388 results about "Time correlation" patented technology

An imaging system substantially simultaneously acquires z-depth and brightness data from first sensors, and acquires higher resolution RGB data from second sensors, and fuses data from the first and second sensors to model an RGBZ image whose resolution can be as high as resolution of the second sensors. Time correlation of captured data from first and second sensors is associated with captured image data, which permits arbitrary mapping between the two data sources, ranging from 1:many to many:1. Preferably pixels from each set of sensors that image the same target point are mapped. Many z-depth sensor settings may be used to create a static environmental model. Non-correlative and correlative filtering is carried out, and up-sampling to increase z-resolution occurs, from which a three-dimensional model is constructed using registration and calibration data.

A base station has a time correlation calculating unit for estimating a variation-with-time characteristic of radio link quality using radio downlink quality information reported by a mobile station, a target error rate setting unit for changing over a target error rate adaptively using this variation-with-time characteristic, and an MCS decision unit for deciding a modulation scheme and / or encoding rate, in such a manner that packet error rate becomes equal to the target error rate, using the radio link quality information as well as reception success / failure information reported by the mobile station. The base station transmits a packet based upon the modulation scheme and / or encoding rate decided.

Embodiments of the present invention relate to a system and method for discovering time correlations among data. The method may include inputting time-series data and summarizing the time-series data at different time granularities. Additionally, the method may involve detecting change points in the time-series data, reducing a comparison of the time-series data to a one-to-one comparison, comparing the time-series data to generate correlation rules, and detecting correlations between the time-series data based on the correlation rules.

The input characteristics of a real-time IDS change continuously with time therefore setting a rigid (time and behavior invariant) classification threshold limits the accuracy that the IDS can potentially achieve. A generic threshold tuning method and system is proposed which can adaptively tune the detection threshold of a real-time IDS in accordance with varying host and network behavior. The method and system perform statistical and information-theoretic analyses of network and host-based IDSs' anomaly based intrusions to reveal a consistent time correlation structure between benign activity periods which is used to predict future anomaly scores and to adapt an IDS' detection threshold accordingly.

An imaging system substantially simultaneously acquires z-depth and brightness data from first sensors, and acquires higher resolution RGB data from second sensors, and fuses data from the first and second sensors to model an RGBZ image whose resolution can be as high as resolution of the second sensors. Time correlation of captured data from first and second sensors is associated with captured image data, which permits arbitrary mapping between the two data sources, ranging from 1:many to many:1. Preferably pixels from each set of sensors that image the same target point are mapped. Many z-depth sensor settings may be used to create a static environmental model. Non-correlative and correlative filtering is carried out, and up-sampling to increase z-resolution occurs, from which a three-dimensional model is constructed using registration and calibration data.

A wireless mesh network is configured to manage a power grid. Each node within the wireless mesh network is configured to detect and classify voltage fluctuations in power supplied by an upstream transformer coupled to the power grid. When a given node detects a particular type of fluctuation (i.e., an “event”), the node generates a timestamped event classification that reflects the type of event and a time when the event occurred. A server configured to manage the wireless mesh network receives timestamped event classifications from each node within the wireless mesh network and then performs a time correlation with the received timestamped event classifications to determine which nodes detected similar events. When two or more nodes detected the same event at similar times, the server determines that those nodes are coupled to the same transformer.

The invention discloses a traffic state prediction method for the urban road network based on key road sections, which is characterized by comprising the steps of first, carrying out data preprocessing; second, establishing a spatial weight matrix of the road network; third, establishing a time correlation matrix; fourth, recognizing key road sections by using a time-space correlation matrix; andfifth, establishing a deep convolution neural network, predicting the state of the road network in the future, and carrying out evaluation on a prediction model. The traffic state prediction method predicts the urban traffic flow state from a level of the wide-range road network, thereby being conducive to guiding the traffic flow from a macroscopic perspective, and fully exploring time-space correlation characteristics of the traffic flow. The key road sections in the road network are recognized, so that the training time of the model can be greatly reduced compared with a method of taking historical states of all road sections as input data, and the prediction efficiency is improved; and the convolution neural network is adopted to serve as the prediction model, and the prediction resultis also more accurate.

