Search

Recently the Wavelet Transform has gained a lot of popularity in the field of signal and image processing, this is due to its capability of providing both time and frequency information simultaneously.In this paper, focus on used Discrete Wavelet Transform 2-dimenssion )2D- DWT) based on conventional approach, convolution, to image processing and implementation by using FPGA(Field Programmable Gate Array) ,due to many researches were implemented on this Hardware in recent years ,using VHDL. In this paper, has been proposed two VHDL architectures to implementation the conventional of the Daubechies 5/3-tap biorthogonal filter bank, a simple – straightforward one and an optimized one, substituting the multipliers used for scaling with shift – add operations. The architecture of optimized approaches were designed and implemented on FPGA, type of Xilinx XC3S500E Spartan-3E.

A parallel-coupled band pass filter is presented in this work. The filter was designed at center frequency of 2GHz, on two different substrate (r=9) and (r=16) with thickness (h=0.635mm). A brief description of design calculation steps is also included. Filter's response is characterized and their performance was compared from the output results like insertion loss, VSWR and bandwidth frequency. The Insertion loss and VSWR for substrate (r=9) were (-1.24dB) and (1.5dB) respectively and the bandwidth was (960MHz). Where for (r=16) the output results were to (-2.8dB), (2.5dB) and (800MHz) respectively. The Q-factor and filters size were reduces as r and electrical losses increases for a certain filter design.

In this paper, the solution of constrained nonlinear programming problems by a Sequential Quadratic Programming (SQP) is considered. The aim of the present work is to promote global convergence without the need to use a penalty and Barrier functions in the mixed interior-exterior point method. Instead, a new concept of a “filter” that aims to minimize the objective function and its approach that allows appoint to be accepted if reduces the objective function and satisfies the constraint violation function. If that point is rejected a new point is tested.