Ning Xu, 56236 Bayberry Cmn, Fremont, CA 94539

7760109, Jul 20, 2010

Feb 1, 2006

11/345737

Alan Broad - Palo Alto CA, US
Rahul Kapur - San Francisco CA, US
Jaidev Prabhu - San Jose CA, US
Martin Albert Turon - Berkeley CA, US
Ning Xu - San Jose CA, US

Memsic, Inc. - Andover MA

G08C 19/00
G08B 9/00
G08B 5/22
H04B 1/10
G01R 21/00
H04L 12/28

34082569, 34082573, 340 729, 34028602, 455 64, 702 62, 370389

A plurality of modules interact to form an adaptive network in which each module transmits and receives data signals indicative of proximity of objects. A central computer accumulates the data produced or received and relayed by each module for analyzing proximity responses to transmit through the adaptive network control signals to a selectively-addressed module to respond to computer analyzes of the data accumulated from modules forming the adaptive network. Interactions of local processors in modules that sense an intrusion determine the location and path of movements of the intruding object and control cameras in the modules to retrieve video images of the intruding object. Multiple operational frequencies in adaptive networks permit expansions by additional networks that each operate at separate radio frequencies to avoid overlapping interaction. Additional modules may be introduced into operating networks without knowing the operating frequency at the time of introduction. New programs are distributed to all or selected modules under control of the base station.

8005108, Aug 23, 2011

Oct 31, 2007

11/930832

Alan S Broad - Palo Alto CA, US
Ning Xu - San Jose CA, US

Memsic Transducer Systems Co., Ltd. - Wuxi, Jiangsu Province

H04J 3/26

370432, 3405391, 340 1033

A plurality of modules interact to form an adaptive network in which each module transmits and receives data signals indicative of physical properties sensed at the modules. A new module is joined in the adaptive network in an expedient manner. The new module transmits a burst of beacon messages after the interactive module is activated to discover neighboring interactive modules deployed and operating in the adaptive network. The neighboring interactive module stays in a sleep-mode of low-power expenditure. The beacon messages persist for a first interval longer than a second interval during which the neighboring interactive modules remain in the sleep mode. After receiving the beacon messages, one or more neighboring interactive modules transmit response messages to the new interactive module. The new interactive module receives the response messages and selects a neighboring interactive module for communication based on the received response messages. The new module can also include an indicator for indicating discovery of a neighboring interactive module with which a reliable wireless link can be established.

8115593, Feb 14, 2012

May 11, 2006

11/433194

Alan Broad - Palo Alto CA, US
Rahul Kapur - San Francisco CA, US
Jaidev Prabhu - San Jose CA, US
Martin Albert Turon - Berkeley CA, US
Ning Xu - San Jose CA, US
Xin Yang - San Leandro CA, US
Matt Miller - Grass Valley CA, US

Memsic Transducer Systems Co., Ltd. - Wuxi, Jiangsu Province

G08B 5/22

340 733, 340 732, 370238, 370252, 370351, 37039521

A plurality of modules interact to form an adaptive network in which each module transmits and receives data signals indicative of proximity of objects. A central computer accumulates the data produced or received and relayed by each module for analyzing proximity responses to transmit through the adaptive network control signals to a selectively-addressed module to respond to computer analyses of the data accumulated from modules forming the adaptive network. Interactions of local processors in modules that sense an intrusion determine the location and path of movements of the intruding object and control cameras in the modules to retrieve video images of the intruding object. Multiple operational frequencies in adaptive networks permit expansions by additional networks that each operate at separate radio frequencies to avoid overlapping interaction. Additional modules may be introduced into operating networks without knowing the operating frequency at the time of introduction. Remote modules operating as leaf nodes of the adaptive network actively adapt to changed network conditions upon awaking from power-conserving sleep mode.

2012016, Jun 28, 2012

Jan 24, 2012

13/356987

Alan Broad - Palo Alto CA, US
Rahul Kapur - San Francisco CA, US
Jaidev Prabhu - San Jose CA, US
Martin Albert Turon - Berkeley CA, US
Ning Xu - San Jose CA, US
Xin Yang - San Leandro CA, US
Matt Miller - Grass Valley CA, US

G06F 1/32

713323

A plurality of modules interact to form an adaptive network in which each module transmits and receives data signals indicative of the proximity of objects. A central computer accumulates the data produced or received and relayed by each module. One of the modules is operable as a leaf node having a sleep mode to conserve energy and an interactive mode. The central computer can send a message to the leaf node commanding it to stay awake in order to receive subsequent communications.

