704 results about "Density based" patented technology

According to a first aspect, there is provided a method for controlling uplink access transmissions in a radio communication system, wherein a user equipment determines a delay time for transmitting a signal on an uplink access channel, wherein the delay time is randomly determined based upon a probability distribution that increases in density with increasing delay. According to a second aspect, there is provided a method for controlling uplink access transmissions in a radio communication system, wherein from a base station of the radio communication system, time variable information is signalled in downlink to user equipments located in an area covered by the base station, wherein the information is used to determine delay times for transmitting signals on an uplink access channel and wherein the information varies based upon a probability distribution which increases in density with increasing time.

The present invention provides methods and compositions for isolating and detecting rare cells from a biological sample containing other types of cells. In particular, the present invention includes a debulking step that uses a microfabricated filters for filtering fluid samples and the enriched rare cells can be used in a downstream process such as identifies, characterizes or even grown in culture or used in other ways. The invention also include a method of determining the aggressiveness of the tumor or of the number or proportion of cancer cells in the enriched sample by detecting the presence or amount of telomerase activity or telomerase nucleic acid or telomerase expression after enrichment of rare cells. This invention further provides an efficient and rapid method to specifically remove red blood cells as well as white blood cells from a biological sample containing at least one of each of red blood cells and white blood cells, resulting in the enrichment of rare target cells including circulating tumor cells (CTC), stromal cells, mesenchymal cells, endothelial cells, fetal cells, stem cells, non-hematopoietic cells etc from a blood sample. The method is based upon combination of immuno-microparticles (antibody coated microparticles) and density-based separation. The final enriched target cells can be subjected to a variety of analysis and manipulations, such as flowcytometry, PCR, immunofluorescence, immunocytochemistry, image analysis, enzymatic assays, gene expression profiling analysis, efficacy tests of therapeutics, culturing of enriched rare cells, and therapeutic use of enriched rare cells. In addition, depleted plasma protein and white blood cells can be optionally recovered, and subjected to other analysis such as inflammation studies, gene expression profiling, etc.

A tube and float system for use in separation and axial expansion of the buffy coat is provided. The system includes a transparent, or semi-transparent, flexible sample tube and a rigid separator float having a specific gravity intermediate that of red blood cells and plasma. The sample tube has an elongated sidewall having a first cross-sectional inner diameter. The float consists of a main body portion and one or more support members protruding from the main body portion to engage and support the sidewall of the sample tube. The main body portion and the support members of the float have a cross-sectional diameter less than that of the first cross-sectional inner diameter of the tube when the sample tube is expanded, such as by centrifugation. The main body portion of the float together with an axially aligned portion of the sidewall define an annular volume therebetween. The support members protruding from the main body portion of the float traverse said annular volume to produce one or more analysis areas. During centrifugation, the centrifugal force enlarges the diameter of the tube to permit density-based axial movement of the float in the tube. Thereafter, the centrifugal force is reduced to cause the tube sidewall to return to its first diameter, thereby capturing the float and trapping the buffy coat constituents in the analysis area. The buffy coat constituents can then be evaluated or measured.

The invention discloses a density clustering-based self-adaptive trajectory prediction method which comprises a trajectory modeling stage and a trajectory updating stage, wherein in the trajectory modeling stage, rasterizing treatment is carried out on a newly generated movement report, so that moving points can be obtained and are divided into six moving point subsets; the six moving point subsets are clustered by adopting a limited area data sampling-based density clustering algorithm, so that a new trajectory cluster can be formed; the new trajectory cluster and an old trajectory cluster in the same period of time are merged with each other according to the similarity of the trajectory points, and the trajectory points of the merged trajectory cluster and the area of influence are updated; the trajectory points are combined according to the time sequence, so that a complete user movement trajectory can be obtained; in the trajectory updating stage, the user movement trajectory generated in the trajectory modeling stage is corrected. The density clustering-based self-adaptive trajectory prediction method is used for user movement trajectory prediction in the mobile communication scene; furthermore, when the new user movement trajectory is generated, the whole trajectory data is not needed to be modeled again.

