Hewlett Packard Enterprise Development LP

Deutschetelekom Ag

Leland Stanford Junior University

Hewlett-packard Development Company, L.p.

Kyu-Han Kim

Christina Vlachou

Jatinder Pal Singh

Souvik Sen

Shruti Sanadhya

Bernd Girod

Puneet Jain

Sangki Yun

Alexander W Min

Ioannis Pefkianakis

Nischal Murthy Piratla

Jeonghun Noh

Zhenyun Zhuang

Sachin Kumar Agarwal

Syed Rafiul Hussain

Ayon Chakraborty

Xiufeng Xie

Peter Pogrzeba

Yuanjie Li

Muhammad Kumail Haider

Jeongkeun Lee

David Chen

David D Lee

Shuai Wang

Paul T Congdon

Jun Qing Xie

Qun Yang Lin

Jeongyeup Paek

Angela Nicoara

Cheng-Hsin Hsu

Yurong Jiang

Martin Robert Fink

Nathaniel Stone

Jeremy Gummeson

Jindan Zhu

Theodore A Stone

Soteris Demetriou

Abhinav Parate

Animesh Srivastava

Dongyao Chen

Sam Tsai

Eduardo Alberto Cuervo Laffaye Cuervo

Swetank Kumar Saha

Prashant Mohapatra

Dhruv Gupta

Qianru Li

Raghav Hampapur Venkatnarayan

Xing Liu

Uma Parthavi Moravapalle

Wenguang Mao

Kassem Fawaz

David Lee

Martin R Fink

Christina Viachou

Systems and methods for mitigating cyberattacks are described herein. A computing system can detect illegitimate network traffic associated with a cyberattack in network traffic. The computing system can determine an amplification factor of the cyberattack based in part on a probability distribution of the illegitimate network traffic. The computing system can determine a filter to demotivate a generation of the illegitimate network traffic. The determined filter can reduce the amplification factor of the cyberattack. The computing system can implement the determined filter to block the illegitimate network traffic.

Systems and methods for mitigating cyberattacks

Examples described herein provide method and systems for high spatial reuse for mmWave Wi-Fi. Examples may include identifying, by a network device, a plurality of millimeter-wave (mmWave) propagation paths between the network device and a set of neighboring devices including a target neighboring device, based on power delay profiles (PDPs) of beam training frames received by the network device from each of the neighboring devices using a plurality of mmWave beams, and determining, by the network device for each of neighboring devices in the set, an estimated angle of arrival (AoA) of each identified mmWave propagation path between the network device and the neighboring device, based on the PDPs of the received beam training frames from the neighboring device. Examples may include selecting, by the network device, one of the mmWave beams that maximizes a signal to interference and noise ratio (SINR) along the estimated AoA of each identified mmWave propagation path between the network device and the target neighboring device, and communicating, by the network device, with the target neighboring device using the selected mmWave beam.

High spatial reuse for mmWave Wi-Fi

A video annotation system for deep learning based video analytics and corresponding methods of use and operation are described that significantly improve the efficiency of video data frame labeling and the user experience. The video annotation system described herein may be deployed at a network edge and may support various intelligent annotation functionality including annotation tracking, adaptive video segmentation, and execution of predictive annotation algorithms. In addition, the video annotation system described herein supports team collaboration functionality in connection with large-scale labeling tasks.

Video annotation system for deep learning based video analytics

Embodiments are directed to passive multi-person location tracking utilizing signal polarization. An embodiment of a system includes multiple receivers, including a first receiver at a first location and a second receiver at a second location, each receiver including one or more receiver antennas to receive polarized radio signals; a transmitter located at a third location to transmit polarized radio signals, the transmitter including antennas for a first signal at a first polarization direction and a second signal at a second, different polarization direction; and a processing system to receive channel state information from each receiver, and to track a plurality of individuals utilizing the received channel state information, wherein the tracking of the plurality of individuals is based at least in part on analysis of a polarization parameter and a set of location parameters generated for each of multiple reflection paths based on the channel state information.

Passive multi-person location tracking utilizing signal polarization

Systems and methods described herein provide a centralized solution for interference mitigation that focuses on efficient spectrum management. Wireless Access Points (APs) collect interference measurements for wireless channels and generate corresponding interferer airtime estimates. The APs also determine Packet Error Rates (PERs) for wireless client devices currently being served via the channels by the APs. The APs send the interferer airtime measurements and the PERs to a centralized controller. If an interferer airtime estimate and a PER for a particular channel satisfy predefined threshold conditions, the controller modifies an allocation of wireless bandwidth for an AP that is currently using the channel and sends a message to the AP indicating how the allocation has been modified.

Centralized spectrum management for interference mitigation

Enhanced spatial reuse of radio spectrum in WLAN operation is described. An example of a storage medium includes instructions for connecting with multiple client devices at an access point in a basic service set (BSS) for transmission over a channel in a network environment; estimating a communication quality value for each of the client devices based at least in part on received signal strength values at the access point and at the client device; prioritizing spatial reuse operation based on the estimated communication quality values for the client devices; establishing a clear channel assessment (CCA) threshold for a spatial reuse operation in a transmission to a first client device, the first client device being selected based on an estimated communication quality value; and dynamically adjusting the CCA threshold based at least in part on success or failure of data transmission between the access point and the first client device.

