421 results about "Temporal correlation" patented technology

A depth image coding method may calculate a depth offset of a depth image, may generate a prediction mode based on the depth offset, may minimize a prediction error of the depth image having a low correlation between adjacent points of view and a low temporal correlation and may enhance a compression rate. The depth offset may be calculated based on a representative value of adjacent pixels included in a template as opposed to using a depth representative value of pixels in a block and header information may not be needed to encode an offset and the offset may be generated by a depth image decoding apparatus. When a plurality of objects is included in a block, a depth offset is calculated for each of the plurality of objects and a motion vector is calculated for each of the plurality of objects and the depth image may be accurately predicted.

In a closed-loop wireless communication system, a codebook-based precoding feedback compression mechanism is provided to remove redundancy from the precoding feedback that is caused by channel correlation in time and frequency. Redundancy due to temporal correlation of the transmission channel is removed by sending precoding feedback only if there is a change in the precoder state for the channel to the receiver. Redundancy due to frequency correlation is removed by run length encoding the precoding feedback, thereby compressing the precoding feedback prior in the frequency domain. By compressing the precoding feedback, the average rate of precoder feedback is reduced.

The present invention efficiently detects various DDoS attacks for large scale Internet with the temporal correlation of traffic flows on the two directions of a single link, the spatial correlation of DDoS attack traffic at different locations and powerful machine learning algorithms. With these techniques, the present invention effectively detects and identifies attack sources without modifying existing IP forwarding mechanisms and without a global upgrade to Internet backbone routers. More importantly, the present invention can detect synchronized DDoS attacks even if the volume of attack traffic is extremely small at the location that is close to the attack source.

A disparity prediction stage and a motion prediction stage predict a disparity vector and a motion vector by extending a MPEG-2 structure into a view axis and using spatial/temporal correlation. A disparity/motion compensation stage compensates an image reconstructed by the disparity prediction stage and the motion prediction stage by using a sub-pixel compensation method. A residual image encoding stage performs an encoding to provide a better visual quality and a three-dimensional effect of an original image and the reconstructed image. A bit rate control stage controls a bit rate for assigning an effective amount of bit to each frame on the reconstructed image according to a bit rate. An entropy encoding stage generates a bit stream on multi-view video source data according to the bit rate.

In a wireless communication network, the location of an addressable receiver relative to the locations of a plurality of addressable sources of electromagnetic radiation is found using probabilistic models of the signal strength measured at the addressable receiver. The inventive method provides location determination on a finer spatial scale than was heretofore available. A region of interest is calibrated via a discrete-space radio map storing probability distributions of received signal strength at the measurement locations. The stored probability distributions are compensated for temporal variability and biases, such as through temporal correlations of the sampled received signal strength. A measurement of the signal strength at the addressable receiver from each of the plurality of addressable sources is used in conjunction with the discrete-space radio map to identify one of the coordinates thereof that maximizes the conditional probability P(x|s), where x is the radio map location and s is a vector of measured signal strengths. Small spatial scale variability can be compensated for using a perturbation technique. The method further implements a continuous-space estimator to return an estimated user location that falls between discrete-space radio map locations.

Provided are a method of efficiently transmitting motion model parameters using temporal correlation between video frames and a video encoding and decoding method and apparatus, in which motion estimation and motion compensation are performed by generating a plurality of reference pictures that are motion-compensated using motion model parameters. Motion model parameters are encoded based on temporal correlation between motion vectors of representative points expressing the motion model parameters, global motion compensation is performed on a previous reference video frame using motion model parameters in order to generate a plurality of transformation reference pictures, and a current video frame is encoded using the plurality of transformation reference pictures.

A 3-D wireframe model expressed in terms of nodes and vertices receives a 3-D video or like signal representative of a scene expressed in terms of a reference model, I frames and P frames. A DPCM coder takes advantage of the temporal correlation of the displacement of each vertex along every axis in the 3-D space. The 3-D signal is a set of non-zero displacements of all vertices and all nodes (s_l[n, v]) at time t_l. The decoded set (animation frame) of the previous instance is used as the predicted value (s_i-l[n, v]). The prediction error e_l[n, v], i.e. the difference between the current displacement set and the predicted one, is computed and quantised (e_l[n, v]). Finally, the quantised samples are entropy coded (c_i[n, v]) using an adaptive arithmetic coding algorithm to handle the unknown data statistics. The predictive scheme described above prevents quantization error accumulation. A DPCM decoder first decodes arithmetically the received samples (‘e’ [n, v]) and computers the decoded samples (s_i′ [n, v]).

Human behavior alerts are determined from a video stream through application of video analytics that parse a video stream into a plurality of segments, wherein each of the segments are either temporally related to at least one of a plurality of temporally distinct transactions in an event data log; or they are each associated with a pseudo transaction marker if not temporally related to at least one of the temporally distinct transactions and an image analysis indicates a temporal correlation with at least one of the distinct transactions is expected. Visual image features are extracted from the segments and one-SVM classification is performed on the extracted features to categorize segments into inliers or outliers relative to a threshold boundary. Event of concern alerts are issued with respect to the inlier segments associated with the associated pseudo transaction marker.

