Linear Sensing Response to Ethanol by Indium Oxide Nanoparticle Layers

Indium oxide nanoparticles having well-defined particle sizes were synthesized using a chemical capping method. These nanoparticles were used for making the nanoparticle layers without altering the size and morphology of these particles. These nanoparticles and nanoparticle layers were characterized using XRD, TEM, HRTEM and AFM. The ethanol sensing behavior of the nanoparticle layers were studied at different ethanol concentrations. It was observed that the sensor response was linear to the ethanol concentration in the range of 10–1000 ppm. The ethanol sensing behavior has been explained on the basis of the creation of a depletion region due to the adsorbed oxygen and release of the electron in the conduction band in the presence of ethanol (as it takes away the adsorbed oxygen). The explanation has been supported by EDAX results.</jats:p

Multimodal Image Fusion Using Curvelet and Genetic Algorithm

694-696Fusion of medical images of different modalities always have the advantages in efficient medical diagnosis. Magnetic resonance image (MRI) and Computed tomography (CT) are twp such modalities which are generally fused. The existing fusion techniques like wavelet transformation have proved to be good in medical image fusion. However, they have failed to retain certain quality with respect to the original. In this paper, one such attempt is made by combining the popular Curvelet transformation (CTr) with Genetic Algorithm (GA). The performance of the proposed method is evaluated in terms of PSNR and MSE while fusing MRI and CT of brain. The results clearly mentioned that the Curvelet and the GA-CTr combination have better fusion characteristics than the WT

Spatial Distribution of Heavy Metals in Sediments of the Negombo Lagoon, Sri Lanka

Heavy metals accumulate in the sediments of aquatic environments due to poor water solubility. Their toxic effect poses a significant threat to living organisms. Negombo Lagoon, a vital aquatic ecosystem in Sri Lanka, has become vulnerable to heavy metals mainly from urbanization-related anthropogenic activities. Previous research in this respect has sampling points restricted to the boundary area. Since the heavy metal concentration is a static parameter, continuous research needs to keep the data updated. This study aims to investigate the spatial distribution of several heavy metals (Cr, Ni, Co, Cu, As, Cd, and Pb) in the surficial sediment of the Negombo Lagoon. Fifteen grab sediment samples were collected from the lagoon and analyzed for heavy metal concentration and grain size. The range of concentrations of each metal in test samples were between (78.07 - 222.68 mg/kg) Cr, (376.7-1298.05 mg/kg) Ni, (15.875-43.74 mg/kg) Co, (32.45-112.79 mg/kg) Cu, (20.17-55.81 mg/kg) As, (0.30-1.4 mg/kg) Cd, and (16.57-70.97 mg/kg) Pb. Heavy metal concentrations and sediment grain sizes show significant spatial variation over the Negombo lagoon area. Heavy metals were highly concentrated in locations, where finer sediments are accumulated (i.e., towards the eastern and southern part of the lagoon). Heavy metal concentrations were found to be increased with the decreasing grain size. High heavy metal concentrations are also found at places where there is a river discharge. Among the sources which feed heavy metals into Negombo Lagoon anthropogenic activities such as municipal and industrial waste disposal, rapid urbanization, shipping, and naval activities etc. have a significant impact.</jats:p

Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection

The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions. The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.</jats:p