Nonlinear optical characteristics of self-assembled films of ZnO

In the present work, we have investigated the nonlinear optical properties of self-assembled films formed from ZnO colloidal spheres by z-scan technique. The sign of the nonlinear component of refractive index of the material remains the same; however, a switching from reverse saturable absorption to saturable absorption has been observed as the material changes from colloid to self-assembled film. These different nonlinear characteristics can be mainly attributed to ZnO defect states and electronic effects when the colloidal solution is transformed into self-assembled monolayers. We investigated the intensity, wavelength and size dependence of saturable and reverse saturable absorption of ZnO self-assembled films and colloids. Values of the imaginary part of third-order susceptibility are calculated for particles of size in the range 20–300 nm at different intensity levels ranging from 40 to 325MW/cm2 within the wavelength range of 450–650 nm.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.