International Business Machines Corporation

Atmel Corporation

Shanghai Hanshi Information Technology Co., Ltd.

Tianma Micro-electronics Co., Ltd.

Shanghai Tianma Am-oled Co., Ltd.

O2micro International Limited

Sheng Huang

Xiao Yan Chen

Kai Liu

Yao Liang Chen

Shao Chun Li

He H Liu

Zhong Xian Li

Hua Fang Tan

Steven M Kagan

Kathryn Allyn Bassin

Jian Ding

Xiao Min Xu

Chen Wang

Howard M Hess

Susan Eileen Skrabanek

Jun Zhu

Li Wang

Wei Lu

Jian Dong Ding

Yun Jie Qiu

Susan E Smith

Mudhakar Srivatsa

Ning Duan

Lin Xu

Raghu Kiran Ganti

Ramya Raghavendra

Lorenzo Bedarida

Xinlin Wang

Guoqiang Hu

Emmanuel Racape

Xiaobao Wang

Chao Hua Zhang

Yitang Zhuang

Ying Liu

Laszlo Lipcsei

Yi Qi

Ya Ming Zhang

Junchi Yan

Shuai Yang

Liyuan Luo

Bojia Lv

Wei Zhu

Song Li

Yaoliang Chen

Footshen Wong

Xiaomin Xu

Xiaoyan Chen

Lalitha Krishnamoorthy

Ning Yu

Ming Lou

Kun Ding

Wangpei Li

Xiaoping Jin

Guo Rong Sun

The disclosure discloses a commodity image information acquisition system, method and apparatus. The system comprises: AIoT management server configured to transmit a product information list to a shelf label management server, and to transmit a notification of starting to take photos to an AI vision system; the shelf label management server configured to determine the electronic shelf label to be notified, and to transmit an information display notification; when the electronic shelf label to be notified performs an information display action, to generate matching relationship between the information display action and the barcode of the commodity and transmit the matching relationship to the AI vision system; the AI vision system configured to shoot the information display action and obtain the shot content; and to perform parse and obtain a recognition result; the commodity information management system configured to associate the electronic shelf label with the commodity.

Commodity image information acquisition system, method and apparatus

The disclosure discloses a method and a device for image stitching, the method including: performing stitching feature extraction on target images based on deep-learning neural-network model to obtain stitching features of the target images; selecting a reference image and a to-be-registered image from the target images; taking the stitching feature of the reference image under a minimum feature size and on the to-be-stitched side as a first stitching feature; calculating the similarity measure value between the first stitching feature and each of second stitching features to obtain a target stitching feature, based on the second stitching features of the to-be-registered image under the minimum feature size; repeating the above steps until the target stitching feature of the to-be-registered image under the maximum feature size is determined, and performing image stitching of the reference image and the to-be-registered image on the to-be-stitched side.

Method and apparatus for image stitching

The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

Unified real time rule analytics using common programming model on both edge and cloud

A computer-implemented method is provided that includes receiving a search request to perform a search in a time series database, where the time series database stores a plurality of sequences of measured values arranged in chronological order, extracting from the search request a condition for performing the search in the time series database, the condition including a target subsequence to be searched for within the plurality of sequences of measured values within the time series database, selecting a search algorithm to perform the search from a plurality of search algorithms registered to the time series database, based on one or more characteristics of the condition and historical statistic data of one or more historical searches conducted on the time series database, and obtaining a search result from the time series database, utilizing the search algorithm.

Searching in a database

A system, computer program product, and method for computing a correlation matrix with respect to massive of time-series is described where a threshold &#x3b5; is specified. The correlations greater than &#x3b5; do not need to be computed. A distance tree is constructed and used. The distance tree organizes the time-series by their correlation estimations. The correlation similarity is computed with MapReduce function by taking advantage of the distance tree. In an efficient MapReduce manner, there is a small I/O waste for overlapped partition. The load is balanced for uneven data distribution and there is early pruning for unnecessary computation. There is no reliance on dimensional reduction and no reliance on coordinates.

Massive time series correlation similarity computation

A method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

Trajectory data compression

A method and apparatus for determining similarity among gene sequences, for compressing a gene sequence, and for decompressing a gene sequence. The method for determining similarity between a first gene sequence and a second gene sequence includes: moving a sliding window of a predefined length on the first gene sequence and the second gene sequence respectively; extracting a first part Stringof the first gene sequence within the sliding window, and a second part Stringof the second gene sequence within the sliding window during the imovement of the sliding window; and determining similarity between the first gene sequence and the second gene sequence based on the first part Stringand the second part String. Also provided is an apparatus for the above method.

Managing gene sequences

A method for processing a time series includes dividing, with a processing device, the time series into a plurality of windows by time; extracting at least one group of similar subsequences from a current window among the plurality of windows; and updating a candidate list on the basis of comparison between similar subsequences in each group of the at least one group with k characteristic subsequences in the candidate list; wherein the k characteristic subsequences are k characteristic subsequences with a greatest number of occurrences in at least processed parts of the time series.

Processing time series

A method includes generating a test model based on at least one of test group dependencies and test group constraints and generating a resource base. The method includes generating a cost model and generating a resource allocation plan based on the test model, the resource base, and the cost model.

System and method for resource modeling and simulation in test planning

A plurality of messages comprising a stream of sensed data is received from an Internet of Things (IoT) device. A plurality of rules is identified. Responsive to a received message of the plurality of messages, the plurality of rules are distributed into a rule index tree based upon a cost model which minimizes a cost for analyzing the plurality of rules to determine whether or not each of the plurality of rules can be applied to the received message. The rule index tree is used to identify a set of rules of the plurality of rules that can be applied to the received message. The set of rules is applied to the received message to generate an outgoing message.

Applying a plurality of rules to a multiplicity of streaming messages to perform intelligent data analytics

The present disclosure relates to methods and systems for storing and querying data. According to the embodiments of the present invention, two-layer indexes are created for multi-dimension data, wherein the primary index is created based on two or more dimensions to retrieve respective data units of the data, while the secondary index is created based on specific dimensions to retrieve respective data blocks in the data unit. Correspondingly, when receiving a multi-dimension query request for data, the primary retrieval first determines a data unit including the target data based on a primary index, and then the secondary retrieval quickly locates a data block including the target data based on the secondary index. In this way, the multi-dimension retrieval can be efficiently performed. Moreover, by appropriately setting the size of a smallest data block, the I/O efficiency of data access will be significantly enhanced.

Storing and querying multidimensional data using first and second indicies

A method for managing a time series database, includes: monitoring multiple operations that access the time series database, so as to identify types of the multiple operations, the types of the multiple operations comprising at least one of the query types or insert types; with respect to a storage mode among multiple storage modes, obtaining costs that the multiple operations access the time series database based on the types, respectively; selecting a storage mode with the minimum cost from the multiple storage modes; and during a predetermined time period, storing into the time series database data values that are collected from multiple measurement points according to the selected storage mode. In one embodiment, there is provided an apparatus for managing a time series database. By means of the method and apparatus of the present invention, the storage and query efficiency with respect to the time series database can be increased.

Method and apparatus for managing time series database

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Shanghai, China

Sheng Huang