Abstract: Disclosed is an automatic depression detection method using audio-video, including: acquiring original data containing two modalities of long-term audio file and long-term video file from an audio-video file; dividing the long-term audio file into several audio segments, and meanwhile dividing the long-term video file into a plurality of video segments; inputting each audio segment/each video segment into an audio feature extraction network/a video feature extraction network to obtain in-depth audio features/in-depth video features; calculating the in-depth audio features and the in-depth video features by using multi-head attention mechanism so as to obtain attention audio features and attention video features; aggregating the attention audio features and the attention video features into audio-video features; and inputting the audio-video features into a decision network to predict a depression level of an individual in the audio-video file.

Abstract: A bi-directional interaction network (BINet)-based person search method, system, and apparatus are provided. The method includes: obtaining, as an input image, a tth frame of image in an input video; and normalizing the input image, and obtaining a search result of a to-be-searched target person by using a pre-trained person search model, where the person search model is constructed based on a residual network, and a new classification layer is added to a classification and regression layer of the residual network to obtain an identity classification probability of the target person. The method improves the accuracy of the person search.

Abstract: A neurovascular coupling analytical method based on an electroencephalogram and functional near-infrared spectroscopy includes: S100: acquiring an electroencephalogram signal and a brain hemodynamic signal; S110: extracting an event-related potential signal from the electroencephalogram signal; S120: extracting a time characteristic from the event-related potential signal; S130: extracting a hemodynamic response function from the brain hemodynamic signal; S140: extracting an amplitude characteristic and time characteristics from the hemodynamic response function; and S150: analyzing influence of the time characteristic of the event-related potential signal on the amplitude characteristic and the time characteristics of the hemodynamic response function to obtain a coupling result. The time characteristic of the event-related potential signal is a delay.

Abstract: The field of high-speed data acquisition and network data processing, and particularly relates to an Ethernet data stream recording method, an Ethernet data stream recording system, and an Ethernet data stream recording device for a high-speed data acquisition system. It is intended to solve problems such as a low utilization rate of CPU, poor system compatibility, difficulty in packaging and deployment and low reliability of system transmission of the traditional high-speed data acquisition system.

Abstract: The present invention relates to ground support equipment for aerospace engineering, and particularly relates to an assembly and test operation robot for a space station experimental cabinet. The assembly and test operation robot comprises a mobile lifting platform, a comprehensive monitoring system, a rotating clamping mechanism, a multifunctional adapter and a science experimental cabinetet, wherein the mobile lifting platform is used for regulating the horizontal position and the height position of the science experimental cabinetet to realize assembly and transportation functions of the experimental cabinet; the rotating clamping mechanism is installed on the mobile lifting platform to realize clamping and rotation functions of the science experimental cabinet; the multifunctional adapter is installed on the rotating clamping mechanism to carry the science experimental cabinet; and the comprehensive monitoring system is used to monitor the assembly state of the science experimental cabinet in real time.

Abstract: Disclosed is a micro-expression recognition method based on a multi-scale spatiotemporal feature neural network, in which spatial features and temporal features of micro-expression are obtained from micro-expression video frames, and combined together to form more robust micro-expression features, at the same time, since the micro-expression occurs in local areas of a face, active local areas of the face during occurrence of the micro-expression and an overall area of the face are combined together for micro-expression recognition.

Abstract: A device capable of simultaneous independent motion measurement of multiple probes in an atomic force microscope includes at least two cantilever arms arranged in parallel. The end of each cantilever arm is provided with a needle tip. The surface of each cantilever arm is provided with a grating structure with a periodic distribution rule for reflecting laser irradiated on the grating structure and receiving the laser through reflected light detectors. The discrimination and motion measurement includes the steps of irradiating the measurement laser of different wavelengths on the back surfaces of multiple probes through the same light path at the same time, adopting the grating structures of different feature sizes as physical labels of the multiple probes and reflecting high-order reflected light of the laser of different wavelengths by the grating structures at different angles to separate the light path.

