Aiming at the problems of dynamic action modeling and multi-scale temporal fusion in skeleton action recognition， an efficient Multi-scale Spatio-Temporal Decoupled Contrastive Learning Framework （MSTDCLF） was proposed. Firstly， a Multi-scale Spatio-Temporal Feature enhancement module （MSTF） was designed to combine depth separable convolution and dilated convolution， so as to model short-term motion features and long-term behavior patterns simultaneously. Secondly， the semantic response between joints and feature channels was further strengthened by embedding the channel-spatial joint attention mechanism. Thirdly， a residual network with attention mechanism was used to solve the gradient decay problem of deep network structure. Finally， a Bidirectional Gated Spatio-temporal Context Modeling （BGSCM） was proposed， and a spatio-temporal enhancement branch was constructed on the basis of Bidirectional Long Short-Term Memory （BiLSTM） network， and the decoupled features were transmitted in joint topology and temporal axis through the gating mechanism， thereby suppressing noise interference and establishing complete action evolution dependency. Experimental results show that MSTDCLF has the accuracies of 87.5% （Cross-Subject （CS）） and 93.0% （Cross-View （CV）） on the NTU RGB+D 60 dataset， and the accuracies of 79.3% （CS） and 80.6% （crosS-Setup （SS）） on the NTU RGB+D 120 dataset， all of which are better than those of the suboptimal method SCD-Net （Spatiotemporal Clues Disentanglement Network）. Ablation experiments verify the effectiveness of the multi-scale design and bidirectional gating mechanism， indicating that MSTDCLF can achieve efficient behavior representation in skeleton behavior recognition and improve recognition accuracy effectively.

Aiming at the problem of artificial artifacts due to phase disorder in frequency-domain speech enhancement algorithms， which limits the denoising performance and decreases the speech quality， a speech enhancement algorithm based on Multi-Scale Ladder-type Time-Frequency Conformer Generative Adversarial Network （MSLTF-CMGAN） was proposed. Taking the real part， imaginary part and magnitude spectrum of the speech spectrogram as input， the generator first learned the local and global feature dependencies between temporal and frequency domains by using time-frequency Conformer at multiple scales. Secondly， the Mask Decoder branch was used to learn the amplitude mask， and the Complex Decoder branch was directly used to learn the clean spectrogram， and the outputs of the two decoder branches were fused to obtain the reconstructed speech. Finally， the metric discriminator was used to judge the scores of speech evaluation metrics， and high-quality speech was generated by the generator through minimax training. Comparison experiments with various types of speech enhancement models were conducted on the public dataset VoiceBank+Demand by subjective evaluation Mean Opinion Score （MOS） and objective evaluation metrics.Experimental results show that compared with current state-of-the-art speech enhancement method CMGAN （Comformer-based MetricGAN）， MSLTF-CMGAN improves MOS prediction of the signal distortion （CSIG） and MOS predictor of intrusiveness of background noise （CBAK） by 0.04 and 0.07 respectively， even though its Perceptual Evaluation of Speech Quality （PESQ） and MOS prediction of the overall effect （COVL） are slightly lower than that of CMGAN， it still outperforms other comparison models in several subjective and objective speech evaluation metrics.

Correctly identifying the entities in judgment documents is an important foundation for building legal knowledge graph and realizing smart courts. However， commonly used Named Entity Recognition （NER） models cannot solve the problem of polysemous word representation and entity boundary recognition errors in judgment document well. In order to effectively improve the recognition effect of various entities in the judgment documents， a Bidirectional Long Short-Term Memory with a sequential Conditional Random Field （BiLSTM-CRF） based on Joint Learning and BERT （Bidirectional Encoder Representation from Transformers） （JLB-BiLSTM-CRF） model was proposed. Firstly， the input character sequence was encoded by BERT to enhance the representation ability of word vectors. Then， the long text information was modeled by BiLSTM network， and the NER tasks and Chinese Word Segmentation （CWS） tasks were jointly trained to improve the boundary recognition rate of entities. Experimental results show that this model has the precision of 94.36%， the recall of 94.94%， and the F1 score of 94.65% on the test set， which are 1.05 percentage points， 0.48 percentage points and 0.77 percentage points higher than those of BERT-BiLSTM-CRF model respectively， verifying the effectiveness of JLB-BiLSTM-CRF model in NER tasks for judgment documents.

Aim to balance the problem of global optimal and local optimal in multi-modal function, an improved quantum genetic algorithm with immune operator is introduced. It carries both the quality of celerity of common quantum genetic algorithm and the quality of global searching of immune clone algorithm. It not only overcomes the flaw of the common quantum genetic algorithm which relapses into local optimum result but also avoids the flaw of the common immune clone algorithm which computes slowly. With the experiment of the global optimization of the multimodal function, the result indicates that this algorithm can settle the problem of searching the global optimization result in given range with faster speed and better result ,and it also shows us that it gets more robust stability compared to the common genetic algorithm and the common quantum genetic algorithm.

The estimation of the content popularity in the Content Distributed/Delivery Network (CDN) system mainly relies on the experience of administrators, which implies strong subjectivity and cannot guarantee the Quality of Service (QoS). In the paper, the authors firstly preprocessed the data, and obtained the initial knowledge base to predict the film popularity. This paper used data mining techniques to learn the existing knowledge and predict the popularity of films. Thus, the films in the CDN system could be deployed more effectively and efficiently. The movie popularity predicted by Bayesian network classier was compared with the movie popularity predicted by decision tree. On the premise of the same correct classification rate and other classification parameters, the time taken to build model in the Bayesian network classifier can be shorter. Therefore, the Bayesian network classifier was preferred. The method can solve the inaccurate deployment caused by the administrators subjectivities and improve the efficiency of the CDN system.

After the first switched on or after a prolonged signal-fade, the receiver of Radio Data System (RDS) must achieve synchronization quickly. The standard algorithm, which calculates the product (modulo-two) of the received binary sequence multiplied by the parity-check matrix to check whether the receiver has achieved synchronization, is very slow. A data frame synchronous fast algorithm based on look-up table was proposed in this paper. The standard algorithm was simplified into the problem of solving three bytes remainder for the received binary sequence. Whether the receiver is synchronized can be determined by checking the remainder table. The fast algorithm improved the operating speed and reduced the demanding memory space. Simulation result indicated the validity and efficiency of the proposed algorithm.

To accurately calculate the Ie-eff coefficient when evaluating VoIP speech quality with the ITU-T G.107 E-model, a real-time evaluation algorithm based on Markov model was presented. Through the establishment of 3-state Markov model for Ppl and 2-state Markov model for BurstR, formulas were derived and corresponding statistics algorithm was given. Commercial test results indicate that this algorithm can accurately evaluate the VoIP speech quality in real-time environment.