1927 results about "Slide window" patented technology

An adaptation to Carrier Interferometry synthesis and analysis provides for complex coding and decoding in a sliding window transform. Coding and decoding functionality can be extended to spatial processing in systems employing multiple transceiver elements. Poly-amplitude codes permit successive interference cancellation in spatial and frequency-domain processing. Handoffs in cellular systems are facilitated by selecting spectral/base station combinations that optimize link performance.

Methods of imaging objects based on wavelet retrieval of scenes utilize wavelet transformation of plural defined regions of a query image. By increasing the granularity of the query image to greater than one region, accurate feature vectors are obtained that allow for robust extraction of corresponding regions from a database of target images. The methods further include the use of sliding windows to decompose the query and target images into regions, and the clustering of the regions utilizing a novel similarity metric that ensures robust image matching in low response times.

Improving the utilization of physical resources and reducing energy consumption in a cloud data center includes providing a plurality of virtual machines in the cloud data center; periodically reallocating resources of the plurality of virtual machines according to a current resource demand of the plurality of virtual machines in order to minimize a number of active physical servers required to handle a workload of the physical servers; maximizing a mean inter-migration time between virtual machine migrations under the quality of service requirement based on a Markov chain model; and using a multisize sliding window workload estimation process for a non-stationary workload to maximize the mean inter-migration time.

Methods and systems are provided for sequence number checking. Sequence numbers of data packets are compared to a “sliding” window. The sliding window indicates a range of sequence numbers considered valid (or invalid). The size of the sliding window may be a particular value or varied. If a sequence number is “below” the sliding window, then it may be considered invalid. If a sequence number is within the sliding window, then it may be further checked to determine if a duplicate sequence number has been received. If a sequence number is “above” the sliding window, then it may be considered valid and the sliding window is advanced. The sliding window and sequence numbers are processed using multiple level bitmaps, which indicate a historical state of sequence numbers received. Furthermore, the multiple level bitmaps may comprise summary bits to summarize a state of subsequent bits.

A system and method summarizes multimedia stored in a compressed multimedia file partitioned into a sequence of segments, where the content of the multimedia is, for example, video signals, audio signals, text, and binary data. An associated metadata file includes index information and an importance level for each segment. The importance information is continuous over as closed interval. An importance level threshold is selected in the closed interval, and only segments of the multimedia having a particular importance level greater than the importance level threshold are reproduced. The importance level can also be determined for fixed-length windows of multiple segments, or a sliding window. Furthermore, the importance level can be weighted by a factor, such as the audio volume.

Apparatuses and methods are provided for determining real time traffic conditions. A candidate road is divided into road segments by perpendicular bisectors. A spatial sliding window is positioned over at least a portion of a road segment, wherein the spatial sliding window corresponds to a front end of the road segment in a direction of travel of the road segment. Real time probe data is received from mobile devices in probe vehicles or on travelers of the at least portion of the road segment within the spatial sliding window. The real time probe data is analyzed, and a computer program assists in determining the real time traffic conditions of the at least portion of the road segment within the spatial sliding window. Based on the analysis, the real time traffic conditions are reported.

A method of rate control using a picture-based lookahead sliding window in a dual-pass transcoder/encoder compressed video architecture extracts statistics from an input video signal according to a simple compression standard, the input video signal being a compressed video signal for transcoding or an uncompressed video signal for encoding. A trans-factor is calculated for a current picture based on previous pictures in a sliding window to predict the complexity of the current picture, the trans-factor being a ratio of global complexity measures for the simple compression standard versus a sophisticated compression standard. Bits for the current picture are then allocated based on the complexity of future pictures in the sliding window. After encoding the current picture according to the sophisticated compression standard, the target bits of and the picture complexity in the sliding window, as well as the trans-factor, are updated as the window is moved by one picture. The extraction of the statistics is achieved in a transcoder by using a simple compression standard decoder to produce the statistics from the compressed video signal as the input video signal, and in an encoder by using a simple compression encoder to generate the statistics from the uncompressed video signal as the input video signal.

Computer implemented method, system and computer usable program code for detecting topic shift boundaries in a multimedia stream. A computer implemented method for detecting topic shift boundaries in a multimedia stream includes receiving a multimedia stream, and performing multimodal analysis on the multimedia stream to locate a plurality of temporal positions within the multimedia stream at which topic changes have an increased likelihood of occurring to provide a sequence of multimedia portions. Characteristics for a sliding window for each multimedia portion in the sequence of multimedia portions are automatically determined, and topic shift boundaries are detected in each multimedia portion by applying a text-based topic shift detector over the media stream's text transcript using a sliding window, wherein the sliding window used with each multimedia portion has the characteristics determined from its respective multimedia portion.

