Shilei Wen

Guangyu Sun

Haifeng Wang

Peng Wang

Di Zhang

Ying Li

Ignacio Javier Alvarez

Xiao Xi Liu

Fei Fei Li

Yi Zhao

Lei Xu

Lirong Wang

Fei Li

Mingyu Chen

Hu Tiger Chen

Yingze Bao

Tian Wu

Huan Ren

Jingdong Wang

Bo A Zhang

Xubin Li

Xiaolong Liu

Le Kang

Qianying Zhu

Zeyu Liu

Jie Zhu

Yujie Wang

Yingzhe Shen

Qixin Huang

Xiahui Gui

Xuesong Shi

Min Liu

Yijun Cao

Yaowen Xing

Yangchao Xia

Jiaqian Luo

Peikun Liu

Tong Zhou

Pin Zhang

Haitao Ji

Xiangbo Su

Ying Zhu

Ning An

Yanbin Sun

Shaoming Zhang

Chuping Dai

Honghong Li

Robert Watts

Yongjian Cai

Huijun He

Xixue Liu

Qiang Hu

Fuwen Zhang

Jiangsong Zhang

Huankun Zhang

Zhihua Zhu

Jiangbo Guo

Qiman Wu

Xiao Liu

Errui Cn Ding

The disclosure provides techniques for registering a camera into a map via a robot. A method for registering a camera into a map may include: collecting an observation of a calibration marker from an image captured by the camera, the calibration maker being attached to a robot capable of moving in a view of the camera; calculating, for the observation of the calibration marker, a transform T (camera, marker) between a camera frame of the camera and a marker frame of the calibration marker; obtaining, for the observation of the calibration marker, a transform T (world, robot) between a world frame of the map and a robot frame of the robot; and performing, when more than a predetermined number of observations of the calibration marker are collected during movement of the robot, a calibration process to calculate a transform T (world, camera) between the world frame and the camera frame based on the transform T (camera, marker) and the transform T (world, robot) for each observation of the calibration marker.

Camera registration via robot

Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Joint perception model training method, joint perception method, device, and storage medium

The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. Specifically, the present disclosure relates to an O-RAN based network system.

Method and device for O-RAN-based performance optimization and configuration

A method and apparatus for supporting a multiple-input multiple-output (MIMO) by a service management and orchestration (SMO) entity in a communication system using an open radio access network (O-RAN) includes receiving, from a first entity, first data, the first entity including at least one of an O-RAN centralized unit (O-CU) and an O-RAN distributed unit (O-DU), the first data including MIMO related information collected from the first entity, determining, based on the first data, a configuration for applying at least one of a single-user-multiple-input-multiple-output (SU-MIMO) and a multi-user-multiple-input-multiple-output (MU-MIMO), and transmitting, to a second entity that controls the first entity in the O-RAN, information on the configuration.

Method and device of communication in a communication system using an open radio access network

A method, an apparatus, device and a storage medium for generating a target re-recognition model are provided. The method may include: acquiring a set of labeled samples, a set of unlabeled samples and an initialization model obtained through supervised training; performing feature extraction on each sample in the set of the unlabeled samples by using the initialization model; clustering features extracted from the set of the unlabeled samples by using a clustering algorithm; assigning, for each sample in the set of the unlabeled samples, a pseudo label to the sample according to a cluster corresponding to the sample in a feature space; and mixing a set of samples with a pseudo label and the set of the labeled samples as a set of training samples, and performing supervised training on the initialization model to obtain a target re-recognition model.

Method and apparatus for generating target re-recognition model and re-recognizing target

Techniques related to training and implementing convolutional neural networks for object recognition are discussed. Such techniques may include applying, at a first convolutional layer of the convolutional neural network, 3D filters of different spatial sizes to an 3D input image segment to generate multi-scale feature maps such that each feature map has a pathway to fully connected layers of the convolutional neural network, which generate object recognition data corresponding to the 3D input image segment.

3D object recognition using 3D convolutional neural network with depth based multi-scale filters

A technology is described for mapping a physical environment. An example method may include receiving laser point data for laser light reflected from the physical environment and detected by a laser sensor. Points included in the laser point data can be correlated to grid cells in an environment map that represents the physical environment. Error ranges for the points correlated to the grid cells can be determined based in part on an error distribution. Occupation probabilities can then be calculated for the grid cells in the environment map using an interpolation technique and grid cell occupation probabilities for adjacent error grid cells selected based in part on the error ranges of the points, and the grid cells in the environment map can be updated with the occupation probabilities.

