Microsoft Technology Licensing, LLC

Hefei University of Technology

Lei Wu

Xian-Sheng Hua

Mingjing Li

Linjun Yang

Yigang He

Zhijie Yuan

Deqin Zhao

Lifen Yuan

Zhen Cheng

Yesheng Sun

Shipeng Li

Wei-Ying Ma

Zhiwei Li

Peng Chen

Chaolong Zhang

Qibin Feng

Guoqiang Lv

Shuai Luo

Ying Long

Zi Wang

Liming Zhu

Jiechao Sheng

The present disclosure provides a light field display device, including: a backlight layer; a light control array layer; and a control layer. The backlight layer, the light control array layer, and the control layer are arranged side by side, and images on the backlight layer, the light control array layer, and the control layer are superimposed to form a three-dimensional image.

Light field display device and display method thereof

An analog circuit fault diagnosis method using a single testable node comprises the following steps: (1) obtaining prior sample data vectors under each fault mode; (2) computing a statistical average of the prior sample data vectors under each of the fault modes; (3) decomposing a signal by an orthogonal Haar wavelet filter set; (4) extracting the feature factor of the prior sample fault modes; (5) extracting a fault-mode-to-be-tested feature factor; (6) computing a correlation coefficient matrix and correlation metric parameters between the feature factor of the prior sample fault modes and the feature factor of the fault-mode-to-be-tested; and (7) determining a fault mode according to a maximal correlation principle by comparing the correlation metric parameters. The method can convert a single signal into a plurality of signals without losing original measurement information, and extract an independent fault mode feature factor reflecting variations of a circuit structure in different fault modes, can be used to study an associated mode determination rule and successfully complete classification of circuit fault modes.

Analog circuit fault diagnosis method using single testable node

A wireless sensor network sensor network charging method for multi-charge nodes, including the following steps: (1) establishing a WSNs model; (2) dividing field ranges of charging trolleys; and (3) charging the charging trolleys: (a) initializing: l=0 and j=0, wherein l is the total number of received alarm nodes, and j is the serial number of an alarmed node; (b) receiving an alarm signal, updating values of l and j, generating a shortest charging path s, and computing an energy discriminate vector; (c) if a vector element satisfies Q&#x2264;5% B and j=1, 2, . . . , l, then executing a charging task on l alarm nodes according to the shortest charging path scorresponded thereby, otherwise, returning to continuously update the values of l and j; (d) executing the charging task; and (e) determining whether the charging trolleys are required to go back to a parking lot for recharging, enabling the charging trolleys to go back to the parking lot for recharging if yes, and otherwise, returning to continuously update the values of l and j and the vector element Q.

Wireless sensor network charging method for multi-charge nodes

Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation.

Media tag recommendation technologies

Image-based semantic distance technique embodiments are presented that involve establishing a measure of an image-based semantic distance between semantic concepts. Generally, this entails respectively computing a semantic concept representation for each concept based on a collection of images associated with the concept. A degree of difference is then computed between two semantic concept representations to produce the aforementioned semantic distance measure for the pair of corresponding concepts.

Image-based semantic distance

Techniques for identifying copied images based on an original image are described. The identifying copied image is based on creating unique and identifiable features that in turn are used to generate multiple histograms. The histograms are generated by patches of the image, where the patches are created by equally dividing the image. The combined patch histograms are representative of the image.

Patch-based texture histogram coding for fast image similarity search

Systems and methods for visual language modeling for image classification are described. In one aspect the systems and methods model training images corresponding to multiple image categories as matrices of visual words. Visual language models are generated from the matrices. In view of a given image, for example, provided by a user or from the Web, the systems and methods determine an image category corresponding to the given image. This image categorization is accomplished by maximizing the posterior probability of visual words associated with the given image over the visual language models. The image category, or a result corresponding to the image category, is presented to the user.

Visual language modeling for image classification

Growing community of inventors

Anhui, China

Lei Wu