A just-in-time (JIT) compiler typically generates code from bytecodes that have a sequence of assembly instructions forming a "template". It has been discovered that a just-in-time (JIT) compiler generates a small number, approximately 2.3, assembly instructions per bytecode. It has also been discovered that, within a template, the assembly instructions are almost always dependent on the next assembly instruction. The absence of a dependence between instructions of different templates is exploited to increase the size of issue groups using scheduling. A fast method for scheduling program instructions is useful in just-in-time (JIT) compilers. Scheduling of instructions is generally useful for just-in-time (JIT) compilers that are targeted to in-order superscalar processors because the code generated by the JIT compilers is often sequential in nature. The disclosed fast scheduling method has a complexity, and therefore an execution time, that is proportional to the number of instructions in an instruction block (N complexity), a substantial improvement in comparison to the N2 complexity of conventional compiler schedulers. The described fast scheduler advantageously reorders instructions with a single pass, or few passes, through a basic instruction block while a conventional compiler scheduler such as the DAG scheduler must iterate over an instruction basic block many times. A fast scheduler operates using an analysis of a sliding window of three instructions, applying two rules within the three instruction window to determine when to reorder instructions. The analysis includes acquiring the opcodes and operands of each instruction in the three instruction window, and determining register usage and definition of the operands of each instruction with respect to the other instructions within the window. The rules are applied to determine ordering of the instructions within the window.

The invention relates to a condition monitoring data stream anomaly detection method, in particular to a condition monitoring data stream anomaly detection method based on an improved gaussian process regression model. The problem that an existing method for processing monitoring data stream anomaly detection is poor in effect is solved. The method comprises the steps that firstly, the historical data sliding window size is determined; secondly, the types of a mean value function and a covariance function are determined; thirdly, the hyper-parameter initial value is set to be the random number from 0 to 1; fourthly, q data closest to the current time t are extracted; fifthly, the gaussian process regression model is determined; sixthly, prediction is conducted by means of the nature of the gaussian process regression model; seventhly, PI of normal data at the time t+1; eighthly, monitoring data are compared with the PI; ninthly, whether the real monitoring data need to be marked to be abnormal or not is judged; tenthly, beta (xt+1) corresponding to the monitoring value at the time t+1 is calculated; eleventhly, the real value or prediction value and the t+1 are added into DT; twelfthly, new DT is created. The condition monitoring data stream anomaly detection method based on the improved gaussian process regression model is applied in the field of network communication.

A sliding window of flash blocks is used to reduce wasted space occupied by stale data in a flash memory. The sliding window slides downward over a few flash blocks. The oldest block is examined for valid pages of data, and the valid pages are copied to the end of the sliding window so that the first block has only stale pages. The first block can then be erased and eventually re-used. A RAM usage table contains valid bits for pages in each block in the sliding window. A page's valid bit is changed from an erased, unwritten state to a valid state when data is written to the page. Later, when new host data replaces that data, the old page's valid bit is set to the stale state. A RAM stale-flags table keeps track of pages that are full of stale pages.

Methods directed to longest prefix matching and systems directed to IP address lookups are presented. The methods and systems relate in particular to IPv6 and comprise finding the longest prefix match (LPM) for an IP address. The method of the invention results in the use of filters to perform LPM. In embodiments of the invention, partial address filtering is used to further reduce filtering requirements. Reducing the number of filtering operations has the advantage of making the LPM algorithm faster and less costly to implement than prior art approaches. Also described is an “ideal offset filter” that extracts a fixed sized sliding window of bits from the IP address being processed.

The invention provides a human body behavior identification method based on an inertial sensor. The method comprises the steps of acquiring human body behavior data of testees by means of the inertial sensor, conducting sliding window segmentation on the acquired human body behavior data, conducting feature extraction on a triaxial accelerated speed subsequence and a triaxial angular speed subsequence which are obtained after sliding window segmentation, conducting feature fusion on a feature vector to form a sample set of the human body behaviors of the testees, conducting feature selection on the sample set by means of the least correlated maximum redundant algorithm and the Bayes regularization sparse polynomial logistic regression algorithm, obtaining the classification feature vectors of all human body behaviors of all the testees, obtaining a classifier model of each human body behavior by means of a fuzzy least square support vector machine, and obtaining a human body behavior identification result after the human body behavior data are tested by means of the fuzzy least square support vector machine. By means of the human body behavior identification method, self-adaptation and identification efficiency can be improved.

