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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 Unified Fast Algorithm for Most Commonly used Transforms using Mixed Radix and Kronecker product Dr. H.B. Kekre Dr. Tanuja Sarode Rekha Vig Senior Professor, Department of Associate Professor, Department of Asst. Prof. and Research Scholar, Computer Science, Computer Science, Thadomal Dept. of Elec. and Telecom. Mukesh Patel School of Technology Shahani College of Engineering Mukesh Patel School of Technology Management and Engineering Mumbai, India Management and Engineering Mumbai, India Mumbai, India Abstract— In this paper we present a unified algorithm with The precursor of the transforms was the Fourier series to some minor modifications applicable to most of the transforms. express functions in finite intervals. It was given by Joseph There are many transforms used in signal and image processing Fourier, French mathematician and physicist who initiated the for data compression and many other applications. Many authors Fourier series and its applications to problems of heat transfer have given different algorithms for reducing the complexity to and vibrations[8]. Using the Fourier series, just about any increase the speed of computation. These algorithms have been practical function of time can be represented as a sum of sines developed at different points of time. The paper also shows how and cosines, each suitably scaled, shifted and "squeezed" or the mixed radix system of counting can be used along with "stretched". Later the Fourier transform was developed to Kronecker product of matrices leading to fast algorithm reducing remove the requirement of finite intervals and to accommodate the complexity to logarithmic order. The results of use of such transforms have been shown for both 1-D and 2-D (image) signals all types of signals[3]. Laplace transform technique followed and considerable compression is observed in each case. which converted the frequency representation into a two- dimensional s-plane, what is termed the "complex frequency" Keywords-Orthogonal transforms, Data compression, Fast domain. algorithm, Kronecker product, Decimation in Time, Decimation in DFT is a transform for Fourier analysis of finite-domain Frequency, mixed radix system of counting discrete-time functions, which only evaluates enough frequency components to reconstruct the finite segment that is I. INTRODUCTION analyzed. Variants of the discrete Fourier transform were used Image transforms play an important role in digital image by Alexis Clairaut[30] in 1754 to compute an orbit, which has processing as a theoretical and implementation tool in been described as the first formula for the DFT, and in 1759 by numerous tasks, notably in digital image filtering, restoration, Joseph Louis Lagrange[30], in computing the coefficients of a encoding, compression and analysis [1]. Image transforms are trigonometric series for a vibrating string. The data which both often linear ones. If they are represented by transform matrix T considered had periodic patterns and was discrete samples of then (1) represents the transformation, an unknown function, and since the approximating functions were finite sums of trigonometric functions, their work led to F = [T]f (1) some of the earliest expressions of the discrete Fourier where, f and F are the original and transformed image transform[8]. Technically, Clairaut's work was a cosine-only respectively. Unitary transforms are also energy conserving series (a form of discrete cosine transform), while Lagrange's ∑| F work was a sine-only series (a form of discrete sine transforms so that ∑[ f ] i 2 = k |2 , thus they are used transform[10]); a