The Research About Simulation Network To Coaxial-Cable And Rectification Project To HDB3 Signal

The signal's high frequency part attenuates seriously on the digital baseband transmission systems, so that the signal rectification is necessary according to the characteristic of the channel.The research background of this thesis is a myriameter coaxial-cable which was equipped on the domestic top research ship, the Dayang Yihao. The main task of the paper is to set up a hardware network to simulate the coaxial-cable, according to the frequency response characteristic tested on the ocean. Based on this experiment foundation, the signal rectification method is researched in the light of the character of the HDB3 coding signal, which is transferred through the simulation network.This thesis research the characteristic of the coaxial-cable with artificial neural network (ANN). As an important part of ANN, back propagation neural network (BP) is of outstanding self-organize ability, self-adapt ability, extend ability, robustness and fault tolerance, so the non-linear mapping with high precision is obtained. As one of the non-linear transfer functions in BP network, tan-s function is suitable especially on the aspects changing acutely and can accelerate the training and convergence process of the network.At present, the programmable technology develops rapidly and holds the balance on EDA design. The structure of FPGA can make full use of its parallel processing function to execute the ANN. The combination on these aspects is also pursued enthusiastically.In this paper, the STAM (Symmetric Table and Addition Method) algorithms is introduced and applied to the performance of tan-s function on the FPGA. The simulative result is also displayed later. There are several methods on the non-linear function realization. But these algorithms usually need to occupy larger hardware resource or have longer reaction times. However, the STAM algorithms can significantly reduce the requirements for the hardware resource by looking up several tables directly, and need not to execute iteratively. It can rapidly perform one time lookup results in one clock period with high precisions, where the computation error is less than one ulp. Compared to other algorithms, the STAM algorithms is more suitable for the hardware realization obviously. At the last chapter, a rectification project of combination network based on integrated differential amplifer to HDB3 coding distortion is researched.