Investigations On The Development Of An Ann Model & Visual Manipulation Approach For 2-D Dft Computation In Image Processing

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

Novel [3+2] annulation reaction of nitrones with Burgess reagent and a few related reactions

Burgess reagent first prepared by E. M. Burgess in 1968, is a mild and selective dehydrating agent for secondary and tertiary alcohols and due to the amphipolar nature it is gainfully employed in a number of creative synthetic ventures. A close examination of the structure of Burgess reagent reveals that it can act as a 1,2-dipole. To the best of our knowledge, no attempts have been made to tap full synthetic potential of the amphipolar nature of this reagent and no reports on 1,3-dipolar addition to a σ-bond in acyclic systems are available in literature. In this context, we propose to unravel novel applications of Burgess reagent based on its amphipolar nature. Rich and multifaceted chemistry of nitrones form the basis of many successful chemical transformations used in attractive synthetic strategies. For the last 50 years special attention has been given to nitrones due to their successful application as building blocks in the synthesis of various natural and biologically active compounds. Our interest in nitrones stems out of its unique character: i.e. it is a 1,3-dipole exhibiting distinct nucleophilic activity. We reasoned that 1,3-dipole possessing significant nucleophilicity should react with amphipolar Burgess reagent with elimination of triethylamine to give the corresponding five-membered ring product by formal dipolar addition to a σ bond. To test this hypothesis we studied the reaction of nitrones with Burgess reagent. This thesis reveals our attempts to explore the [3+2] annulation reaction of nitrones with Burgess reagent which was found to be followed by a rearrangementinvolving C-to-N aryl migration, ultimately resulting in diarylamines and carbamates. We have also examined the reaction of cyanuric chloride with nitrones in DMF with a view to exploit the nucleophilicty of nitrones and to unravel the migratory aptitude, if any, observed in this reaction

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images