Abstract: A terminal device is disclosed, including a display module, an optical fingerprint sensor, at least two backlight assemblies, and a light emitter. The display module includes a display screen and a light-transmissive cover that covers the display screen. The light emitter and the display screen are both fastened onto an inner surface of the light-transmissive cover, the optical fingerprint sensor is provided on a side of the display screen facing away from the light-transmissive cover, and the at least two backlight assemblies are provided sequentially between the display screen and the optical fingerprint sensor. Along a direction from the display screen to the optical fingerprint sensor, areas of light-transmissive regions of the at least two backlight assemblies are gradually reduced. Light emitted by the light emitter experiences a finger, and then successively passes through the display module and the light-transmissive regions and is projected onto the optical fingerprint sensor.

Abstract: An electronic device is provided. The electronic device includes: a device body, a display module, a backlight module, and a fingerprint identification assembly. The display module is disposed on a surface of the device body. The backlight module includes a bearing frame and a light guide assembly. The bearing frame is fixedly disposed in the device body. The bearing frame is provided with a first through hole. The light guide assembly is disposed on the bearing frame. A first gap is formed between the light guide assembly and the display module. The fingerprint identification assembly includes a transmitter and a receiver. The transmitter is disposed in the first gap. The receiver is disposed on a side of the bearing frame away from the light guide assembly and is corresponding to a position of the first through hole.

Abstract: An electronic device is provided. The electronic device includes: a device body, a display module, a backlight module, and a fingerprint identification assembly. The display module is disposed on a surface of the device body. The backlight module includes a bearing frame and a light guide assembly. The bearing frame is fixedly disposed in the device body. The bearing frame is provided with a first through hole. The light guide assembly is disposed on the bearing frame. A first gap is formed between the light guide assembly and the display module. The fingerprint identification assembly includes a transmitter and a receiver. The transmitter is disposed in the first gap. The receiver is disposed on a side of the bearing frame away from the light guide assembly and is corresponding to a position of the first through hole.

Abstract: A implicit structured light decoding method, a computer equipment and a computer-readable storage medium. The method includes: traversing an image captured by a camera to acquire a grayscale value of each pixel point and an ideal neighborhood grayscale distribution; extracting and outputting an updated output image according to the grayscale value of each pixel point and the ideal neighborhood grayscale distribution and in combination with a preset output image; classifying stripe central points in the updated output image into different stripes; determining a correspondence between stripes in the updated output image and stripes in a structured light image according to the different stripes; and decoding all stripe central points by using triangulation method in combination with the correspondence between the extracted stripes and the projected stripe pattern. This solution can efficiently and robustly decode the implicit stripe-based structured light on a basis of ensuring precision.

Abstract: A video blind denoising method based on deep learning, a computer device and a computer-readable storage medium. The method includes: taking a video sequence from a video to be denoised, taking the middle frame in the video sequence as a noisy reference frame, performing an optical flow estimation on the image corresponding to the noisy reference frame and each other frame in the video sequence, to obtain optical flow fields; transforming, according to the optical flow fields, the image corresponding to each other frame in the video sequence to the noisy reference frame for registration respectively, to obtain multi-frame noisy registration images; taking the multi-frame noisy registration images as an input of a convolutional neural network, taking the noisy reference frame as the reference image, performing iterative training and denoising by using the noise2noise training principle, to obtain the denoised image. This solution may achieve the blind denoising of a video.

Abstract: A terminal device is disclosed, including a display module, an optical fingerprint sensor, at least two backlight assemblies, and a light emitter. The display module includes a display screen and a light-transmissive cover that covers the display screen. The light emitter and the display screen are both fastened onto an inner surface of the light-transmissive cover, the optical fingerprint sensor is provided on a side of the display screen facing away from the light-transmissive cover, and the at least two backlight assemblies are provided sequentially between the display screen and the optical fingerprint sensor. Along a direction from the display screen to the optical fingerprint sensor, areas of light-transmissive regions of the at least two backlight assemblies are gradually reduced. Light emitted by the light emitter experiences a finger, and then successively passes through the display module and the light-transmissive regions and is projected onto the optical fingerprint sensor.

Abstract: A video blind denoising method based on deep learning, a computer device and a computer-readable storage medium. The method includes: taking a video sequence from a video to be denoised, taking the middle frame in the video sequence as a noisy reference frame, performing an optical flow estimation on the image corresponding to the noisy reference frame and each other frame in the video sequence, to obtain optical flow fields; transforming, according to the optical flow fields, the image corresponding to each other frame in the video sequence to the noisy reference frame for registration respectively, to obtain multi-frame noisy registration images; taking the multi-frame noisy registration images as an input of a convolutional neural network, taking the noisy reference frame as the reference image, performing iterative training and denoising by using the noise2noise training principle, to obtain the denoised image. This solution may achieve the blind denoising of a video.

