Abstract: The present application provides a VR-based standardized training system for multi-person online cooperated first-aid nursing of trauma, which is used for providing medical staff with an efficient and convenient-to-update and maintain standardized training environment for multi-person online cooperated first-aid nursing of trauma by introducing the VR technology, so as to significantly improve the training effect of the first aid of trauma of the medical staff.

Abstract: A method and a device for timing correction, a computing device, and a storage medium applied to an integrated circuit are provided. In the method, the integrated circuit includes normal logic cells and spare correction cells, determining a timing path with a timing error and a first normal logic cell in the timing path that does not meet a timing requirement in the integrated circuit; setting a search range around the first normal logic cell and determining at least one spare correction cell within the search range; testing and obtaining a timing result of the at least one spare correction cell used in the integrated circuit; determining a target spare correction cell according to the timing result, wherein the target spare correction cell is at least one of the at least one spare correction cell. The method for timing correction and etc. ensure the correctness of the chip design.

Abstract: The present disclosure discloses a method for quantifying a bearing capacity of foundation containing shallow-hidden spherical cavities, comprising: in Step 1, constructing a spatial axisymmetric calculation model for stability analysis of the foundation containing shallow-hidden spherical cavities; in Step 2, solving the model to obtain a general solution which reflects the spatial stress distribution of surrounding rock containing shallow-hidden spherical cavities; in Step 3, obtain a mathematical expression by derivation for calculating the bearing capacity of the foundation containing shallow-hidden spherical cavities; and in Step 4: completing the determination of the foundation bearing capacity. Benefits: This method has many advantages such as comprehensive consideration, high accuracy and reliability of calculation results, and may provide the scientific basis for the development of prevention and control against the instability of the foundation containing shallow-hidden cavities.

Abstract: Implementations of the subject matter described herein relate to object detection based on deep neural network. With a given input image, it is desired to determine a class and a boundary of one or more objects within the input image. Specifically, a plurality of channel groups is generated from a feature map of an image, the image including at least a region corresponding to a first grid. A target feature map is extracted from at least one of the plurality of channel groups associated with a cell of the first grid. Information related to an object within the region is determined based on the target feature map. The information related to the object may be a class and/or a boundary of the object.

Abstract: The present disclosure discloses a method for quantifying a bearing capacity of foundation containing shallow-hidden spherical cavities, comprising: in Step 1, constructing a spatial axisymmetric calculation model for stability analysis of the foundation containing shallow-hidden spherical cavities; in Step 2, solving the model to obtain a general solution which reflects the spatial stress distribution of surrounding rock containing shallow-hidden spherical cavities; in Step 3, obtain a mathematical expression by derivation for calculating the bearing capacity of the foundation containing shallow-hidden spherical cavities; and in Step 4: completing the determination of the foundation bearing capacity. Benefits: This method has many advantages such as comprehensive consideration, high accuracy and reliability of calculation results, and may provide the scientific basis for the development of prevention and control against the instability of the foundation containing shallow-hidden cavities.

Abstract: Implementations of the subject matter described herein relate to object detection based on deep neural network. With a given input image, it is desired to determine a class and a boundary of one or more objects within the input image. Specifically, a plurality of channel groups is generated from a feature map of an image, the image including at least a region corresponding to a first grid. A target feature map is extracted from at least one of the plurality of channel groups associated with a cell of the first grid. Information related to an object within the region is determined based on the target feature map. The information related to the object may be a class and/or a boundary of the object.

Abstract: Techniques for setting depth values for invalid measurement regions of depth images are described herein. A computing device may set the depth values based on evaluations of depth values of neighboring pixels and of corresponding pixels from time-adjacent depth images. Alternately or additionally, the computing device may utilize a texture image corresponding to the depth image to identify objects and may set depth values for pixels based on depth values of other pixels belonging to the same object. After setting the depth values, the computing device may normalize the depth values of the pixels. Further, the computing device may generate reduced representations of the depth images based on a depth reference model or a depth error model and may provide the reduced representations to an encoder.

Abstract: In some examples, a layered encoding component and a layered decoding component provide for different ways to encode and decode, respectively, video streams transmitted between devices. For instance, in encoding a video stream, a layered encoding component may analyze the content of successive video frames and determine different types of encoding techniques to use for different ones of the video frames. Further, in some cases, some of the encoding techniques may be used on less than an entire video frame. In another example, in decoding a video stream, a layered decoding component may receive video frames encoded with different types of encoding. The layered decoding component may decode the differently encoded video frames and combine them to reconstruct a video stream.

