3775 results about "Euclidean vector" patented technology

Pattern recognition, wherein a sequence of feature vectors is formed from a digitized incoming signal, the feature vectors comprising feature vector components, and at least one feature vector is compared with templates of candidate patterns by computing a distortion measure. A control signal based on at least one time-dependent variable of the recognition process is formulated, and the distortion measure is computed using only a subset of the vector components of the feature vector, the subset being chosen in accordance with said control signal. This reduces the computational complexity of the computation, as the dimensionality of the vectors involved in the computation is effectively reduced. Although such a dimension reduction decreases the computational need, it has been found not to significantly impair the classification performance.

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

Embodiments include a motion estimation method performed in a parallel processing system that determines a list of several candidate motion vectors for a macroblock of a video image and retains them through multiple computation passes. All candidate motion vectors are used as potential neighboring predictors, so that the best combination of differential vectors rises to the top of the candidate list. Numerous combinations of differential motion vectors are considered during the process that compares motion vectors among up to eight neighboring macroblocks, instead of simply between pairs of macroblocks. The motion estimation system is configured to use a large number of compute engines, such as on a highly parallel GPU platform. This is achieved by having no dependencies between macroblocks except one per pass. This allows the number of calculations per pass to be very large.

An interframe prediction processor designed for high-speed video coding and decoding. The interframe prediction processor has an address selector and a predictor calculator. The address selector receives information about MBAFF type and coding type. When performing motion compensation for a given MBAFF picture with reference to blocks surrounding a current block, the address selector provides memory addresses of reference motion vectors according to the coding type of the current block pair, as well as to the coding type of the reference blocks. The predictor calculator that determines a motion vector predictor for each constituent block of the current block pair based on reference motion vectors read out of the provided memory addresses.

This invention extends routing mechanisms that use link metrics for route selection so that: A link metric cross correlation vector is determined for all links, where each element in the vector corresponds to some other link, and reflects the change in the link metric value if a data flow would already use this other link. The invention further describes a specific embodiment where all cross-correlating links are adjacent to each other, i.e., they terminate or originate in a common node. A mechanism is described to create an extended routing graph. This extended graph permits the use of standard polynomial time algorithms that simultaneously construct the optimal route and find the optimal route metric (such as shortest-path algorithms) also for the adjacent link cross-correlating case.

A plurality of motion vector candidates is extracted, which includes motion vectors of a first set of blocks and a second set of blocks each adjacent to an object block. The first set of blocks has the same size as the object block. The second set of blocks has a smaller size than the object block. A first predicted motion vector of the object block is selected from the plurality of motion vector candidates. A residual cost of each neighboring motion vector is calculated. Each neighboring motion vector points a point within a predetermined range from a point pointed by the first predicted motion vector. A second predicted motion vector having the minimum residual cost is selected from the plurality of neighboring motion vectors. If the first predicted motion vector is equal to the second predicted motion vector, the first predicted motion vector is set as the motion vector of the object block. If the first predicted motion vector is not equal to the second predicted motion vector, a search region pointed by the first predicted motion vector is searched by block matching.

The invention discloses a geographic and geomorphic characteristic construction method based on laser radar and image data fusion and belongs to the automatic control field. The method specifically comprises 1) obtaining 3D laser point clouds and panoramic pictures of the surrounding environment of a ground unmanned mobile platform at present; 2) matching the 3D laser point clouds and the panoramic pictures and obtaining matched images; 3) dividing the 3D laser point clouds based on different distribution characteristics corresponding to each laser point and carrying out clustering based on a dynamic clustering algorithm of each distribution characteristic to obtain a plurality of region classes; 4) finding passable region classes in the plurality of region classes based on travel ability of the ground unmanned mobile platform; 5)obtaining landform identification vectors of the passable region classes by utilizing a denseness SIFT algorithm; and 6) carrying out landform classification on the passable region classes based on the landform identification vectors and by utilizing a classifier. The method is suitable for passable geographic and geomorphic characteristic construction of the ground unmanned mobile platform.

