Aiming at the problem that the number of parameters， calculation amount and accuracy of general gesture recognition algorithms are difficult to balance， a lightweight gesture recognition algorithm for basketball referee was proposed. The proposed algorithm was reconstructed on the basis of YOLOV5s （You Only Look Once Version 5s） algorithm： Firstly， the Involution operator was used to replace CSP1_1 （Cross Stage Partial 1_1） convolution operator to expand the context information capturing range and reduce the kernel redundancy. Secondly， the Coordinate Attention （CA） mechanism was added after the C3 module to obtain stronger gesture feature extraction ability. Thirdly， a lightweight content aware upsampling operator was used to improve the original upsampling module， and the sampling points were concentrated in the object area and the background part was ignored. Finally， the Ghost-Net with SiLU （Sigmoid Weighted Liner Unit） as the activation function was used for lightweight pruning. Experimental results on the self-made basketball referee gesture dataset show that the calculation amount， number of parameters and model size of this lightweight gesture recognition algorithm for basketball referee are 3.3 GFLOPs， 4.0×10⁶ and 8.5 MB respectively， which are only 79%， 44% and 40% of those of YOLOV5s algorithm， mAP@0.5 of the proposed algorithm is 91.7%， and the detection frame rate of the proposed algorithm on the game video with a resolution of 1 920×1 280 reaches 89.3 frame/s， verifying that the proposed algorithm can meet the requirements of low error， high detection rate and lightweight.

With the continuous development of blockchain technology， the block transmission delay has become a performance bottleneck of the scalability of the blockchain system. Remote Direct Memory Access （RDMA） technology， which supports high-bandwidth and low-delay data transmission， provides a new idea for block transmission with low latency. Therefore， a block catalogue structure for block information sharing was designed based on the characteristics of RDMA primitives， and the basic working process of block transmission was proposed and implemented on this basis. Experimental results show that compared with TCP（Transmission Control Protocol） transmission mechanism， the RDMA-based block transmission mechanism reduces the transmission delay between nodes by 44%， the transmission delay among the whole network by 24.4% on a block of 1 MB size， and the number of temporary forks appeared in blockchain by 22.6% on a blockchain of 10 000 nodes. It can be seen that the RDMA-based block transmission mechanism takes advantage of the performance of high speed networks， reduces block transmission latency and the number of temporary forks， thereby improving the scalability of the existing blockchain systems.

There are very few paired high and low resolution images in the real world. The traditional single image Super-Resolution （SR） methods typically use pairs of high-resolution and low-resolution images to train models， but these methods use the way of synthetizing dataset to obtain training set， which only consider bilinear downsampling as image degradation process. However， the image degradation process in the real word is complex and diverse， and traditional image super-resolution methods have poor reconstruction performance when facing real unknown degraded images. Aiming at those problems， a single image super-resolution method was proposed for real complex scenes. Firstly， high- and low-resolution images were captured by the camera with different focal lengths， and these images were registered as image pairs to form a dataset CSR（Camera Super-Resolution dataset） of various scenes. Secondly， to simulate the image degradation process in the real world as much as possible， the image degradation model was improved by the parameter randomization of degradation factors and the nonlinear combination degradation. Besides， the dataset of high- and low-resolution image pairs and the image degradation model were combined to synthetize training set. Finally， as the degradation factors were considered in the dataset， residual shrinkage network and U-Net were embedded into the benchmark model to reduce the redundant information caused by degradation factors in the feature space as much as possible. Experimental results indicate that compared with the BSRGAN （Blind Super-Resolution Generative Adversarial Network） method， under complex degradation conditions， the proposed method improves the PSNR by 0.7 dB and 0.14 dB， and improves SSIM by 0.001 and 0.031 respectively on the RealSR and CSR test sets. The proposed method has better objective indicators and visual effect than the existing methods on complex degradation datasets.

The energy frequency distribution of non-stationary signal could not be got correctly with short-time Fourier transform. A new method was proposed to separate the audio sources from a single mixture based on Hilbert-Huang transform. Hilbert transform combined with Intrinsic Mode Functions (IMFs) constituted Hilbert Spectrum (HS) of mixture, which was a time-frequency representation of a non-stationary signal. The HS of mixture was used to derive the independent source subspaces. The time domain source signals were reconstructed by applying the inverse transformation. The simulated results show that the proposed method is efficient and improves the separation performance. It was observed that HS-based TF representation performed better than using STFT.