Abstract. In this technical survey, we introduce the state-of-the-art signal-to-noise rate (SNR) estimation techniques for direct sequence ultra-wide bandwidth (DS-UWB) system. The SNR estimation is important to improve the system performance in a multiuser wireless sensor network (WSN). We demonstrate the effectiveness of using regression analysis of features extracted from code mapping to derive a new SNR estimator. And compared the result with another estimator derived by long short-time memory (LSTM) recurrent neural networks (RNN) in terms of their root-mean-squared error. Numerical result show that with a proper length of sequence the proposed method can reach an excellent performance when operating in a DS-UWB system.

This paper studies the construction of self-learning model based on network environment. The application in the electronic circuit course is based on the characteristics and development of advanced education and teaching mode at home and abroad. By making full use of the network environment, applying the network' Interactive Q & A and record common problems, teachers can grasp students' learning status all the time. Teachers preparing teaching on the learning state of students will lead to a substantial increase in classroom teaching efficiency and teaching effectiveness. The learners will have a comprehensive grasp of the basic knowledge of the course and basic ideas. Through the course teaching practice, the model can fully mobilize the enthusiasm of students and cultivate learners' active thinking and self-learning ability, significantly improving the quality of teaching.