Abstract: A weakly supervised image semantic segmentation method based on an intra-class discriminator includes: constructing two levels of intra-class discriminators for each image-level class to determine whether pixels belonging to the image class belong to a target foreground or a background, and using weakly supervised data for training; generating a pixel-level image class label based on the two levels of intra-class discriminators, and generating and outputting a semantic segmentation result; and further training an image semantic segmentation module or network by using the label to obtain a final semantic segmentation model for an unlabeled input image. By means of the new method, intra-class image information implied in a feature code is fully mined, foreground and background pixels are accurately distinguished, and performance of a weakly supervised semantic segmentation model is significantly improved under the condition of only relying on an image-level annotation.

Abstract: Disclosed are a target speaker separation system, an electronic device and a storage medium. The system includes: first, performing, jointly unified modeling on a plurality of cues based a masked pre-training strategy, to boost the inference capability of a model for missing cues and enhance the representation accuracy of disturbed cues; and second, constructing a hierarchical cue modulation module. A spatial cue is introduced into a primary cue modulation module for directional enhancement of a speech of a speaker; in an intermediate cue modulation module, the speech of the speaker is enhanced on the basis of temporal coherence of a dynamic cue and an auditory signal component; a steady-state cue is introduced into an advanced cue modulation module for selective filtering; and finally, the supervised learning capability of simulation data and the unsupervised learning effect of real mixed data are sufficiently utilized.

Abstract: An MPI reconstruction method, device, and system based on a RecNet model include obtaining a one-dimensional (1D) MPI signal on which imaging reconstruction is to be performed, taking the 1D MPI signal as an input signal, and inputting the input signal and a velocity signal of an FFP corresponding to the input signal into a trained magnetic particle reconstruction model RecNet for image reconstruction to obtain a two-dimensional (2D) MPI image, where the magnetic particle reconstruction model RecNet is constructed based on a domain conversion network and an improved UNet network. The MPI reconstruction method, device, and system obtain a high-quality and clear magnetic particle distribution image without obtaining the system matrix.

Abstract: A vehicle-mounted angiography machine with a double-robotic-arm type structure is used for interventional surgery diagnosis and treatment on patients on the site of cardiovascular diseases and on the way and occupies a small space. The vehicle-mounted angiography machine includes a six-degree-of-freedom robotic lower arm at the lower portion of an ambulance body space, a three-degree-of-freedom mobile table located in the middle of the space for moving a patient, and a seven-degree-of-freedom robotic upper arm at the upper portion of the space. An end portion of the upper arm is connected to a flat panel detector of the angiography machine, and an end portion of the lower arm is connected to an anode ball tube of the angiography machine, Large-scale isocentric detection is realized by cooperation of the central motion of the upper arm and the lower arm and the up-down, left-right and front-back motion of the table.

Abstract: An image processing method, a related device, and a computer storage medium are provided. The method includes: obtaining a feature intensity image corresponding to a training image, where an intensity value of a pixel in the feature intensity image is used to indicate importance of the pixel for recognizing the training image, and resolution of the training image is the same as resolution of the feature intensity image; and occluding, based on the feature intensity image, a to-be-occluded region in the training image by using a preset window, to obtain a new image, where the to-be-occluded region includes a to-be-occluded pixel, and the new image is used to update an image recognition model. According to the embodiments of the present application, a prior-art problem that a model has low accuracy and relatively poor generalization performance because of limited training data can be resolved.

Abstract: An MPI reconstruction method, device, and system based on a RecNet model include obtaining a one-dimensional (1D) MPI signal on which imaging reconstruction is to be performed, taking the 1D MPI signal as an input signal, and inputting the input signal and a velocity signal of an FFP corresponding to the input signal into a trained magnetic particle reconstruction model RecNet for image reconstruction to obtain a two-dimensional (2D) MPI image, where the magnetic particle reconstruction model RecNet is constructed based on a domain conversion network and an improved UNet network. The MPI reconstruction method, device, and system obtain a high-quality and clear magnetic particle distribution image without obtaining the system matrix.

Abstract: A method, a system and a device for difference automatic calibration in cross modal target detection are provided. The method includes: obtaining a plurality of modals of color-thermal images to be detected, and constructing an input image set; acquiring a feature map of each modal image in the input image set, and extracting a shared candidate region; aligning the shared candidate region, and obtaining a feature representation of the each shared candidate region through pooling; obtaining a category of the feature representation of the shared candidate region based on a preset classifier, and obtaining a position of the shared candidate region through deviation regression; adopting non-maximum value suppression to obtain a shared candidate region with the highest probability according to the category of the feature representation of the shared candidate region, and returning to the image to be detected for marking according to the position of the shared candidate region.

