1503 results about "Binary tree" patented technology

Disclosed are an apparatus for recognizing voice using multiple acoustic models according to the present invention and a method thereof. An apparatus for recognizing voice using multiple acoustic models includes a voice data database (DB) configured to store voice data collected in various noise environments; a model generating means configured to perform classification for each speaker and environment based on the collected voice data, and to generate an acoustic model of a binary tree structure as the classification result; and a voice recognizing means configured to extract feature data of voice data when the voice data is received from a user, to select multiple models from the generated acoustic model based on the extracted feature data, to parallel recognize the voice data based on the selected multiple models, and to output a word string corresponding to the voice data as the recognition result.

An improved method for creating an index based on a path-compressed binary trie in a database system comprising database tables and indexes on those tables is described. For a given index to be created, a path-compressed binary trie for the given index is determined. The path-compressed binary trie comprises internal nodes and leaf nodes. Based on a traversal of the path-compressed binary trie, an index is created comprising a first array of internal nodes encountered during the traversal, and a second array of leaf nodes encountered during the traversal. The database system employs said first and second arrays for providing index-based access for a given key value.

The present invention provides a storage system for storing variable sized objects. The storage system is preferably a transaction-based system that uses variable sized objects to store data. The storage system is preferably implemented using arrays disks that are arranged in ranks. Each rank includes multiple stripes. Each stripe may be read and written as a convenient unit for maximum performance. A rank manager is able to dynamically configure the ranks to adjust for failed and added disks by selectively shortening and lengthening the stripes. The storage system may include a stripe space table that contains entries describing the amount of space used in each stripe. An object map provides entries for each object in the storage system describing the location (e.g., rank, stripe and offset values), the length and version of the object. A volume index translates regions of logical storage into object identifiers. The storage system may implement various types of formats such as I-node, binary tree and extendible hashing formats.

The present invention provides a storage system for storing variable sized objects. The storage system is preferably a transaction-based system that uses variable sized objects to store data. The storage system is preferably implemented using arrays disks that are arranged in ranks. Each rank includes multiple stripes. Each stripe may be read and written as a convenient unit for maximum performance. A rank manager is able to dynamically configure the ranks to adjust for failed and added disks by selectively shortening and lengthening the stripes. The storage system may include a stripe space table that contains entries describing the amount of space used in each stripe. An object map provides entries for each object in the storage system describing the location (e.g., rank, stripe and offset values), the length and version of the object. A volume index translates regions of logical storage into object identifiers. The storage system may implement various types of formats such as I-node, binary tree and extendible hashing formats.

In a hierarchical taxonomy of document, the categories of information may be structured as a binary tree with the nodes of the binary tree containing information relevant to the search. The binary tree may be ‘trained’ or formed by examining a training set of documents and separating those documents into two child nodes. Each of those sets of documents may then be further split into two nodes to create the binary tree data structure. The nodes may be generated to maximize the likelihood that all of the training documents are in either or both of the two child nodes. In one example, each node of the binary tree may be associated with a list of terms and each term in each list of terms is associated with a probability of that term appearing in a document given that node. New documents may be categorized by the nodes of the tree. For example, the new documents may be assigned to a particular node based upon the statistical similarity between that document and the associated node.

A method for locating objects, such as images having fixed aspect ratios, within a predefined space, such as a printed page, is provided. The method comprises generating a binary tree structure comprising at least one node and a plurality of leaves, each leaf corresponding to an image and being associated with one node. The method further comprises characterizing a bounding box for each node in the binary tree structure, the bounding box establishing a boundary for all leaves associated with the node. The method also comprises manipulating bounding boxes to fit within the predefined space. The method may be selectively repeated to reposition images within the binary tree structure or to reform the binary tree structure and characterize and manipulate using the reformed binary tree structure. The end result is at least one potential layout having all images located within the predefined space.

