Ting W Wang

20100119241, May 13, 2010

Sep 25, 2009

12/567398

KAI YANG - Murray Hill NJ, US
Ting Wang - West Windsor NJ, US

NEC Laboratories America, Inc. - Princeton NJ

H04B 10/06

398208

Polarization multiplexing, optical communications systems can suffer from chromatic dispersion and polarization mode dispersion, resulting in channel delay spread. These errors can be compensated quickly and simply in the frequency domain. By obviating the need for a cyclic prefix, the complexity of the equalization can be reduced by more than a factor of twenty.

20120251117, Oct 4, 2012

Mar 30, 2012

13/435413

Ankitkumar N. Patel - East Brunswick NJ, US
Philip Nan Ji - Princeton NJ, US
Jason P. Jue - Plano TX, US
Ting Wang - West Windsor NJ, US
Yoshiaki Aono - Tokyo, JP

NEC LABORATORIES AMERICA, INC. - Princeton NJ

H04J 14/02

398 79

A method includes determining a line rate selection for a flexible optical wavelength-division-multiplexing WDM network, determining a traffic routing in said network, and determining simultaneously a channel routing, wavelength assignment and spectrum allocation in said network based on an auxiliary graph.

20120263469, Oct 18, 2012

Apr 13, 2012

13/445965

Jingjing Zhang - East Brunswick NJ, US
Ting Wang - West Windsor NJ, US
Naoto Saeki - Tokyo, JP

NEC CORPORATION - Tokyo
NEC LABORATORIES AMERICA, INC. - Princeton NJ

H04J 14/00

398 66

A method for reducing energy consumption of a passive optical network includes optical network units of the network which infer their downstream queue status rather than being explicitly notified by an optical line terminal of the network. Based on the inferred queue status, the optical network units make their own sleep mode decisions without assistance from optical line terminal. Both downstream traffic inference and sleep decision making at the optical network units are based on common information possessed by optical line terminal and optical network units. Accordingly, the optical line terminal can accurately infer the status of each optical network unit if the sleep control scheme implemented at an optical network unit is known by the optical line terminal.

20130318615, Nov 28, 2013

May 23, 2012

13/478290

Mihai Christodorescu - Briarcliff Manor NY, US
Reiner Sailer - Scarsdale NY, US
Douglas L. Schales - Ardsley NY, US
Marc Ph. Stoecklin - Riverdale NY, US
Ting Wang - Elmsford NY, US

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

G06F 21/00
G06F 17/10

726 25

Methods for determining cyber-attack targets include collecting and storing network event information from sensors to extract information regarding an attacker; forming an attack scenario tree that encodes network topology and vulnerability information including paths from known compromised nodes to a set of potential targets; calculating a likelihood for each of the paths using a processor; calculating a probability distribution for the set of potential targets to determine which potential targets are most likely pursued by the attacker; calculating a probability distribution over a set of nodes and node vulnerability types already accessed by the attacker; determining a network graph edge to remove which minimizes a defender's expected uncertainty over the potential targets; and removing the determined network graph edge.

20130318616, Nov 28, 2013

Jun 4, 2012

13/487774

Mihai Christodorescu - Briarcliff Manor NY, US
Reiner Sailer - Scarsdale NY, US
Douglas L Schales - Ardsley NY, US
Marc Ph Stoecklin - Riverdale NY, US
Ting Wang - Elmsford NY, US

International Business Machines Corporation - Armonk NY

G06F 21/00

726 25

Systems for determining cyber-attack target include a network monitor module configured to collect network event information from sensors in one or more network nodes; a processor configured to extract information regarding an attacker from the network event information, to form an attack scenario tree that encodes network topology and vulnerability information including a plurality of paths from known compromised nodes to a set of potential targets, to calculate a likelihood for each of the paths, to calculate a probability distribution for the set of potential targets to determine which potential targets are most likely pursued by the attacker, to calculate a probability distribution over a set of nodes and node vulnerability types already accessed by the attacker, and to determine a network graph edge to remove that minimizes a defender's expected uncertainty over the potential targets; and a network management module configured to remove the determined network graph edge.

20220329068, Oct 13, 2022

Apr 10, 2022

17/717112

- Princeton NJ, US
Yuanda XU - Jersey City NJ, US
Yue TIAN - Princeton NJ, US
Sarper OZHARAR - Princeton NJ, US
Ting WANG - West Windsor NJ, US

NEC LABORATORIES AMERICA, INC - Princeton NJ

H02J 3/00
G01H 9/00
G01D 5/353

Distributed fiber optic sensing (DFOS) and artificial intelligence (AI) systems and methods for performing utility pole hazardous event localization that advantageously identify a utility pole that has undergone a hazardous event such as being struck by an automobile or other detectable impact. Systems and methods according to aspects of the present disclosure employ machine learning methodologies to uniquely identify an affected utility pole from a plurality of poles. Our systems and methods collect data using DFOS techniques in telecommunication fiber optic cable and use an AI engine to analyze the data collected for the event identification. The AI engine recognizes different vibration patterns when an event happens and advantageously localizes the event to a specific pole and location on the pole with high accuracy. The AI engine enables analyses of events in real-time with greater than 90% accuracy.

20210312801, Oct 7, 2021

Apr 4, 2021

17/221814

- Princeton NJ, US
Eric COSATTO - Red Bank NJ, US
Ting WANG - West Windsor NJ, US

NEC LABORATORIES AMERICA, INC - Princeton NJ

G08G 1/04
G06T 11/20
G06T 5/20
G06T 5/00
G06K 9/46
G06K 9/62
H04B 10/071

Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously provide traffic monitoring. and traffic management which improves the safety and efficiency of a roadway.

20210160260, May 27, 2021

Feb 4, 2021

17/167330

- Armonk NY, US
Jiyong JANG - Ossining NY, US
Douglas Lee SCHALES - Ardsley NY, US
Marc Philippe STOECKLIN - White Plains NY, US
Ting WANG - White Plains NY, US

H04L 29/06
G06N 20/00
G06F 16/28
G06F 16/901

Unknown and reference signatures are accessed. The unknown and reference signatures indicate patterns that correspond to known threats to resources (such as computer systems and/or computer networks) in a computer environment and comprise a multitude of descriptive elements having information describing different aspects of a corresponding signature. A set of similarity measures is created of the unknown and reference signatures from different perspectives, each perspective corresponding to a descriptive element. The set of similarity measures are integrated to generate an overall similarity metric. The overall similarity metric is used to find appropriate categories in the reference signatures into which the unknown signatures should be placed. The unknown signatures are placed into the appropriate categories to create a mapping from the unknown signatures to the reference signatures. The mapping is output for use by an IDPS for determining whether a threat has occurred to the resources in the computer environment.