Abstract: A implicit structured light decoding method, a computer equipment and a computer-readable storage medium. The method includes: traversing an image captured by a camera to acquire a grayscale value of each pixel point and an ideal neighborhood grayscale distribution; extracting and outputting an updated output image according to the grayscale value of each pixel point and the ideal neighborhood grayscale distribution and in combination with a preset output image; classifying stripe central points in the updated output image into different stripes; determining a correspondence between stripes in the updated output image and stripes in a structured light image according to the different stripes; and decoding all stripe central points by using triangulation method in combination with the correspondence between the extracted stripes and the projected stripe pattern. This solution can efficiently and robustly decode the implicit stripe-based structured light on a basis of ensuring precision.

Abstract: It is possible to reduce singling overhead in a radio access network by coordinating authentication of a group of UEs (e.g., IoT devices, etc.) via a master device. In particular, the master device may aggregate UE identifiers (UE_IDs) for UEs in the group, and send an identity message carrying the UE_IDs and a master device identifier (MD_ID) to a base station, which may then relay the identity message to a Security Anchor Node (SeAN). The SeAN may send an authentication data request carrying the UE_IDs and MD_ID to a Home Subscriber Server (HSS), which may return an authentication data response that includes a group authentication information. The group authentication information may then be used to achieve mutual authentication between the SeAN and each of the master device, group of UEs, and individual UEs.

Abstract: A polycarbonate composition includes the following components in parts by weight: a. 30 to 87 parts of a PC resin; b. 8 to 50 parts of a rubber-modified graft polymer; c. 5 to 25 parts of a fire retardant; and d. 0 to 10 parts of other aids; wherein a sum of parts by weight of the four components a, b, c, and d is 100. Phenols which are added in a polycarbonate composition formula, based on a total weight of the polycarbonate composition does not exceed 100 ppm. A K value of a component I of the PC resin is adjusted between 300000 and 500000 and the K value of a component II is adjusted to be less than 300000.

Abstract: A polycarbonate composition, includes the following components in parts by weight: a. 30 to 87 parts of a PC resin; b. 8 to 50 parts of a rubber-modified graft polymer; c. 5 to 25 parts of a fire retardant; and d. 0 to 10 parts of other aids; wherein a sum of parts by weight of the four components a, b, c, and d is 100. Phenols which are added in a polycarbonate composition formula based on a total weight of the polycarbonate composition does not exceed 100 ppm. A K value of a component I of the PC resin is adjusted between 300000 and 500000 and the K value of a component II is adjusted to be less than 300000, A structural formula of the phenols is shown as Formula (1).

Abstract: It is possible to reduce singling overhead in a radio access network by coordinating authentication of a group of UEs (e.g., IoT devices, etc.) via a master device. In particular, the master device may aggregate UE identifiers (UE_IDs) for UEs in the group, and send an identity message carrying the UE_IDs and a master device identifier (MD_ID) to a base station, which may then relay the identity message to a Security Anchor Node (SeAN). The SeAN may send an authentication data request carrying the UE_IDs and MD_ID to a Home Subscriber Server (HSS), which may return an authentication data response that includes a group authentication information. The group authentication information may then be used to achieve mutual authentication between the SeAN and each of the master device, group of UEs, and individual UEs.

Abstract: Implementations disclosed herein provide a method comprising comparing high-latency data sectors of a storage band, the high-latency data sectors having latency above a predetermined threshold, with target sectors for storing new data to determine one or more of the high-latency data sectors that may be skipped during retrieval of at-rest data from the storage band.

Abstract: Implementations disclosed herein provide a method comprising comparing high-latency data sectors of a storage band, the high-latency data sectors having latency above a predetermined threshold, with target sectors for storing new data to determine one or more of the high-latency data sectors that may be skipped during retrieval of at-rest data from the storage band.

Abstract: Implementations disclosed herein provide a method comprising comparing high-latency data sectors of a storage band, the high-latency data sectors having latency above a predetermined threshold, with target sectors for storing new data to determine one or more of the high-latency data sectors that may be skipped during retrieval of at-rest data from the storage band.

Abstract: The present invention relates to the technical field of the human computer interaction, more particularly to, a system and method for human computer interaction. The system for human computer interaction comprises a projection unit, a first image sensing unit, a second image sensing unit, an interface unit, an image processing unit, a projected interface processing unit, and a controlling unit. The system for human computer interaction provided by the present invention may easily project the human computer interaction interface on all kinds of planes encountered in people's daily life, to realize display of the human computer interaction interface everywhere, and improve users' experience.

Abstract: Implementations disclosed herein provide a method comprising comparing high-latency data sectors of a storage band, the high-latency data sectors having latency above a predetermined threshold, with target sectors for storing new data to determine one or more of the high-latency data sectors that may be skipped during retrieval of at-rest data from the storage band.

Abstract: A method or system comprises determining a target sector of a write operation in response to a request for the write operation to a band in a storage device, determining a plurality of down-track data sectors in the band, and performing a band rewrite operation on the target data sector and the down-track data sectors.

Abstract: The present invention relates to the technical field of the human computer interaction, more particularly to, a system and method for human computer interaction. The system for human computer interaction comprises a projection unit, a first image sensing unit, a second image sensing unit, an interface unit, an image processing unit, a projected interface processing unit, and a controlling unit. The system for human computer interaction provided by the present invention may easily project the human computer interaction interface on all kinds of planes encountered in people's daily life, to realize display of the human computer interaction interface everywhere, and improve users' experience.