Apparatus and computer-readable media are disclosed for establishing secure multicast communication among multiple multicast proxy service nodes of domains of a replicated directory service that spans a wide area network. Domains are organized in a logical tree. Each domain has a logical tree that organizes the multicast proxy service nodes, a group manager at the root node, a multicast key distribution center, multicast service agent, directory service agent and key distribution center. Multicast proxy service nodes store a group session key and a private key. Replication of the directory performs key distribution. A multicast group member joins or leaves the group by publishing message. The local key distribution center and multicast service agent obtain the publisher's identity from a local directory service agent. Based on the identity, a secure channel is established with the directory service agent in the group member's domain. Keys of the binary tree branch that contains the joining or leaving node are updated, and an updated group session key and a new private key are received.

Binning of predictor values used for generating a data mining model provides useful reduction in memory footprint and computation during the computationally dominant decision tree build phase, but reduces the information loss of the model and reduces the introduction of false information artifacts. A method of binning data in a database for data mining modeling in a database system, the data stored in a database table in the database system, the data mining modeling having selected at least one predictor and one target for the data, the data including a plurality of values of the predictor and a plurality of values of the target, the method comprises constructing a binary tree for the predictor that splits the values of the predictor into a plurality of portions, pruning the binary tree, and defining as bins of the predictor leaves of the tree that remain after pruning, each leaf of the tree representing a portion of the values of the predictor.

The invention provides a virtual assembly-oriented collision detection method based on an AABB (Axis Aligned Bounding Box)-OBB (Oriented Bounding Box) mixed bounding box. A pre-processing stage comprises the following steps of: splitting a model of a deformable object, establishing an OBB layered binary tree layer by layer, and establishing an AABB of the entire model. A real-time stage comprises the following steps of: maintaining the AABB at the peak of the binary tree, and carrying out rough collision detection on the entire model; maintaining an OBB at the other nodes, and carrying out accurate collision detection by using the OBB; and finally, carrying out patch-level collision detection. The method comprises the following steps of: loading a three-dimensional model; splitting the model, and establishing a layered binary tree; carrying out collision detection by using the AABB in the rough collision detection stage; carrying out collision detection by using the OBB in the accurate collision detection stage; and finally, carrying out patch-level collision detection. The method provided by the invention can ensure that in an aircraft engine assembling process, a spherical nut wrench is used for disassembling and assembling spherical nuts, and an internal hexagonal wrench is used for disassembling and assembling plug screws.

The invention discloses target detection system and method based on covariance and a binary-tree support vector machine. The system comprises a video data collecting unit, an image preprocessing unit and a background modeling vehicle partitioning and displaying unit. The video data collecting unit is used for acquiring information in real time and carrying out digitization and system conversion on analog videos. The image preprocessing unit comprises an FPGA (Field Programmable Gate Array) and a DSP (Digital Signal Processor), wherein the FPGA is used as a coprocessor; and the DSP is used as a main processor to accomplish the background modeling of video images, the partitioning and the extraction of vehicle targets and the realization of a model identification algorithm. Utilizing the combination of the FPGA and the DSP, the invention can realize multi-video real-time model identification through combining with the model identification algorithm based on the covariance features of the images and the support vector machine. The invention can be widely used for a plurality of fields of intelligent traffic management, intelligent video monitoring and the like.

In a hierarchical taxonomy of document, the categories of information may be structured as a binary tree with the nodes of the binary tree containing information relevant to the search. The binary tree may be ‘trained’ or formed by examining a training set of documents and separating those documents into two child nodes. Each of those sets of documents may then be further split into two nodes to create the binary tree data structure. The nodes may be generated to maximize the likelihood that all of the training documents are in either or both of the two child nodes. In one example, each node of the binary tree may be associated with a list of terms and each term in each list of terms is associated with a probability of that term appearing in a document given that node. New documents may be categorized by the nodes of the tree. For example, the new documents may be assigned to a particular node based upon the statistical similarity between that document and the associated node.