157 results about "Streaming algorithm" patented technology

A falling detection method and an electronic system using the same are provided. The falling detection method includes: obtaining a video stream, and performing recognition on a person in an image of the video stream to obtain at least one human body feature; calculating based on the human body feature to obtain at least one falling related feature; calculating at least one movement trend in a plurality of directions of the person in the video stream by using an optical flow algorithm; inputting the falling related feature and the movement trend to a falling classifier, such that the falling classifier generates an output result indicating whether a falling event happens to the person; and sending an alarm according to the output result.

The invention discloses a light stream based vehicle motion state estimating method which is applicable to estimating motion of vehicles running of flat bituminous pavement at low speed in the road traffic environment. The light stream based vehicle motion state estimating method includes mounting a high-precision overlook monocular video camera at the center of a rear axle of a vehicle, and acquiring video camera parameters by means of calibration algorithm; preprocessing acquired image sequence by histogram equalization so as to highlight angular point characteristics of the bituminous pavement, and reducing adverse affection caused by pavement conditions and light variation; detecting the angular point characteristics of the pavement in real time by adopting efficient Harris angular point detection algorithm; performing angular point matching tracking of a front frame and a rear frame according to the Lucas-Kanade light stream algorithm, further optimizing matched angular points by RANSAC (random sample consensus) algorithm and acquiring more accurate light stream information; and finally, restructuring real-time motion parameters of the vehicle such as longitudinal velocity, transverse velocity and side slip angle under a vehicle carrier coordinate system, and accordingly, realizing high-precision vehicle ground motion state estimation.

The subject invention provides for a system and method that facilitates concurrent data streaming. In particular, the invention involves initiating a first operation from the optical media at time t_x and initiating at least a second operation from the optical media at time t_y while the first operation is currently in progress, wherein t_x≠t_y. The first operation includes reading a real-time data stream to a first buffer. The second operation includes one of reading a real-time data stream and a non-real-time data stream to at least a second buffer. Furthermore, a utility-based analysis can be performed to determine whether to access the first buffer rather than to access the surface of the optical media in order to conduct the second operation. Moreover, the first and at least second operations can be performed in parallel.

A distinct-count estimate is obtained in a guaranteed small footprint using a two level hash, distinct count sketch. A first hash fills the first-level hash buckets with an exponentially decreasing number of data-elements. These are then uniformly hashed to an array of second-level-hash tables, and have an associated total-element counter and bit-location counters. These counters are used to identify singletons and so provide a distinct-sample and a distinct-count. An estimate of the total distinct-count is obtained by dividing by the distinct-count by the probability of mapping a data-element to that bucket. An estimate of the total distinct-source frequencies of destination address can be found in a similar fashion. By further associating the distinct-count sketch with a list of singletons, a total singleton count and a heap containing the destination addresses ordered by their distinct-source frequencies, a tracking distinct-count sketch may be formed that has considerably improved query time.

A browsing interface for browsing Internet information that organizes information downloaded from the Internet in a page-based manner, complete with a functional thickness representation. A number of insertion methods are available for insertion of newly downloaded Internet information. A browsing data file contains information and parameters which control the display of information on the browsing interface. A streaming algorithm pre-downloads data based on predicted user data requests to minimize interruption of the browsing process.

The invention relates to streaming algorithms useful for obtaining summaries over unaggregated packet streams and for providing unbiased estimators for characteristics, such as, the amount of traffic that belongs to a specified subpopulation of flows. Packets are sampled from a packet stream and aggregated into flows and counted by implementation of Adaptive Sample-and-Hold (ASH) or Adaptive NetFlow (ANF), adjusting the sampling rate based on a quantity of flows to obtain a sketch having a predetermined size, the sampling rate being adjusted in steps; and transferring the count of aggregated packets from SRAM to DRAM and initializing the count in SRAM following adjustment of the sampling rate.