Included in the invention are systems and methods of full time recording network traffic to a hierarchical data storage. Also included in the invention are systems and methods of retrieval of recorded network traffic from a hierarchically organized network data repository. Additionally included in the invention are systems and methods of efficiently filtering data in a hierarchically organized network data repository. Systems and methods of displaying recorded network data utilizing the retrieval systems are also included in the invention. Further included in the invention are systems and methods of providing sliding time window selection user interfaces. Detailed information on various example embodiments of the inventions are provided in the Detailed Description below, and the inventions are defined by the appended claims.

The invention discloses an accompanying robot path planning method and system based on obstacle virtual expansion. The method includes a step of constructing an environment map, namely a step of constructing a two-dimensional occupancy grid map according to an actual scene, with each grid being labeled as an obstacle zone or a walkable zone; a step of setting initial coordinate positions of an accompanying robot and a movable target in the grid map; a step of constructing a sliding window for the robot; a step of subjecting the obstacle zones to expansion processing, namely a step of performing initial expansion on grids where an obstacle is according to a shortest distance between the center of the robot and a body edge, determining the number of grid layers expanded on the basis of the minimum impassable zone, adopting the grids in the expanded zones as obstacle virtual expansion grids, and labeling the grade of danger of obstacle influences on the obstacle virtual expansion grids; and a step of planning a path for the accompanying robot based on an A* algorithm and an incremental path planning process. Incremental path updating is performed by adopting a path of the last moment,thus saving path planning time and increasing the response speed of the accompanying robot.

A log file processing system sorts records from large log files and groups them by session without making a complete copy of the log files by capturing a subset of the log files in a sliding memory window and identifying all records in the window that form a complete user session. Records belonging to a complete session are output for analyzing, and the remaining records are output as raw log data for additional processing. Using a ring buffer to implement the sliding memory window, data structures are used to group records by session, to identify completed sessions, and to index into the ring buffer to retrieve records for completed sessions that are to be directly analyzed. Any records remaining in the ring buffer at the end of slide window processing may be output as raw log file data and are processed as incomplete or malformed session records. An embodiment of the log file processing system provides a significant improvement on the speed of data extraction from log files into analyzable session data.

The invention provides a massive structured data storage method, a massive structured data query method, a massive structured data storage system and a massive structured data query system, which all support high-speed loading. The distributed storage method for massive structured data comprises the following steps of: receiving the data which is loaded at high speed from a user; and caching the loaded data in a distributed way by utilizing a double-sliding window structure, and storing the cached data in the distributed way after a fixed period. By the distributed storage method for the massive structured data, newly-loaded data can be cached, so that the query efficiency of application such as streaming data which frequently uses recently-loaded data in post data query can be improved.

The invention discloses a monocular vision SLAM algorithm based on a semi-direct method and sliding window optimization. The monocular vision SLAM algorithm comprises the steps that 1) color image frames acquired by a monocular color camera are uploaded to a computer through a third-party image acquisition interface; 2) the algorithm initialization process is performed, the camera pose transformation relationship between the two initial frames is established, initial map points are established, the two initial frames act as key frames and the initial map points are inserted in the map and thesliding window; and 3) the map points observed in the previous frame are projected to the current frame, and bundle set optimization computation based on the luminosity error is performed between thetwo frames of images so as to obtain the camera pose transformation between the two frames to track the movement of the camera. The monocular vision SLAM algorithm has faster operation speed, and theused equipment is simple and easy to calibrate so as to have higher practical value and wider application occasions.

A method is presented for throttling data transmissions within a data processing system. Information about a data transfer from a server to a client is received within the application layer of a server, which stores the information about the data transfer along with information about a number of recent data transfers from the server to the client to create a sliding window of historical information about data transfers. The data transfer from the application layer of the server is delayed within the application layer of the server for an amount of time that is approximately equal to a computed delay time value in response to a determination that an average data transfer rate over the number of recent data transfers from the server to the client may exceed a data transfer rate threshold parameter.

