107 results about "Nearest neighbour" patented technology

A digital camera has a sensor for sensing an image, a processor for modifying the sensed image in accordance with instructions input into the camera and an output for outputting the modified image where the processor includes a series of processing elements arranged around a central crossbar switch. The processing elements include an Arithmetic Logic Unit (ALU) acting under the control of a writeable microcode store, an internal input and output FIFO for storing pixel data to be processed by the processing elements and the processor is interconnected to a read and write FIFO for reading and writing pixel data of images to the processor. Each of the processing elements can be arranged in a ring and each element is also separately connected to its nearest neighbors. The ALU receives a series of inputs interconnected via an internal crossbar switch to a series of core processing units within the ALU and includes a number of internal registers for the storage of temporary data. The core processing units can include at least one of a multiplier, an adder and a barrel shifter. The processing elements are further connected to a common data bus for the transfer of a pixel data to the processing elements and the data bus is interconnected to a data cache which acts as an intermediate cache between the processing elements and a memory store for storing the images.

The invention relates to a WLAN indoor KNN positioning method based on near-neighbor point number optimization, which relates to the field of mode identification and solves the problem of reduced positioning precision caused by improper near-neighbor point number selection in the traditional WLAN indoor KNN positioning method. The WLAN indoor KNN positioning method comprises the following steps of: firstly establishing a complete WLAN positioning scene and a position fingerprint database; then, pre-estimating the position of a testing point according to the collected signal intensity at the testing point and the pre-stored position fingerprint data by utilizing a KNN positioning method with the near-neighbor number as 2; then obtaining the theoretical expected error of the testing point at a pre-estimated position by the KNN positioning method when the near-neighbor point numbers are 1 and 2, and selecting the near-neighbor point number corresponding to the KNN positioning method with higher theoretical precision as the optimum near-neighbor point number for estimating the position of the testing point; and finally realizing WLAN indoor KNN positioning by utilizing the KNN positioning method under the optimum near-neighbor point number. The invention is applicable to indoor positioning.

The invention discloses a method for countering a deception false target by utilizing a netted radar system, which mainly solves the problem that deception probability is high in the event of countering the deception false target only by utilizing target position information fusion. The method comprises the following steps of: (1), carrying out coordinate transformation on measurements of node radars, namely, taking various node radars as a polar coordinate system of a reference original point, and transforming to a uniform rectangular coordinate system of the netted radar system; (2), matching measurements through a nearest neighbour association method so as to obtain an association measurement sequence; (3), identifying true and false targets by utilizing target position information, and reserving the association measurement sequence passing fusion testing; (4), obtaining a practical speed vector set corresponding to the reserved association measurement sequence; and (5), carrying out identification of true and false targets by utilizing target speed information, and further reducing the deception probability of netted radar. The method disclosed by the invention effectively reduces the deception probability of the netted radar and can be used for the netted radar to effectively resist deception interference.

This invention relates to a data distribution and getting method based on network information, in which, said method includes: a resource getting node sends a request for getting resources to a resource index server, which searches for it in a resource state library to look for addresses of all resource peer points capable of providing said specific resources currently and feeds back a neighbor list to the resource getting node based on the neighbornhood principle, the getting node sends an obtaining request to the peer points on the list based on the addresses provided by the neighbor list and receives or uses resources, which fully utilizes a physical network topological information to get the lastest neighbor list and reduces multiple data paths resulted in using overlay network in the physical network.

The invention discloses a method for automatically detecting the printing defects of a remote controller panel based on an SURF (Speed-Up Robust Feature) algorithm. The method disclosed by the invention comprises the following steps: carrying out histogram equalization on a to-be-detected sample image by making a remote controller template image, respectively acquiring feature points of the template image and the to-be-detected image through the SURF algorithm, matching the feature points by utilizing a partitioned and accelerated nearest neighbour matching method, acquiring a homography matrix according to a matching result, carrying out affine transformation on the to-be-detected image to obtain a correction image by utilizing the homography matrix, processing difference images of the template image and the correction image superposed with a mask, carrying out binaryzation and morphological processing on a difference image result, judging whether a to-be-detected sample is qualified, if the to-be-detected sample has a defect, locating the position of the defect; if the to-be-detected sample does not have a detect, judging that the to-be-detected sample is qualified, and completing detection. The method disclosed by the invention is capable of effectively detecting defects in the to-be-detected sample and accurately locating the positions of the defects.

The invention discloses a KNN algorithm-based missing data filling method. The method comprises the following steps of: automatically recognizing feature attributes with missing values in a data set; traversing the feature attributes with missing values and gradually filling the missing values by utilizing a KNN algorithm; in the process of filling the missing value of each feature attribute, iterating KNN algorithm parameters k to obtain a KNN model cluster configured by different parameters; and selecting an optimum model according to an optimal objective function, and carrying out missing value filling on missing data by utilizing the model. The algorithm parameters k have great influences on the KNN algorithm, and an optimization strategy is applied to the construction of missing value filling models, so that the model precision can be greatly improved, and correspondingly, the quality of the filled data is greatly improved.

