Abstract: The present disclosure provides for determining a logistics product.

Abstract: The embodiments of this application disclose a method for generating sign language video performed by a computer device. The method includes the following steps: acquiring acquiring listener text, the listener text conforming to grammatical structures of a hearing-friendly person; performing summarization extraction on the listener text to obtain summary text, a text length of the summary text being shorter than a text length of the listener text; converting the summary text into sign language text, the sign language text conforming to grammatical structures of a hearing-impaired person; and generating the sign language video based on the sign language text.

Abstract: Disclosed are a method and an apparatus for end-to-end SAR image recognition, and a storage medium. According to the disclosure, a generative adversarial network is used to enhance data and improve data richness of a SAR image, which is beneficial to subsequent network training; a semantic feature enhancement technology is also introduced to enhance semantic information of a SAR deep feature by a coding-decoding structure, which improves performances of SAR target recognition; and meanwhile, an end-to-end SAR image target recognition model with high integrity for big scenes like the Bay Area is constructed, which is helpful to improve a synthetic aperture radar target recognition model for big scenes like the Bay Area from local optimum to global optimum, increases the stability and generalization ability of the model, reduces the network complexity, and improves the target recognition accuracy.

Abstract: The present disclosure relates to a method for enhancing sematic features of SAR image oriented small set of samples, comprising: acquiring a sample set of an SAR target image, and performing transfer learning and training on the sample set to obtain a initialized deep neural network of an SAR target image, the sample set comprising an SAR target image and an SAR target virtual image; performing network optimization on the deep neural network by an activation function, and extracting features of the SAR target image by the optimized deep neural network to obtain a feature map; and mapping, by an auto-encoder, the feature map between a feature space and a semantic space to obtain a deep visual feature with an enhanced semantic feature.

Abstract: The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

Abstract: Disclosed are a method and an apparatus for end-to-end SAR image recognition, and a storage medium. According to the disclosure, a generative adversarial network is used to enhance data and improve data richness of a SAR image, which is beneficial to subsequent network training; a semantic feature enhancement technology is also introduced to enhance semantic information of a SAR deep feature by a coding-decoding structure, which improves performances of SAR target recognition; and meanwhile, an end-to-end SAR image target recognition model with high integrity for big scenes like the Bay Area is constructed, which is helpful to improve a synthetic aperture radar target recognition model for big scenes like the Bay Area from local optimum to global optimum, increases the stability and generalization ability of the model, reduces the network complexity, and improves the target recognition accuracy.

Abstract: The present disclosure relates to a method for enhancing sematic features of SAR image oriented small set of samples, comprising: acquiring a sample set of an SAR target image, and performing transfer learning and training on the sample set to obtain a initialized deep neural network of an SAR target image, the sample set comprising an SAR target image and an SAR target virtual image; performing network optimization on the deep neural network by an activation function, and extracting features of the SAR target image by the optimized deep neural network to obtain a feature map; and mapping, by an auto-encoder, the feature map between a feature space and a semantic space to obtain a deep visual feature with an enhanced semantic feature.

Abstract: The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

Abstract: A waveguide probe for the detection of pathogens in a sample which comprises a laser, a first and a second tubes that converge at a point to form a proximal end. A magnet is positioned in the end to configure to focus paramagnetic microspheres attached to antigen/antibody/optically labeled antibody complexes in the field of view. The proximal end is polished to form an aperture.

Abstract: A waveguide probe for the detection of pathogens in a sample which comprises a laser, a first and a second tubes that converge at a point to form a proximal end. A magnet is positioned in the end to configure to focus paramagnetic microspheres attached to antigen/antibody/optically labeled antibody complexes in the field of view. The proximal end is polished to form an aperture.

Abstract: A magnetic focusing immunosensor for the detection of pathogens comprising a laser, an exciting fiber and a collecting fiber, a fiber optic magnetic probe in communication with the collecting and exciting fibers and means for detecting, collecting and measuring fluorescent signals in communication with the collecting fiber. The probe and the collecting and exciting fibers are configured to focus paramagnetic microspheres attached to antigen/antibody/optically labeled complexes in a predetermined pattern in the field of view of the collecting fiber while blocking background interference.

Abstract: A magnetic focusing immunosensor for the detection of pathogens comprising a laser, an exciting fiber and a collecting fiber, a fiber optic magnetic probe in communication with the collecting and exciting fibers and means for detecting, collecting and measuring fluorescent signals in communication with the collecting fiber. The probe and the collecting and exciting fibers are configured to focus paramagnetic microspheres attached to antigen/antibody/optically labeled complexes in a predetermined pattern in the field of view of the collecting fiber while blocking background interference.