The invention relates to a robot positioning method with fusion of visual features and IMU information. The invention puts forward a method of fusion of monocular vision with IMU. Visual front-end pose tracking is performed and the post of a robot is estimated by using feature points; an IMU deviation model, an absolute scale and a gravity acceleration direction are estimated by using pure visualinformation; IMU solution is performed to obtain high-precision pose information and thus an initial reference is provided to optimize the search process, and the initial reference, a state quantity and visual navigation information are used for participating in optimization; and a rear end employs a sliding-window-based tightly coupled nonlinear optimization method to realize pose and map optimization. And the computational complexity is fixed while the speedometer is calculated based on a sliding window method, so that the robustness of the algorithm is enhanced.

The invention discloses a character detection method and device based on deep learning. The method comprises the steps: designing a multilayer convolution neural network structure, and enabling each character to serve as a class, thereby forming a multi-class classification problem; employing a counter propagation algorithm for the training of a convolution neural network, so as to recognize a single character; minimizing a target function of the network in a supervision manner, and obtaining a character recognition model; finally employing a front-end feature extracting layer for weight initialization, changing the node number of a last full-connection layer into two, enabling a network to become a two-class classification model, and employing character and non-character samples for training the network. Through the above steps, one character detection classifier can complete all operation. During testing, the full-connection layer is converted into a convolution layer. A given input image needs to be scanned through a multi-dimension sliding window, and a character probability graph is obtained. A final character region is obtained through non-maximum-value inhibition.

A coherent optical receiver for a data-frame format in which a data frame has two or more pilot-symbol blocks, each having a cyclic prefix or suffix, and one or more payload-symbol blocks, each of which is concatenated with at least one adjacent block without a guard interval between them. The receiver uses optical signals corresponding to the pilot-symbol blocks to perform data-frame synchronization, frequency-offset correction, and channel-estimation procedures, which are robust even in the presence of certain transmission impairments. The receiver applies block-overlap processing with a sliding window to recover the payload data in a manner that substantially cancels the adverse effects of inter-block interference caused by the absence of guard intervals in the payload portion of the data frame. In one embodiment, the receiver performs channel-estimation and block-overlap processing for polarization-division-multiplexed signals in a manner that enables a combined, intertwined application of channel-response-compensation and polarization-demultiplexing procedures.

Provided are methods, devices, and systems for maintaining a SIP survivable network. A SIP endpoint maintains an ordered list of the controllers that are available to it and registers with a subset containing the first N controllers in the list, where N is a plurality, but not all, of the controllers in the list, and obtains services from one or more of the controllers of the subset. If one of the controllers of the subset becomes unavailable, the endpoint registers with the (N+1)^st controller in the list, thereby adding it to the subset. If and when the one controller becomes available again, the endpoint de-registers with the (N+1)^st controller, thereby removing it from the subset, and if necessary re-registers with one controller. The subset thus forms a sliding window on the list.

The invention discloses an electromyographic signal gesture recognition method based on a hidden markov model. The method comprises the following steps of: executing smoothing filtering for electromyographic signals; extracting a multi-feature feature set for each window data through a sliding window, and executing normalization and feature dimension reduction of minimum redundancy maximum correlation criterion for feature vectors; designing three classes of hidden markov model classifiers, and optimizing parameters of the hidden markov model classifiers; obtaining classifier models through training with hidden markov classifier model parameters and training data; inputting test data into the models trained well, and according to likelihood output by each class of hidden markov model, determining that the class corresponding to the maximum likelihood is the recognized class. According to the method provided by the invention, three classes of common hidden markov model classifiers are recognized based on a new feature set. By application of a classification method based on the hidden markov model, different gestures of the same testee can be recognized accurately, and gestures of different testees can be relatively recognized accurately.

Training data object images are clustered as a function of motion direction attributes and resized from respective original into same aspect ratios. Motionlet detectors are learned for each of the sets from features extracted from the resized object blobs. A deformable sliding window is applied to detect an object blob in input by varying window size, shape or aspect ratio to conform to a shape of the detected input video object blob. A motion direction of an underlying image patch of the detected input video object blob is extracted and motionlet detectors selected and applied that have similar motion directions. An object is thus detected within the detected blob and semantic attributes of an underlying image patch extracted if a motionlet detectors fires, the extracted semantic attributes available for use for searching for the detected object.

