5574 results about "NODAL" patented technology

Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, and the like, which is associated with each Z-node in the Z-web.

A system that incorporates an interactive graphical user interface for visualizing clusters (categories) and segments (summarized clusters) of data. Specifically, the system automatically categorizes incoming case data into clusters, summarizes those clusters into segments, determines similarity measures for the segments, scores the selected segments through the similarity measures, and then forms and visually depicts hierarchical organizations of those selected clusters. The system also automatically and dynamically reduces, as necessary, a depth of the hierarchical organization, through elimination of unnecessary hierarchical levels and inter-nodal links, based on similarity measures of segments or segment groups. Attribute/value data that tends to meaningfully characterize each segment is also scored, rank ordered based on normalized scores, and then graphically displayed. The system permits a user to browse through the hierarchy, and, to readily comprehend segment inter-relationships, selectively expand and contract the displayed hierarchy, as desired, as well as to compare two selected segments or segment groups together and graphically display the results of that comparison. An alternative discriminant-based cluster scoring technique is also presented.

Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

A real-time body status monitoring system (RBMS) is presented in this invention. A wearable monitoring apparatus (WMA) worn by users consists of one or a few sensor nodes and a computing module. Sensor nodes communicate with the computing module via either wired or wireless protocols. RBMS incorporates a monitoring center that connects and serves many WMAs. Together with the sensors and context-aware information fusion and analysis, the system in the invention goes beyond sampling rare events that may be of profound diagnostic, prognostic, or therapeutic importance. It measures the physiological responses to therapeutic interventions during daily activities, which constitute direct and practical health indicators for the patient.

Described herein are networked (smart) living and work spaces, including a variety of different wireless nodes that may be distributed, including wall-mounted and/or retrofitted over existing electrical outlets and light switches, for providing data streams to a digital hub or master controller. For example, described herein are wall-mounted, interactive sensing and audio-visual node device for a networked living/working space.

A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller. Examples of Wearable Electromyography-Based Controllers include articles of manufacture, such as an armband, wristwatch, or article of clothing having a plurality of integrated EMG-based sensor nodes and associated electronics.

A method is described for providing a named entity classification in a computing system having a processor, comprising the steps of the processor reading, from an LOD (Linking Opening Data) set, an LOD node corresponding to a to-be-classified named entity. The processor also determining a type attribute of the LOD node corresponding to the to-be-classified named entity as a tagged type of the to-be-classified named entity and further reading a candidate type. Finally, the processor computing, based on the tagged type, a possibility of the to-be-classified named entity belonging to the candidate type.

The invention discloses a convolution neural network parallel processing method based on a large-scale high-performance cluster. The method comprises the steps that (1) a plurality of copies are constructed for a network model to be trained, model parameters of all the copies are identical, the number of the copies is identical with the number of nodes of the high-performance cluster, each node is provided with one model copy, one node is selected to serve as a main node, and the main node is responsible for broadcasting and collecting the model parameters; (2) a training set is divided into a plurality of subsets, the training subsets are issued to the rest of sub nodes except the main mode each time to conduct parameter gradient calculation together, gradient values are accumulated, the accumulated value is used for updating the model parameters of the main node, and the updated model parameters are broadcast to all the sub nodes until model training is ended. The convolution neural network parallel processing method has the advantages of being capable of achieving parallelization, improving the efficiency of model training, shortening the training time and the like.

Apparatus and methods for high-level neuromorphic network description (HLND) framework that may be configured to enable users to define neuromorphic network architectures using a unified and unambiguous representation that is both human-readable and machine-interpretable. The framework may be used to define nodes types, node-to-node connection types, instantiate node instances for different node types, and to generate instances of connection types between these nodes. To facilitate framework usage, the HLND format may provide the flexibility required by computational neuroscientists and, at the same time, provides a user-friendly interface for users with limited experience in modeling neurons. The HLND kernel may comprise an interface to Elementary Network Description (END) that is optimized for efficient representation of neuronal systems in hardware-independent manner and enables seamless translation of HLND model description into hardware instructions for execution by various processing modules.

A computerized method for detecting premium attacks by an attack classification system is described. Based on received analytic information, the attack classification system generates logical representations for different portions of the analytic information represented as a nodal graph. The logical representations include objects, properties, and relationships between the objects and the properties. The attack classification system filters at least one relationship from the relationships and forms a first cluster further filtering the relationships. Being a logical representation of objects, properties and the remaining relationships, the first cluster is analyzed to determine features and introduce the features into the nodal graph. An analysis of the features determines whether the objects, properties and relationships forming the first cluster are associated with a premium attack by at least applying rule-based constraints to the features of the first cluster to determine whether they correspond to cluster features commonly present in premium attacks.

