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1137 results about "Near neighbor" patented technology

Laminated magnetorestrictive element of an exchange coupling film, an antiferromagnetic film and a ferromagnetic film and a magnetic disk drive using same

A magnetoresistive element comprises an exchange coupling film having a under layer, an antiferromagnetic film and a ferromagnetic film, which are laminated in that order, the under layer including a metal having a face centered cubic crystal structure or hexagonal closest packing crystal structure which have a longer nearest neighbor atomic distance than that of the antiferromagnetic film. With this construction, it is possible to improve the exchange coupling field and to satisfy a stable output over a long period of time. A magnetoresistive element having a dual spin valve structure has a magnetization adjusting layer, which is antiferromagnetically connected to a pinned layer via an anti-parallel connection layer, to adjust the value of the product of the saturation magnetization of each of the magnetization adjusting layer and the pinned layer by the thickness thereof. Moreover, a magnetoresistance head use a giant magnetoresistance effect, and has at least one pair of pinned layer and free layer arranged via a non-magnetic spacer layer. The pinned layer has a pair of ferromagnetic layers which have different compositions and different coercive forces and which are antiferromagnetically connected to each other via a connection layer, so that the effective exchange coupling field of the pinned layer is 200 Oe or more.

Personalized commodity recommending method and system which integrate attributes and structural similarity

The invention discloses a personalized commodity recommending method which integrates attributes and structural similarity. In the method, users and commodities are used as nodes with characteristic information to be mapped to a network by integrating the attribute information and structural similarity information, and an information network chart is established according to the purchasing relation between customers and the commodities; and interests and preference among user node pairs are measured by the integrated attributes and structural similarity in the information network chart, and the nearest neighbor is selected by the interests and the preference to improve the accuracy of recommending. On the basis of the recommending method, the invention also discloses a personalized commodity recommending method which integrates the measurement of the attributes and the structural similarity. In the system, the interests and the preference of the users are measured accurately by a computing method of integrating the similarity of the attributes and the similarity of node structure backgrounds in the information network chart, and the generation efficiency of the nearest neighbor is improved by utilizing clustering technology. The method and the system can be applied to electronic commerce, and provide personalized commodity recommending for the users.

Optimizing layout of an application on a massively parallel supercomputer

A general computer-implement method and apparatus to optimize problem layout on a massively parallel supercomputer is described. The method takes as input the communication matrix of an arbitrary problem in the form of an array whose entries C(i, j) are the amount to data communicated from domain i to domain j. Given C(i, j), first implement a heuristic map is implemented which attempts sequentially to map a domain and its communications neighbors either to the same supercomputer node or to near-neighbor nodes on the supercomputer torus while keeping the number of domains mapped to a supercomputer node constant (as much as possible). Next a Markov Chain of maps is generated from the initial map using Monte Carlo simulation with Free Energy (cost function) F=Σi,jC(i,j)H(i,j)—where H(i,j) is the smallest number of hops on the supercomputer torus between domain i and domain j. On the cases tested, found was that the method produces good mappings and has the potential to be used as a general layout optimization tool for parallel codes. At the moment, the serial code implemented to test the method is un-optimized so that computation time to find the optimum map can be several hours on a typical PC. For production implementation, good parallel code for our algorithm would be required which could itself be implemented on supercomputer.

Peer-to-peer enterprise storage

A peer-to-peer storage system includes a storage coordinator that centrally manages distributed storage resources in accordance with system policies administered through a central administrative console. The storage resources, or “nodes,” are otherwise unused portions of storage media, e.g., hard disks, that are included in the devices such as personal computers, workstations, laptops, file servers, and so forth, that are connected to a corporate computer network, and are thus otherwise available only individually to the respective devices. The storage coordinator assigns the nodes to various “replication groups” and allocates the storage resources on each of the nodes in a given group to maintaining dynamically replicated versions of the group files. The storage nodes in a given group perform dynamic file replication and synchronization operations by communicating directly, that is, peer-to-peer, using a message-based protocol. The storage coordinator also manages distributed searches of file content on the network by selecting one node from each group to search through the associated group files. The selected nodes report the search results back to the storage coordinator, which organizes the results and provides them to the user. Thereafter, in response to a request for various files by the user, the storage coordinator instructs the nodes that are near neighbors of the user to provide the requested files. The storage coordinator thus ensures that the amount of the network bandwidth consumed by the search operation is minimized.