The invention belongs to the technical field of computer science, and discloses an online public opinion text information emotion polarity classification processing system and method, the online public opinion text emotion polarity is widely applied to a public opinion monitoring system, however, a feature engineering extraction module of a traditional machine learning method is large in text information loss, and the accuracy of a classification model is not high enough. The method comprises the steps of preprocessing data; the method comprises the following steps of: constructing a word vector in a way of pre-training a model fin-tuning through BERT; the BERT model calculates the correlation between the characters in the sentence and each of the other characters; the constructed word vector can better solve the problems of'one-word polysemy 'and'synonym' of Chinese; the loss of word vector representation is greatly reduced; in the classification model, firstly Bi-LSTM is used for effectively learning context information, then Attention is used for capturing main semantic information and effectively filtering valuable public opinion information, finally softmax classification is used, and the performance of an obtained public opinion text emotion polarity classification result is better than that of a current mainstream algorithm.

Displaying requested data from a database includes providing a display containing a scroll bar with a scroll tab therein, determining a total amount of the requested data, sizing the scroll tab in the scroll bar according to the total amount of the requested data and according to an amount of data displayable in the display, and fetching a predetermined amount of data from a first portion of the database. The predetermined amount may be independent of the size of the display. The predetermined amount may be less than the total amount of the requested data. Sizing the scroll tab may include choosing a size that indicates to a user that all of the requested data is being fetched from the database and/or may include choosing a size that is inversely proportional to the total amount of the request data in response to the total amount of the requested data being less than a predetermined value. Choosing a size may include choosing a predetermined size in response to the total amount of the requested data being greater than the predetermined value.

The invention discloses a multi-label-based lightweight rapid crowd counting method. A simple and efficient trunk feature extraction network is designed according to the size of a receptive field, anda dense context module is arranged in the trunk feature extraction network, so that information transmission of a network layer is ensured, and the network expression capability is improved; six multi-scale intermediate supervision branches are designed, so that the network can be converged more quickly and stably; an up-sampling module is designed, the resolution is improved step by step, and the quality of a density map is improved, so that accurate counting and accurate positioning are realized; three labels are designed, a crowd counting task based on density is explicitly converted intoa foreground and background segmentation task to assist a regression task of a crowd density map, prediction of the density map and the segmentation map is achieved at the same time, and estimation errors are effectively reduced. Test results of UCF _ CC _ 50, ShanghaiTeck and UCF-QNFR data sets show that the prediction performance of the method is superior to that of a current mainstream algorithm, the prediction speed reaches real time, and the method can be conveniently deployed in terminal equipment.

The invention belongs to the field of computer vision. The invention particularly relates to complex backgrounds, shielding and other problems. The invention discloses a target tracking method based on discriminant dictionary learning. Firstly, a target and a background sample are obtained according to the local correlation of the target in time and space; secondly, a dictionary learning model isestablished based on sparse representation, an error item is used for capturing abnormal values generated by shielding and the like, a non-convex MCP function is used for punishing a sparse coding matrix and an error matrix, and inconsistent constraint items are applied to the dictionary so as to improve dictionary robustness and discrimination; mM-IALM optimization method is used for solving theproposed non-convex dictionary learning model so as to obtain better convergence; And calculating a candidate target reconstruction error from the obtained dictionary to construct a target observationmodel, and realizing accurate tracking of the target based on a Bayesian reasoning framework. Simulation results show that compared with an existing mainstream algorithm, the method has higher tracking precision and robustness under the environments of illumination change, scale change, shielding, background clutter and the like.

The invention discloses a no-reference image quality evaluation method based on a self-attention mechanism GAN network, and particularly relates to the field of image processing, which is composed ofthree parts: a generation network, a discrimination network and a quality evaluation network. The generation network performs feature extraction and recovery reconstruction on an input distorted image, and the discrimination network distinguishes the reconstructed image from an undistorted image as much as possible. According to the method, the adversarial learning intensity is enhanced by addingthe self-attention module and improving the model structure, and a more reliable simulation'reference graph' is output; experiments are carried out on LIVE and TID2013 data sets, results show that theSARAN algorithm provided by the invention is superior to a current mainstream algorithm in overall objective evaluation of quality evaluation of non-reference distorted images, has good performance for different distortion types, and shows that the SARAN algorithm has high correlation with a subjective evaluation result, and better meets the perception consistency of a visual perception system (HVS) on the image quality.

