Intel Corporation

Apple Inc.

David Arditti Ilitzky

Candy Yiu

Shilpa Talwar

Satish Chandra Jha

Geng Wu

Tanay Karnik

Qian Li

Ignacio Javier Alvarez

Ankit Prakash Gupta

Arvind Srinivasan

Dave A Cavalcanti

Julio Cesar Zamora Esquivel

Rafael De La Guardia Gonzalez

Robert G Southworth

Thomas A Tetzlaff

David Israel Gonzalez Aguirre

Javier Felip Leon

David Gomez Gutierrez

Vaibhav Vaidya

Christopher Schaef

Suhwan Kim

Bongjin Jung

Daniel S Lake

Carl S Marshall

Stefano Pellerano

Harry G Skinner

Feng Xue

Dawei Ying

Guangjie Li

Jose I Parra Vilchis

Leobardo E Campos Macias

Rodrigo Aldana Lopez

Jose Rodrigo Camacho Perez

Ching-Yu Liao

Harish Krishnamurthy

Paulo Lopez Meyer

Hector A Cordourier Maruri

Lu Lu

Vesh Raj Sharma Banjade

Seong-Youp Suh

Hassan Ghozlan

Paul H Dormitzer

Yaser M Fouad

Joonbeom Kim

Jingwen Bai

Sreenivas Kasturi

Ranganadh Karella

Shu-Ping Yeh

Eduardo Alban

Mustafa Akdeniz

Jonathan A Dama

Xuebin Yang

Sriram Kabisthalam Muthukumar

Dileep J Kurian

Patrick Mead

David Gómez Gutiérrez

Rocio Hernandez Fabian

Linda Patricia Osuna Ibarra

Karl S Papadantonakis

Victor Palacios Rivera

Pranjali S Deshmukh

John Greth

Arturo Veloz

Saurabh Bhandari

Gaspar Mora Porta

Mika Nystroem

Venkatesh Rajendran

Tiefeng Shi

Edgar Borrayo

Alberto Alcocer Ochoa

Carlos Ornelas

Bernard Deadman

Rodrigo Aldana López

Jorge Hermosillo

Jorge Romero Aragon

Ilya Klochkov

Mark Edward Davis

Yassir Mosleh

Song Noh

Carlos A Flores Fajardo

Jorge Carballido Gamio

Rodrigo Varela Leos

Paulino Mendoza

Edgar Borrayo Sandoval

Leobardo Campos Macías

Humberto Caballero Barragan

Valeri Kontorovich Mazover

Fernando Ramos Alarcon Barroso

Miryam Lomeli Barajas

Timo Huusari

Bradley Alan Jackson

Daniel Tong

Philippe Auzas

Jonathan Byrne

John Tell

Mengkun Ke

Xiaoyun May Wu

Apparatuses, methods and storage medium associated with compensating for a sensor deficiency in a heterogeneous sensor array are disclosed herein. In embodiments, an apparatus may include a compute device to aggregate perception data from individual perception pipelines, each of which is associated with respective one of different types of sensors of a heterogeneous sensor set, to identify a characteristic associated with a space to be monitored by the heterogeneous sensor set; detect a sensor deficiency associated with a first sensor of the sensors; and in response to a detection of the sensor deficiency, derive next perception data for more than one of the individual perception pipelines from sensor data originating from at least one second sensor of the sensors. Other embodiments may be disclosed or claimed.

Compensating for a sensor deficiency in a heterogeneous sensor array

An unmanned aerial vehicle can be configured to adjust a beam direction, provide path information, act as a base station, act as a cluster head, include an improved directional antenna or array of directional antennas, communicate in a collaboration using belief propagation, receive communications from a serving station aiding in navigation or improved signal performance, or the like.

Unmanned aerial vehicle communication

Examples describe an egress port manager that uses an adaptive jitter selector to apply a jitter threshold level for a buffer, wherein the jitter threshold level is to indicate when egress of a packet segment from the buffer is allowed, wherein a packet segment comprises a packet header and wherein the jitter threshold level is adaptive based on a switch fabric load. In some examples, the jitter threshold level is to indicate a number of segments for the buffer's head of line (HOL) packet that are to be in the buffer or indicate a timer that starts at a time of issuance of a first read request for a first segment of the packet in the buffer. In some examples, the jitter threshold level is not more than a maximum transmission unit (MTU) size associated with the buffer. In some examples, a fetch scheduler is used to adapt an amount of interface overspeed to reduce packet fetching latency while attempting to prevent fabric saturation based on a switch fabric load level, wherein the fetch scheduler is to control the jitter threshold level for the buffer by forcing a jitter threshold level based on switch fabric load level and latency profile of the switch fabric.

Dynamic virtual cut-through and dynamic fabric bandwidth allocation between virtual cut-through and store-and-forward traffic

A signal processing device includes a prediction and correction engine configured to receive a signal including a target signal, and to execute a single-moment filter, based on a current measurement sample of the signal and a model of the target signal, to obtain a single-moment state estimate and a single-moment state estimate error covariance for the target signal, a covariance renormalizer configured to determine a multi-moment state estimate error covariance for the target signal based on a prior single-moment state estimate error covariance, corresponding to a sample prior to the current measurement sample, and the single-moment state estimate error covariance, and a multi-moment prediction and correction engine configured to execute a multi-moment filtering extension based on the current measurement sample and the multi-moment state estimate error covariance to obtain a multi-moment state estimate, and further configured to determine an estimate for the target signal based on the multi-moment state estimate.