true cosine+sine DFT was used by Gauss[7] i k in 1805 for trigonometric interpolation of asteroid orbits. for data compression using energy compaction in transformed elements. In most cases the transform matrices are unitary. i.e. Equally significant is a small calculation buried in Gauss’ treatise on interpolation that appeared posthumously in 1866 as T-1 = Tt (2) an unpublished paper, which shows the first clear and t The columns of T are the basis vectors of transform. In indisputable use of the fast Fourier transform (FFT)[5][6], case of 2-D transforms, the basis vectors correspond to the which is generally attributed to Cooley and Tukey[4] in 1965. basis images. Thus a transform decomposes a digital image to a It is a very efficient algorithm for calculating the discrete weighted sum of basis images. Fourier transform, before which the use of DFT, though useful in many applications was very limited. 194 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 Digital applications becoming more popular with the advent then, -1 f = Ct µ C F (8) of computers led to use of square waves as basis functions to represent digital waveforms. Rademacher and J.L. Walsh [11] -1 where µ C = [CCt]-1. independently presented the first use of square functions which led to development of more transforms based on square functions, e.g. Haar[13], Walsh. All of these have fast B. Properties of Kronecker Product algorithm for calculations and hence are used extensively. 1. (A + B) ⊗ C = A ⊗ C + B ⊗ C Hadamard[12, 27] matrices having elements +1 and -1 are also used as transforms. The other most commonly used transforms 2. ( A ⊗ B ) ⊗ C = A ⊗ ( B ⊗ C ) are Group Theoretic transforms[29] Slant transform[14] KLT 3. a (A ⊗ B) = (aA ⊗ B) = (A ⊗ aB) where a is scalar and fast KLT [9,15]. 4. (A ⊗ B)t = At ⊗ Bt These transforms are used in various applications and different transforms may be more suitable in different 5. (A ⊗ B)-1 = A-1 ⊗ B-1 applications. The applications include image analysis[1,22,23], 6. Π k =1 ( Ak ⊗ Bk ) = (Π k =1 Ak ) ⊗ (Π k =1 Bk ) L L L image filtering[1], image segmentation[21], image reconstruction[1,16], image restoration[1], image compression[1,16,17-20,24-26], Scaling operation[2], Pattern 7. det (A ⊗ B) = (det A)m (det B)n where A is mxm matrix analysis and recognition[28] etc. and B is nxn matrix. In this paper we present general fast transform algorithm 8. Iff A and B are unitary matrices then A ⊗ B is also for mixed radix system from which not only all other fast UNITARY matrix. transform algorithms can be derived but one can generate composite transforms with fast algorithms. Key to this fast algorithm is Kronecker product of matrices. Image transforms III. KRONECKER PRODUCT LEADS TO FAST ALGORITHM such as DFT, sine, cosine, Hadamard, Haar and slant can be Let C = A ⊗ B where A is mxm, B is nxn hence C is mn x mn. factored as Kronecker products of several smaller sized matrices. This makes it possible to develop fast algorithms for Thus F= [C]f, is written in an expanded form as given below: their implementation. Next section describes the Kronecker product and properties of Kronecker product. II. KRONECKER PRODUCT OF MATRICES A. Kronecker Product Kronecker product of two matrices A and B is defined as C = A ⊗ B = [aij B ] (3) Where C is m1n1 x m2n2 , A is m1 x m2 and B is n1 x n2 Matrix [C] is given by ⎡ a11 B a12 B .......... a1m B ⎤ ⎢ a B a B .......... a B ⎥ ⎢ 21 22 2m ⎥ ⎢ . . . . ⎥ [C ] = ⎢ ⎥ (4) ⎢ . . . . ⎥ ⎢ . . . . ⎥ ⎢ ⎥ ⎢a m1 B a m 2 B .......... a mm B ⎥ ⎣ ⎦ Let us partition the input and output sequences into m partitions of n elements each and also the matrix into nxn For matrix C to be orthogonal matrices A and B both have to blocks as shown above. be orthogonal. Now if AAt = µA diagonal matrix and BBt = µB diagonal matrix then In a compact form the above matrix equation can be written as CCt = µA ⊗ µB = µC (5) is also a diagonal matrix. To get this result, use F0 = a0,0[B] f0 + a0,1[B] f1+ ……+ a0,m-1[B] fm-1 (9.1) (A ⊗ B) (C ⊗ D) = AC ⊗ BD (6) F1 = a1,0[B] f0+ a1,1[B] f1+ ……+ a1,m-1[B] fm-1 (9.2) . Thus if . F=[C]f (7) Fm-1 = am-1,0[B] f0+ am-1,1[B] f1+ .