Ye E He

20130128966, May 23, 2013

Nov 16, 2012

13/679184

Minqiang Jiang - Campbell CA, US
Ye He - West Lafayette IN, US
Jin Song - Shenzhen, CN
Haoping Yu - Carmel IN, US

FUTUREWEI TECHNOLOGIES, INC. - Plano TX

H04N 7/26

37524012

A video codec comprising a processor configured to generate a prediction block for a current block, compute a difference between the current block and the prediction block to generate a residual block, scan a plurality of prediction residuals located in the residual block following a scanning order, and if the plurality of residual values comprise at least one non-zero prediction residual, entropy encode the at least one non-zero prediction residual. A method comprising generating a prediction block for a current block, computing a difference between the current block and the prediction block to generate a residual block, scanning a plurality of prediction residuals located in the residual block, and if the plurality of residual values comprise at least one non-zero prediction residual, entropy encoding the at least one non-zero prediction residual.

20130114716, May 9, 2013

Nov 2, 2012

13/668094

Minqiang Jiang - Campbell CA, US
Ye He - West Lafayette IN, US
Jin Song - Shenzhen, CN
Haoping Yu - Carmel IN, US

FUTUREWEI TECHNOLOGIES, CO. - Plano TX

H04N 7/32

37524014

A video codec comprising a processor configured to compute a reconstructed pixel based on a residual pixel and a first prediction pixel and compute a second prediction pixel in a directional intra prediction mode based on the reconstructed pixel, wherein the first and second prediction pixels are located in a same block of a video frame. A method for intra prediction comprising computing a prediction pixel adaptively based on a plurality of reconstructed neighboring pixels, wherein a distance between the prediction pixel and each of the plurality of reconstructed neighboring pixels is one.

20160223597, Aug 4, 2016

Feb 3, 2015

14/612499

Ye He - Mountain View CA, US
Abhay Gupta - Cupertino CA, US
Vivek Garud - Cupertino CA, US
Alex Shyr - Mountain View CA, US

G01R 19/04
G01R 21/133
G01R 19/25
B60L 11/18

The present invention is directed to systems and methods of disaggregating and detecting energy usage associated with electric vehicle charging from a whole-house consumption signal. In general, methods of the present invention may include: identifying by an electronic processor potential interval candidates of electric vehicle charging, based at least in part upon long and decreasing patterns; determining by the electronic processor intervals associated with the charging of an electric vehicle, based at least in part on evaluating each potential interval candidate; determining by the electronic processor an initial point of charging for each interval associated with the charging of an electric vehicle; and accounting by the electronic processor for feedback of any incorrectly detected signals.

20160112720, Apr 21, 2016

Dec 22, 2015

14/978426

- Plano TX, US
Minqiang Jiang - Campbell CA, US
Ye He - West Lafayette IN, US
Jin Song - Shenzhen, CN
Haoping Yu - Carmel IN, US

H04N 19/593
H04N 19/91
H04N 19/176
H04N 19/182

A video codec including a memory and a processor operably coupled to the memory. The processor is configured to compute a reconstructed pixel based on a residual pixel and a first prediction pixel and compute a second prediction pixel in a directional intra prediction mode based on the reconstructed pixel, where the first and second prediction pixels are located in a same block of a video frame. A method for intra prediction including computing a prediction pixel adaptively based on a plurality of reconstructed neighboring pixels, where a distance between the prediction pixel and each of the plurality of reconstructed neighboring pixels is one.

20150142347, May 21, 2015

Nov 17, 2014

14/543805

Rahul Mohan - Sunnyvale CA, US
Abhay Gupta - Cupertino CA, US
Ye He - Mountain View CA, US
Vivek Garud - Cupertino CA, US

G01R 21/133
G01W 1/10

702 60

Systems and methods of the present invention are directed to disaggregating the contribution of solar panels from a whole house energy profile. Methods of disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, may include steps of: predicting solar energy generation for the specific house by estimating a solar capacity of the solar panels, predicting solar intensity associated with the specific house, and multiplying estimated solar capacity with predicted solar intensity; and subtracting the predicted solar energy generation from the low frequency whole house energy consumption data, thereby disaggregating the contribution of energy produced by the solar panels. Computerized systems of the same may apply machine learning models such as radial basis function, support vector, or neural network machines.

20150142695, May 21, 2015

Nov 17, 2014

14/543824

Ye He - Mountain View CA, US
Abhay Gupta - Cupertino CA, US
Vivek Garud - Cupertino CA, US

G06Q 10/06
G06Q 50/06
G06N 99/00

705348

The present invention is directed to systems and methods for performing energy disaggregation of a whole-house energy usage waveform, based at least in part on the whole-house energy usage profile, training data, and predetermined generic models, including: a module for pairing impulses identified in the whole-house energy usage waveform to indicate an appliance cycle, pairing impulses with at least one up transition with at least one down transition; a module for bundling impulses that are representative of an appliance cycle; a classification module, which upon determination of a type of appliance associated with bundles, is configured to classify the bundles of transitions in accordance with bundles exhibited by similar appliances with similar characteristics; and utilizing such pairing module and module for bundling to perform energy disaggregation. Moreover, the present invention sets forth graphical user interfaces for the presentation of such data and the receipt of user-supplied validation and information

20140207298, Jul 24, 2014

Jan 20, 2014

14/159058

Abhay Gupta - Cupertino CA, US
Ye He - Mountain View CA, US
Vivek Garud - Cupertino CA, US
Raul Mohan - Sunnyvale CA, US

G05B 15/02

700291

Aspects in accordance with embodiments of the invention may include a method for remotely setting, controlling, or modifying settings on a programmable communicating thermostat (PCT) in order to customize settings to a specific house and user, including steps of: receiving at a remote processor information entered into the PCT by the user; receiving at the remote processor: non-electrical information associated with the specific house or user; and energy usage data of the specific house; performing by the remote processor energy disaggregation on the energy usage data; determining by the remote processor a custom schedule for the PCT based upon the information entered by the user, the non-electrical information associated with the specific house or user, and disaggregated energy usage data; revising by the remote processor, the custom schedule for the PCT based upon additional user input or seasonal changes; providing the custom schedule for the PCT to the PCT.