The invention relates to a method, a device and equipment for optimizing the analysis performance of an intelligent video, and the method comprises the steps: (1) carrying out a reference piperine test on a video file for the acceleration of an offline video file, and setting an optimal file slice number; slicing the video file, and issuing a slicing task to the GPU; calling a GPU to decode the slice file, and calling back a decoding result to an algorithm directly through a video memory address, and reducing the performance loss without the video memory-main memory copy, wherein the video analysis algorithm takes the decoded video memory address, calls a GPU for algorithm acceleration and outputs an analysis result; (2) optimizing and expanding the number of paths for real-time video stream algorithm analysis; and calling the GPU to decode each path of real-time video, calling back a decoding result to the algorithm directly through a video memory address, setting double caches by analgorithm end, storing decoded data in multiple paths, transmitting the decoded data to the algorithm for GPU batch processing, and switching the two cache functions after batch processing is completed to achieve the purpose of minimum system delay.

The invention discloses a rapid inter-frame mode selection method for an AVS (Advanced Audio Video Coding Standard) coder. The method comprises the following steps of: preselecting an optimal mode from a 16*16 mode, a 16*8 mode, a 8*16 mode and a 8*8 mode by virtue of a vision perception judging model and pixel point edge information; breaking through the dependence on data of interframe mode selection by virtue of a rapid interframe mode selection algorithm facilitating the realization of hardware to ensure that the interframe mode selection does not need to wait the relevant reconstruction data and becomes the premise for realizing an efficient interruption-free mode selection stream line; designing 5-level stream algorithm which can be realized oriented at the hardware and is capable of efficiently calculating rate distortion cost values to allow the application and popularization of the practical hardware for the interframe mode selection on the basis of rate distortion cost values to be possible; and finally, determining an optimal mode according to cost values of three candidate modes including a preset optimal mode, a direct mode and an intra mode. The invention also discloses an AVS interframe mode selection device. According to the invention, the coding property of the hardware coder is remarkably improved.

The invention firstly provides a laser radar SLAM algorithm-CPFG (Closet Probability and Feature Grid) algorithm based on a feature probability grid map. The algorithm utilizes three-dimensional laser radar data to create and update a grid map of line, surface and Gaussian distribution characteristics and occupancy probability in real time, and combines a robust mahalanobis distance as an optimization function to carry out real-time pose estimation. The algorithm mainly comprises three steps of point cloud preprocessing, matching of the point cloud and the characteristic probability grid map and pose estimation, and updating of the feature probability grid map. Compared with several current mainstream algorithms, the laser radar SLAM algorithm has better performance in the aspects of real-time performance and positioning precision. Afterwards, attitude information of inertial navigation is fused, the high displacement precision of the laser radar SLAM and the low attitude drift characteristic of inertial navigation are combined, the relative positioning precision of the method can reach about one thousandth, and the method has wide application prospects in the field of unmanned driving positioning.

The embodiment of the invention discloses a data processing method and device, a server and a storage medium, and the method comprises the steps of obtaining initial medical data from at least one hospital information system, the initial medical data comprising medical sub-data of at least two dimensions; on the basis of different dimensions corresponding to the medical sub-data, adopting a distributed message publishing and subscribing system to carry out shunting processing on the initial medical data, and obtaining multiple pieces of branch data; analyzing and processing each piece of branch data through a streaming computing platform to obtain a plurality of pieces of analyzed data; associating and storing each of the analytic data in an object- In the relational database, associatingall the stored analysis data to form target medical data corresponding to the initial medical data; when a query request sent by a client is received, adopting a streaming algorithm to carry out object-object query on the client; and searching the target medical data in the relational database, and sending the target medical data to the client, so that the processing efficiency of the medical datacan be improved.