Interference mitigation and multi-moment filtering

Example predictive wireless feedback control systems disclosed herein include a receiver to receive measurements of a controlled system via a first wireless link. Disclosed example systems also include an observer to output estimated values of states of the controlled system based on a state space model that is updated based on the measurements. Disclosed example system further include a predictor to predict future values of the states of the controlled system based on the estimated values of the states, a first latency of the first wireless link and an upper limit of a second latency associated with a second wireless link that is to communicate values of a control signal to an actuator associated with the controlled system. In disclosed examples, the predictor is to output the predicted future values of the states to a controller that is to determine the control signal.

Predictive wireless feedback control loops

Examples describe a manner of scheduling packet segment fetches at a rate that is based on one or more of: a packet drop indication, packet drop rate, incast level, operation of queues in SAF or VCT mode, or fabric congestion level. Headers of packets can be fetched faster than payload or body portions of packets and processed prior to queueing of all body portions. In the event a header is identified as droppable, fetching of the associated body portions can be halted and any body portion that is queued can be discarded. Fetch overspeed can be applied for packet headers or body portions associated with packet headers that are approved for egress.

Maintaining bandwidth utilization in the presence of packet drops

Examples describe use of multiple meta-data delivery schemes to provide tags that describe packets to an egress port group. A tag, that is smaller than a packet, can be associated with a packet. The tag can be stored in a memory, as a group with other tags, and the tag can be delivered to a queue associated with an egress port. Packets received at an ingress port can be as non-interleaved to reduce underrun and providing cut-through to an egress port. A shared memory can be allocated to store packets received at a single ingress port or shared to store packets from multiple ingress ports.

Multi-path packet descriptor delivery scheme

Example wireless feedback control systems disclosed herein include a receiver to receive a first measurement of a target system via a first wireless link. Disclosed example systems also include a neural network to predict a value of a state of the target system at a future time relative to a prior time associated with the first measurement, the neural network to predict the value of the state of the target system based on the first measurement and a prior sequence of values of a control signal previously generated to control the target system during a time interval between the prior time and the future time, and the neural network to output the predicted value of the state of the target system to a controller. Disclosed example systems further include a transmitter to transmit a new value of the control signal to the target system via a second wireless link.

Wireless feedback control loops with neural networks to predict target system states

An apparatus for autonomous vehicles includes a perception pipeline having independent classification processes operating in parallel to respectively identify objects based on sensor data flows from multiple ones of a plurality of sensors. The apparatus also includes a sensor monitoring stage to operate in parallel with the perception pipeline and to use the sensor data flows to estimate and track a confidence level of each of the plurality of different sensors, and nullify a deficient sensor when the confidence level associated with the deficient sensor fails to meet a confidence threshold.

Multimodal sensing in autonomous driving vehicles with self-healing capabilities

An apparatus is provided which comprises: a first Power Management Unit (PMU); and a second PMU, wherein the first PMU is to manage transition of the apparatus from a low power state to a first active state, wherein the second PMU is to manage transition of the apparatus from the first active state to a second active state, and wherein the second PMU is to be powered down while the apparatus is to be in the low power state.

Hierarchical power management unit for low power and low duty cycle devices

Methods and apparatus relating to heterogeneous compute architecture hardware/software co-design for autonomous driving are described. In one embodiment, a heterogeneous compute architecture for autonomous driving systems (also interchangeably referred to herein as Heterogeneous Compute Architecture or 'HCA' for short) integrates scalable heterogeneous processors, flexible networking, benchmarking tools, etc. to enable (e.g., system-level) designers to perform hardware and software co-design. With HCA system engineers can rapidly architect, benchmark, and/or evolve vehicle system architectures for autonomous driving. Other embodiments are also disclosed and claimed.

Heterogeneous compute architecture hardware/software co-design for autonomous driving

Systems, apparatuses and methods may provide for early pre-charge with respect to peak power events. Application performance may improve by pre-charging a supercap just prior to initiating a system wake up from a qualified system wake-source trigger. Additionally, the pre-charging of the supercap may be controlled by a time defined pre-charge period and may also be controlled by a predetermined threshold voltage.

Early pre-charge enablement for peak power application in net zero energy devices

Systems and methods for detecting rogue vehicles within a plurality of vehicles connected via a vehicular ad-hoc network (VANET) are provided. Sensors on each VANET vehicle provide host vehicle data and data associated with other nearby vehicles. Each VANET vehicle multicasts information that includes location, velocity, and preferred future travel path to the other VANET vehicles. Using data from sensors and data received from other VANET vehicles the host vehicle generates a dynamic set of safe vehicle operating behaviors. Nearby vehicles that do not comply with the determined safe vehicle operating behaviors or perform illegal/unsafe acts are identified as rogue vehicles. Data associated with identified rogue vehicles is transmitted to all VANET vehicles. Each VANET vehicle determines a preferred future travel path based on the received information associated with rogue vehicles, the preferred future travel path information received from other VANET vehicles, and the host vehicle's safe vehicle operating behaviors.

Rogue vehicle detection and avoidance

Growing community of inventors

Guadalajara, Mexico

David Arditti Ilitzky