A system obtains neuro-response data as well as survey based data during discrete choice modeling to evaluate subject decision making processes. A discrete choice model evaluates a decision made by a subject as a function of multiple variables. Neuro-response data vectors and orthogonal survey based data vectors are weighted and combined to generate multi-dimensional vectors. The multi-dimensional vectors are used to estimate the effectiveness of changing particular variables in modifying subject behavior.

In a motion vector generation apparatus 1 according to the present invention, when motion vectors are sequentially generated for a plurality of images, a target point is determined at a sub-pixel level in any of the plurality of images, a corresponding point corresponding to the target point is searched at the sub-pixel level in another image different from this image, and a motion vector is calculated on the basis of the target point and the corresponding point. Then, the corresponding point is defined as a new target point, and the aforementioned process is repeated to sequentially generate the motion vectors. Thus, the motion vector generation apparatus 1 of the present invention can compute consecutive motion vectors by performing correspondence search at the sub-pixel level in the plurality of images.

A method for tracking objects in three dimensions in a radar system is provided that includes receiving spherical coordinates of an estimated location of each object of a plurality of detected objects, a range rate of each object, and variances for the spherical coordinates and the range rate of each object, determining whether or not each object is currently being tracked, updating a tracking vector for an object based on the object spherical coordinates, range rate, and variances when the object is currently being tracked, and initializing a tracking vector for an object when the object is not currently being tracked, wherein a tracking vector for an object is a process state vector for an extended Kalman filter designed to track an object, elements of the tracking vector including Cartesian coordinates of the object location, the object velocity in three directions, and the object acceleration in three directions.

The invention provides a contact network three-dimensional reconstruction method based on SIFT and LBP point cloud registration. The method comprises the first step of obtaining initial three-dimensional point cloud data of the environment where parts of a contact network to be reconstructed are located through motion-sensing peripheral Kinect for Windows, and conducting denoising, simplifying, partitioning clustering, fusing and other preprocessing operations on the initial three-dimensional point cloud data to obtain single-view-angle point cloud data of the parts of the contact network to be reconstructed, the second step of extracting key points through an SIFT algorithm, constructing description vectors of the key points by means of LBP features of uniform patterns and determining the corresponding relations between the key points in different point clouds according to the distances between the vectors, the third step of completing point cloud registration through a rough registration method and an ICP fine registration method and obtaining the complete three-dimensional point cloud data of the parts of the contact network to be reconstructed, and the fourth step of completing three-dimensional reconstruction through the Poisson surface reconstruction method and obtaining a three-dimensional model. According to the method, the key factor is point cloud registration which is the key step influencing the three-dimensional reconstruction speed; the description vectors of the key points are constructed by means of the LBP features of the uniform patterns, so that vector dimensions are reduced, the matching speed of the corresponding relations is increased, registration is accelerated, and the three-dimensional reconstruction speed is increased.

The invention relates to an entity relationship joint extraction method and system based on an attention mechanism. The method comprises the following steps of: converting an entity marked in trainingdata and a triple of a relationship into a form that each word corresponds to a predefined type of tag; Mapping each word in the sentences of the training data into a corresponding word vector, inputting the word vectors into a neural network model based on an attention mechanism, and performing training through a back propagation algorithm to obtain a label prediction model; And inputting the sentences needing to be subjected to entity relationship extraction into the trained label prediction model, predicting a label corresponding to each word, and obtaining entity relationship triples existing in the sentences according to the corresponding relationship between the labels and the words in the triples. The system comprises a preprocessing module, a model training module and a result processing module. According to the method, by more effectively utilizing the key information in the sentences, the joint extraction performance of the relational entities is improved, and the method hasgood practicability.

Techniques for deriving eigenvectors based on steered reference and used for spatial processing. A steered reference is a pilot transmission on one eigenmode of a MIMO channel per symbol period using a steering vector for that eigenmode. The steered reference is used to estimate both a matrix Σ of singular values and a matrix U of left eigenvectors of a channel response matrix H. A matrix Ũ with orthogonalized columns may be derived based on the estimates of Σ and U, e.g., using QR factorization, minimum square error computation, or polar decomposition. The estimates of Σ and U (or the estimate of Σ and the matrix Ũ) may be used for matched filtering of data transmission received via a first link. The estimate of U or the matrix Ũ may also be used for spatial processing of data transmission on a second link (for reciprocal first and second links).

