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By Anil K. Jain

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5. 6. 7. 8. 9. 10. S0(n) = X(n); Sj(n) = 0 Vj; Vn for j = 1 to J for n = 1 to M/2j for k = max(1; 2n-L ? 1) to 2n Sj(n) = Sj(n) ? Sj-1(k)G(2n-k) Wj(n) = Wj(n) ? Sj-1(k)H(2n-k) endfor(k) endfor(n) endfor(j) output = SJ(n), Wj(n), j = 1, 2, …, J. The parallel version of above algorithm may be derived with assumption of decomposition into a coarser approximation and finer detail at each scale as a process. Let, j be the current scale and length of the signal is a power of two. Process 0 at root For number of [data packets] Initialize data packets to xo’s 1.

In particular, fixed-point effects in implementation of DWT are studied. When an algorithm is implemented using fixed-point arithmetic, the quality of results can be significantly different from those obtained using floating-point arithmetic. In fixed-point arithmetic, the range of allowable data (including input data, intermediate results, and final output) is limited to a prespecified number Ni of integer bits and Nf of fractional bits. In algorithms where the dynamic range of data is large, as in the case of an orthogonal wavelet transform, fixed-point effects can severely limit the quality of results obtained.

It is easier for one to solve a particular class of problem efficiently on a nonshared memory machine based on specific or fixed interconnection network by exploiting topologies of their choice. The message-passing multicomputers are lattices of processing elements (PE) nodes connected by a message-passing network. The basic computational paradigm is that of concurrency of processes, where processes are instances of programs. 1 Parallel Algorithm The DWT may be computed recursively as a series of convolutions and decimations [52].

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