Feng Yang

US Patent:

6573999, Jun 3, 2003

Filed:

Jul 14, 2000

Appl. No.:

09/615964

Inventors:

Feng Yang - San Jose CA

Assignee:

Nanometrics Incorporated - Milpitas CA

International Classification:

G01B 1105

US Classification:

356632, 356630

Abstract:

A method of measuring film thickness is based on the films attenuation of optical characteristics, such as absorption band or absorption bands, of underlying material. The thickness of the film is determined based on a correlation between the thickness of the film and the strength of the absorption band (such as a peak or valley area) of the underlying material. The correlation is generated using an identified absorption band of the underlying material, which may be determined empirically or using a library of information, and the reflectance spectra produced by calibration samples, each having a different thickness of film.

US Patent:

7209404, Apr 24, 2007

Filed:

Aug 5, 2005

Appl. No.:

11/198563

Inventors:

Sho-Mo Chen - Cupertino CA, US
Fei Ye - Cupertino CA, US
Feng Yang - Plano TX, US

Assignee:

ForteMedia Inc. - Cupertino CA

International Classification:

G11C 5/14

US Classification:

365226, 365227

Abstract:

Disclosed methods and apparatus provide embedded memory architectures that lower the overall operational power consumption of memory arrays without sacrificing memory access speed. Because in large memory arrays the leakage current is a considerable portion of the overall power consumption, leakage reduction in memory arrays, manufactured by advanced processing technologies, is a major challenge. To reduce leakage, methods and apparatus are presented for memory access and for power- and ground-supply monitoring and management at memory sub-array level.

US Patent:

20120167170, Jun 28, 2012

Filed:

Dec 28, 2010

Appl. No.:

12/979698

Inventors:

Weidong Shi - Windsor, CA
Jun Yang - Milpitas CA, US
Feng Yang - Stanford CA, US
Yingen Xiong - Mountain View CA, US

International Classification:

G06F 7/04

US Classification:

726 2

Abstract:

A method for providing passive user identification may include causing selective processing of data indicative of characteristics of a user of a device by aggregating one or more modality specific biometric classification processes conducted in background operation of the device, comparing the selectively processed data to a profile of a currently logged in or default user to determine a likelihood that the user corresponds to the currently logged in or default user, and selectively implementing an active authentication process based on a result of the determining. A corresponding apparatus and computer program product are also provided.

US Patent:

20220415039, Dec 29, 2022

Filed:

Nov 26, 2019

Appl. No.:

17/762289

Inventors:

- Mountain View CA, US
Hossein Talebi - San Jose CA, US
Peyman Milanfar - Menlo Park CA, US
Feng Yang - Sunnyvale CA, US

International Classification:

G06V 10/98
G06V 10/82
G06V 20/40
G06N 3/04

Abstract:

A trained model is retrained for video quality assessment and used to identify sets of adaptive compression parameters for transcoding user generated video content. Using transfer learning, the model, which is initially trained for image object detection, is retrained for technical content assessment and then again retrained for video quality assessment. The model is then deployed into a transcoding pipeline and used for transcoding an input video stream of user generated content. The transcoding pipeline may be structured in one of several ways. In one example, a secondary pathway for video content analysis using the model is introduced into the pipeline, which does not interfere with the ultimate output of the transcoding should there be a network or other issue. In another example, the model is introduced as a library within the existing pipeline, which would maintain a single pathway, but ultimately is not expected to introduce significant latency.

US Patent:

20220358537, Nov 10, 2022

Filed:

Aug 6, 2020

Appl. No.:

17/760535

Inventors:

- Mountain View CA, US
Luying Li - Sunnyvale CA, US
Feng Yang - Sunnyvale CA, US
Junjie Ke - East Palo Alto CA, US
Xiyang Luo - Mountain View CA, US
Hao Feng - Sunnyvale CA, US
Chao-Hung Chen - Milpitas CA, US
Wenjing Kang - Santa Clara CA, US
Zheng Xia - Palo Alto CA, US
Yicong Tian - Mountain View CA, US
Xia Li - Sunnyvale CA, US
Han Ke - Sunnyvale CA, US

International Classification:

G06Q 30/02

Abstract:

Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.

US Patent:

20220335560, Oct 20, 2022

Filed:

May 12, 2019

Appl. No.:

17/764445

Inventors:

- Mountain View CA, US
Feng Yang - Sunnyvale CA, US
Xiyang Luo - Pasadena CA, US

International Classification:

G06T 1/00
G06T 9/00
G06T 3/40
G06N 3/04

Abstract:

A computer-implemented method that provides watermark-based image reconstruction to compensate for lossy encoding schemes. The method can generate a difference image describing the data loss associated with encoding an image using a lossy encoding scheme. The difference image can be encoded as a message and embedded in the encoded image using a watermark and later extracted from the encoded image. The difference image can be added to the encoded image to reconstruct the original image. As an example, an input image encoded using a lossy JPEG compression scheme can be embedded with the lost data and later reconstructed, using the embedded data, to a fidelity level that is identical or substantially similar to the original.

US Patent:

20220301141, Sep 22, 2022

Filed:

Aug 6, 2020

Appl. No.:

17/612372

Inventors:

- Mountain View CA, US
Xiyang Luo - Mountain View CA, US
Feng Yang - Sunnyvale CA, US
Junjie Ke - East Palo Alto CA, US
Yicong Tian - Mountain View CA, US
Chao-Hung Chen - Milpitas CA, US
Xia Li - Sunnyvale CA, US
Luying Li - Sunnyvale CA, US
Wenjing Kang - Santa Clara CA, US

International Classification:

G06T 7/00
G06V 10/82

Abstract:

Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include training machine learning models on images. A request is received to evaluate quality of an image included in a current version of a digital component generated by the computing device. The machine learning models are deployed on the image to generate a score for each quality characteristic of the image. A weight is assigned to each score to generate weighted scores. The weighted scores are combined to generate a combined score for the image. The combined score is compared to one or more thresholds to generate a quality of the image.

US Patent:

20230130410, Apr 27, 2023

Filed:

Apr 17, 2020

Appl. No.:

17/918170

Inventors:

- Mountain View CA, US
Feng Yang - Sunnyvale CA, US
Hossein Talebi - San Jose CA, US

International Classification:

G06T 9/00
G06T 3/40

Abstract:

Methods, systems, and computer programs encoded on a computer storage medium, that relate to generating quantization tables that are used during digital image compression of a digital image. Multiple training images are obtained. A model can be trained using the training images to generate a quantization table that can be used during encoding of an input image. For each training image, a quantization table can be obtained using the model. Using the quantization table, an encoded digital image is obtained for the training image. Using the encoded digital image and the training image, an image quality loss and a compression loss can be determined. An overall loss of the model can be determined by combining the image quality loss and the compression loss for the training image. The model can be updated based on the overall loss.