Abstract: A deep-sea crawling robot and a crawling method thereof are provided. The deep-sea crawling robot includes a crawling robot body and a countertop, wherein a top of the crawling robot body is fixedly connected to the countertop, left and right sides of a bottom of the crawling robot body are respectively provided with crawler belts, and left and right sides of the countertop are respectively provided with weight-increasing auxiliary crawling mechanisms; and an outer plate is fixedly connected to a front side or back side of the countertop, a multifunctional propelling mechanism is arranged on the outer plate, and a rapid dewatering mechanism is arranged in a chamber of the countertop. The deep-sea crawling robot can not only accelerate the propulsion and crawling speed when it is on the seabed but also further enhance its stability during the crawling process on the seabed.

Abstract: A bottom touching assisting device suitable for deep-sea submersibles and an implementation method thereof are provided. Four support columns are arranged within a mounting box body at a bottom of a deep-sea submersible, the support columns and the mounting box body are connected in a sliding manner through sliders, and the support column is sleeved with a threaded sleeve in a threaded connection manner. In conjunction with a drive component and a pressing mechanism, smooth vertical movement of the support column is achieved when the threaded sleeve rotates. This allows a bottom end of the support column to extend from the mounting box body.

Abstract: A computer device obtains a first face image (IMA) and a second face image (IFA). The device obtains M first image blocks corresponding to facial features from the first face image (IMA), and obtains N second image blocks corresponding to facial features from the second face image (IFA). The device transforms the M first image blocks and the N second image blocks to a feature space to generate M first feature blocks and N second feature blocks. The device selects a subset of the first feature blocks and a subset of the second feature blocks according to a specified control vector. The device generates a first composite feature map based the selected subset of the first feature blocks and the selected subset of the second feature blocks. The device inversely transforms the first composite feature map back to an image space to generate a third face image.

Abstract: Aspects disclosed herein generally related to a method and system for efficient blind source separation using a topological approach. The method and system comprise locating and separating the audio streams by constructing and simplifying contour tree in a built time-frequency smooth weighted histogram in the subsystems included. Thus, in one example, the audio streams can be separated and reproduced in a faster, more reliability, higher quality and more robust way.

Abstract: Aspects disclosed herein generally relate to a method and a system for improving voice separation by eliminating overlaps or overlapping points. The time-frequency points from the two recorded mixtures are separated by using a Degenerate unmixing estimation technique (DUET) algorithm. The method or system further eliminates the overlapping time-frequency points which belongs to neither of the original resources of sounds.

Abstract: A computer device obtains a first face image (IMA) and a second face image (IFA). The device obtains M first image blocks corresponding to facial features from the first face image (IMA), and obtains N second image blocks corresponding to facial features from the second face image (IFA). The device transforms the M first image blocks and the N second image blocks to a feature space to generate M first feature blocks and N second feature blocks. The device selects a subset of the first feature blocks and a subset of the second feature blocks according to a specified control vector. The device generates a first composite feature map based the selected subset of the first feature blocks and the selected subset of the second feature blocks. The device inversely transforms the first composite feature map back to an image space to generate a third face image.