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Iaetsd finger print recognition by cordic algorithm and pipelined fft

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Iaetsd finger print recognition by cordic algorithm and pipelined fft

2. 2. option to system designers as they continue to face the challenges of minimizing the cost and power targets with the increased performance. The basic principle underlying the CORDIC-based computation, and present its iterative algorithm for different operating modes and planar coordinate system[7]. All of the trigonometric functions can be computed or derived from functions using vector rotations, which will be discussed in the following sections. Vector rotation can also be used for polar to rectangular and rectangular to polar conversions, for vector magnitude, and as a building block in certain transforms such as the DFT and DCT. The CORDIC algorithm provides an iterative method of performing vector rotations by arbitrary angles using only shifts and adds operations. Fig 1.Rotation of two dimensional vector As shown in Fig 1, the rotation of a Two-dimensional vector p0=[x0 y0] through an angle θ to obtain a rotated vector pn= [xn yn] could be performed by the matrix product pn=R.p0 where R is the rotation matrix: R= …… (1) By factoring out the cosine term in (1), the rotation matrix R can be rewritten as R=[(1+tan2 𝜃)-1/2 ] …………… . .(2) and can be interpreted as a product of a scale-factor K=[(1+tan2 𝜃)-1/2 ] with a pseudo rotation matrix Rc,given by RC= ……………… …. (3) The pseudo rotation operation rotates the vector p0 by an angle𝜃 and changes its magnitude by a factor K=cos𝜃 to produce a pseudo-rotated vector p’n= Rcp0 To achieve simplicity of hardware realization of the rotation, the key ideas used in CORDIC arithmetic are to (i) decompose the rotations into a sequence of elementary rotations through predefined angles that could be implemented with minimum hardware cost; and (ii) to avoid scaling, that might involve arithmetic operation, such as square-root and division. The second idea is based on the scale-factor contains only the magnitude information but no information about the angle of rotation[5][8]. Generalized Block Diagram The proposed architecture is designed using the butterfly structure using CORDIC, angle generator, ram, multiplexer, demultiplexer and registers. This architecture (Fig 2) can be classified into input block, core block and output block. The input block will have the ram, demultiplexer, register and multiplexer arrangement, input for the system is going to be binary data input. Input block will have a RAM where the data will be saved by incremental addressing and that data will enter in to the demux unit, output of demux unit is saved in the register. The register chosen for saving the data is based on the select line of the demux, output of the register are applied to the muxing unit. The multiplexer unit will produce input for the core block[6][7]. The core block consists of the butterfly structure of the FFT which is designed using the CORDIC algorithm to replace the complex multipliers. An angle generator is used to generate the twiddle factor angle for rotation to the pipe-lined CORDIC structure. The core block will be designed for the radix 2 and radix-4 FFT structure. The output from the core block also will have the demux - mux arrangement with registers, the data output will be stored before sending the data out. ISBN-13: 978-1535448697 www.iaetsd.in Proceedings of ICRMET-2016 ©IAETSD 201647
3. 3. Input core black output Figure 2: General block diagram for FFT. FFT Design In CORDIC The Discrete Fourier transform(DFT) converts a time- domain sequence into an equivalent frequency-domain sequence. The inverse discrete Fourier transform performs the reverse operation and converts a frequency-domain sequence into an equivalent time-domain sequence. The fast Fourier transform (FFT) is a very efficient algorithm technique based on the discrete Fourier transform but with fewer computations required. The FFT is one of the most commonly used operations in digital signal processing to provide a frequency spectrum analysis. Two different procedures are introduced to compute an FFT: the decimation-in-frequency and the decimation-in-time. Decimation in Frequency DIF –FFT algorithm The Decimation-In-Frequency(DIF) radix-2 FFT partitions the DFT computation into even-indexed and odd-indexed outputs it is shown in figure 3, which can each be computed by shorter-length DFTs of different combinations of input samples. Recursive application of this decomposition to the shorter-length DFTs results in the full radix-2 decimation-in- time FFT. Figure 3 : SFG of Computation of 8 point DFT using DIF FFT The decimation, however, causes shuffling in data. The entire process involves v = log2 N stages of decimation, where each stage involves N/2 butterflies of the type shown in the Figure 4. Figure 4:Butterfly structure Here WN = e –j 2 / N, is the Twiddle factor. Consequently, the computation of N-point DFT via this algorithm requires (N/2) log2 N complex multiplications[1][2]. Finger Print Recognition System Finger Print data angle sin & cos Fig : finger print recognition System. Data Base size(256*256) Pipelined FFT CORDIC Memory Storage Test Features data Matching Output Input block Butterfly structure Angle generator Output block ISBN-13: 978-1535448697 www.iaetsd.in Proceedings of ICRMET-2016 ©IAETSD 201648
4. 4. The design of the finger print recognition using CORDIC and pipelined FFT as shown in figure.. Finger print data base can be collect and resize it, this data is processed by pipelined FFT and CORDIC then it stored in the memory. the storage data match with test feature date then performance of the pipelined FFT and finger print recognition implemented. Conclusion The architecture is dedicated to the computation of trigonometric and arctangent functions. Nevertheless, it can be adapted to all functions by reprogramming the FPGA. The module uses radix-2 number representation, this leads to small circuits by replacing the costly multiplications by a small number of additions. The obtained operators provide very small average error. In that way this algorithm suitable for FFT and finger print recognition. References [1] Dr.P Malathi and Dr Manisha sharma " Pipelined cordic architecture for FFT processors implementation on FPGA" IJAREEEIE,2015. [2] George Joseph ana Vijyakumar K " Variants of cordic for trigonomatric function and its applications",ISSN 2091-2730,2015. [3] Yidong Liu and Lihang Fan " A modified cordic FPGA implementation for wave generation" springer science business media New york,2013. [4] Yasodai A and Ramprasad A " A new memory reduced radix-4 cordic processor for FFT operation " IOSR-JVSP ,2013. [5] Hunny Pahuja, Lavish Kansal and Paramdeep Singh " Cordic algorithm implementation in FPGA computation of sine and cosine signals " international journal of scientific and engineering research,2011. [6] Ramanpreeth and Parminder Singh Jassal " FPGA implementation of cordic algorithm architecture " international journal of scientific knowlegde,2011. [7] Leena vachani and Promod k.mehor " Efficient cordic algorithm and architecture for low area and high throughput implementation" IEEE,2009. [8] Volder, J.," The CORDIC Trigonometric Computing Technique," IRE Trans. Electronic Computing, Vol EC-8, pp330-334 Sept 1959. . ISBN-13: 978-1535448697 www.iaetsd.in Proceedings of ICRMET-2016 ©IAETSD 201649

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