Abstract: A method for processing low resolution degraded image, a system, a storage medium, and a device therefor are provided. The present disclosure adopts a dual branch processing model, which includes an image restoration branch and an image super-resolution branch. At the same time, a fusion module is used to fuse and learn image features from the two domains, thereby improving the problem of error accumulation and high computational cost caused by the two-stage processing method.

Abstract: The present application discloses a face image restoration method, a system, a storage medium and a device, the restoration model adopted in the above method starts from structured information of a face, generates a structured face graph based on features of the to-be-restored face image, and restores face image by the structured face graph and the decoder, which can solve the problem that it is difficult to capture and learn structured information based on the traditional convolution operation, and improve the indicators of the face restoration and enriches the visualization effect.

Abstract: The present application discloses a batch-type image distribution method based on an IPFS, which includes: obtaining a list of cluster nodes needed to download images; calculating a caching ratio of an image layer of images to be distributed of each node, comparing the caching ratio of each node with a threshold of a preset node, and obtaining a complete image from an image repository; adding the node with the complete image to a list of source nodes, and adding remaining nodes needed to download images to a list of demand nodes, and constructing an IPFS network, and exporting and sharing the image; calculating target demand nodes using an optimization algorithm, and inputting the target demand nodes into the IPFS network for image download, and importing the downloaded image into a container and starting the container.

Abstract: Disclosed are an indoor non-contact human activity recognition method and system. The method comprises: collecting an indoor reflected signal by using an antenna array; filtering the reflected signal to obtain a noise-removed reflection signal; and inputting the noise-removed reflected signal to a pre-trained human activity recognition model, and determining a human activity category, the human activity recognition model being a pre-trained CNN network model based on a transfer learning algorithm. The recognition method and system have the advantages that: the antenna array is configured for collecting human actions to carry out activity recognition indoors, which can be applied to home-based care scenes; original data is denoised, so that most of high-frequency noises can be removed, and a phase change of the signal is reserved; a CNN structure is adopted for training so as to reduce a complexity of the system location-free sensing.

Abstract: The present application discloses a low-light image enhancement method based on wavelet transform and Retinex-Net, and belongs to the field of image enhancement technology. In order to solve the problems of colour distortion and lack of details such as edges and textures when Retinex-Net processes some images, the present application adds the wavelet transform, fuses the high-frequency components with regional characteristics, and processes only the low-frequency component with Retinex-Net, to retain more details of edges and textures in the image, and transfers the low-frequency and high-frequency components from the HSV space to the value space for value fusion and stretching, to effectively improve the colour distortion of the image, and also improve the expression of details of the image. The present application retains more detail information while avoiding colour distortion, and the enhancement effect is satisfactory.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: Provided is a display apparatus. The display apparatus includes: a liquid crystal display panel and a backlight module, wherein a first sealed air cavity is formed inside the liquid crystal display panel, and the backlight module is located on one side of the liquid crystal display panel and forms a second sealed air cavity with the liquid crystal display panel; and a sound production board and a sound production exciter, wherein the sound production board is fixed onto the surface of the backlight module away from the liquid crystal display panel, a vibration output terminal of the sound production exciter is fixed onto the surface of the sound production board away from the backlight module, and the sound production exciter is used for exciting, by means of the vibration output terminal, the sound production board to vibrate, so as to drive the backlight module to vibrate.

Abstract: The present application discloses a task scheduling method based on an improved chimpanzee optimization algorithm, including obtaining a task to be scheduled and a task scheduling model pre-established by using a chimpanzee optimization algorithm, performing iterative computation of chimpanzees in the task scheduling model by the chimpanzee optimization algorithm; and ending the iterative computation in response to that an iteration termination condition is reached, outputting an optimal solution, and obtaining an optimal scheduling scheme. The present application solves the problem of the traditional chimpanzee optimization algorithm in the prior art that is prone to falling into the local optimum, and the imbalance between the global exploration capacity and the local exploitation capacity, the improved chimpanzee optimization algorithm has different aspects of performance enhancement compared to general intelligence algorithms of population.

Abstract: A display apparatus includes a liquid crystal display panel and a backlight module, a first sealed air cavity formed inside the liquid crystal display panel, and the backlight module being located on one side of the liquid crystal display panel and forming a second sealed air cavity with the liquid crystal display panel; and a sound-emitting board and a sound-emitting exciter, the sound producing board being fixed to the surface of the backlight module far away from the liquid crystal display panel, and a vibration output terminal of the sound-emitting exciter being fixed to the surface of the sound-emitting board far away from the backlight module.

Abstract: A method for image motion deblurring, an apparatus, an electronic device and medium therefor are provided. The method includes obtaining a motion-blurred image to be deblurred; inputting the obtained blurred image into a pre-constructed and pre-trained image motion deblur model based on a multi-scale feature fusion module and a local channel information interaction module to obtain a clear image; wherein, the image motion deblur model is obtained through extracting characteristic information of different spatial scales and frequencies through the multi-scale feature fusion module for feature fusion, and exchanging local channel information of the fused feature map with local channel information in an one-dimensional convolution manner through the local channel information interaction module, and then training a dataset with a objective of minimizing a loss function based on adversarial loss and content loss.