The invention relates to a semi-supervised learning-based multi-gesture facial expression recognition algorithm which comprises the steps of acquiring n front expression images and n side expression images of n persons to form a training set X and a testing set S, segmenting face regions of the front expression images and the side expression images, and carrying out illumination compensation on the face regions by using a histogram equalization method; then extracting expression characteristics of the images by adopting a linear discriminant analysis method, carrying out expression recognition on samples in the testing set S; marking each unmarked sample in the training set X by using marked samples in the training set X by adopting an Euclidean distance nearest neighbour method; re-sampling the training set X by adopting a round-robin mode to obtain a new training set Xr; scheduling a basic classifying device to calculate a mark ht of each sample in the training set X at the tth circle by using the new training set Xr, and calculating a mark ft of each sample in the testing set S at the tth circle by using the new training set Xr; and finally, calculating a classifying error rate epsilon t of the basic classifying device to side samples in the training set, and updating weights of all training samples in the training set X until reaching the circle ending condition.

The present invention is directed to the creation of macroscopic materials and objects comprising aligned nanotube segments. The invention entails aligning single-wall carbon nanotube (SWNT) segments that are suspended in a fluid medium and then removing the aligned segments from suspension in a way that macroscopic, ordered assemblies of SWNT are formed. The invention is further directed to controlling the natural proclivity or nanotube segments to self assemble into or ordered structures by modifying the environment of the nanotubes and the history of that environment prior to and during the process. The materials and objects are “macroscopic” in that they are large enough to be seen without the aid of a microscope or of the dimensions of such objects. These macroscopic ordered SWNT materials and objects have the remarkable physical, electrical, and chemical properties that SWNT exhibit on the microscopic scale because they are comprised of nanotubes, each of which is aligned in the same direction and in contact with its nearest neighbors. An ordered assembly of closest SWNT also serves as a template for growth of more and larger ordered assemblies. An ordered assembly further serves as a foundation for post processing treatments that modify the assembly internally to specifically enhance selected material properties such as shear strength, tensile strength, compressive strength, toughness, electrical conductivity, and thermal conductivity.

In a mobile communication network providing an Internet service, the arrived packets are delivered from the nearest neighbor router device to the mobile terminal device according to the need such as when a prescribed application is activated or when a prescribed packet has arrived, by using message exchanges between the nearest neighbor router device and the mobile terminal device, so that it becomes possible to prevent the unnecessary packet attacks from the global Internet.

The method for automatic determination of validity or invalidity of input from miniaturized keypads or keyboards, in which more than one key is operated at the same time, includes designating a number of keys as principal keys and assigning each key neighboring each principal key as nearest neighbor or next nearest neighbor and then testing each signal produced by key operation. The signal is valid if it is produced by operation of only one single key. However testing continues if two or more keys produce the signal. Then the signal is valid when the two or more keys are correlated in an unambiguous principal key-nearest neighbor key relationship. If not, testing continues further and the signal is valid if the plural keys are correlated in an unambiguous principal key-nearest neighbor key-next nearest neighbor key relationship. Otherwise the signal is an invalid key input.

The invention discloses a distributed mass short text KNN (K Nearest Neighbor) classification algorithm and a distributed mass short text KNN classification system based on information entropy feature weight quantification. The algorithm mainly includes text preprocessing, weights for measuring features with information entropies, a document vector representation method and a KNN classification algorithm adopting indexing and caching techniques, and the whole algorithm is implemented on the basis of a MapReduce computing framework. By utilizing the information entropies of features in a training set, the invention effectively measures the effects of words in texts on classification, and moreover, based on the MapReduce computing framework, the algorithm can utilize mass clusters to efficiently classify mass short texts. The mass short text classification algorithm provided by the invention has the advantages of high accuracy, high operating efficiency and strong scalability.

In the method, image background is obtained through morphology as demean value image containing target and noise can be obtained by deducting image background from original image, N/S ratio of processing result can be raised by applying pretreatment process based on elliptic paraboloid volume, sing frame detection result can be obtained by selecting detecting threshold with constant false alarm ratio standard, false target is further removed by utilizing solidity of target motion trail, target tracking applies most near neighbour coorelation method and target state is refreshed by applying kalman filtering based on uniform straight-line motion model and trail is utilized to predict possible position of target for carrying on stable tracking if target is lost.

We disclose a transformation-based method for indexing high-dimensional data to support similarity search. The method, iDistance, partitions the data into clusters either based on some clustering strategies or simple data space partitioning strategies. The data in each cluster can be described based on their similarity with respect to a reference point, and hence they can be transformed into a single dimensional space based on such relative similarity. This allows us to index the data points using a B<+>-tree structure and perform similarity search using range search strategy. As such, the method is well suited for integration into existing DBMSs. We also study two data partitioning strategies, and several methods on selection of reference points. We conducted extensive experiments to evaluate iDistance, and our results demonstrate its effectiveness.