The present disclosure provides systems and methods for making latency measurements and using these measurements in routing in optical networks. In an exemplary embodiment, a method is defined whereby two nodes sharing a line automatically determine whether both nodes are capable of making a latency measurement and then which node will initiate and which node participates in making the latency measurement. In another exemplary embodiment, an on-demand latency measurement may be made between any two arbitrary nodes within a domain. Routing messages may be used to disseminate the latency of links via a signaling and routing protocol. Advantageously, the present invention provides measurement of latency and latency variation of customer circuits (i.e., SNCs) using an in-band, non-intrusive calculation with a high-degree of accuracy. Furthermore, the present invention may consider these calculations for circuit routing based on the latency and circuit acceptance based on maximum latency restrictions.

The objective of the present invention is to detect, using a universal method, any “movement” inside of the object being monitored, while maintaining the security of a container. The movement inside of the object to be monitored includes 1) a human movement when a human enters into a container to be monitored, 2) a movement to bring a foreign article in, 3) a movement to take cargo out. To achieve the objective, this invention uses the concept of a so-called “inside-seal”. In actual configuration, a plurality of communication units (communication nodes) are installed on the walls of the container. These communication units have a predetermined powered communication capability and form a communication network communicating with each other. A communication status between each node and all of the other nodes provided in the network, and a network graph matrix is generated which defines the nodal relationship between the nodes. Since the matrix is determined not only by the property of the object to be monitored, but also by the spatial condition within the container, it is possible to detect even a small change in the space. According to the first preferred embodiment, each node transmits a low power electric wave which can reach only neighboring nodes, and each node can transmit data to remote nodes only by relaying the data to the neighboring nodes. The relaying counts (HOP counts) of each node to communicate with all of the other nodes are obtained, and based on these relaying counts, a network graph matrix is generated which defines the relaying counts to communicate between all nodes. According to the second preferred embodiment, each node transmits UWB waves to all of the other nodes, which can reach to all of the other nodes, and the distances between all the nodes are measured and a network graph matrix between all the nodes is generated.

To reduce power consumption of a semiconductor integrated circuit and to reduce delay of the operation in the semiconductor integrated circuit, a plurality of sequential circuits included in a storage circuit each include a transistor whose channel formation region is formed with an oxide semiconductor, and a capacitor whose one electrode is electrically connected to a node that is brought into a floating state when the transistor is turned off. By using an oxide semiconductor for the channel formation region of the transistor, the transistor with an extremely low off-state current (leakage current) can be realized. Thus, by turning off the transistor in a period during which power supply voltage is not supplied to the storage circuit, the potential in that period of the node to which one electrode of the capacitor is electrically connected can be kept constant or almost constant. Consequently, the above objects can be achieved.

In various embodiments, an implant for interfacing with a bone structure includes a web structure including a space truss. The space truss includes two or more planar truss units having a plurality of struts joined at nodes and the web structure is configured to interface with human bone tissue. In some embodiments, a method is provided that includes accessing an intersomatic space and inserting an implant into the intersomatic space. The implant includes a web structure including a space truss. The space truss includes two or more planar truss units having a plurality of struts joined at nodes and the web structure is configured to interface with human bone tissue.

The probability of a computer file being malware is inferred by iteratively propagating domain knowledge among computer files, related clients, and/or related source domains. A graph is generated to include machine nodes representing clients, file nodes representing files residing on the clients, and optionally domain nodes representing source domains hosting the files. The graph also includes edges connecting the machine nodes with the related file nodes, and optionally edges connecting the domain nodes with the related file nodes. Priors and edge potentials are set for the nodes and the edges based on related domain knowledge. The domain knowledge is iteratively propagated and aggregated among the connected nodes through exchanging messages among the connected nodes. The iteration process ends when a stopping criterion is met. The classification and associated marginal probability for each file node are calculated based on the priors, the received messages, and the edge potentials associated with the edges through which the messages were received.

Within the context of a cross-referenced data-base, an initial “base-set” of results to a query is generated using any conventional search engine tool. The base-set is then expanded by adding to it entries referencing entries in the original set or referenced by those entries, in a possibly iterative manner. The resulting collection of entries and references is represented as a mathematical graph or network, amendable to graph theoretic analysis. Connected components within the graph form top-level clusters, and articulation nodes within these clusters are calculated. These articulation nodes serve as both navigational “gateways” and anchors for sub-clusters. Sub-clusters, consisting of the transitive descendants of the articulation nodes, are associated with each articulation node. The articulation nodes themselves then form a graph, which is analyzed further for prominence, and a hierarchy of articulation nodes is calculated. The resulting hierarchy consisting of the top-level clusters and the sub-clusters associated with the articulation nodes is then presented visually to users in a manner enabling them to easily navigate through the space of expanded search results.