116 results about "Euclidean distance matrix" patented technology

The invention discloses a fingerprint map matching method based on Euclidean distances, and relates to the technical field of fingerprint locating. The fingerprint map matching method based on the Euclidean distances aims to solve the problems that a traditional WKNN algorithm is low in locating accuracy, and the value of k can have large influences on locating results. The fingerprint map matching method based on the Euclidean distances comprises the first step of evenly distributing n receiving machines for AP in an area to be measured to measure the RSS vectors of m points to be measured, a second step of measuring the RSS vectors as (RSS1, RSS2, ..., RSSn) of located points at the located points, a third step of calculating the Euclidean distances from the located points to the m points in the fingerprint map in sequence, a fourth step of ranking the Euclidean distances in the third three from small to larger, a fifth step of calculating the weighing coefficients ql of the front k points, and a sixth step of carrying out summing after weighing is carried out on the physical coordinates of the obtained first k points obtained in the fourth step by using corresponding weighting coefficients to obtain the physical coordinates of the located points. The fingerprint map matching method based on the Euclidean distances is applied to the fingerprint locating area.

A method for determining the geometry and/or the localisation of an object comprising the steps of:

• • sending one or more signals by using one transmitter;
  • receiving by one or more receivers the transmitted signals and the echoes of the transmitted signals as reflected by one or more reflective surfaces
  • building by a computing module a first Euclidean Distance Matrix (EDM) comprising the mutual positions of the receivers;
  • adding to the EDM matrix a new row and a new column, the new row and a new column comprising time of arrivals of said echoes and computing its rank or distance to an EDM matrix
  • determining the geometry and/or the position of the object based on said rank or distance.

The invention discloses an identification method of container numbers, which comprises the following steps: extracting the container numbers: cutting an image containing the container numbers by an image processing technology to obtain useful container numbers, and performing the image standardized processing on the cut container number again; and identifying characters: obtaining information after the image standardized processing, and identifying the cut characters by using a neural network technology and an Euclidean distance method. The invention greatly reduces the labor cost, quickens the working efficiency and improves the work accuracy.

The invention is applicable to the field of remote-sensing image classification and provides a spectral angle and Euclidean distance based remote-sensing image classification method. The spectral angle and Euclidean distance based remote-sensing image classification method comprises the steps of preprocessing remote-sensing images to filter out noise; screening effective information for classification; segmenting the remote-sensing images into multiple homogenous image map spots serving as minimum research units; calculating mean values and variances of training samples at all wave bands; calculating mean values and variances of testing samples at all wave bands; further calculating Euclidean distances and spectral angles; determining the comprehensive similarity as the sum of weights of the spectral angles and the Euclidean distances and determining weights; calculating the comprehensive similarity of classification objects and surface features to enable the type of the surface features with minimum comprehensive similarity to serve as the final type of the classification objects. The spectral angle and Euclidean distance based remote-sensing image classification method integrates the advantages of two classifiers, achieves complementation of different classification methods, determines optimal weight through verification at minimum intervals, effectively improves classification accuracy, ensures classification efficiency, achieves algorithm automation and is high in classification efficiency.

The invention relates to an automobile location method, and especially an indoor parking lot location method. The method comprises the steps of 1, setting a reference node for a location scene, collecting an RSSI of the reference node, and establishing a Radio Map composed of a coordinate of the reference node and the RSSI of the reference node; 2, carrying out Euclidean distance operation by employing the RSSI collected by a terminal and the RSSI in a fingerprint library; and 3, determining the minimum reference node coordinate obtained through the Euclidean distance operation as the location of a target. According to the indoor parking lot location method provided by the invention, the environment parameters in the location scene are precise, the data collection workload is low, the calculation quantity is small, the location precision is high, and the cost is low.

Provided is a high-dimensional data accurate neighbor quick searching method based on euclidean distance. The method includes expressing high-dimensional data into a vector form, embedding the high-dimensional data into a two-dimensional space formed by mean value and variance and meanwhile building a sampling index of the original high-dimensional data. When neighbor searching is conducted, the sampling index is first utilized to obtain a filtering threshold when a searching point is input, then the filtering threshold is utilized to filter non-neighbor data in the two-dimensional space to obtain a candidate data set, finally the distances between all candidate data points and the searching point are calculated in a linear traversal mode, and the nearest neighbor point of the searching point is calculated. The method has the advantage of being capable of quickly processing the high-dimensional data and capable of searching for the accurate neighbor point.