Imaging based symptomatic classification and cardiovascular stroke risk score estimation

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular stroke risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which computes: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic ground truth information about the training patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular stroke risk score value on the basis of the four set of wall features.

Method and system for communicating with and tracking RFID transponders

An RFID system and method for communicating between a host computer, one or more interrogators connected to the host computer, and a large body of transponders distributed within an area covered by the interrogators. Each transponder originally has a common identification code, and upon initialization by the host computer internally generates a unique identification code based upon an internally generated random number. The host, through the interrogators, reads each of the identification codes associated with each transponder by iteratively transmitting a read identification code command along with a controlled variable. Each transponder compares the received controlled variable to an internally generated random number, and selectively transmits its identification code based upon the outcome of this comparison. After the completion of each read identification code iteration, the host adjusts the controlled variable based upon the responses received in the previous iteration. Preferably, communications between the interrogators and the transponders are DSSS signals in TDMA format, and the transponders use the random number generator to assign a time slot for transmission of their response. Each interrogator includes an antenna system utilizing a switch matrix to connect multiple antennas having different polarizations, which ensures that all transponders within the range of the interrogator receive the signals from the interrogator. In a further aspect, the interrogators are arranged in groups, each group in nearest neighbor format, to reduce the time for reading the transponders and the emissions generated when more than one interrogator is active at the same time.

Binocular camera-based panoramic image splicing method

The invention provides a binocular camera-based panoramic image splicing method. According to the method, a binocular camera is arranged at a certain point of view in the space, the binocular camera completes photographing for once and obtains two fisheye images; a traditional algorithm is improved according to the defect of insufficient distortion correction capacity of a latitude-longitude correction method in a horizontal direction; corrected images are projected into the same coordinate system through using a spherical surface orthographic projection method, so that the fast correction of the fisheye images can be realized; feature points in an overlapping area of the two projected images are extracted based on an SIFT feature point detection method; the search strategy of a K-D tree is adopted to search Euclidean nearest neighbor distances of the feature points, so that feature point matching can be performed; an RANSAC (random sample consensus) algorithm is used to perform de-noising on the feature points and eliminate mismatching points, so that image splicing can be completed; and a linear fusion method is adopted to fuse spliced images, and therefore, color and scene change bluntness in an image transition area can be avoided.

Android device wireless fidelity (WiFi) indoor locating method based on position fingerprint identification algorithm

The invention discloses an Android device wireless fidelity (WiFi) indoor locating method based on a position fingerprint identification algorithm. The Android device WiFi indoor locating method based on the position fingerprint identification algorithm aims at solving the problems that when a terminal receiver works in a city with dense building clusters or indoors, signal strength is greatly reduced due to influence by buildings, thus locating accuracy is low and location even can not be completed. Based on the foundation of the traditional position fingerprint identification algorithm, a k-nearest neighbor (KNN) matching algorithm and a coordinate computing method based on weight are adopted so that the position of a point to be measured is obtained, errors brought by signal fluctuation is effectively reduced, the position of the terminal receiver can be accurately located, and reaction is fast. Compared with a tradition indoor locating method, the Android device WiFi indoor locating method based on the position fingerprint identification algorithm can accurately locate the position of a requester in the condition of a complex environment, and is fast in reaction, efficient, accurate and especially suitable for indoor position location of an Android device terminal.

Data-driven and task-driven image classification method

The invention discloses a data-driven and task-driven image classification method. The data-driven and task-driven classification method comprises the steps that a convolutional neural network structure is designed according to the scale of data sets and image content; a convolutional neural network model is trained through the given classified data sets; feature expression is extracted from training set images through a trained convolution neural network; images to be tested are input into the trained convolutional neural network and are classified. The data-driven and task-driven image classification method is based on nonlinear convolution feature learning, and the model can be adapted to the data sets through a date driving mode, so that the specific data set can be better described; errors of K-nearest neighbors can be directly optimized through a task-driving mode, and therefore a better performance can be obtained with respect to a K-nearest neighbor task; efficient training can be conducted through a GPU in the training stage, and efficient K-nearest neighbor image classification can be achieved just through a CPU in the testing stage; in this way, the data-driven and task-driven image classification method is quite suitable for a large-scale image classification task, a retrieval task and the like.
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