…+ am-1,m-1[B] fm-1 (9.m) 195 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 It is seen that the coefficients computed by operation of other. The algorithm thus obtained is given in pictorial form in matrix [B] onto vectors f0, f1, ……., fm-1 (9.1) are directly Fig. 1. used in (9.2) to (9.m), thus reducing the number of computations. III. DECIMATION IN FREQUENCY By computing multiplication by matrix [B] to vectors f we can In this algorithm, input sequence (f0, f1, ……., fmn-1) appears in compute intermediate coefficients vectors say G0, G1, …Gm-1, order whereas output sequence appears in a shuffled form, so that, hence the name Decimation in Frequency (DIF) as shown in Gi = [B] fi for i = 0,1, 2, .., m-1 (10) Fig. 1. For number of computations required, let M be the total multiplications required, then Hence we get F0 = a0,0G0 + a0,1G1 + ………………+ a0,m-1Gm-1 (11.1) M = n2m + m2n = nm(n+m) (16) F1 = a1,0G0 + a1,1G1 + ………………+ a1,m-1Gm-1 (11.2) Without this algorithm we require (nm)2 multiplications. Since . (n+m) < nm for all values of m and n except 2 there is a reduction in number of multiplications. Similarly for additions . Fm-1 = am-1,0G0 + am-1,1G1 + …………+ am-1,m-1Gm-1 (11.m) A = nm(n-1) + mn(m-1) = nm(n+m-2) (17) For calculations of F0, F1, ……., Fm-1 the coefficients G0, G1, ……, Gm-1 can be used thus reducing computations In general if sequence length is N and if N= n1n2n3……nr, then considerably. This algorithm can be made elegant as follows. we get Let G0, G1, ……, Gm-1 be written as, M = N(n1 + n2 + n3 + ……. + nr) (18) ⎡ g 00 ⎤ ⎡ g10 ⎤ ⎡ g m −1,0 ⎤ and, ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ g 01 ⎥ ⎢ g11 ⎥ ⎢ g m −1,1 ⎥ G0 = ⎢. ⎥ , G1 = ⎢. ⎥ ,…., Gm −1 = ⎢. ⎥ (12) A = N(n1 + n2 + n3 + ……. + nr - r) (19) ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢. ⎥ ⎢g ⎥ ⎢g ⎥ ⎢ ⎥ Let n = 2r then ⎣ 0,n−1 ⎦ ⎣ 1,n −1 ⎦ ⎢ g m −1, n −1 ⎥ ⎣ ⎦ Now collecting the first elements of output vectors F0, M = N(2 + 2 + 2 + …… r times ) F1, ……., Fm-1 and forming a new vectors as given by. = N(2r) = 2Nlog2n (20) ⎡ F0 ⎤ ⎡ a a 01 ....... a 0 m −1 ⎤ ⎡ g 00 ⎤ ⎢ ⎥ ⎢ 00 ⎢ ⎥ ⎢ Fn ⎥ ⎢ a10 a11 ........ a1m −1 ⎥ ⎥ ⎢ g10 ⎥ Normally without this algorithm we require M = N2 ⎢. ⎥ ⎢ . ⎢. ⎥ multiplications i.e. M = N(n1n2n3……nr) multiplication and . . ⎥ ⎢ ⎥ =⎢ ⎥ ⎢ ⎥ (13) with this algorithm we require M = N(n1 + n2 + n3 + …. + nr). ⎢. ⎥ ⎢ . . ....... . ⎥ ⎢. ⎥ Thus the product is replaced by sum of those factors. The ⎢. ⎥ ⎢ . . . ⎥ ⎢. ⎥ reduction is of logarithmic order. ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ F( m −1) n ⎥ ⎣a m−1,0 a m −1,1 ....... a m −1,m−1 ⎥ ⎦ ⎢ g ( m−1),0 ⎥ ⎣ ⎦ ⎣ ⎦ A. Relation between A ⊗ B and B ⊗ A Thus by taking second elements of vectors F0, F1,.., Fm-1 we get these by operating matrix [A] on second elements of Consider the sequence f(n) represented by vector f G0, G1, … Gm-1. In general taking ith elements of vectors F0, F1, transformed to vector F given by F = [C] f ; where C = …, Fm-1, we get these by operating matrix[A] on ith elements A ⊗ B. of G0, G1, …, Gm-1. This algorithm has been obtained by shuffling output elements of B matrix by a perfect shuffle Now matrix Sn. So the output F comes in a shuffled form Fs where 1. If we shuffle the input sequence f and output F has Fs = [Sm] F (14) to remain same it is necessary