Huawei Technologies Co., Limited

Mingxuan Yuan

Yu Huang

Qiang Yang

Jia Zeng

Huamin Qu

Weixiong Rao

Bing Ni

Fangzhou Zhu

Xialiang Tong

Huiling Zhen

Miaohui Chen

Yanhua Li

Wanqian Luo

Naixing Wang

Chi-Yin Chow

Kai Yan Lee

Haoyang Chen

An automatic test pattern generation-based circuit verification method, comprises determining a to-be-detected first logic cone from a fan-out logic cone corresponding to the target line; determining, based on the first logic cone, a to-be-detected second logic cone from a fan-in logic cone corresponding to the target line; generating a first conjunctive normal form (CNF) based on the first logic cone and the second logic cone, and detecting the target line by using the first CNF to obtain a first detection result; and if the first logic cone is a partial region in the fan-out logic cone, and the first detection result meets a first specified condition corresponding to the first logic cone, determining a first verification result of the target line based on the first detection result.

Automatic test pattern generation-based circuit verification method and apparatus

A method for obtaining an emission probability includes obtaining a plurality of measurement reports (MRs) of a terminal in a target region and an engineering parameter of at least one base station in the target region, obtaining, based on parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs, processing, using a regression model, location information in each of the plurality of MRs and the feature vector corresponding to each of the plurality of MRs, to obtain a single-point positioning model, calculating, based on the single-point positioning model, the location information in each of the plurality of MRs, and the feature vector corresponding to each of the plurality of MRs, an emission probability of the feature vector corresponding to each of the plurality of MRs.

Method and apparatus for obtaining emission probability, method and apparatus for obtaining transition probability, and sequence positioning method and apparatus

A terminal positioning method and a network device, where the network device obtains radio signal sampling information of a first terminal at a current moment. The first terminal is any terminal in a target region, and the target region is a preset geographic region. The network device obtains position information of the first terminal at the current moment by prediction based on the radio signal sampling information of the first terminal at the current moment and a predictive model of the target region. The predictive model is obtained by extensive data training in the target region, has relatively strong error tolerance and error-correction capabilities, and can accurately reflect a relationship between radio signal sampling information and position information of a terminal. Terminal positioning accuracy is effectively improved.

Terminal positioning method and network device

The present disclosure provides a data visualization method and apparatus. The method includes: displaying a distribution curve diagram by using a display device, where the distribution curve diagram is used to represent time-based distribution of multiple pieces of spatial-temporal data, and the distribution curve diagram includes a timeline, a curve changing with the timeline, and an irregular region enclosed by the curve; and displaying a tree map in the irregular region by using the display device, where the tree map divides the irregular region into m sub-regions that are in a one-to-one correspondence with m regions, and an area of each of the m sub-regions is used to indicate an amount of spatial-temporal data distributed in a region corresponding to each sub-region. The present disclosure implements visualization of spatial-temporal data by using the tree map.

Data visualization method and apparatus

A data visualization method includes acquiring a spatiotemporal behavior data set generated by network users, determining, according to a time point at which or a time period in which each piece of spatiotemporal behavior data is generated, an amount of spatiotemporal behavior data that is in the spatiotemporal behavior data set and generated at a time point or in a time period falling within each time period in N time periods, and presenting a column bar corresponding to an itime period in the N time periods, where the column bar is in a radial direction of a first circle representing a full time domain and intersects the first circle and the length of a column bar represents a quantity of behaviors in a time period corresponding to the column bar.

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Hong Kong, China

Mingxuan Yuan