A method for determining a process variable of a product in a tank based on a time delay of electromagnetic waves. The method further comprises forming a measurement signal comprising a sequence of values, each value representing a time correlation between a pulse of a reference signal and a reflected signal, sampling and digitizing this measurement signal to form a digital signal, identifying a time window of the digital signal including the surface echo peak, determining a relative time period between a reference time corresponding to the predefined reference and a beginning of the time window, time-to-frequency transforming the digital signal in the time window to obtain a phase spectrum, determining a relative phase shift of the spectrum and using the relative phase shift to calculate a corresponding time shift, and determining the time delay by adding the relative time period and the time shift.The invention is based on the realization that major improvement of measurement performance, compared to amplitude detection only, can be achieved by discrimination of the phase difference between the reflected signal and a reference. The detection is further independent of the pulse waveform and modulation, significantly reducing the requirements on pulse modulation.

A time correlated single photon counting system having a programmable delay generator triggered by a laser fire event detector. The system may be used for chemical agent detection based on Rayleigh scattering using optical time domain reflectometry techniques. The system may also be used for Raman detection using frequency to time transformations.

Disclosed are systems and methods that time correlate input at a point of entry of consumer data of least two identifications for products, prescriptions, vouchers, or codes, uses positive correlation determinations for triggering output of predetermined information. The time correlation of identifications may be used to build a time correlation database, and thereafter use that database for targeted communications. Related inventions include an intelligent Y cable or the like to filter data transmitted from a scanner at a point of entry of consumer data, and a novel POS CS configured to receive bar codes that would otherwise cause the POS CS to enter an error condition.

The invention provides a road section travel time forecasting method based on LSTM. The method comprises forecasting model generation and forecasting of travel time in a future period of time. The step of forecasting model generation comprises the steps that A1 data normalization processing is carried out on the history travel time data of a designated road section; and A2 the normalized history travel time data are used to train an LSTM recursive neural network to acquire a forecasting model. The step of forecasting of travel time in the future period of time comprises the steps that B1 data normalization processing is carried out on current travel time data through the same method used in step A1; the forecasting model is input to forecast the travel time in the future period of time; and inverse normalization processing is carried out on a forecasting result to acquire the travel time in the future period of time. A short-time correlation mode and a long-time association mode of travel time data can be excavated. The method automatically adjusts the contribution of historical information to current forecasting according to the current state, and has the advantages of high prediction accuracy and good robustness.

There is provided a radio communication device having a high quality and accuracy regardless of fluctuation degree of the transmission path characteristic. The radio communication device estimates the fluctuation speed of the transmission path characteristic from the time correlation of the reception signal, judges the diversity technique used between the communication devices from the estimated value, and feeds back the judgment information so as to switch the diversity between the communication devices, thereby increasing the diversity gain. The feedback information is minimized by defining a correspondence table. Moreover, diversity switching is performed at a timing considering a control delay and a propagation path delay so as to prevent an instantaneous disconnection of the communication.

A method is provided for sharing emotional reactions to social media. A virtual social venue is provided for sharing streaming media content with a plurality of users. Users are provided with a tool or tools to express emotional reactions, such as clapping, laughing, cheering, whistling, expressions of surprise, and booing, to the media content. The users' emotional reactions are stored in time association with the streaming media content. If and when other users subsequently view and / or listen to the shared media content, the stored emotional reactions of previous viewers and / or listeners are presented along and in time correlation with the shared media content.

The invention discloses a wireless sensor network abnormal event detecting method based on multi-attribute correlation and belongs to an anomaly detection technology in data mining.The wireless sensor network abnormal event detecting method comprises the specific steps that non-space-time attribute dependency model is established based on a Bayesian network, the attribute correlation confidence is calculated according to a conditional probability table, the similarity of points to be detected and abnormal points in a non-space-time attribute correlation mode is reflected; time correlation detection is performed based on a sliding window model, readings simultaneously meeting the time correlation mode and abnormal event attribute correlation mode are marked as temporal abnormal points to detect whether abnormal events occur or not by cooperating with neighbor node information.In addition, the wireless sensor network abnormal event detecting method calculates recent abnormal nodes closest to an abnormal event area center by adopting a Ritter's smallest enclosing circle algorithm, uploads the abnormal information of the abnormal nodes and can effectively reduce the data transmission amount and reduce the energy consumption of sensor nodes.The wireless sensor network abnormal event detecting method can be applied to wireless sensor network event detection of multiple sending components.