to shuffle the columns of [C] by the same shuffle. Fs is obtained from F by dividing F into n groups of m 2. If we shuffle the output elements of vector F and elements each sequentially and then picking up first element want their values remain same the rows of matrix [C] are to be of each group and then second element and so on. To obtain F from Fs we get shuffled by same shuffle. F = [Sm]-1Fs = [Sn] Fs (15) Let fs = [Sn] f ⇒ f = [Sn]-1 fs Here [Sm] and [Sn] are known to be PERFECT SHUFFLE And Fs = [Sn] F ⇒ F = [Sn]-1 Fs MATRICES, where m x n = N. They are also inverse of each 196 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 Substituting in the above equation we get remainder. The process can be continued till we obtained mn-1. Thus the n-tuple (mn-1, mn-2, ……, m0) is obtained representing [Sn]-1Fs = [C] [Sn]-1fs number N. Fs = [Sn] [C] [Sn]-1fs = [Sn] [C] [Sn]tfs As an example of mixed radix system application = [Sn] [C] [Sm] consider the Kronecker product of three orthogonal matrices = [B ⊗ A]fs (21) given below. Thus replacing B and A with each other to implement [B ⊗ A] ⎡1 1 1⎤ ⎡1 1 ⎤ in (21) and giving input in a shuffled form fs will result in T2 = ⎢ ⎥ T3 = ⎢− 2 1 1⎥ ⎢ ⎥ output coming in a normal form, giving us a new algorithm ⎣1 − 1⎦ ⎢ 0 − 1 1⎥ which can be named as decimation in time. This is given in a ⎣ ⎦ pictorial form in Fig. 2. ⎡ 1 1 1 1 1⎤ ⎢− 4 1 1 1 1⎥ IV. DECIMATION IN TIME ⎢ ⎥ T5 = ⎢ 0 − 3 1 1 1⎥ In this algorithm the input sequence appears in a shuffled ⎢ ⎥ form using [Sm] as shuffling matrix. Output is in a normal ⎢ 0 0 − 2 1 1⎥ order as shown in Fig. 2. ⎢ 0 ⎣ 0 0 − 1 1⎥ ⎦ V. MERGING OF DIT AND DIF ALGORITHMS ⎡5 ⎤ Using rectangular array of size mxn and filling them as ⎢20⎥ shown in Fig. 3, we can unify DIF and DIT algorithms. By ⎡3 ⎤ ⎢ ⎥ ⎡ 2⎤ using two dimensional array [f] filled up by input sequence μ m0 = ⎢ ⎥ μ m1 = ⎢6⎥ ⎢ ⎥ μ m2 = ⎢12 ⎥ ⎣ 2⎦ ⎢ ⎥ columnwise as shown in Fig. 3 and operating by matrix [B] on ⎢2⎥ ⎣ ⎦ ⎢6 ⎥ all columns of f-array we get g-array. On this operating by ⎢2 ⎥ matrix [A] on rows we get [F] array. Operation of [B] and [A] ⎣ ⎦ can be interchanged, as shown in DIT path, where intermediate array is named as p-array. It may be noted that g and p array Giving a transformation matrix T = T5 ⊗ T3 ⊗ T2 (22) will have different elements but finally we obtain same F-array. Algorithm given is very simple to understand. This works very Table 1 shows Fast algorithm using mixed radix system well for Walsh, Hadamard and Haar transforms. With little column one is the input sequence subscript i.e n as fn. Column modifications it also gives fast algorithm for DFT and other 2, 3 and 4 shows the values of m2, m1, m0 representing n in a Fourier transform family like DCT and DST and also Group mixed radix system with radices r3= 5, r2=3, and r1=2. Next Theoretic Transforms. If N has more than 2 factors then we column is input sequence f ={0,1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ,11, have to consider multidimensional array for filling and reading. 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, This is easily achieved by using mixed radix system of 28, 29}. Intermediate stage g1 is computed from input counting, for indexing input and output sequences. sequence f by operating T2 on pairs of two input numbers such that m2, m1 are constant and m0 is varying thus following fifteen pairs of input sequence (0, 1), (2, 3), (4, 5), (6, 7), (8, VI. MIXED RADIX SYSTEM 9), (10, 11),…. and so on are obtained. Intermediate stage 2 is Let N be any integer consisting of radix r, then N can be computed from intermediate stage 1 by operating T3 on stage written as N = mn-1rn-1 + ………….. + m2r2 + m1r1 + m0 in 1, such that