The present invention is directed to a method of controlling a communications link and apparatus configured to perform this method. This invention is particularly related to but in no way limited to MIMO (multiple inputs multiple outputs) wireless communications systems. The method comprises the steps of determining at the receiver the quality of the communications link and based on this, selecting a group of transmission parameters and an element from this group. These selections are then communicated to the transmitter. The transmission parameter may be the transmission configuration such as the modulation and coding scheme. The invention minimises the required feedback signalling from the receiver to the transmitter by exploiting temporal correlation of the parameter being controlled, whilst allowing rapid selection of the parameter.

Problem The problem is to facilitate viewing of temporal correlation between patient's chief complaint and doctor's interview results associated with the patient's chief complaint. Means of Solution The temporal correlation can be viewed by providing an electronic medical information system equipped with a control server comprising an input means for inputting, among the information written on the chart, the patient's chief complaint information into a chief complaint information file and for inputting the doctor's consultation information associated with the patient's chief complaint information into a consultation information file; an accumulation means for accumulating the chief complaint information and consultation information; a calculation means for scoring, with respect to each date of consultation, the latest chief complaint information and consultation information input by the input means, and the past chief complaint information and consultation information accumulated by the accumulation means, respectively; a generation means for automatically generating, based on the scores, a list by which the temporal variation of the chief complaint information and consultation information can be viewed.

An apparatus and method is provided for efficiently determining the source of problems in a complex system based on observable events. By splitting the problem identification process into two separate activities of (1) generating efficient codes for problem identification and (2) decoding the problems at runtime, the efficiency of the problem identification process is significantly increased. Various embodiments of the invention contemplate creating a causality matrix which relates observable symptoms to likely problems in the system, reducing the causality matrix into a minimal codebook by eliminating redundant or unnecessary information, monitoring the observable symptoms, and decoding problems by comparing the observable symptoms against the minimal codebook using various best-fit approaches. The minimal codebook also identifies those observable symptoms for which the greatest benefit will be gained if they were monitored as compared to others.

By defining a distance measure between symptoms and codes in the codebook, the invention can tolerate a loss of symptoms or spurious symptoms without failure. Changing the radius of the codebook allows the ambiguity of problem identification to be adjusted easily. The invention also allows probabilistic and temporal correlations to be monitored. Due to the degree of data reduction prior to runtime, extremely large and complex systems involving many observable events can be efficiently monitored with much smaller computing resources than would otherwise be possible.

A spatio-temporal joint filter and a spatial joint filter for noise reduction are disclosed. The spatio-temporal joint filter includes a spatial joint filter including the first and second sub filters having different characteristics and includes a temporal joint filter. When the present invention is adequately used, an edge/detail region of an image is well preserved, an aggressive noise reduction is performed on a flat region, and the temporal flicker problems are eliminated. Additionally, it has an intrinsic motion compensation effect by using the spatio-temporal correlation between the adjacent frames.

The present invention relates to a method and apparatus for channel temporal correlation estimation and MIMO mode selection. An embodiment of the invention under LTE system utilizes SRS symbols for temporal correlation estimation and performs MIMO mode selection based on the said temporal correlation estimation.

The invention discloses a traffic prediction method based on adaptive spatial self-attention map convolution, belongs to the traffic field and the deep learning field, and provides an adaptive spatial self-attention graph convolution network (ASSAGCN) for traffic prediction. The ASSAGCN is formed by stacking two residual error blocks. Each residual block is composed of a graph convolution module (GCN), a multi-head spatial self-attention module (MHSSA), a gating fusion module (GF) and a multi-receptive-field cavity causal convolution module (MRDCC), Wherein the GCN performs modeling on local spatial correlation of a road network based on connectivity; the MHSSA is used for capturing implicit spatial correlation of a road network and aggregating information of each node globally; the GF fuses the output of the GCN and the output of the MHSSA; and the MRDCC is used to model temporal correlation. An input layer adopts a simple full-connection layer to map input to a high-dimensional space to improve the expression ability of the model, and an output layer adopts two 1 * 1 convolutional layers. The method can capture the potential spatial correlation in the road network, and adapts to the dynamic change of the road network structure.

The present invention describes a speech enhancement method using microphone arrays and a new iterative technique for enhancing noisy speech signals under low signal-to-noise-ratio (SNR) environments. A first embodiment involves the processing of the observed noisy speech both in the spatial- and the temporal-domains to enhance the desired signal component speech and an iterative technique to compute the generalized eigenvectors of the multichannel data derived from the microphone array. The entire processing is done on the spatio-temporal correlation coefficient sequence of the observed data in order to avoid large matrix-vector multiplications. A further embodiment relates to a speech enhancement system that is composed of two stages. In the first stage, the noise component of the observed signal is whitened, and in the second stage a spatio-temporal power method is used to extract the most dominant speech component. In both the stages, the filters are adapted using the multichannel spatio-temporal correlation coefficients of the data and hence avoid large matrix vector multiplications.

The invention provides a small area-level ultra-short-term load prediction and visualization method based on a Long Short-Term Memory (LSTM) network, the method comprising: step 1: an input and outputvariable of the model is determined; step 2: The input and output data sets are separately preprocessed; step 3: the depth LSTM load prediction model is constructed and the random search method is used to find the appropriate hyperparameters until the test set prediction error is minimized. step 4: The t-SNE visualization technology is used to visually characterize the network hidden layer vector, and the correlation coefficient heat map is formed according to the hidden layer vector to perform correlation quantitative analysis, thereby reflecting the network's ability to extract feature datafrom the input data. The method aims to utilize the feature extraction capabilities of the deep learning model and the LSTM temporal correlation learning capabilities to achieve higher prediction accuracy than the machine learning model.