The invention discloses a method for classifying network abnormal events. The method disclosed by the invention comprises the following steps of: firstly, extracting flow characteristic parameters from network flow data; and, determining a historical time window and constructing abnormal characteristic multi-time sequences according to a detected abnormal time point, wherein the accuracy for classifying abnormal events of a backbone network can be effectively improved by sufficiently utilizing the time correlation of a flow behavior; simultaneously, different kinds of abnormal events having similar characteristics can be effectively distinguished by analyzing the similarity between the abnormal characteristic multi-time sequences; and the real-time requirement for classifying the abnormalevents of the backbone network can be ensured by utilizing an information entropy to process mass data.

Signal processing architectures for direct acquisition of spread spectrum signals using long codes. Techniques are described for achieving a high of parallelism, employing code matched filter banks and other hardware sharing. In one embodiment, upper and lower sidebands are treated as two independent signals with identical spreading codes. Cross-correlators, in preferred embodiments, are comprised of a one or more banks of CMFs for computing parallel short-time correlations (STCs) of received signal samples and replica code sequence samples, and a means for calculating the cross-correlation values utilizing discrete-time Fourier analysis of the computed STCs. One or more intermediate quantizers may optionally be disposed between the bank of code matched filters and the cross-correlation calculation means for reducing word-sizes of the STCs prior to Fourier analysis. The techniques described may be used with BOC modulated signals or with any signal having at least two distinct sidebands.

The invention discloses an automatic navigation method and device. The method comprises the following steps of: acquiring a scene video of a current geographical location photographed by a multi-objective camera system; calculating a spatial position and gesture information of the camera in the multi-objective camera system; performing time correlation three-dimensional matching on continuous two-frame images at the same camera time to generate a first environmental object depth map; performing spatial correlation three-dimensional matching on continuous two-frame images at different camera times to generate a second environmental object depth map; constructing a local obstacle map according to the first environmental object depth map and the second environmental object depth map; and mapping the local obstacle map to a safe distance space to determine a safety path from a current position to a predetermined target. The method effectively solves limitation of a conventional depth algorithm in viewing angel and movement, can perform self-position positioning, obstacle sensing and automatic generation of the safety path, is high in positioning precision and optimum in safety path, and can be applied to mobile platforms such as robots or entertainment unmanned aerial vehicle.

The invention belongs to the technical field of wireless communication, and particularly relates to a dynamic heterogeneous network flow prediction method based on a deep space-time neural network. Aiming at the problems of small coverage area, low prediction precision, short prediction time and the like of the existing mobile data traffic prediction method, the dynamic heterogeneous network traffic prediction method based on the deep space-time neural network is studied. Considering the characteristics of user mobility, flow data space-time correlation and the like, deeply researching a wide-coverage long-term mobile data flow prediction mathematical model description method in the dynamic heterogeneous network; On the basis, a space-time related convolutional long-short time memory network model is studied to predict the long-term trend of the mobile traffic in the dynamic heterogeneous network; A space-time related three-dimensional convolutional neural network model is studied to capture micro-fluctuation of a mobile flow sequence in the dynamic heterogeneous network; And fusing the long-term trend prediction model and the short-term change model of the mobile traffic, therebyrealizing wide-coverage and high-precision long-term mobile traffic prediction in the dynamic heterogeneous network.

A method for archiving a collaboration session regarding visual content is disclosed. A collaboration session is established from a collaboration server with participant nodes. The visual content is transmitted to the participant nodes. A first multimedia data stream, an annotation data object associated with an annotation to the visual content in timed correlation to a first time reference within the first multimedia data stream, and view parameters are received. A first session record entry associated with the multimedia data stream, the annotation data object, and the view parameters is stored into a session record. The session record includes a plurality of session record entries including the first session record entry. The session record is retrievable by other participant nodes for playing back of the collaboration session in sequence with the session record entries. The first session record entry is transmitted to other participant nodes.

The invention discloses an anomaly detection method based on a time series. The anomaly detection method based on the time series comprises intra-node data correlation analysis and inter-node data correlation analysis. The intra-node data correlation analysis describes time correlation of sensor node data and is to compute the data correlation between current data and historical data. The inter-node data correlation analysis describes spatial data correlation between different sensor nodes and is to compute the data correlation between the sensor node data and data of neighbor nodes of sensor nodes. The anomaly detection method based on the time series can be used for effectively detecting abnormal data, and good performance can still be kept under the influence of wrong data. Meanwhile, an approximation method of the time series data is applied so that the time series data can be effectively compressed, a method for measuring the similarity between the compressed time series is put forward and used for clustering analysis of the time series, and consequently anomaly detection based on the time series is achieved.