An indoor localization method for estimating the location of nodes (n1, n2), at least one node (n1) being connected to at least another node (n2) by at least two different channels (ch1, ch1′), comprising the following steps:

• • measuring a distance between the connected nodes,
  • for each channel, building a Euclidean Distance Matrix (EDM) based on the measured distances,
  • estimating the location of the nodes (n1, n2) based on the built Euclidean Distance Matrices.

The invention discloses a small current system grounding fault positioning method. The method includes the following steps: extracting the zero sequence currents prior to and after the moment of a fault of each feeder terminal on a faulty feeder; moving a data window point by point and calculating the feature value of zero sequence current of respective feeder terminal under each data window; establishing a feature value curve for each feeder terminal; acquiring the sampling point corresponding to the maximum feature value of each feature value curve of the feeder terminal which is closest tothe bus on the faulty feeder; acquiring the feature values that correspond to the same sampling points that are acquired from the feature value curves of the rest of the feeder terminals; calculatingthe Euclidean distance of the feature values of the adjacent two feeder terminals; ranking the Euclidean distances from long to short and selecting N biggest Euclidean distances; if one Euclidean distance is greater than the sum of the rest of the N-1 Euclidean distances, taking the interval between two feeder terminals that correspond to the Euclidean distance as a fault interval. According to the invention, the method herein can increase reliability of fault positioning and reduce communication pressure of data channels.

The invention discloses a hybrid spatial modulation method based on Euclidean distance and antenna selection, and the method comprises the steps: supposing that a transmitting end is provided with Ns transmitting antennas, selecting Nt transmitting antennas to transmit data, and enabling a receiving end to have Nr receiving antennas, wherein the receiving end employs a maximum likelihood method to detect a received symbol y; enabling an antenna selection method based on system capacity or an antenna selection method based on Euclidean distance to be applied to an ESM, so as to increase the minimum Euclidean distance; enabling the receiving end to detect the state of a channel before each transmission of information, calculating the minimum square Euclidean distances when the SM and ESM schemes are used for data transmission, and selecting the scheme with the larger minimum square Euclidean distance to actually transmit the symbol. The method provided by the invention enables the number of transmitting antennas not to be limited through the absorption of the advantages of SM and ESM, and avoids the interference with the antennas. In particular, a channel adaptive method can enable a system to adapt to the continuous change of a channel better.

The invention relates to a cylindrical surface image matching method combining with SURF feature extraction and curve fitting, comprising steps of arranging two images (A and B) on an upper position and a lower position in a left-right alignment manner, adopting a SURF characteristic detection algorithm to perform characteristic detection on the two images, finding out a matched pair set, calculating the angle between the straight line defined by each matched pair in the characteristic point matched pair set and the horizontal direction and the angle and an Euclidean distance between the two characteristic points in each matched pair, establishing an angle set K of the image matched pair and an Euclidean distance set D of the image, performing curve fitting on the angle set K of the matched pair, wherein the independent variable of the curve fitting is the abscissa X1i of the matched pair, the ordinate of the curve fitting is angle Theta I, removing the mismatching, performing curve fitting on the Euclidean distance set D of the matched pair, wherein the independent variable of the curve fitting is the abscissa X1i of the characteristic point in the image A, and removing the mismatching. The invention can more accurately remove the mismatching of the cylindrical object.

Disclosed are systems and methods of sensor fusion for exemplary use with robotic navigation control. Systems and methods include providing local estimates of a target location from a plurality of expert modules that process sensor data. The local estimates are weighted based upon a Mahalanobis distance from an expected estimated value and based upon a Euclidean distance between the local estimates. The local estimates are fused in a Bayesian fusion center based upon the weight given to each of the local estimates.

The invention discloses a high-spectrum image segmentation method based on pixel space information, mainly solving the problem that similar physiognomies can not be favorably segmented by the prior method. The high-spectrum image segmentation method comprises the following steps: solving and normalizing a pixel characteristics matrix and a pixel space Euclidean distance matrix of high-spectrum data; weighting the pixel characteristics matrix and the pixel space Euclidean distance matrix, adding the two weighted matrixes to form a joint dissimilarity matrix and adjusting weighted parameters toacquire a plurality of groups of joint dissimilarity matrixes; using an isometric mapping algorithm to reduce the dimension of each group of joint dissimilarity matrix and acquiring a plurality of groups of mapping results; counting and analyzing each group of mapping result, finishing the primary segmentation of a high-spectrum image; and carrying out category correction to primarily segmented boundary points to acquire a final image segmentation result. The method can effectively find the nuance of different physiognomies in the high-spectrum image and can be applied to martial object recognition, mineral exploration and environmental condition analysis.

