Intel Corporation

Maria E Ramirez Loaiza

Gabriel Arrobo Vidal

Satish Chandra Jha

Mats Gustav Agerstam

S M Iftekharul Alam

Vijay Sarathi Kesavan

Ned M Smith

Justin E Gottschlich

Alexander F Heinecke

Marcos Emanuel Carranza

Yi Zhang

Kathiravetpillai Sivanesan

Srikathyayani Srikanteswara

Zongrui Ding

Venkatesan Nallampatti Ekambaram

Kuilin Clark Chen

Ravikumar Balakrishnan

Shengtian Zhou

Cesar Martinez-Spessot

David J McCall

Mohammad Mejbah Ul Alam

Stepan Karpenko

Nageen Himayat

Qian Li

Bahareh Sadeghi

Sara S Baghsorkhi

Vallabhajosyula S Somayazulu

Jason Martin

Eve M Schooler

Yiting Liao

Roya Doostnejad

Robert Colby

Jeffrey Ota

Fatemeh Fazel Sarjoui

William R Dieter

Methods, apparatus, systems and articles of manufacture are disclosed to identify and manage IoT protocols and associated devices. An example apparatus includes a gateway device to communicate according to a first protocol. The example gateway device includes a plugin agent to discover a first device and probe the first device to gather data regarding a protocol of the first device. The example plugin agent is to transmit the gathered data to a plugin manager to determine whether the first device is to communicate via the first protocol, and, when the first device is unable to communicate via the first protocol, determine a plugin for the gateway device to enable the gateway device to communicate with the first device, the plugin agent to provision the plugin for the gateway device with respect to the first device.

Framework for IoT protocol identification and management

Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

System and method using collaborative learning of interference environment and network topology for autonomous spectrum sharing

Systems and techniques for machine generation of content names in an information centric network (ICN) are described herein. For example, a node may obtain content. An inference engine may be invoked to produce a name for the content. Once the content is named, the node may respond to an interest packet that includes the name of the content. The response is a data packet that includes the content.

Machine generated content naming in an information centric network

Systems and techniques for an information centric network (ICN) distributed search with approximate cache and forwarding information lookup. For example, a search interest packet may be received. Here, the search interest packet includes search criteria and a signal indicating that it is a search interest packet. A search for content&#x2014;including content in a local content store&#x2014;that meets the search criteria may then be performed. Once complete, a data packet that includes the results of the search may be transmitted towards an author of the search interest packet.

Information centric network distributed search with approximate cache

Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Methods and apparatus to provide machine assisted programming

Systems and techniques for information centric network (ICN) approximate computation caching are described herein. For example, an interest packet that includes a feature set of input data may be received. A node may then perform a search of a local data store using the feature set to determine an approximate computation result cached in the local data store. Here, the approximate computation result may be based on input data that differs from the input data named in the interest packet. The node may then return the approximate computation result to an author of the interest packet in response to the search.

Information centric network approximate computation caching

Systems and techniques for information-centric network data cache management are described herein. A demand metric may be calculated for a content item requested from an information-centric network (ICN). A resistance metric may be calculated for each cache node of a set of cache nodes in the ICN based on the demand metric. A topology of the set of cache nodes may be evaluated to identify a transmission cost for each cache node of the set of cache nodes. An influencer node may be selected from the set of cache nodes based on the resistance metric for the influencer node and the transmission cost for the influencer node. The content item may be cached in a data cache of the influencer node.

Information-centric network data cache management

Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimator model to estimate a first data structure based on the first input, the PTV estimator model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Methods, systems, articles of manufacture and apparatus for code review assistance for dynamically typed languages

Systems and techniques for node-density aware interest packet forwarding in a dynamic ad hoc information centric network (ICN) are described herein. For example, a next interest packet to forward may be obtained at a network node. A time period to hold the next interest packet before forwarding may be calculated based on node density in the network. The node may then broadcast the next interest packet upon expiration of a timer set to the time period and started when the next interest packet was obtained.

Node-density aware interest packet forwarding in ad hoc network

For example, an apparatus may include logic and circuitry configured to cause a wireless communication device to determine at least one video quality parameter representing an estimated quality of at least one video stream to be streamed via the wireless communication device to a display device over a wireless communication medium; to determine a scheduling policy parameter based at least on the video quality parameter; and to provide the scheduling policy parameter to a Media Access Control (MAC) scheduler to schedule wireless transmission of the at least one video stream to the display device.

Apparatus, system and method of wireless video streaming

Methods, apparatus, systems and articles of manufacture are disclosed provide an apparatus to allocate bandwidth between devices, the apparatus comprising a comparator to determine whether a first dataflow to a first device is below a first fair share throughput attributed to the first device; and a weight adjustor to, in response to the comparator determining that the first dataflow is below the first fair share throughput attributed to the first device adjust the first fair share throughput such that the first dataflow is closer to the first fair share throughput; and adjust a second fair share throughput attributed to a second device such that a second dataflow to the second device is closer to the second fair share throughput.

Methods and apparatus to enable multi-AP WLAN with a limited number of queues

An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Methods and apparatus to detect anomalies of a monitored system

Growing community of inventors

Hillsboro, OR, United States of America

Maria E Ramirez Loaiza