The invention relates to a WLAN (Wireless Local Area Network) indoor single-source linear WKNN (Weighted K-Nearest Neighbor) locating method based on reference point position optimization, belonging to the field of mobile computing. The invention aims to solve the problem of selection of numbers and positions of optimal reference points in a WKNN locating algorithm under the existing WLAN indoor single-source linear environment. Aiming at the special single-source linear scene, firstly, a closed solution form of the WKNN locating algorithm with theoretic expected precision is computed at an offline state by aiming at the concrete practical single-source linear locating environment; then, reference points are subjected to optimal distribution by utilizing the relationship between an expected error and the position of the reference point as well as the relationship between the expected error and the size of a target region to meet the minimum expected error criterion, and a corresponding single-source average position fingerprint database is established; and finally, the position coordinates of a locating terminal are estimated by using a 4-neighbor-point WKNN locating method according to a signal intensity sample collected in real time at the present stage.

The invention relates to a method for detecting a phishing webpage based on nearest neighbour and similarity measurement, which comprises the following steps: a picture of a whole image of a webpage is taken as a start point, and the characteristic of unchanged dimension conversion is extracted; similar characteristics at phishing webpage detection stage are quickly queried, and are then submitted to a machine leaning and matching module to carry out identification; the machine leaning and matching module extracts characteristic data transmitted during a system training stage to carry out training, so that a parameter of webpage similarity threshold can be optimized; during the phishing webpage detection stage, the characteristic data transmitted by the characteristic extracting module is received, the similarity between webpages is calculated, and finally, the phishing webpage is judged according to the webpage similarity threshold; in addition, a sorting method-Bayesian addable regression tree is added to predict suspicious webpages; and the characteristics during the phishing webpage detection process are extracted to be used as an evidence of the phishing webpage detection, so that the high accuracy can be ensured, and simultaneously, the webpage detection time can be remarkably reduced.

The invention provides an improvement-based KNN (K Nearest Neighbor) text classification method. The method comprises the following steps: preprocessing a training text, computing the feature vector of each training sample, and constructing a feature vector spatial model of a training set; defining a density and a distance, defining a density and a distance, defining a whole sample space into a plurality of spherical regions and outliers according to types, and storing as a training set library; during testing, judging whether a text to be tested falls into a certain spherical region, judging the type of the text to be tested according to a corresponding mark number, otherwise, using the outliers and the center point of each sphere as a training set library, calling a KNN algorithm, and judging the type of the text to be tested. By adopting the method provided by the invention, the classification speed, classification accuracy and data skew sensitivity are considered. The method can be well applied to the classification problem of non-spherical distribution, and is particularly suitable for a text classification problem having a high-dimension feature vector and a distribution irregularity feature.

The invention relates to a KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting the grayscale nonuniformity of an MR (Magnetic Resonance) image, belonging to the field of image processing. The method comprises the following steps of: firstly constructing a grayscale nonuniform field model by utilizing surface fitting knowledge and using a group of orthonormalization basis functions, and establishing energy functions; and then solving model parameters according to an energy function minimization principle to realize grayscale nonuniformity correction and image segmentation, wherein subordinate functions are solved by adopting an iterative algorithm and the KNN algorithm in the model parameter solving process, therefore a partial volume effect is greatly reduced while a grayscale nonuniform field is eliminated, and the influence of noises on the correction and the segmentation of the grayscale nonuniformity of the MR image is reduced. The subordinate functions are solved with KNN through the following steps of: firstly acquiring an accurate smooth normalization histogram by using a kernel estimation algorithm; then respectively solving a threshold value TCG between cerebrospinal fluids and gray matters and a threshold value TGW between the gray matters and white matters by using a maximum between-cluster variance method; carrying out rough sorting on the KNN sorting algorithm by utilizing the two threshold values; and finally accurately sorting points to be fixed by adopting the traditional KNN sorting algorithm.

The invention provides a special vehicle instrument type identification and calibration method based on image characteristics, and belongs to the field of image processing and mode identification. The method includes the steps of firstly collecting several special vehicle instrument images according to each type of several special vehicle instruments, and reserving representative images as training samples through manual judgment, secondly carrying out normalization to quality of the images through an image preprocessing method according to each training sample, then extracting a disk of the instrument images and carrying out the size normalization to the instrument images according to radius of the disk, thirdly respectively extracting color features and Gabor texture features of the special vehicle instrument images after the normalization, and finally aiming at all training samples, and building models for each type of instruments through respectively using of the color features and the Gabor texture features as vector quantities. After the feature models of the training samples of each type of instruments are built, with regards to the collected real-time images, the method can also be used to carry out image preprocessing and image quality normalization, respectively carries out mode match aiming at the feature templates of each type of instrument training samples with the two features, and obtains instrument classifying results through a nearest neighbour rule.

A method of authenticating access points (1, 2, 3) on a wireless network (4) comprises sending a list of nearest neighbours (2, 3) from a trusted access point (1) to a controller (6). At the trusted access point (1), test data related to each of the listed nearest neighbours is received from the controller. Each neighbour (2, 3) is interrogated using its respective test data and the response compared with an expected response. A reliability indication is returned to the controller (6).