Abstract Classification of chromosomes have impct in the recent research. The chromosomes are non-rigid material and they are several times touching each other or they overlap each other in the metaphase images. Now many techniques are required to understand the overlapping chromosomes. This paper presents a novel method for segmenting chromosomes based upon computational geometry. In the proposed approach first the... Classification of chromosomes have impct in the recent research. The chromosomes are non-rigid material and they are several times touching each other or they overlap each other in the metaphase images. Now many techniques are required to understand the overlapping chromosomes. This paper presents a novel method for segmenting chromosomes based upon computational geometry. In the proposed approach first the contour line is traced for the overlapping chromosomes and then all the cut points are traced for the overlapping chromosomes. Then based on computational geometry method a specific number of cut points are selected and they are used for separating the two chromosomes. We have found that 87.4% of the images were correctly segmented using the proposed method. Read More Read Less

Abstract Today’s learners are using multimedia on a daily basis. From computers to cell phones, it’s very difficult to get through a day without being exposed to multimedia. Prior research from Mayer and colleagues has revealed the multimedia principle, which communicates that two representations that explain for one another are better for learning than just one. While much of this research focused on... Today’s learners are using multimedia on a daily basis. From computers to cell phones, it’s very difficult to get through a day without being exposed to multimedia. Prior research from Mayer and colleagues has revealed the multimedia principle, which communicates that two representations that explain for one another are better for learning than just one. While much of this research focused on cognitive load and learning, it did not focus on learner preference. As a result, a survey was presented to learners to discover their preferences for the multimedia, modality, redundancy, and coherence principles in a multimedia environment. Overall, participants agreed that they preferred multiple representations to a single one. However, the most surprising results were found when learners were presented redundant representations and irrelevant details. Learners indicated they preferred redundant text and sound with images to image and text or images and sound and that they preferred highly detailed and colored images to simple images. This indicates that while learners may learn better if we following the multimedia principles, they might decrease learner interest or motivation, which could have an impact on instruction. Read More Read Less

Abstract Super-resolution framework uses multiple noisy low resolution images from camera to generate a higher resolution image that has better spatial resolution than any of the available low resolution images. Super-resolution is an ill-posed problem. A inherent difficulty is the challenge of inverting the image observation model without amplifying the effect of noise in the measured data. Classically, the issue is addressed by... Super-resolution framework uses multiple noisy low resolution images from camera to generate a higher resolution image that has better spatial resolution than any of the available low resolution images. Super-resolution is an ill-posed problem. A inherent difficulty is the challenge of inverting the image observation model without amplifying the effect of noise in the measured data. Classically, the issue is addressed by incorporating regularization in the cost function to constraint the space of solutions. The main focus of this paper is to develop a regularization function that would preserve edges with improved resolution of super-resolved image. The proposed method is also compared with the classical super resolution methods and experimental results show the effectiveness and robustness of this method both visually and quantitatively. Read More Read Less