m2 and m0 are constant and m1 is varying thus we case of mixed radix form N can be written as N = mn-1r1r2…rn-1 get following ten 3-tuples (15, 25, 35), (-15, -15, -15), (17, 27, + ……... + m2r1r2 + m1r1 + m0 where r1, r2, ……, rn-1 are 37), (-15, -15, -15),….. and so on. Final output F = g3 is different radices. When r1 = r2 = ……= rn-1 = r then mixed computed from intermediate stage 2 such that m1 and m0 are radix system reduces to fixed radix system. Thus mixed radix constant and m2 is varying thus we get following 5-tuples (75, system is general and fixed radix system is a special case of 81, 87, 93, 99), (30, 30, 30, 30, 30), (10, 10, 10, 10, 10), (-45, - mixed radix system. Now we can decompose N in case fixed 45, -45, -45, -45), (0, 0, 0, 0, 0) and (0, 0, 0, 0, 0) and radix by dividing N by r successively to get coefficients m0, operating by T5. we get final output sequence F = {435, 60, m1, ……, mn-1 as remainders. In case of mixed radix N can be 36, 18, 6, 150, 0 ,0 ,0 ,0, 50, 0, 0, 0, 0, -225, 0, 0, 0, 0, 0, 0, 0, decomposed by dividing N by r1 to obtain m0 as remainder 0, 0, 0, 0, 0, 0} as shown in (23). and quotient can then be divided by r2 to obtain m1 as 197 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 Figure 1. Decimation in Frequency domain (Perfect Shuffle [Sm]) Note that there are two perfect shuffles [Sm] and [Sn] and [Sm].[Sn] = I where mn = N and also [Sm] = [Sn]t. Figure 2. Decimation in Time domain ( Perfect Shuffle [Sn]) 198 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 Figure 3. Merging of decimation in time and frequency algorithms. 435 0 60 1 36 2 18 3 6 4 150 5 0 6 0 7 0 8 0 9 50 10 0 11 0 12 0 13 0 14 -225 * 15 (23) 0 0 = 16 17 0 18 0 19 0 20 0 21 0 22 0 23 0 24 0 25 0 26 0 27 0 28 0 29 199 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 TABLE I. FAST ALGORITHM USING MIXED RADIX SYSTEM 5 3 2 Intermediate stages Final Stage Input Output Subscript of sequence Stage 1 Stage 2 sequence f m2 m1 m0 f g1 g2 g3 = F 3*2 2 1 0 0 0 0 0 15 75 435 1 0 0 1 1 25 30 60 2 0 1 0 2 35 10 36 3 0 1 1 3 -15 -45 18 4 0 2 0 4 -15 0 6 5 0 2 1 5 -15 0 150 6 1 0 0 6 17 81 0 7 1 0 1 7 27 30 0 8 1 1 0 8 37 10 0 9 1 1 1 9 -15 -45 0 10 1 2 0 10 -15 0 50 11 1 2 1 11 -15 0 0 12 2 0 0 12 19 87 0 13 2 0 1 13 29 30 0 14 2 1 0 14 39 10 0 15 2 1 1 15 -15 -45 -225 16 2 2 0 16 -15 0 0 17 2 2 1 17 -15 0 0 18 3 0 0 18 21 93 0 19 3 0 1 19 31 30 0 20 3 1 0 20 41 10 0 21 3 1 1 21 -15 -45 0 22 3 2 0 22 -15 0 0 23 3 2 1 23 -15 0 0 24 4 0 0 24 23 99 0 25 4 0 1 25 33 30 0 26 4 1 0 26 43 10 0 27 4 1 1 27 -15 -45 0 28 4 2 0 28 -15 0 0 29 4 2 1 29 -15 0 0 200 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011 A. Inverse Transform: Let μ m2 μ m1 μ m0 = μ m2 ⊗ μ m1 ⊗ μ m0 ⎡5 ⎤ ⎢20⎥ ⎢ ⎥ ⎡3 ⎤ ⎡2⎤ μ m2 μ m1 μ m0 = ⎢12 ⎥ ⊗ ⎢6⎥ ⊗ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ 2⎦ ⎢6 ⎥ ⎢ 2 ⎥ ⎣ ⎦ ⎢2 ⎥ ⎣ ⎦ where, m0, m1, and m2 are suffixes given in column 2, 3, and 4 of Table 1. To obtain original sequence f back from transformed sequence F first divide each of Fk by μ m2 μ m1 μ m0 where m2m1m0 is the mixed radix representation of subscript k of F and then multiply the sequence by T5t ⊗ T3t ⊗ T2t refer to the 4th properties of Kronecker product given in section II. For fast inverse mixed radix algorithm same algorithm given in Table Figure 4. a) Original Fingerprint image b) Reconstructed 1 is valid with respective matrices replaced by their transpose. image with 98% energy components gives 62.24% compression and 7.86% error c) Reconstructed image with The number of multiplications required are N2 and 98.5% energy components gives 54.32% compression and additions N(N-1) where N is the input sequence length i.e. 6.8% error d) Reconstructed image with 99% energy