A method and system for controlling drilling mud density in drilling operations. The mud required at the wellhead is combined with a base fluid of a different density to produce diluted mud in the riser. By combining the appropriate quantities of drilling mud with base fluid, riser mud density at or near the density of seawater may be achieved, thereby permitting greater control over the pressure in the wellbore and various risers. Blowout preventers may also be used in combination with the process to control these pressures. Concentric risers are disclosed, wherein an annulus defined within one riser is utilized to carry the different density base fluid to the injection point for injection into the drilling mud, while an annulus defined within another riser is utilized to carry the combination fluid and cuttings back to the drilling rig. Cuttings are separated in the usual manner at the surface. The diluted mud is passed through a centrifuge system to separate drilling mud from the different density base fluid. The centrifuge system may also be utilized to separate the recovered drilling fluid into a substantially barite portion and a substantially drilling fluid portion, wherein the two portions are stored locally at the rig and recirculated during drilling operations.

The invention discloses a hard disk failure prediction method for a cloud computing platform. The hard disk failure predication method comprises the following steps: marking SMART log data of a hard disk as a normal hard disk sample and a faulted hard disk sample according to a hard disk maintenance record in a prediction time window; then, dividing the denoised normal hard disk sample into k non-intersected subsets by adopting a K-means clustering algorithm; combining the k non-intersected subsets with the faulted hard disk sample respectively; generating k groups of balance training sets according to an SMOTE (Synthetic Minority Oversampling Technique) so as to obtain k support vector machine classifiers for predicting the faulted hard disk. In the prediction stage, test sets can be clustered by using a DBSCAN (Density-based Spatial Clustering Of Applications With Noise), a sample in a clustered cluster is predicted as the normal hard disk sample, a noise sample is predicted by each classifier obtained by training, and further a final prediction result is obtained by voting. According to the method disclosed by the invention, hard disk fault prediction is carried out by using the SMART data of the hard disk, and relatively high fault recall ratio and overall performance can be obtained.

Methods and systems are provided for generating an electronic map display. In one implementation, a method is provided for determining a route distance based on information for a route, comparing the route distance with a distance threshold, defining a search area when the route distance is less than the distance threshold, the search area including boundaries, calculating, using a processor, a points of interest (POI) density of the search area, comparing the POI density with a first density threshold and a second density threshold, adjusting the boundaries of the search area based on a result of comparing the POI density with the first density threshold and the second density threshold, identifying POIs in the adjusted search area, and providing POI information for an electronic map display, the POI information being associated with one or more POIs identified in the adjusted search area.

A method and an apparatus for detecting an interest degree of a crowd in a target position are disclosed. The interest degree detection method includes projecting a depth image obtained by photographing onto a height-top-view, the depth image including the crowd and the target position; dividing the height-top-view into cells; determining density of the crowd in each cell; determining a moving speed and a moving direction of the crowd in each cell; determining orientation of the crowd in each cell; and determining, based on the density, the moving speed, the moving direction and the orientation of the crowd, the interest degree of the crowd in each cell in the target position. According to this method, the interest degree of the crowd in the target position can be detected accurately, even at a crowded place where it is difficult to detect and track a single person.

An input image that includes an eye is divided into a plurality of blocks, and a mosaic image in which each of the blocks has a density value is produced. Among all the blocks, one having a small distance from a center point of the mosaic image and having a small density value is determined to be a block including the center of the pupil of the eye. Then, based on the determined block, a center position of the pupil in the input image is derived. Based on the density value of the block including the center of the pupil, a density threshold value is set. Then, based on the density threshold value, a two-valued image is produced from the input image. Among regions in the two-valued image which are darker than the density threshold value, one including the center position of the pupil is extracted as a pupil region.