Grid occupancy mapping using error range distribution

The present disclosure relates to real-time localization error correction of an autonomous vehicle (AV). A processor for real-time localization error correction of the AV is provided. The processor is configured to retrieve a reference landmark around the AV from a map aggregating server (MAS), wherein the AV is configured to interact with the MAS for real-time localization; detect, in real time, a ground truth landmark corresponding to the reference landmark, according to image data captured by one or more image capture devices installed on the AV; and determine a deviation between the ground truth landmark and the reference landmark as a real-time correction value for the real-time localization of the AV.

Real-time localization error correction of autonomous vehicle

A method for switching transmission methods in a multiple input multiple output (MIMO) system may include: receiving scenario identification related data from a user equipment (UE) in a current transmission method; determining a target transmission method of the UE from at least two predefined transmission methods based on the scenario identification related data; and switching the current transmission method of the UE to the target transmission method.

Method and device for switching transmission methods in massive MIMO system

Methods, apparatus, systems, and articles of manufacture to simulate sensor data are disclosed. An example apparatus includes a noise characteristic identifier to extract a noise characteristic associated with a feature present in first sensor data obtained by a physical sensor. A feature identifier is to identify a feature present in second sensor data. The second sensor data is generated by an environment simulator simulating a virtual representation of the real sensor. A noise simulator is to synthesize noise-adjusted simulated sensor data based on the feature identified in the second sensor data and the noise characteristic associated with the feature present in the first sensor data.

Methods and apparatus to simulate sensor data

Systems, apparatuses and methods may provide for technology that generates a sequence of predictive maps based on a sequence of historical maps and overlays the sequence of predictive maps on one another to obtain a map overlay. The technology may also apply an attenuation factor to the map overlay. In one example, the map overlay includes a grid of cells and each cell includes an occupation probability in accordance with the attenuation factor.

Predictive map generation technology

A method performed by a terminal in a wireless communication system, including receiving, from a base station, a physical downlink control channel (PDCCH); predicting aggregation level information including information about an aggregation level of a control channel element (CCE) in which the PDCCH is received based on an aggregation level prediction model; determining a detection order for detecting the PDCCH based on the aggregation level information; and etecting the PDCCH based on the determined detection order.

Method and apparatus for detecting physical downlink control channel based on predicted information

A SnBiSb series low-temperature lead-free solder and a preparation method thereof, which belongs to the technical field of low-temperature soldering. The lead-free solder includes by weight the following composition: 32.8-56.5% of Bi, 0.7-2.2% of Sb, with the remainder being Sn, wherein the weight percentages of Bi and Sb satisfy a relationship of b=0.006a2&#x2212;0.672a+19.61+c, wherein the symbol a represents the weight percentage of Bi, the symbol b represents the weight percentage of Sb, and the range of c is &#x2212;1.85&#x2264;c&#x2264;1.85. The solder alloy has a peritectic or near peritectic structure with a low melting point, and has an excellent mechanical performance and reliability, and applicable to the field of low-temperature soldering.

SnBiSb series low-temperature lead-free solder and its preparation method

Disclosed is an underground coal preparation process adopting a water medium, comprising: feeding exploited raw coal into a &#x3a6;25 mm raw coal classifying screen for screening; performing dry coal preparation on a classifying screen oversize product with a particle size greater than 25 mm and feeding a classifying screen undersize product with a particle size less than 25 mm into a special underground compact jigging machine for sorting to obtain jigging machine overflow clean coal, jigging middling coal and jigging gangue; screening and dewatering the jigging machine overflow clean coal to obtain a clean coal product, and screening and dewatering the jigging middling coal to obtain a middling coal product, sorting slime water to be sorted and generated during sorting to obtain coarse clean coal slime and coarse middling coal slime, and performing circulating water treatment on a final liquid.

Underground coal separation process

The present disclosure provides a pedestrian re-identification method and apparatus, computer device and readable medium. The method comprises: collecting a target image and a to-be-identified image including a pedestrian image; obtaining a feature expression of the target image and a feature expression of the to-be-identified image respectively, based on a pre-trained feature extraction model; wherein the feature extraction model is obtained by training based on a self-attention feature of a base image as well as a co-attention feature of the base image relative to a reference image; identifying whether a pedestrian in the to-be-identified image is the same pedestrian as that in the target image according to the feature expression of the target image and the feature expression of the to-be-identified image. According to the pedestrian re-identification method of the present disclosure, the accuracy of the pedestrian re-identification can be effectively improved when the feature extraction model is used to perform the pedestrian re-identification.

Pedestrian re-identification method, computer device and readable medium

Growing community of inventors

Beijing, China

Zhigang Wang