Abstract: A method and system for reconstructing a magnetic particle distribution model based on time-frequency spectrum enhancement are provided. The method includes: scanning, by a magnetic particle imaging (MPI) device, a scan target to acquire a one-dimensional time-domain signal of the scan target; performing short-time Fourier transform to acquire a time-frequency spectrum; acquiring, by a deep neural network (DNN) fused with a self-attention mechanism, a denoised time-frequency spectrum; acquiring a high-quality magnetic particle time-domain signal; and reconstructing a magnetic particle distribution model. The method learns global and local information in the time-frequency spectrum through the DNN fused with the self-attention mechanism, thereby learning a relationship between different harmonics to distinguish between a particle signal and a noise signal.

Abstract: An atomic force microscope has dual probes composed of a hinge structure, two cantilever beams and needle tips arranged on free ends of the cantilever beams. The hinge structure is a U-shaped body having two ends respectively extended with a first cantilever beam and a second cantilever beam. The free end of the first cantilever beam and the free end of the second cantilever beam are respectively provided with a first needle tip and a second needle tip. The integrated dual probes is operated by the driving function of the probe clamp. Therefore, only a set of motion control and measurement system of the atomic force microscope is required to realize the rapid in-situ switching function of the dual probes.

Abstract: The present disclosure belongs to a field of biomedical imaging technology, and in particularly to a non-uniform excitation field-based method and system for performing a magnetic nanoparticle imaging. The present disclosure includes: separating the non-uniform excitation field into independent space and current time functions by a spatialtemporal separation method; calculating a normalized signal peak through the current time function; constructing a reconstruction mathematical model based on the normalized signal peak and an imaging subunit volume; and quantitatively reconstructing a spatial distribution of a nanoparticle by combining the normalized signal peak, a non-uniform spatial function of the excitation field and the reconstruction mathematical model, so as to achieve the magnetic nanoparticle imaging of a to-be-reconstructed object.

Abstract: A video generation method includes: obtaining a target face image and a source face image; extracting a feature of each of the source face image and the target face image through a face feature encoder, to obtain corresponding source feature codes and target feature codes; generating swapped face feature codes through a face feature exchanger according to the source feature codes and the target feature codes; generating an initial swapped face image through a face generator according to the swapped face feature codes; and fusing the initial swapped face image with the target face image through a face fuser, to obtain a final swapped face image. The face feature encoder performs hierarchical encoding on the face feature to reserve semantic details of a face, and the face feature exchanger performs further processing based on the hierarchical encoding, to obtain hierarchical encoding of a swapped face feature with semantic details.

Abstract: Disclosed is a hierarchical generated audio detection system, comprising an audio preprocessing module, a CQCC feature extraction module, a LFCC feature extraction module, a first-stage lightweight coarse-level detection model and a second-stage fine-level deep identification model; the audio preprocessing module preprocesses collected audio or video data to obtain an audio clip with a length not exceeding the limit; inputting the audio clip into CQCC feature extraction module and LFCC feature extraction module respectively to obtain CQCC feature and LFCC feature; inputting CQCC feature or LFCC feature into the first-stage lightweight coarse-level detection model for first-stage screening to screen out the first-stage real audio and the first-stage generated audio; inputting the CQCC feature or LFCC feature of the first-stage generated audio into the second-stage fine-level deep identification model to identify the second-stage real audio and the second-stage generated audio, and the second-stage generated au

Abstract: The control system based on multi-unmanned aerial vehicle (UAV) cooperative strategic confrontation includes a management module, a UAV formation module, a situation assessment module, a decision-making module, and a cooperative mission assigning module of both sides in a confrontation. The management module is configured to store state information acquired by the UAV formation module. The UAV formation module is configured to acquire state information of UAVs and execute a control instruction. The situation assessment module is configured to acquire situation assessment information according to the state information. The decision-making module is configured to acquire a countermeasure based on the situation assessment information. The cooperative mission assigning module is configured to generate control instructions for the UAVs based on the countermeasure and in combination with a confrontation target and an optimal situation assessment value.