Jie Chen

20210377903, Dec 2, 2021

Jun 2, 2020

16/890493

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Wenjie Zhao - Princeton NJ, US

H04W 64/00
H04W 16/14
H04W 4/029
H04W 24/02
H04L 12/24
H04W 16/18

The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include identifying, by a device comprising a processor, predicted resource usage of a first antenna covering a geographic zone. Further, the method can include selecting, by the device, a group of geographic siting locations within the geographic zone for potentially siting ones of a group of second antennas. In addition, selecting, by the device, a spatial arrangement in relation to the first antenna, of a subset of the group of geographic siting locations can occur, with a selected spatial arrangement including an arrangement to maintain the predicted resource usage in the condition.

20210081673, Mar 18, 2021

Sep 9, 2020

17/016260

- Princeton NJ, US
Asim KADAV - Jersey City NJ, US
Jie CHEN - Bellevue WA, US

NEC LABORATORIES AMERICA, INC - Princeton NJ

G06K 9/00

Aspects of the present disclosure describe systems, methods, and structures that provide action recognition with high-order interaction with spatio-temporal object tracking. Image and object features are organized into into tracks, which advantageously facilitates many possible learnable embeddings and intra/inter-track interaction(s). Operationally, our systems, method, and structures according to the present disclosure employ an efficient high-order interaction model to learn embeddings and intra/inter object track interaction across the space and time for AR. Each frame is detected by an object detector to locate visual objects. Those objects are linked through time to form object tracks. The object tracks are then organized and combined with the embeddings as the input to our model. The model is trained to generate representative embeddings and discriminative video features through high-order interaction which is formulated as an efficient matrix operation without iterative processing delay.

20180227876, Aug 9, 2018

Apr 5, 2018

15/946163

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Sam Houston Parker - Cranbury NJ, US
Huahui Wang - Bridgewater NJ, US
Wenjie Zhao - Princeton NJ, US

H04W 64/00
G01S 5/10

Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.

20170079005, Mar 16, 2017

Nov 23, 2016

15/359843

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Sam Houston Parker - Cranbury NJ, US
Huahui Wang - Bridgewater NJ, US
Wenjie Zhao - Princeton NJ, US

H04W 64/00
G01S 5/10

Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.

20150148059, May 28, 2015

Nov 26, 2013

14/090166

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Sam Houston Parker - Cranbury NJ, US
Huahui Wang - Bridgewater NJ, US
Wenjie Zhao - Princeton NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04W 64/00

4554561

Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.