The present invention discloses an improved MK model and WKNN algorithm combined mixed indoor positioning method. According to the method, an improved MK model is used to establish a signal spread model which is more suitable for a complex indoor environment, a nearest adjacent point is helped to filtered in a positioning state, thus a nearest adjacent point with a large difference does not participate the positioning of a WKNN algorithm, according to the Euclidean distance formula, the distance of each nearest adjacent point to an AP is calculated, the distances are taken as weights to be substituted into the formula of the WKNN algorithm to obtain a final estimated position value.

The invention discloses a noise elimination method for an impact noise image. The method specifically comprises the following steps: establishing a mark matrix F of a noise polluted image I; dividing the image I and the mark matrix F into M*N grids according to impact noise pollution density rho; extracting an image block T<m,n> and a mark block L<m,n>, which are composed of pixels in the (m,n)th grid in the image I and the matrix F respectively; establishing a pollution pixel set E and a non-pollution pixel set P in the image block T<m,n> through traversing elements in the mark block L<m,n>; obtaining a linear predication system parameter Psi; according to the linear predication system parameter Psi and an Euclidean distance matrix De, calculating to obtain a pollution pixel value shown in the specification; carrying out matrix transposition operation on the pollution pixel value shown in the specification to obtain a noise elimination pixel value E; obtaining a noise-eliminated image block T<m,n>; writing the image block T<m,n> back to the image and replacing pixels of the image in the (m,n)th grid; and traversing all the grids in the image I.

The invention relates to a multi-label cooperative positioning method based on weighted MDS. The multi-label cooperative positioning method comprises the steps of: placing target tags in an indoor scene, and estimating a region range in which the target tags are located according to distances from the target tags to readers; establishing an Euclidean distance matrix D<^>; calculating to obtain a scalar product matrix Bs<^> according to a relationship between a scalar product matrix and the Euclidean distance matrix; and adopting a weighted MDS algorithm for positioning the target tags, whereinthe positioning is implemented by regarding a unit matrix I<M+N> as the weight of the algorithm at first to obtain positions of the target tags, regarding the positions as initial estimated positionsof the target tags in the following iterative calculation, utilizing the obtained estimated positions of the target tags, adopting residual vector variance as the new weight W of the algorithm, acquiring new estimated positions of the target tags again until the positioning precision meets the requirement, and outputting the finally-obtained estimated positions X0<^> of the target tags.

The invention discloses an self-adaptive integrated unbalanced data classification method based on Euclidean distance, which comprises the following steps of: firstly, obtaining a plurality of diversified balance subsets by using a random balance method, then establishing and obtaining a plurality of basic classifiers on each balance subset; and adding a classifier pre-selection algorithm before the dynamic selection algorithm. After a screened basic classifier is obtained, a new dynamic selection algorithm is provided, and by evaluating the condition of the sample classifier in the surrounding area of a to-be-classified sample, the capability is stronger when more minority class samples belong to the correct classification range. And finally, a prediction result obtained by the selected basic classifier by adopting a distance-based adaptive integration rule is output. According to the method, basic classifiers can be established on the generated diversified subsets, meanwhile, a dynamic selection algorithm is provided, the sub-classifier with the highest classification capacity can be selected out, finally, the proposed integration rule can provide a better output result, and finally, the unbalanced data classification precision is effectively improved.

The invention discloses a vehicle movement pattern mining method based on a frequent pattern tree. The method comprises the following steps that 1 pattern mining is to be conducted on vehicles, track separation is conducted on passenger running tracks of the vehicles, and a plurality of sub-running tracks are obtained; 2 line segment clustering is conducted on the sub-running tracks based on euclidean distance, and a plurality of clusters are obtained; 3 the euclidean distance of any two clusters is calculated, and proximity relations of the clusters are determined according to the euclidean distance; 4 the frequent pattern tree is obtained according to the construction of the proximity relation of the clusters. By means of a motion pattern recognition method, space division is not needed to be conducted on the running tracks, coding is not needed to be conducted on track line segments additionally, the method is simpler, and the recognition effect is good.

Provided is a constellation mapping method. The constellation mapping method comprises the step that mapping is carried out on bit data in an irregular constellation mapping mode to obtain constellation symbols, wherein the irregular constellation mapping mode meets the following conditions that (1) at least two different pieces of the bit data are mapped to the same constellation symbol; (2) at least one first constellation symbol and one second constellation symbol exist in a set of constellation symbols so that the minimum value of the Euclidean distances between all the constellation symbols and the first constellation symbol can be different from the minimum value of the Euclidean distances between all the constellation symbols and the second constellation symbol, wherein the minimum value is larger than zero. According to the technical scheme, Shaping gain of a communication system is improved, channel estimation is improved, and inter-subcarrier interference is eliminated.