Few-shot Text-To-Speech （TTS） aims to synthesize speech that closely resembles the original speaker using only a small amount of training data. However， this approach faces challenges in quickly adapting to new speakers and improving the similarity between generated speech and speakers while ensuring high speech quality. Existing models often overlook changes in model features during different adaptation stages， leading to slow improvement of speech similarity. To address these issues， a meta-learning-guided model for adapting to new speakers was proposed. The model was guided by a meta-feature module during the adaptation process， ensuring the improvement of speech similarity while maintaining the quality of generated speech during the adaptation to new speakers. Furthermore， the differentiation of adaptation stages was achieved through a step encoder， thereby enhancing the speed of model adaptation to new speakers. The proposed method was evaluated on the Libri-TTS and VCTK datasets using subjective and objective evaluation metrics. Experimental results show that the Dynamic Time Warping-Mel Cepstral Distortion （DTW-MCD） of the proposed model are 7.450 2 and 6.524 3， respectively. It surpasses other meta-learning methods in terms of synthesized speech similarity and enables faster adaptation to new speakers.

Edge Computing （EC） and Simultaneous Wireless Information and Power Transfer （SWIPT） technologies can improve the performance of traditional networks， but they also increase the difficulty and complexity of system decision-making. The system decisions designed by optimization methods often have high computational complexity and are difficult to meet the real-time requirements of the system. Therefore， aiming at Wireless Sensor Network （WSN） assisted by EC and SWIPT， a mathematical model of system energy efficiency optimization was proposed by jointly considering beamforming， computing offloading and power control problems in the network. Then， concerning the non-convex and parameter coupling characteristics of this model， a joint optimization method based on deep reinforcement learning was proposed by designing information interchange process of the system. This method did not need to build an environmental model and adopted a reward function instead of the Critic network for action evaluation， which could reduce the difficulty of decision-making and improve the system real-time performance. Finally， based on the joint optimization method， an Improved Deep Deterministic Policy Gradient （IDDPG） algorithm was designed. Simulation comparisons were made with a variety of optimization algorithms and machine learning algorithms to verify the advantages of the joint optimization method in reducing the computational complexity and improving real-time performance of decision-making.

The supply chain faces many challenges in the development process， including how to ensure the authenticity and reliability of information as well as the security of the traceability system in the process of product traceability， the security of products in the process of logistics， and the trust management in the financing process of small and medium enterprises. With characteristics of decentralization， immutability and traceability， blockchain provides efficient solutions to supply chain management， but there are some technical challenges in the actual implementation process. To study the applications of blockchain technology in the supply chain， some typical applications were discussed and analyzed. Firstly， the concept of supply chain and the current challenges were briefly introduced. Secondly， problems faced by blockchain in three different supply chain fields of information flow， logistics flow and capital flow were described， and a comparative analysis of related solutions was given. Finally， the technical challenges faced by blockchain in the practical applications of supply chain were summarized， and future applications were prospected.

To resist recaptured image's attack towards face recognition system, an algorithm based on predicting face image's gradient direction was proposed. The contrast of real image and recaptured image was enhanced by adaptive Gauss homomorphic's illumination compensation. A Support Vector Machine (SVM) classifier was chosen for training and testing two kinds of pictures with convoluting 8-direction Sobel operator. Using 522 live and recaptured faces come from domestic and foreign face databases including NUAA Imposter Database and Yale Face Database for experiment, the detection rate reached 99.51%; Taking 261 live face photos using Samsung Galaxy Nexus phone, then remaked them to get 522 samples library, the detection rate was 98.08% and the time of feature extraction was 167.04s. The results show that the proposed algorithm can classify live and recaptured faces with high extraction efficiency.

Concerning the characteristics of complex components of irregular images and random alignment of irregular spot without proper fitting mathematics model, an adaptive segmentation algorithm with improved Pulse Coupled Neural Network （PCNN） was proposed in the paper. On the basis of basic PCNN model, the neurons feedback input function and dynamic threshold function were modified and multi-level output model for the neuron output was designed to implement the segmentation process as well. Simulation experiment shows that the improved PCNN has better robustness and can realize the adaptive segmentation of irregular image.

For complex multi-peaks function with high dimensions, canonical Particle Swarm Optimization Algorithm (PSOA) has big chance falling in premature convergence for the fast losing of population diversity. With the disadvantages, the intuitionistic fuzzy population entropy was presented as the estimate of the diversity of the population in this paper. By applying the intuitionistic fuzzy population entropy as parameter in velocity updated mechanism, the improved PSOA can prevent premature convergence, which can also provide the PSOA with adaptabily. The experiments show the Improved PSOA is significantly superior to canonical PSOA.

To satisfy the protocol and requirements of different services flow of the stratospheric telecommunication platform, a scheduling scheme that could meet Medium Access Control (MAC) layer QoS and dynamic bandwidth allocation was proposed. The proposed scheduling scheme allocated up link and down link bandwidth not only to Mobile Station (MS) by Mobile Center Station (MCS) but also to all types of traffic in MS simultaneously and dynamically. At last, the simulation on the slot of different services flow, different delay characteristics of the traffic and different algorithms in the same services prove the feasibility of the scheme.