The invention provides a service fault positioning method, device, computer equipment and a storage medium. The method comprises steps: an early warning message containing the identity and fault timeof abnormal business is acquired; according to the identity of the abnormal business, each sub service node correlated to the abnormal business is acquired; from a service link corresponding to the service to which the abnormal business belongs, the link set of each sub service node is acquired, and the edge between the nodes in the service link is used for characterizing called data between the nodes; according to the fault time of the abnormal business and the called data corresponding to each node in the link set, each time correlation between each node in the link set and the abnormal business is determined; and according to each time correlation, a target fault node pair in the link set is determined. Thus, automatic positioning of a fault node can be realized, the fault positioning speed and the accuracy are improved, and the technical problems of slow positioning speed and poor accuracy of a manual fault positioning mode in the prior art can be solved.

Methods, structures, and systems are disclosed for implementing legal intercept of data which provide real-time correlation of broadband user information to network addresses (or other identifiers) across multiple and different authentication systems and user databases. In certain embodiments, an intercept coordinator module interacts with each authentication system to determine real-time a target address for a target user device, which it then uses to update mediation devices, external databases, etc., involved in performing a lawful intercept under the CALEA process. Probes are not required within the network to perform authentication system captures. A modular interface system provides support for existing CALEA equipment, and support for implementing additional interface modules for new or updated CALEA equipment. Exemplary intercept coordinator modules may communicate with multiple AAA systems, in multiple different sub-nets or networks, including geographically distant networks, and provides for pooling of common CALEA equipment resources for use in multiple networks simultaneously.

The invention provides a moving target tracking method based on an image, and aims to the problem of tracking failure of the conventional tracking method based on a convolutional neural network duringsize or scale change of a moving target. According to the method, feature extraction is performed on the image through a pre-trained network by employing a deep learning technology, a target point istracked by employing a correlation filter method, the tracking accuracy is improved through acquisition of information of time correlation by employing a C3D network, and multi-scale tracking characteristics are provided through combination of a DenseNet network and a network structure of a feature pyramid network so that accurate tracking during change of the size or the scale of the moving target can be realized, and the method can be widely applied to tracking of the moving target based on the image.

The invention relates to a photon-counting imaging laser radar for filtering noise in real time by adopting an adjacent pixel element threshold value method, and the radar relates to the technical field of laser radar. The problem that the photon-counting imaging laser radar noise filtering technology can not meet the real-time application requirement is solved. A pulse laser signal enters a beam splitter to form two signals; one signal is processed through a PIN detector and is outputted to an adjacent pixel element threshold value processing module; the other signal is collimated through a optical transmitting system to radiate a target; the other signal is spread by the target and outputted an echo signal, the echo signal passes through a receiving optical system and a Gm-APD single-photon detector array, the adjacent pixel element threshold value processing module records the echo signal comprising time correlation and compares the signal with an initial signal, so as to obtain the round-trip time difference of the pixel corresponding to the Gm-APD single-photon detector array, the round-trip time difference is calculated to obtain a distance value of the corresponding pixel target, and a target 3D distance image is obtained finally. The radar is suitable for filtering the noise in laser imaging.

Time-correlation methods for determining pulse characteristics from a modelocked ultrafast laser include a cross-correlation method and an auto-correlation method. In the cross-correlation method, pulses from the laser and pulses from another modelocked laser are incident on a two-photon detector that responds when the pulses overlap in time. The lasers are synchronized to the same frequency and the phase difference between pulses from the two lasers is varied to vary the temporal pulse overlap while recording the detector response. Pulse characteristics are determined from recorded data representing the detector response as a function of phase difference. In the auto-correlation method, pulses from one laser are divided into two components. One component follows a fixed delay path before being temporally overlapped at the detector with another component that has not been delayed. The temporal overlap is varied by varying the pulse repetition frequency. Pulse characteristics are determined from recorded data representing the detector response as a function of phase difference.