total multiplications required for this problem is 302 = 900 and components gives 44.32% compression and 5.53% error additions required are 30*29= 870. Where in case of proposed mixed radix fast algorithm total multiplications required are VII. CONCLUSION 30*(5+3+2) = 300 and total additions required are 30*(5+3+2- 3) =210, thus reducing the number of computations by a factor In this paper we propose generalized Fast Algorithm using more than 3. Kronecker product. The given algorithm is very simple to understand and works very well for Walsh, Hadamard and Haar transforms. With little modifications it also gives fast In case of two-dimensional signals like images, these algorithm for DFT and other Fourier transform family like composite transforms generated by using mixed radix systems DCT and DST and also Group Theoretic Transforms. If N has can be used for compression. Given below is an example, more than 2 factors then we have to consider multidimensional where an image is transformed using one such composite array for filling and reading. It has also been shown how this transform generated using one 4x4 Walsh matrix(Matrix A), algorithm can be easily applied using mixed radix system of one 3x3 Kekre’s transform matrix(Matrix B), one 5x5 DCT counting. The application of the proposed method to a one (Discrete Cosine Transform) matrix(Matrix C) and one 5x5 dimensional number sequence and two- dimensional image Kekre’s transform matrix(Matrix D). The Kroenecker product shows that this method can be used to generate considerable taken in the order D ⊗ C ⊗ B ⊗ A of these matrices produces a amount of compression. 300x300 size composite transform and has been used on a 300x300 fingerprint image. In transform domain certain coefficients are selected such that the total energy of these REFERENCES coefficients is equal to some percentage of the total energy of the image. The image is reconstructed using these selected [1] Loannis Pitas, “Digital Image Processing Algorithms and Applications”, components. The reconstructed images, compression ratio and Published by Wiley-IEEE, Feb. 2000, ISBN 0471377392 the percentage error for 98%, 98.5% and 99% of total energy [2] M. J. Kieman L. M. Linnett R. J. 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Kekre has received B.E. (Hons.) in Sept. 1976. Telecomm. Engg. from Jabalpur University in [10] P. Yip and K. R. Rao. “ A Fast Computational Algorithm for the 1958, M.Tech (Industrial Electronics) from IIT Discrete Sine Transform.” IEEE Trans. Commun. COM-28, no. 2 Bombay in 1960, M.S.Engg. (Electrical Engg.) (February 1980):304-307. from University of Ottawa in 1965 and Ph.D. [11] J. L. Walsh. “A Closed Set of Orthogonal Functions.” American J. of (System Identification) from IIT Bombay in Mathematics 45 (1923): 5-24. 1970. He has worked Over 35 years as Faculty of Electrical Engineering and then HOD Computer [12] H. Kitajima. “Energy Packing Efficiency of the Hadamard Transform.” Science and Engg. at IIT Bombay. For last 13 IEEE Trans. Comm. (correspondence) COM-24 (November 1976):1256- years worked as a Professor in Department of Computer Engg. at Thadomal 1258 Shahani Engineering College, Mumbai. He is currently Senior Professor [13] J. E. Shore. “On the Applications of Haar Functions,” IEEE Trans. working with Mukesh Patel School of Technology Management and Communications COM-21 (March 1973): 209-216. Engineering, SVKM’s NMIMS University, Vile Parle(w), Mumbai, INDIA. [14] W. K. Pratt, W. H. Chen and L. R. Welch. “Slant Transform Image He has guided 17 Ph.D.s, 150 M.E./M.Tech Projects and several B.E./B.Tech Coding.” IEEE Trans. Comm. COM-22 (August 1974): 1075-1093. Projects. His areas of interest are Digital Signal