Abstract: Disclosed are an image dehazing method and system based on CycleGAN. The method comprises: acquiring a to-be-processed hazy image; and inputting the image into a pre-trained densely connected CycleGAN, and outputting a clear image. The densely connected CycleGAN comprises a generator, the generator comprises an encoder, a converter and a decoder, the encoder comprises a densely connected layer for extracting features of an input image, the converter comprises a transition layer for combining the features extracted at the encoder stage, the decoder comprises a densely connected layer and a scaled convolutional neural network layer, the densely connected layer is used for restoring original features of the image, and the scaled convolutional neural network layer is used for removing a checkerboard effect of the restored original features to obtain a finally output clear image.

Abstract: The present application discloses a text image super-resolution method based on text assistance, including: obtaining a low-resolution text image to be reconstructed; inputting the low-resolution text image into a pre-trained text image super-resolution model, and determining a reconstructed text image based on an output of the text image super-resolution model; a method of constructing and training the text image super-resolution model includes: obtaining a text image dataset; and training the pre-constructed text image super-resolution model by using the text image dataset to obtain the trained text image super-resolution model. Compared to other ordinary super-resolution models, this text image super-resolution model fuses the text sequence features with the image texture features, and fully exploits and utilizes the text information in the low-resolution image, which can help to improve the quality of reconstructed text image.

Abstract: A medical image segmentation method include: 1) acquiring a medical image data set; 2) acquiring, from the medical image data set, an original image and a real segmentation image of a target region in the original image in pair to serve as an input data set of a pre-built constant-scaling segmentation network, the input data set including a training set, a verification set, and a test set; 3) training the constant-scaling segmentation network by using the training set to obtain a trained segmentation network model, and verifying the constant-scaling segmentation network by using the verification set, the constant-scaling segmentation network including a feature extraction module and a resolution amplifying module; and 4) inputting the original image to be segmented into the segmentation network model for segmentation to obtain a real segmentation image.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The present disclosure relates to the field of computer vision image dehazing and discloses a single image dehazing method based on detail recovery. The method includes step 1: constructing a training dataset; step 2: constructing a backbone dehazing network to achieve preliminary image dehazing: based on the U-Net network model, introduce a residual module based on pixel attention mechanism in the encoding region, and an feature enhancement module in the decoding area; step 3: constructing a detail recovery network for image detail recovery, and introducing residual shrinkage module and spatial attention mechanism; step 4: training an overall network model composed of the backbone dehazing network and the detail recovery network; step 5: Testing. The present disclosure can effectively remove fog while improving the problem of edge detail information loss, reducing the blurring of the edges of the dehazing image, and generating a higher-quality dehazing image.

Abstract: The present application belongs to the field of intelligent transportation technology, and in particular to a high-precision spatio-temporal trajectory recovery method based on cell phone signaling data, using a density clustering algorithm to pre-process original cell phone signaling data, using a grid clustering algorithm that fuses spatio-temporal features, introducing POI data from Baidu maps, and carrying out a clustering analysis from the spatio-temporal two dimensions, extracting a plurality of staying points of the signaling user, and then reconstructing a signaling user's travel trajectory based on the staying points, the method provided by the present application enables the user's reconstructed trajectory to have a higher degree of precision, and is more closely fitted to the user's real trajectory.

Abstract: A method of reconstruction of super-resolution of video frame includes inputting a first video frame with a first resolution and a plurality of consecutive frames thereof into a pre-trained super-resolution reconstruction network, and outputting, by the pre-trained super-resolution reconstruction network, a second video frame with a second resolution corresponding to the first video frame. The second resolution is higher than the first resolution. The super-resolution reconstruction network includes a feature extraction subnetwork, a spatial-temporal non-local alignment subnetwork, an attention progressive fusion subnetwork, and an up-sampling subnetwork which are connected in sequence.

Abstract: A multidimensional resource scheduling method in a Kubernetes cluster architecture system is provided. For a computing-intensive service, each server node in the cluster is scored according to CPU idleness and memory idleness; for an ordinary service, each server node in the cluster is scored according to resource requirements of a scheduling task, a resource priority of each server node and resource balance of each server node. The pod scheduling task is bound to a server node with a highest score for execution. This scheduling method meets diverse resource requests of various services, thereby enhancing the flexibility and expandability of the system.

Abstract: The present application discloses a task scheduling method based on an improved particle swarm optimization algorithm, which includes: obtaining task data to be scheduled, encoding particles according to the task data; iterating the particles by a particle swarm optimization algorithm; in response to that the particle swarm optimization algorithm does not fall into a local optimal solution, outputting a scheduling scheme; and in response to that the particle swarm optimization algorithm falls into the local optimal solution, outputting the scheduling scheme by fusing the particle swarm optimization algorithm with a cuckoo search algorithm. The present application introduces a cuckoo search algorithm when the particle swarm optimization algorithm falls into a local optimal solution, solving the dilemma of the particle swarm optimization algorithm falling into a local optimal solution, while improving the global search capability of the algorithm.

Abstract: The present disclosure discloses an Internet-of-Things-oriented machine learning container image download system and a method. The Internet-of-Things-oriented machine learning container image download system includes a master node and a plurality of computing nodes; the master node is configured to store and convert a machine learning model, and build a machine learning container image from the format-converted machine learning model; and issue an image download instruction to each of the computing nodes after image information of the machine learning container image is completely built; and each of the computing nodes is configured to receive the image download instruction, download the machine learning container image, and start a machine learning container; and receive data collected by Internet-of-Things devices, and return a data processing result to the Internet-of-Things devices.