Enhanced spatial reuse of radio spectrum in WLAN operation

Examples provide accurate localization of an object by using a network device. Examples include determining distances between transmitter and receiver antennas of a network device, transmitting, by the transmitter antenna, a wireless signal having a transmit power, receiving, by the receiver antennas, a reflected signal that reflects off of an object, receiving, by the receiver antennas, a static signal that does not reflect off of the object, and processing the static and reflected signals and determining the location of the object, based on the distances between the transmitter and the receiver antennas and the transmit power.

Accurate localization of an object by a network device

Embodiments are directed to interference-aware link-rate adaptation for wireless communication. An embodiment of a storage medium includes instructions to operations including receiving data regarding wireless signals received by an access point, the access point to provide Wi-Fi communication, the wireless signals being communicated in an unlicensed radio spectrum; performing spectral analysis of the received wireless signals to identify a channel of an LTE (Long-Term Evolution) signal; and upon determining that energy level of the LTE signal is below an energy detection (ED) level and that the channel is a primary Wi-Fi channel for the access point, adapting a link rate for the access point based on channel state information for the channel.

Interference-aware link-rate adaptation for wireless communication

An example method of operating a network may include determining whether a flow is to be added to the network based on: a flow type of the flow, a link condition of the flow, and for each possible combination of flow type and link condition out of multiple flow types and multiple link conditions, the number of flows currently carried on the network that correspond to the respective combination.

Determining whether a flow is to be added to a network

Example implementations relate to mobile virtual private network (mVPN) configuration. For example, a system for mVPN configuration may include a configuration selector to intercept an internet protocol (IP) packet in a mobile virtual private network (mVPN) and select a mVPN configuration for the IP packet using a lookup table. The system may further include a configuration adapter to adapt the IP packet according to the selected mVPN configuration.

Mobile virtual private network configuration

A method of adjusting a communication link of a client device of a wireless local area network is described. The method includes monitoring two or more communication links between two or more client devices and a network device in a wireless local area network. The two or more client devices including a first client device and a second client device. The method also includes detecting a degradation in a first communication link between the first client device and the network device. The communication link degradation is not detected in a second communication link between the second client device and the network device. The method further includes determining the degradation in the first communication link is caused at least in part by interference from cellular network communications. The method includes adjusting one or more parameters of the first communication link without affecting the second communication link in response to determining the degradation is cause at least in part by the interference from the cellular network communications.

LTE interference detection and mitigation for Wi-Fi links

An example video analytics device can include a memory, a processor executing instructions stored in the memory, an active region detector to identify a plurality of active regions of a plurality of video streams, wherein the plurality of active regions are sections in the plurality of video streams that change from a first frame to a second frame, and a bin packer communicatively coupled with the processor to combine the plurality of active regions to produce a multi-batch of an individual slice, wherein the multi-batch of the individual slice is a batch including the plurality of active regions that is processed at once.

Video active region batching

Example method includes: transmitting a plurality of probe images from an IoT device at an edge network to a server hosting a target DNN, wherein the plurality of images are injected with a limited amount of noise to probe sensitivities of the target DNN to the red, green, and blue colors; receiving a feedback comprising a plurality of DCT coefficients unique to target DNN from the server hosting the target DNN; computing a plurality of color conversion weights based on the feedback received from the server; converting a set of real-time images from RGB color space to YUV color space using the plurality of color conversion weights unique to the target DNN; compressing the set of real-time images using a quantization table unique to the target DNN by the IoT device; and transmitting the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

Deep neural network color space optimization

Example method includes receiving, at an access point (AP), a plurality of virtual reality (VR) data from each of a plurality untethered mobile devices associated with a multi-user VR environment, receiving, at the AP, an indication of an atomic data block size for data to be transmitted to one or more of the plurality of untethered mobile devices, reducing, by the AP and based on the indication, a transmission duration of a VR data block to be transmitted to one or more of the plurality of untethered mobile devices.

Wi-fi optimization for untethered multi-user virtual reality

This disclosure provides systems through which a wireless access point (AP) can determine the location of a client device with a high degree of accuracy. The AP uses CIR measurements for a plurality of directional beams used to communicate with the client device to determine a respective amplitude of a Line-of-Sight (LOS) path between the AP and the client device for each beam. The AP identifies a high-LOS-amplitude subset of the beams and, based on predefined radiation patterns associated with the beams included in the high-LOS-amplitude subset, determines a direction of the LOS path between the AP and the client device. The AP also determines a Time-of-Flight (ToF) for radio frames exchanged between the AP and the client device via the plurality of directional beams and uses the ToF to determine a distance between the AP and the client device.

Accurate localization of client devices for wireless access points

Growing community of inventors

Palo Alto, CA, United States of America

Kyu-Han Kim