processing, Image Processing [15] H. Hotelling. “Analysis of a Complex of Statistical Variables into and Computer Networks. He has more than 350 papers in National / Principle Components.” J. Educ. Psychology 24 (1933): 417-441 and International Conferences / Journals to his credit. Recently thirteen students 498-520. working under his guidance have received best paper awards. Four of his students have been awarded Ph. D. of NMIMS University. Currently he is [16] Anil K. Jain, “Fundamentals of Digital Image Processing,” Prentice guiding eight Ph.D. students. He is fellow of IETE and life member of ISTE. Hall 1997. [17] A. Habibi and P. A. Wintz. “Image Coding by Linear Transformation and Block Quantization.” IEEE Trans. Commun. Tech. COM-19, no. 1 Dr. Tanuja K. Sarode has received (February 1971): 50-63. M.E.(Computer Engineering) degree from Mumbai University in 2004, Ph.D. from Mukesh [18] P. A. Wintz “Transform Picture Coding,” Proc. IEEE 60, no. 7 (July Patel School of Technology, Management and 1972): 809-823. Engg., SVKM’s NMIMS University, Vile-Parle [19] W. K. Pratt, W. H. Chen and L. R. Welch, “Slant Transform Image (W), Mumbai, INDIA. She has more than 11 Coding,” IEEE Trans. Commun. COM-22, no. 8 (August 1974): 1075- years of experience in teaching. Currently 1093. working as Assistant Professor in Dept. of [20] K. R. Rao, M. A. Narsimhan and K. Revuluri. “Image Data Computer Engineering at Thadomal Shahani Processing by Hadamard –Haar Transforms.” IEEE Trans. Computers Engineering College, Mumbai. She is member of International Association of C-23, no. 9 (September 1975): 888-896. Engineers (IAENG) and International Association of Computer Science and Information Technology (IACSIT). Her areas of interest are Image [21] Chang-Tsun Li, Roland Wilson, “Image Segmentation Using Processing, Signal Processing and Computer Graphics. She has 90 papers in Multiresolution Fourier Transform” Technical report, Department of National /International Conferences/journal to her credit. Computer Science, University of Warwick, September 1995. [22] Andrew R. Davies. Image Feature Analysis using the Multiresolution Fourier Transform. PhD thesis, Department of Computer Science, The University of Warwick, UK, 1993 Rekha Vig has received B.E. (Hons.) in [23] A. Calway. The Multiresolution Fourier Transform: A Genera Purpose Telecomm. Engg. from Jabalpur University in Tool for Image Analysis. PhD thesis, Department of Computer Science, 1994 and M.Tech (Telecom) from MPSTME, The University of Warwick, UK, September 1989. NMIMS University in 2010. She is working as Assisstant Professor in the Department of [24] Dorin, Comaniciu, Richard Grisel, “Image coding using transform Electronics and Telecommunications in Mukesh vector quantization with training set synthesis”, Signal Processing Patel School of Technology Management and Volume 82 , Issue 11 (November 2002) Pages: 1649 – 1663. Engineering, NMIMS University, Mumbai. She [25] Data compression using orthogonal transform and vector quantization has more than 12 years of teaching and United States Patent 4851906. approximately 2 years of industry experience. [26] Robert Y. Li, Jung Kim and N. Al-Shamakhi “Image compression using She is currently pursuing her Ph.D. from NMIMS University, Mumbai. Her transformed vector quantization” Image and Vision Computing 20, areas of specialization are image processing, digital signal processing and 2002, pp-37-45. wireless communication. Her publications include more than 15 papers in IEEE international conferences, international journals and in national conferences and journal. 202 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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