Gopikakumari, R; Dr.Sreedhar, C S(Cochin University Of Science And Technology, July 31, 1998)
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Abstract:
This thesis is an outcome of the investigations carried out on the development of an
Artificial Neural Network (ANN) model to implement 2-D DFT at high speed. A new
definition of 2-D DFT relation is presented. This new definition enables DFT computation
organized in stages involving only real addition except at the final stage of computation. The
number of stages is always fixed at 4. Two different strategies are proposed. 1) A visual
representation of 2-D DFT coefficients. 2) A neural network approach.
The visual representation scheme can be used to compute, analyze and manipulate 2D
signals such as images in the frequency domain in terms of symbols derived from 2x2
DFT. This, in turn, can be represented in terms of real data. This approach can help analyze
signals in the frequency domain even without computing the DFT coefficients.
A hierarchical neural network model is developed to implement 2-D DFT. Presently,
this model is capable of implementing 2-D DFT for a particular order N such that ((N))4 = 2.
The model can be developed into one that can implement the 2-D DFT for any order N upto a
set maximum limited by the hardware constraints. The reported method shows a potential in
implementing the 2-D DF T in hardware as a VLSI / ASIC
Description:
Department of Electronics,
Cochin University of Science and Technology