DESIGN OF A BANDPASS FIR DIGITAL FILTER TO EXTRACT SUB – HZ LOW FREQUENCY SIGNAL. Presented to In Partial Fulfillment of the Requirements of [Project Dated: ABSTRACT Technologies have developed and advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips…
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The aim of this research work is to design and performance evaluation of 0.2 – 0.5 Hz Butterworth, Blackman and Chebyshev digital semi adaptive filters. For this work XILINX and MATLAB software’s were used for the design. As pert of practical research work these designs were translated Using FPGA hardware SPARTAN-3E kit. These were optimized, analyzed, compared and evaluated keeping the sampling frequency at 1Hz for 64 orders. These filters designed using software were tested by passing a sinusoidal test signal of 5 Hz along with noise and the filtered Output signals are presented. Signal to Noise ratios were evaluated, plotted and comparative analysis carried out in this paper. Keywords Digital filter, XILINX and MATLAB software’s, Field Programmable Gate Arrays (FPGA), SPARTAN-3E, DSP Chips, DIP switches Contents ABSTRACT 2 Contents 3 1. INTRODUCTION. 5 2. LITERATURE REVIEW 7 4. RESULTS AND DISCUSSION. 44 6. CONCLUSION 54 Reference list 55 1. INTRODUCTION. 1.1 Problem statement. The possibility of real time and low cost digital filtering emerged in when general purpose of microcomputers were introduced in the 1970s. The earlier designed digital filters had limited precision and speed which limited them to a restricted their application to simple algorithms and low frequency . In the 1980s, in its historical development DSP Chips which were of higher special cost were introduced (Tomlinson 1991). This contained an on chip D/A converter were incorporated and later improvements in their speed and complexity were advanced by ICS . This gave birth to the advanced digital filter design of today ( VolKenberg, 1987). Availability of F.P.G.A series kits helps researchers to take practical designs of semi adaptive band pass filters from low to high frequencies. It is on this basis that this project is designed to provide a high factor and high performance factor in attenuating frequencies which are undesirable for certain applications like the electrocardiogram. 1.2 Objective The objectives of this project are: To inter-grate a low pass and high pass digital filters in a substrate known as band pass filter. To design FIR band pass that can help to extract low frequency of between 0.2-0.5 Hz 1.3 Scope of work The scope of this work is to design and develop a finite impulse response band pass digital filter. The following lists of objectives must be completed with the following in mind: 1. The scope of this project is making literature review which will survey on the magazine, journal, textbooks or conference papers. 2. During literature review, all the information such as theory and design of a digital filter will be explored. 3. Calculation based on Butter-worth and Chebyshev response will be done 4. Entering of the filter characteristics to generate coefficients using the FDAT tool block. 5. After the coefficients have been generated by the use of the FDAT tool block, design simulation using the FIR block will be done. 6. The last step in the finite impulse response band base filter design will be to hardware verify the performance of the hardware in the loop. 7. The preliminary work will use MATLAB tools and the result will compare with the theoretical result. 8. Finally, fabricate and make measurement for analysis result will be made based on the designed device. 1.4 METHODOLOGY This project will start with the background study of band pass basic concept through hole
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This research will attempt ro critically scrutinize other interventions put forward by other researchers such as HARRIS who embraces IIR prototypes, to quantify the quality improvements for several IIR prototypes achievable, to analyze the feasibility of using double precision floating point in MATLAB to solve problems regarding curricular signal processing in relation to getting data for analysis in filtering.
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