The invention discloses a constant false alarm rate detection method adaptive to environmental changes. Correlation of corresponding positions of multiple periodic data and correlation of adjacent data of the same group of data are used for threshold detection, that is, time correlation and space correlation are used for threshold detection, and an interference memory group is initialized respectively to be spectrum information of first periodic data through an initialized part; clutter information of the current data is estimated to be a result when clutter information in former M periods and the spectrum information of the current data are weighted; if the located environment changes greatly or quickly, the weight of the current data information on clutter estimation can be properly increased; and the currently-estimated clutter information is used for setting of a later constant false alarm rate detection threshold. According to the method of the invention, in homogeneous background, a unit average constant false alarm rate is used to achieve optimal performance, and in nonhomogeneous background, an ordered statistical method is used for enabling the detection performance and the false alarm performance to be significantly improved.

The invention discloses a method used for an embedded terminal and capable of improving the playback quality of streaming media and a system used for the embedded terminal and capable of improving the playback quality of the streaming media. The method used for the embedded terminal and capable of improving the playback quality of the streaming media is used for services of online download and playback of embedded terminal equipment. By means of dynamic adjustment of the download rate and the playback rate, the user experience of a download type playback service can be improved. The method used for the embedded terminal and capable of improving the playback quality of the streaming media comprises the following steps: dividing time into periods, and collecting the information of download rate, the quantity of video buffer memory and the playback rate when each period starts. The method used for the embedded terminal and capable of improving the playback quality of the streaming media further comprises the following steps: forecasting the quantity of the video download in the period, namely, estimating the quantity of the download in the period according to historical data of the download rate; and computing decision-making process, namely, calculating the optimal playback rate and the upper limit of the download rate of the period according to the forecasted quantity of the download, the current quantity of the buffer memory and the current playback rate. The method used for the embedded terminal and capable of improving the playback quality of streaming media and the system used for the embedded terminal and capable of improving the playback quality of the streaming media have the advantages of being capable of better utilizing the time correlation of the download rate, finely tuning the playback effect, ensuring that the higher the code rate, the closer the download rate to the playback rate, thus ensuring the smooth playback of high definition videos.

The invention discloses a Brillouin optical time domain reflectometer for single-photon detection based on an edged filter method, which comprises a narrow linewidth laser (1), a pulse modulator (2), a circulator (3), a sensing optical fiber (4), an optical fiber module (5), 3dB coupler (6), a linear edged filter (7), an InGaAs / InP SPAD (single-photon avalanche diode) detector group (8), a signal generator (9), a data processing module (10) and a pulse signal generator (11), wherein the InGaAs / InP SPAD detector group (8) includes two or three InGaAs / InP SPAD detectors. The InGaAs / InP SPAD detector group with a high counting rate and high sensitivity is used as a detection unit, Brillouin frequency shift information is acquired by the edged filter method, time correlation single-photon counting technology is adopted by the data processing module (10), limitations on the bandwidth and sensitivity of a traditional detector are broken through, spatial resolution and measuring precision of the reflectometer can be simultaneously improved, and temperature and strain are simultaneously sensed.

The invention discloses a traffic flow three parameter real time prediction method taking regard of space-time correlation. According to the method, on the basis of acquiring traffic flow rate, speed and occupancy data of a target section and upstream and downstream sections of the target section, a state space model for multivariable short time prediction of traffic flow three parameters is established; according to spatial correlation of various traffic variables at different data acquisition sections, an observation equation of the state space model is established; according to time autocorrelation and cross correlation of the multiple traffic variables at one same data acquisition section, a state equation of the state space model is established; prediction and iteration update of the traffic flow three parameters are realized by employing the Kalman filtering algorithm. The method makes full use of the spatial correlation of the traffic flow three parameters at the different data acquisition sections, the time autocorrelation and the cross correlation of the different traffic variables at one same data acquisition section, the multivariable prediction algorithm is employed, and thereby traffic flow short time prediction accuracy is facilitated.

A method and apparatus for processing step-like output signals (primary signals) generated by non-ideal, for example, nominally single-pole ("N-1P ") devices. An exemplary method includes creating a set of secondary signals by directing the primary signal along a plurality of signal paths to a signal summation point, summing the secondary signals reaching the signal summation point after propagating along the signal paths to provide a summed signal, performing a filtering or delaying operation in at least one of said signal paths so that the secondary signals reaching said summing point have a defined time correlation with respect to one another, applying a set of weighting coefficients to the secondary signals propagating along said signal paths, and performing a capturing operation after any filtering or delaying operations so as to provide a weighted signal sum value as a measure of the integrated area QgT of the input signal.