T-Mobile
Principal Engineer - System Design and Strategy
At&T 2001 - 2015
Engineer V - National Quality and Performance Group
Orange Communications Sa 1999 - 2000
Performance Engineer
Moffet Larson & Johnson 1995 - 1998
Design Engineer
Education:
University of California, Berkeley 2015
Duke University (Via Coursera) 2014
The Johns Hopkins University 2013
University of Washington (Via Coursera) 2013 - 2013
Stanford University 2013 - 2013
Virginia Tech 1990 - 1995
Bachelors, Bachelor of Science, Electrical Engineering
- Bellevue WA, US Kevin HINSON - Seattle WA, US Jie HUI - Mercer Island WA, US Antoine TRAN - Bellevue WA, US Bryan YANG - Seattle WA, US Doru CULIAC - Bellevue WA, US
International Classification:
H04W 4/02 G01S 5/02 G01S 5/00
Abstract:
Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.
Developing A Fingerprint Map For Determining An Indoor Location Of A Wireless Device
- Bellevue WA, US Kevin HINSON - Seattle WA, US Jie HUI - Mercer Island WA, US Antoine TRAN - Bellevue WA, US Bryan YANG - Seattle WA, US Doru CULIAC - Bellevue WA, US
International Classification:
H04W 4/02 G01S 5/00 G01S 5/02
Abstract:
Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.
Profiling Location Information And Network Traffic Density From Telemetry Data
- Bellevue WA, US Kevin HINSON - Seattle WA, US Jie HUI - Mercer Island WA, US Antoine TRAN - Bellevue WA, US Bryan YANG - Seattle WA, US Doru CULIAC - Bellevue WA, US
International Classification:
H04W 4/029 G01S 5/00
Abstract:
A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.
- Bellevue WA, US Kevin HINSON - Seattle WA, US Jie HUI - Mercer Island WA, US Antoine TRAN - Bellevue WA, US Bryan YANG - Seattle WA, US Doru CULIAC - Bellevue WA, US
International Classification:
G01C 21/20 G01C 21/00
Abstract:
Techniques are disclosed for locating a user device in an indoor environment such as a building based at least on a building topology model using one or more Internet of Things (IoT) devices. Certain aspects of the techniques include detecting a presence of a user device in communication with an IoT device in a building, wherein the IoT device is associated with device attributes. The building is identified based at least on a building topology model that is associated with the building and the device attributes. The location of the IoT device is determined based at least on the building topology model. The user device is located relative to the location of the IoT device.
Constrained User Device Location Using Building Topology
- Bellevue WA, US Narciso FAUSTINO - McKinney TX, US Kevin HINSON - Seattle WA, US Jie HUI - Mercer Island WA, US Antoine TRAN - Bellevue WA, US Bryan YANG - Seattle WA, US
International Classification:
H04W 4/021 H04W 4/029
Abstract:
Techniques are described for locating user devices in an indoor environment based at least on a building topology model. The techniques include detecting a presence of a user device in a building. The building may include ingress and egress points connecting defined spaces within the building. The locations of the individual ingress and egress points are identified based at least on the movement data of the user device. A building topology model may be generated using the locations of the individual ingress and egress points. To locate the user device at a given timestamp, one or more of the ingress and egress points passed through by the user device may be identified. The location of the user device may be determined based at least on the locations of the one or more ingress and egress points passed through by the user device mapped to the building topology model.
Mock Road Elementary School Albany GA 1977-1978, Turner Elementary School Albany GA 1978-1982, Brewer Middle School Greenwood SC 1983-1984, Northside Middle School Greenwood SC 1984-1987