2012029, Nov 15, 2012

Jun 26, 2012

13/533428

Alan Broad - Palo Alto CA, US
Rahul Kapur - San Francisco CA, US
Jaidev Prabhu - San Jose CA, US
Martin Albert Turon - Berkeley CA, US
Ning Xu - San Jose CA, US
Xin Yang - San Leandro CA, US
Matt Miller - Grass Valley CA, US

G06F 1/26

713300

A plurality of modules interact to form an adaptive network in which each module transmits and receives data signals indicative of proximity of objects. A central computer accumulates the data produced or received and relayed by each module for analyzing proximity responses to transmit through the adaptive network control signals to a selectively-addressed module to respond to computer analyses of the data accumulated from modules forming the adaptive network. Interactions of local processors in modules that sense an intrusion determine the location and path of movements of the intruding object and control cameras in the modules to retrieve video images of the intruding object. Multiple operational frequencies in adaptive networks permit expansions by additional networks that each operate at separate radio frequencies to avoid overlapping interaction. Additional modules may be introduced into operating networks without knowing the operating frequency at the time of introduction. Remote modules operating as leaf nodes of the adaptive network actively adapt to changed network conditions upon awaking from power-conserving sleep mode. New programs are distributed to all or selected modules under control of the base station.

2013030, Nov 14, 2013

May 8, 2012

13/466984

Prashant Anand - Bangalore, IN
Ramanathan Lakshmikanthan - Santa Clara CA, US
Sun Den Chen - San Jose CA, US
Ning Xu - San Jose CA, US

H04L 12/56

370389

In one aspect, the present invention reduces the amount of low-latency memory needed for rules-based packet classification by representing a packet classification rules database in compressed form. A packet processing rules database, e.g., an ACL database comprising multiple ACEs, is preprocessed to obtain corresponding rule fingerprints. These rule fingerprints are much smaller than the rules and are easily accommodated in on-chip or other low-latency memory that is generally available to the classification engine in limited amounts. The rules database in turn can be stored in off-chip or other higher-latency memory, as initial matching operations involve only the packet key of the subject packet and the fingerprint database. The rules database is accessed for full packet classification only if a tentative match is found between the packet key and an entry in the fingerprint database. Thus, the present invention also advantageously minimizes accesses to the rules database.

2022032, Oct 13, 2022

Apr 1, 2021

17/220543

- San Jose CA, US
Zhe Lin - Fremont CA, US
Jingwan Lu - Santa Clara CA, US
Scott Cohen - Sunnyvale CA, US
Jianming Zhang - Campbell CA, US
Ning Xu - Milpitas CA, US

G06T 3/00
G06K 9/62
G06K 9/68
G06K 9/48
G06T 9/00
G06T 11/00

This disclosure describes one or more implementations of a digital image semantic layout manipulation system that generates refined digital images resembling the style of one or more input images while following the structure of an edited semantic layout. For example, in various implementations, the digital image semantic layout manipulation system builds and utilizes a sparse attention warped image neural network to generate high-resolution warped images and a digital image layout neural network to enhance and refine the high-resolution warped digital image into a realistic and accurate refined digital image.

2022026, Aug 18, 2022

Feb 17, 2021

17/177595

- San Jose CA, US
Jianming Zhang - Campbell CA, US
He Zhang - San Jose CA, US
Yilin Wang - San Jose CA, US
Zhe Lin - Fremont CA, US
Ning Xu - Milpitas CA, US

G06T 7/194
G06T 7/136
G06T 7/11
G06T 5/00
G06T 3/40

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a progressive refinement network to refine alpha mattes generated utilizing a mask-guided matting neural network. In particular, the disclosed systems can use the matting neural network to process a digital image and a coarse guidance mask to generate alpha mattes at discrete neural network layers. In turn, the disclosed systems can use the progressive refinement network to combine alpha mattes and refine areas of uncertainty. For example, the progressive refinement network can combine a core alpha matte corresponding to more certain core regions of a first alpha matte and a boundary alpha matte corresponding to uncertain boundary regions of a second, higher resolution alpha matte. Based on the combination of the core alpha matte and the boundary alpha matte, the disclosed systems can generate a final alpha matte for use in image matting processes.