In the non-contact respiratory state monitoring based on WiFi Channel State Information （CSI）， respiratory state data are easily affected by environmental interference during acquisition， resulting in degraded signal quality. Meanwhile， due to constrained acquisition conditions and privacy protection， the number of samples is often insufficient， leading to a few-shot problem， and the generated samples usually lack consistency with real samples in key discriminative features， thereby reducing the effectiveness of data augmentation. In addition， the small differences among different respiratory states further increase the difficulty of classification and limit the depth of research on multi-state respiratory recognition. To address these challenges， an SiamGAN-CNNBiLSTM-based respiratory state recognition system was proposed. First， a multi-state respiratory dataset consisting of normal breathing， apnea， and sneezing was constructed， and a systematic CSI signal preprocessing pipeline was designed to improve signal quality. Second， an SiamGAN module integrating Generative Adversarial Network （GAN） and Siamese network was developed， so that through the adversarial mechanism of generator and discriminator， samples with higher realism were generated. At the same time， a Siamese encoder was introduced to extract 128-dimensional intrinsic features of the signals， and a Mean Squared Error （MSE） constraint was imposed in the feature space to enhance the consistency between generated and real samples in key discriminative features. By this strategy， the discriminability and generalization of generated samples were strengthened， and the physical properties of the original signals were preserved， so as to avoid the introduction of classification-irrelevant high-frequency noise. Finally， a deep classification network combining Convolutional Neural Network （CNN） and Bidirectional Long Short-Term Memory （BiLSTM） network was constructed to fully mine the spatio-temporal features of respiratory signals， and achieve high-precision recognition of multiple respiratory states. Experimental results show that in the office scenario， SiamGAN-CNNBiLSTM achieves excellent performance with an accuracy improvement of 2.2 percentage points， compared with CNN-BiLSTM method， and an accuracy improvement of 8.3 percentage points， compared with the Transformer method； in corridor scenario， SiamGAN-CNNBiLSTM achieves an accuracy of 96.7%， representing a decrease of only 1.6 percentage points compared with that in office scenario， indicating the robustness of the proposed method across different environments.

Congestion window of TCP Westwood (TCPW) is based on the increase of linear mode at the congestion avoidance phase in high-speed networks. Therefore, it cannot rapidly obtain or maintain the high throughput. During the slow-start stage, the congestion window of TCPW is based on exponential growth mode, which will cause the datagram increases too fast and prompt the probability of congestion. For the above defects, TCPW was improved from two aspects, and the new algorithm was called NLTCPW. During the slow-start stage, send window of NLTCPW got 10 packets faster than TCPW. After that, the increment speed of send window was decelerated. A simple nonlinear mode was used to increase the congestion widow at the congestion stage. The performance analysis of mathematical model and simulation results show that NLTCPW algorithm has better throughput performance, lower packet loss rate and better fairness, and it is friendly and stable in high-speed networks.

In order to improve the splice site recognition accuracy of Fuzzy Support Vector Machine (FSVM), a new method for computing the membership degree of sample was proposed. The initial membership was defined as the distance ratio of the sample to the two cluster centers of positive and negative samples, K-Nearest Neighbor (KNN) was adopted to compute the tightness of the samples, and the multiplication of the tightness and the initial membership degree was used as the ultimate membership. It will not only improve the membership degree of support vector, but also reduce the membership degree of noise sample. This method was applied to recognize the splice site, and the experimental results show that the recognition accuracy of constitutive 5′ and 3′ splice site reaches 94.65% and 88.97% respectively. Compared with the classical support vector machine，the recognition accuracy of constitutive 3′ splice site increases by 7.94%.