The invention proposes an improvement to increase the speed at which a Trickle algorithm can propagate update information in a multi-hop network, while still controlling the number of redundant transmissions and ensuring scalability of the algorithm. It does this by intelligently adapting the size of a listen-only period (t1, t2), depending on the fact if a node has recently received an update or not.

The invention discloses an A3C-SRU-based intelligent vehicle traffic flow converging method and system. The implementation method comprises the following steps of 1, adopting environmental parametersand vehicle parameters by devices such as a digital camera, a multi-line laser radar, a millimeter-wave radar and a gps positioning system; 2, establishing a simulation environment platform by utilizing simulation software according to the environment parameters and the vehicle parameters extracted in the step ; 3, setting parameters and constraint conditions of a reinforcement learning algorithmaccording to the simulation environment in the step 2; 4, training by using an A3C-SRU algorithm according to the simulation environment constructed in the step 2 to obtain a decision of an imported traffic flow scene; and 5, obtaining the optimal action sequence obtained in the step 2 according to the model obtained in the step 4, storing the trained model, and inputting the model into the intelligent vehicle to realize a traffic flow importing task. According to the A3C-SRU-based intelligent vehicle afflux traffic flow algorithm of the invention, real-time afflux traffic flow tasks can be effectively realized according to the settings of the steps 1-5.

The invention discloses an IO disk falling method and device for a storage system and the storage system. The IO disk falling method comprises the steps: obtaining the maximum IO issuing rate according to the used cache proportion of a cache; obtaining the number of fragments according to the IO maximum issuing rate and a preset fragment size, and generating fragments with the number of fragmentsin a cache; storing each piece of received IO data to a respective corresponding fragment; and enabling the fragments meeting the preset disc falling condition to be subjected to disc falling. According to the IO disk falling method, IO data on each fragment form a large IO for disk falling; the IO merging process is simplified; and the delay of the drop time delay of the batch drop IO data is reduced, and the consistency of the upper limit of the actual rate and the upper limit of the rate of the leaky bucket current limiting algorithm is ensured on the basis of meeting the requirement of full flash storage of the batch drop IO data, so that the IO drop can be accurately controlled by subsequently utilizing the drop time delay and the dynamic regulation rate of the fragments.

The invention relates to a crack detection method based on an improved coding and decoding network model, and the method comprises the steps: transmitting a data set after data preprocessing to an encoder for feature extraction, enabling the model to employ an encoder decoder structure, enabling a trunk network to be ResNet34 pre-trained in ImageNet, adding cascaded dual-core cavity convolution to an intermediate layer of the encoder, so that more cracks in the underlying semantic information and spatial structure information are retained and merged in the jump connection stage, the multi-core pooling module is introduced in the decoder stage to obtain crack information of different sizes and fuse the crack information, compared with information obtained through a single pooling layer, the information obtained through the method is more global, and fine cracks in a picture can be effectively detected. Experiments are carried out on different data sets, comparison experiments are carried out with other mainstream algorithm models at the same time, results show that the method provided by the invention has high precision in effect, and the defects of a traditional method are overcome.

Embodiments of the present invention provide a method and a device for selecting optimal network maximum flow algorithm, and relates to the field of network maximum flows, which can determine an optimal network maximum flow algorithm according to different network flow graphs. The method comprises: an algorithm selecting device acquiring a network flow graph, and obtaining a first residual network according to the network flow graph; acquiring an operation algorithm set, the operation algorithm set comprising at least two algorithms; performing preflow push concurrently in the first residual network by using at least two algorithms, to obtain a second residual network, determining, in the second residual network, a quantity of pivotal edges corresponding to the at least two algorithms, and determining that an algorithm corresponding to the maximum value of the quantity of pivotal edges, as an optimal network maximum flow algorithm of the network flow graph. The embodiments of the present invention are used for selecting a network maximum flow algorithm.