Abstract: Techniques for setting depth values for invalid measurement regions of depth images are described herein. A computing device may set the depth values based on evaluations of depth values of neighboring pixels and of corresponding pixels from time-adjacent depth images. Alternately or additionally, the computing device may utilize a texture image corresponding to the depth image to identify objects and may set depth values for pixels based on depth values of other pixels belonging to the same object. After setting the depth values, the computing device may normalize the depth values of the pixels. Further, the computing device may generate reduced representations of the depth images based on a depth reference model or a depth error model and may provide the reduced representations to an encoder.

Abstract: Techniques for setting depth values for invalid measurement regions of depth images are described herein. A computing device may set the depth values based on evaluations of depth values of neighboring pixels and of corresponding pixels from time-adjacent depth images. Alternately or additionally, the computing device may utilize a texture image corresponding to the depth image to identify objects and may set depth values for pixels based on depth values of other pixels belonging to the same object. After setting the depth values, the computing device may normalize the depth values of the pixels. Further, the computing device may generate reduced representations of the depth images based on a depth reference model or a depth error model and may provide the reduced representations to an encoder.

Abstract: The techniques and arrangements described herein provide for layered compression of depth image data. In some examples, an encoder may partition depth image data into a most significant bit (MSB) layer and a least significant bit (LSB) layer. The encoder may quantize the MSB layer and generate quantization difference data based at least in part on the quantization of the MSB layer. The encoder may apply the quantization difference data to the LSB layer to generate an adjusted LSB layer.

Abstract: In some examples, a layered encoding component and a layered decoding component provide for different ways to encode and decode, respectively, video streams transmitted between devices. For instance, in encoding a video stream, a layered encoding component may analyze the content of successive video frames and determine different types of encoding techniques to use for different ones of the video frames. Further, in some cases, some of the encoding techniques may be used on less than an entire video frame. In another example, in decoding a video stream, a layered decoding component may receive video frames encoded with different types of encoding. The layered decoding component may decode the differently encoded video frames and combine them to reconstruct a video stream.

Abstract: In some examples, a layered encoding component and a layered decoding component provide for different ways to encode and decode, respectively, video streams transmitted between devices. For instance, in encoding a video stream, video frames may be analyzed across multiple video frames to determine temporal characteristics, and analyzed spatially within a single given video frame. Further, based at partly on the analysis of the video frames, some video frames may be encoded with a first encoding and portions of other video frames may be encoded using a second layer encoding, where the second layer encoding may use a different type of encoding for different portions of a single given video frame. To decode an encoded video stream, both the base layer encoded video frames and the second layer encoded video frames may be transmitted, decoded, and combined at a destination device into a reconstructed video stream.

Abstract: Techniques for setting depth values for invalid measurement regions of depth images are described herein. A computing device may set the depth values based on evaluations of depth values of neighboring pixels and of corresponding pixels from time-adjacent depth images. Alternately or additionally, the computing device may utilize a texture image corresponding to the depth image to identify objects and may set depth values for pixels based on depth values of other pixels belonging to the same object. After setting the depth values, the computing device may normalize the depth values of the pixels. Further, the computing device may generate reduced representations of the depth images based on a depth reference model or a depth error model and may provide the reduced representations to an encoder.

Abstract: A computing device is described herein that is configured to encode natural video content in accordance with a first encoding scheme and screen content in accordance with a second encoding scheme. The computing device is configured to distinguish between the natural video content of a video frame and the screen content of the video frame based at least in part on temporal correlations between the video frame and one or more neighboring video frames and on content analysis of the video frame.

Abstract: Aspects of the subject matter described herein relate to image restoration for compressed images. In aspects, image restoration is accomplished by recovering spectral information from data corresponding to a compressed image. The spectral information is recovered using an algorithm to search through a solution space of possible solutions while constraints are imposed on the solution space to trim undesirable solutions from the space. An algorithm described herein may be iteratively applied to improve the quality of the recovered image.

Abstract: Aspects of the subject matter described herein relate to image restoration for compressed images. In aspects, image restoration is accomplished by recovering spectral information from data corresponding to a compressed image. The spectral information is recovered using an algorithm to search through a solution space of possible solutions while constraints are imposed on the solution space to trim undesirable solutions from the space. An algorithm described herein may be iteratively applied to improve the quality of the recovered image.