A method of receiving data packets asynchronously transmitted by a plurality of user terminals using a spread-spectrum medium access protocol, comprises a step of cancelling interferences between colliding packets according to an innovative “sliding window” processing algorithm. A gateway receiver is adapted for carrying out interference cancellation using this algorithm. Asynchronous packet transmission from a user terminal is controlled by estimating a parameter indicative of the quality of information transmission through a communication channel; and inhibiting or allowing data transmission depending on a comparison between said estimated parameter and an adaptively varying threshold. A user terminal comprises transmission control means adapted for carrying out such a method. A communication system comprises a plurality of mobile user terminal communicating with a gateway through a satellite channel using an asynchronous spread-spectrum medium access protocol without closed-loop power control, wherein the user terminals and said gateway are of the kind described above.

The invention discloses an electromyographic signal gesture recognition method based on deep learning and attention mechanisms. The method comprises the following steps: performing noise reduction filtering on electromyographic signals; extracting one classic characteristic set from each wind datum by using a sliding window, and establishing a new electromyographic image based on characteristics;designing a deep learning frame based on a convolutional neural network, a circulation neural network and an attention mechanisms, and optimizing network structure parameters of the deep learning frame; performing training with the designed deep learning frame and the training data so as to obtain a classifier model; inputting testing data into the trained deep learning network model, and according to likelihood of a last layer of output, maximally likelihooding corresponding types, that is, recognition types. By adopting the method, electromyographic gesture signals can be recognized on the basis of new characteristic images and deep learning frames based on attention mechanisms. By adopting the electromyographic signal gesture recognition method based on deep learning and attention mechanisms, multiple different gestures of a same subject can be accurately recognized.

The invention discloses a multidirectional water meter reading area detection algorithm employing a full convolution neural network, and the algorithm comprises the following steps: S1, obtaining training data which comprises a water meter image and reading region mark information; S2, training the full convolution neural network through the mark information for the extraction of multilayer cascading features, and obtaining a multichannel characteristic image; S3, carrying out the sliding window scanning of the characteristic image, taking a full connection neural network as a classifier and a regression device, and screening out a rectangular candidate window of a water meter reading region preliminarily; S4, extracting the features of a corresponding region in the characteristic image according to the region position information of the candidate window, taking a second full connection neural network as the classifier and the regression device, and obtaining the center, length, width and angle information of the water meter reading region; S5, finally obtaining a detection result of the multidirectional water meter reading area in a manner of a rotating rectangular frame. The algorithm is accurate, robust and practical.

The invention discloses a binocular vision indoor positioning and mapping method and device. The method comprises the following steps of collecting left and right images in real time, and calculatingthe initial pose of the camera; collecting angular velocity information and acceleration information in real time, and pre-integrating to obtain the state of an inertial measurement unit; constructinga sliding window containing several image frames, and nonlinearly optimizing the initial pose of the camera by taking the visual error term between the image frames and the error term of the measurement value of the inertial measurement unit as constraints to obtain the optimized pose of the camera and measurement value of the inertial measurement unit; constructing word bag models for loop detection, and correcting the optimized pose of the camera; extracting and converting features of the left and right image into words for matching with the word bags of the offline map, optimizing and solving to obtain the optimized pose of the camera if the match is successful, and re-collecting the left and right images and matching the word bags if the match is unsuccessful. The binocular vision indoor positioning and mapping method and device provided by the invention can realize positioning and mapping in an unknown environment and the positioning function in the already constructed scene, andhas good precision and robustness.

The invention relates to a target tracking method based on a TLD (Tracking-Learning-Detection) algorithm. The method comprises the steps of: initially selecting a target region from a first frame of image; by adopting a mid-value optical flow method, according to target region information in a previous frame of image, predicting a determined prediction target as a target tracking result; judging whether a number of pixel points in a current frame of image, which are occupied by the target region in the previous frame of image, is greater than a pixel threshold value; if yes, shortening the target region in the previous frame of image and the current frame of image according to a certain length-width ratio, and carrying out global traversal in a mode of a sliding window so as to obtain to-be-selected targets; if no, carrying out local region partitioning on the current frame of image, and carrying out local traversal in a mode of the sliding window so as to obtain to-be-selected targets; processing at least one to-be-selected target to form a target detection result; and according to the target tracking result and the target detection result, determining a target tracking region. According to the target tracking method disclosed by the invention, a detection module and a learning module of the TLD algorithm are improved, and real-time performance of target tracking is improved.