Microwave studies of a poly vinyl acetate (PVA)-based phantom for applications in medical imaging

A phantom that exhibits complex dielectric properties similar to low-water-content biological tissues over the electromagnetic spectrum of 2000–3000 MHz has been synthesized from carbon black, graphite powder, and poly vinyl acetate (PVA)-based adhesive. The material overcomes various problems that are inherent in conventional phantoms such as decomposition and deterioration due to the invasion of bacteria or mold. The absorption coefficients of the material for various concentrations of carbon and graphite are studied. A combination of 50% poly-vinyl-acetate-based adhesive, 20% carbon, and 30% graphite exhibits a high absorption coefficient, which suggests another application of the material as a good microwave absorber for the interior lining of tomographic chamber in microwave imaging. The cavity-perturbation technique is adopted to study the dielectric properties of the material.

Discrete wavelet transform de-noising in eukaryotic gene splicing

This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank.Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots.Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given.Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms