Intel Corporation

Keith A Ellis

Michael J Nolan

Hugh Martin Carr

Niall Cahill

Ronan O'Malley

David M Boundy

Connor Upton

David Coates

Mark Y Kelly

Michael J McGrath

Marcin Spoczynski

Keith Nolan

Giovani Estrada

Brian McCarson

Damian Kelly

Radhika Loomba

Gabriel Mullarkey

Peter J Barry

Thijs Metsch

Cliodhna Ni Scanaill

Joseph Butler

Jessica C McCarthy

Charlie Sheridan

Aurelian Lazarut

Lisa Sherin

Daire Healy

Perumal Kappuudaiyar

An apparatus is disclosed to compress packets, the apparatus comprising; a data analyzer to identify a new destination address and a protocol identifier of an input packet corresponding to a new destination node and a communication system between the new destination node and a source node; a compression engine to utilize a plurality of compression functions based on the new destination address and the protocol identifier and reduce a size of the input packet; a compression analyzer to identify a reduced packet and a compression function identifier corresponding to the reduced packet, the compression function identifier associated with one of the compression functions; and a source modifier to construct a packet to include the compression function identifier by modifying unregistered values of a protocol identifier by a difference associated with the compression function identifier, the packet to inform the new destination node of a compression function.

Methods and apparatus to compress packets in a computing environment

Methods, apparatus, systems and articles of manufacture are disclosed to facilitate end-user defined policy management. An example apparatus includes an edge node interface to detect addition of a networked user device to a service gateway, and to extract publish information from the networked user device. The example apparatus also includes a device context manager to identify tag parameters based on the publish information from the networked user device, and a tag manager to prohibit unauthorized disclosure of the networked user device by setting values of the tag parameters based on a user profile associated with a type of the networked user device.

Methods and apparatus to facilitate end-user defined policy management

Methods, apparatus, systems and articles of manufacture are disclosed to manage process excursions. An example apparatus includes a digital twin comparer to determine when a product fails to satisfy a tolerance metric of a digital twin, and a fingerprint manager to generate a fingerprint corresponding to a sensor pattern. The example apparatus also includes a node interfacer to determine a number of workstations of a process control system that exhibit the fingerprint, and an excursion statistics calculator to invoke a corrective action for respective ones of the number of workstations, the corrective action based on a threshold count of the number of workstations that exhibit the fingerprint.

Methods, systems, articles of manufacture and apparatus to manage process excursions

In some embodiments, infrastructure data and service data is received for a computing infrastructure. The infrastructure data indicates resources in the computing infrastructure, and the service data indicates services to be orchestrated across the computing infrastructure. An infrastructure capacity model is generated, which indicates a capacity of the computing infrastructure over a particular time window. Service-to-resource placement options are also identified, which indicate possible placements of the services across the resources over the particular time window. Resource inventory data is obtained, which indicates an inventory of resources that are available to add to the computing infrastructure during the particular time window. An infrastructure capacity plan is then generated, which indicates resource capacity allocation options over the time slots of the particular time window. Resource capacities for the services are then allocated in the computing infrastructure.

Automated resource management for distributed computing

Methods, apparatus, systems and articles of manufacture to improve accuracy of a fog/edge-based classifier system are disclosed. An example apparatus includes a transducer to mounted on a tracked object, the transducer to generate data samples corresponding to the tracked object; a discriminator to: generate a first classification using a first model based on a first calculated feature of the first data samples from the transducer, the first model corresponding to calculated features determined from second data samples, the second data samples obtained prior to the first data samples; generate an offset based on a difference between a first model feature the first model and a second model feature of a second model, the second model being different than the first model; and adjust the first calculated feature using the offset to generate an adjusted feature; a pattern matching engine to generate a second classification using vectors corresponding to the second model based on the adjusted feature; and a counter to, when the first classification matches the second classification, increment a count.

Methods and apparatus to improve accuracy of edge and/or a fog-based classification

A non-transitory computer-readable storage medium, an apparatus, and a computer-implemented method to select respective physical infrastructure devices of an edge computing system to implement services requested by respective service-requesting clients. The computer-readable storage medium includes computer-readable instructions that, when executed, cause at least one processor to perform operations comprising, for each candidate physical infrastructure device, calculating a utility score corresponding to each of the services requested, wherein: the utility score corresponds to one of each of the respective service-requesting clients or each subgroup of a plurality of subgroups of the respective service-requesting clients. The utility score is based on location-related attributes and resource-related attributes of said each candidate physical infrastructure device, resource-related attributes of said each of the services requested, and location-related attributes and movement-related attributes of one of said each of the respective service-requesting clients or said each subgroup of the plurality of subgroups of the respective service-requesting clients.

Computer-readable storage medium, an apparatus and a method to select access layer devices to deliver services to clients in an edge computing system

Acoustic imaging device

Methods, apparatus, systems, and articles of manufacture for conditional classifier chaining in a constrained machine learning environment are disclosed. An example apparatus includes a classification controller to select a first model to be utilized to classify a first feature identified from sensor data. A memory controller is to copy the first model to a memory. A machine learning processor is to apply the first model to the first feature to create a first classification output, the first classification output indicating an identified class. The classification controller is to, in response to a determination that the first classification output identifies a second model to be used for classification, instruct the memory controller to load the second model into the memory. The machine learning processor is to apply the second model to the second feature to create a second classification output.

Methods and apparatus for conditional classifier chaining in a constrained machine learning environment

Technologies for collaborative optimization include multiple Internet-of-Things (IoT) devices in communication over a network with an optimization server. Each IoT device selects an optimization strategy based on device context and user preferences. The optimization strategy may be full-local, full-global, or hybrid. Each IoT device receives raw device data from one or more sensors/actuators. If the full-local strategy is selected, the IoT device generates processed data based on the raw device data, generates optimization results based on the processed data, and generates device controls/settings for the sensors/actuators based on the optimization results. If the full-global strategy is selected, the optimization server performs those operations. If the hybrid strategy is selected, the IoT device generates the processed data and the device controls/settings, and the optimization server generates the optimization results. The optimization server may provision plugins to the IoT devices to perform those operations. Other embodiments are described and claimed.

Technologies for adaptive collaborative optimization of internet-of-things systems

Orchestration and/or management of services on deployed computing, electronic, appliance and like devices are described. A service orchestration and/or management system ('SOMS') may be configured to provide for orchestration and/or management of services to one or more deployed devices. The SOMS may obtain information from deployed devices, such as status, capabilities and service metadata. The SOMS may then encapsulate and store the obtained information for later use. The SOMS may also store service information, facilitate creation of services based on stored service definition meta-data, modeling or simulating a service prior to deployment, and deployment of a service to remotely deployed devices followed by activation to begin operation of the service. Other embodiments may be described and claimed.

Orchestration and management of services to deployed devices

Techniques are disclosed for scene analysis including the use of acoustic imaging and computer audio vision processes for monitoring applications. In some embodiments, an acoustic image device is utilized with a microphone array, image sensor, acoustic image controller, and a controller. In some cases, the controller analyzes at least a portion of the spatial spectrum within the acoustic image data to detect sound variations by identifying regions of pixels having intensities exceeding a particular threshold. In addition, the controller can detect two or more co-occurring sound events based on the relative distance between pixels with intensities exceeding the threshold. The resulting data fusion of image pixel data, audio sample data, and acoustic image data can be analyzed using computer audio vision, sound/voice recognition, and acoustic signature techniques to recognize/identify audio and visual features associated with the event and to empirically or theoretically determine one or more conditions causing each event.

Growing community of inventors

Carlow, Ireland

Keith A Ellis