A method of encoding a motion vector includes: selecting one of a plurality of predicted candidate motion vector sets by using motion information of neighboring blocks of a current block; selecting one of predicted candidate motion vectors within a selected predicted candidate motion vector set, as a predicted motion vector; encoding a differential motion vector representing a difference between a current motion vector or motion vector of the current block and a selected predicted motion vector; and encoding a predicted motion vector index indicating the selected predicted motion vector. As a motion vector is encoded after selecting an efficient predicted candidate motion vector set, the size of a differential vector to be encoded can be reduced without necessarily encoding additional information to indicate which set of predicted candidate motion vectors has been selected, resulting in improved compression efficiency of motion vectors and in turn the improved video compression efficiency.

Neural Network Loadflow (NNL) computation method is invented involving input vector composed of net nodal injection of real and reactive powers and diagonal elements of conductance and susceptance matrices multiplied by the squared voltage magnitude components of the flat-start normally used as initial solution estimate guess for loadflow solution by conventional NRL or SSDL methods. Training, and testing/validating input-output data sets are generated by applying uniform and non-uniform scaling factors applied to base case loads at PQ-nodes, resistance and reactance of transmission line branches. These scale factors are increased until loadflow solution by conventional methods such as Newton-Raphson Loadflow and Super Super Decoupled Loadflow methods diverge. Divergence of loadflow methods are due to node voltage, node angle, and numerical instabilities. Voltage magnitude and phase angle values in the solution before divergence are respective stability limits and voltage magnitude and phase angle values in loadflow solution provide direct measure to the respective stability margins. Also Suresh's diakoptiks based feature selection technique is presented for ANNs calculating one node variable with one neuron each in their output layers.

A method re-identifies objects in a pair of images by applying a convolutional neural network (CNN). Each layer in the network operates on an output of a previous layer. The layers include a first convolutional layer and a first max pooling layer to determine a feature map, a cross-input neighborhood differences layer to produce neighborhood difference maps, a patch summary layer to produce patch summary feature maps, a first fully connected layer to produce a feature vector representing higher order relationships in the patch summary feature maps, a second fully connected layer to produce two scores representing positive pair and negative pair classes, and a softmax layer to produce positive pair and negative pair probabilities. Then, the positive pair probability is output to signal whether the two images represent the same object or not.

A method for sparse channel estimation in MIMO OFDM systems with a plurality of subchannels having the same sparsity structure is presented. The inventive method comprises initializing a plurality of residual vectors and observation generating matrices modeling the channel, sending a pilot signal for each subcarrier, converting the pilot signals to tap positions, detecting an optimal tap position, updating the residual vectors by removing the one residual vector having the optimal tap position, updating the generating matrices in accordance with the optimal residual vector, calculating weighted residuals based on the updated residual vectors, and repeating the steps, except initializing, until a stopping condition is met, wherein the updated observation matrices estimate the sparse channel. In one embodiment, the observation generating matrices are omitted. In one embodiment, multiple vectors are removed during one iteration. Pilot placement and pilot allocation techniques are presented to optimize the method.

An integrated rainfall calculation method using X-band dual-polarimetric radar measurement data includes a precipitation classification step of classifying hydrometeors into four types of snow, rain/snow, rain and non-meteorological target through a fuzzy logic technique using a correlation coefficient (cross correlation coefficient, ρ_hv), features of a measured differential propagation phase (Ψ_dp(r)) or differential propagation phase (φ_dp) and a signal-to-noise ratio (SNR) as input variables (input feature vector); a specific differential phase calculation step of separately calculating a specific differential phase by applying a specific differential phase using a total difference of differential phase and signal-attenuation corrected reflectivity for the rain among the classified hydrometeors and applying a specific differential phase calculated using a filtering method for the other hydrometeors; and a rainfall calculation step of calculating rainfall by using a relation between the specific differential phase and the rainfall and using the separately calculated specific differential phase.