The invention discloses a dynamic road segment division based vehicle route guidance method, which is characterized in that a dynamic rod network connected graph is built through dynamic road segment division for the purpose of searching optimal path search and realizing dynamic navigation. The method specifically comprises the following steps: first, acquiring vehicle real-time information through a vehicle networking technology by the traffic center, and utilizing an algorithm of Density-based Spatial Clustering Of Applications With Noise (DBSCAN) to regularly and dynamically divide the regional rods, so as to generate the dynamic rod network connected graph; secondly, sending the position and destination of a vehicle itself to a traffic information center for asking for the optimal path; and finally, generating the optimal path on the dynamic rod network connected graph through utilizing a shortest path algorithm by the traffic information center according to the position and destination of the vehicle, and sending the information to the vehicle and realizing path guidance. The method has the advantages that the generated dynamic rod network connected graph which is accurate and real-time can provide the optimal path guidance for a traveler, thereby alleviating city traffic jam and improving running efficiency.

The invention relates to a rod-shaped object regular three-dimensional modeling method and rod-shaped object regular three-dimensional modeling system based on density peak clustering. The rod-shaped object regular three-dimensional modeling method comprises the steps of performing voxel resampling on original point cloud data which are acquired by an on-vehicle laser radar, eliminating outliers, performing mesh segmentation on a scene point cloud, performing ground point elimination and high-rise point elimination on each grid through elevation straight-through filtering, and respectively performing projection on three coordinate planes of a three-dimensional coordinate system; respectively clustering in three projection planes on each grid in a clustering manner based on a density peak and a distance attribute; limiting the spatial range of the point clouds in a same kind by a regular spatial cubic bounding box, extracting the rod-shaped object in the spatial cubic bounding box according to priori knowledge, and obtaining a rod-shaped member characteristic parameter; and performing modeling for restoring the rod-shaped object. The rod-shaped object regular three-dimensional modeling method and the rod-shaped object regular three-dimensional modeling system can perform quick, efficient and accurate extraction on rod-shaped objects in a majority of environments and integrate with a digital city, thereby realizing fine and true modeling on the rod-shaped objects in the digital city.

The invention discloses a method and a device for positioning the nodes in a wireless sensor network, comprising the following steps: the topology structure of the whole wireless sensor network is obtained; a clustering method based on network density is adopted; after the cluster head is optimized and selected, the whole wireless sensor network is divided into a plurality of clusters which have uniform density and are not superposed to each other based on an density accessibility method; the absolute coordinates of all cluster heads are obtained; a genetic algorithm is used to determine the sequence in which the movable beacon traverses all clusters and determine the moving path of the beacon in the clusters, thus forming an optimum moving path of the movable beacon in the whole wireless sensor network and realizing the positioning of the nodes in the wireless sensor network. The method and the device can ensure the precision of higher node positioning, simultaneously can reduce the moving expense and calculation expense of the beacon, and can improve the real-time performance of the positioning of the nodes.

Embodiments are directed to a density-based clustering algorithm that decomposes and reformulates the DBSCAN algorithm to facilitate its performance on the Map-Reduce model. The DBSCAN algorithm is reformulated into connectivity problem using a density filter method and a partial connectivity detector. The density-based clustering algorithm uses message passing and edge adding to increase the speed of result merging, it also uses message mining techniques to further decrease the number of iterations to process the input graph. The algorithm is scalable, and can be accelerated by using more machines in a distributed computer network implementing the Map-Reduce program.

The invention discloses an indoor passive positioning method based on channel state information and a support vector machine. The method comprises the following steps: firstly preprocessing the acquired channel state information data, performing de-noising and smoothness through the adoption of a density-based spatial clustering of applications with noise and a weight-based moving average algorithm, and then using the principal component analysis algorithm to extract the features. The data after the preprocessing and feature-extracting can reflect the signal change more accurately and the dimension is greatly reduced. The passive positioning adopts two-stage positioning. In the training stage, the large positioning space is divided into sub-regions, the support vector machine classification and regression model is established for each sub-region so as to acquire a statistic model for accurately representing the nonlinear relationship between the position and the signal. The two-stage positioning firstly determines the sub-regions through the classification of the support vector machine, and the precision position is determined in the sub-region through the regression of the support vector machine. The method disclosed by the invention has the beneficial effects that the passive positioning can be performed in the absence of the active participation of the target, and the indoor positioning precision is improved to sub-meter level.

Provided is a vehicle-mounted GPS space-time trajectory big data optimization method and system. The vehicle-mounted GPS space-time trajectory big data optimization method comprises the steps that map matching processing is performed according to original GPS trajectory data and a road-grade traffic network, and a road section corresponding to each trajectory point is determined; the initial optimization rate is determined according to a GPS trajectory error distribution law; preliminary optimization is performed based on density and includes the preliminary optimization performed according to a density evaluation model and the initial optimization rate, so that the trajectory points gathered on the road surfaces are selected, and the points away from the road surfaces are regarded as drifting points to be abandoned; secondary optimization is performed based on angle similarity and includes similarity value calculation conducted on preliminary optimization results by utilizing an angle similarity evaluation model, clustering processing is performed, then the results having highest similarity values in clustering categories are selected as final optimization results, and selected high-quality data are obtained. By means of the vehicle-mounted GPS space-time trajectory big data optimization method and system, overall positioning accuracy based on low-accuracy vehicle-mounted GPS trajectory big data is improved. The optimization method is simple and easy to achieve.

The invention discloses an urban road traffic jam judging method based on vehicle GPS data, and relates to an urban road traffic jam judging method. The problem that an application range of a traffic jam judging method depending detection equipment data is relatively large in limitation because conventional traffic information detection equipment is adopted by an existing urban road traffic jam judging method is solved. The urban road traffic jam judging method comprises the following steps: constructing an urban road link travel time prediction model based on an artificial neural network model; calculating link travel time data of a current moment according to a position vector, a link number vector, a time stamp vector and a speed vector of the current moment according to a vehicle GPS by using the urban road link travel time prediction model; further calculating a link traffic flow velocity and a link traffic flow density based on the link travel time data; with data of the link traffic flow velocity and the link traffic flow density as input conditions, judging a road traffic jam state. According to the urban road traffic jam judging method, the traffic jam state can be rapidly and accurately judged according to the GPS data of the current moment.

The invention relates to a medical image recognition method, which aims at providing the method which can more accurately recognize the type of a new medical image. The method comprises the construction of a classification library of association rules, the update thereof and the medical image recognition step, the construction of the classification library of the association rules and the update step thereof comprise the following steps: data of medical image samples are prepared and carried out the pre-treatment; a density clustering-based medical image segmentation method is adopted to respectively recognize local region to be analyzed in each sample medical image; the characteristics of the local region in each sample medical image are respectively extracted to construct a medical image sample database T, the characteristics comprise: mean, variance, inclination, kurtosis, energy, entropy and clustering characteristics; the characteristic values are carried out the discretization; the characteristic values are carried out the discretization; a frequent itemset in the medical image sample database is excavated; and the classification library of the association rules is constructed according to the frequent itemset.

The invention discloses a density peak clustering algorithm based on the density adaptive distance, and aims at solving the problem that a density peak clustering algorithm based on the Euclidean distance is incapable of processing a data set of complex structure effectively. The density peak clustering algorithm based on the density adaptive distance is realized by that (1) the density adaptive distance is calculated according to the Euclidean distance and the adaptive similarity, so that a data space distribution structure is described in a better way; (2) an input parameter, namely the cutoff distance, of the algorithm is calculated according to the proportion of the total number of neighbor points of data points to the total number of a data set sample on the basis of the density adaptive distance; (3) according to the cutoff distance and the density adaptive distance, the local density of each data point as well as the shortest distance from the data point to a point of higher local density are calculated, a decision diagram is drafted, and a clustering center is selected; and (4) each residual point is distributed to a cluster to which the nearest neighbor point of the higher local density belongs, and a clustering result is obtained. Experiments on artificial data sets and UCI real data sets show that the density peak clustering algorithm based on the density adaptive distance, compared with the density peak clustering algorithm based on the Euclidean distance, can handle the data set of complex structure and is higher in accuracy.

A density-based data clustering method, comprising a parameter-setting step for setting a scanning radius and a minimum threshold value, a dividing step for dividing a space of a plurality of data points according to the scanning radius, a data-retrieving step for retrieving one data point out of the plurality of data points as a core data point, a searching step for calculating a distance between the core data point and each of the query points, a grouping determination step for determining whether a number of the neighboring points is smaller than the minimum threshold value.