Modeling of single mode optical fiber having a complicated refractive index profile by using modified scalar finite element method

A numerical method based on modified scalar finite element method (SC-FEM) is presented and programmed on MATLAB platform for optical fiber modeling purpose. We have estimated the dispersion graph, mode cut off condition, and group delay and waveguide dispersion for highly complicated chirped type refractive index profile fiber. The convergence study of our FEM formulation is carried out with respect to the number of division in core. It has been found that the numerical error becomes less than 2 % when the number of divisions in the core is more then 30. To predict the accurate waveguide dispersion characteristics, we need to compute expression (d^2 (Vb))/(dV^2 ) numerically by the FEM method. For that the normalized propagation constant b (in terms of β) should be an accurate enough up to around 6 decimal points. To achieve this target, we have used 1 million sampling points in our FEM simulations. Further to validate our results we have derived the higher order polynomial expression for each case. Comparison with other methods in calculation of normalized propagation constant is found to be satisfactory. In traditional FEM analysis a spurious solution is generated because the functional does not satisfy the boundary conditions in the original waveguide problem, However in our analysis a new term that compensate the missing boundary condition has been added in the functional to eliminate the spurious solutions. Our study will be useful for the analysis of optical fiber having varying refractive index profile

Seeding the Green Future - Participatory organic cotton breeding

BackgroundWorldwide, India is the most important country for organic cotton production with 67% market share. In India, organic cotton production is challenged by 95% genetically modified (GM) cotton cultivation.Public breeding and seed multiplication were neglected and local non-GM seeds supply were eroded. With the continuous growth of the organic market it is important to maintain non-GM germplasm, to enlarge the offer of organic cultivars with a better performance that meet the demand of the market, and to rebuild the seed sovereignty of organic smallholder cotton farmers. Participatory breeding of Gossypiumhirsutumand traditionalG. arboreumcotton offers a great opportunity for developing locally adapted cultivars for increasing genetic diversity

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering

Intelligent Recommendation System for Higher Education

Education domain is very vast and the data is increasing every day. Extracting information from this data requires various data mining techniques. Educational data mining combines various methods of data mining, machine learning and statistics; which are appropriate for the unique data that comes from educational sector. Most of the education recommendation systems available help students to choose particular stream for graduate education after successful schooling or to choose particular career options after graduation. Counseling students during their course of graduate education will help him to comprehend subjects in better ways that will results in enhancing his understanding about subjects. This is possible by knowing the ability of student in learning subjects in past semesters and also mining the similar learning patterns from the past databases. Most educational systems allow students to plan out their subjects (particularly electives) during the beginning of the semester or course. The student is not fully aware about what subjects are good for his career, in which field he is interested in, or how would he perform. Recommending students to choose electives by considering his learning ability, his area of interest, extra-curricular activities and his performance in prerequisites would facilitate students to give a better performance and avoid their risk of failure. This would allow student to specialize in his domain of interest. This early prediction benefits the students to take necessary steps in advance to avoid poor performance and to improve their academic scores. To develop this system, various algorithms and recommendation techniques have to be applied. This paper reviews various data mining and machine learning approaches which are used in educational field and how it can be implemented

Performance Evaluation of Differential Evolution Algorithm Using CEC 2010 Test Suite Problems

Differential evolution algorithm (DE) is a novel parallel direct search evolutionary algorithm. Here we measure the performance of differential evolution algorithm on CEC 2010 test suite problems. It has found that the performance of standard differential evolution algorithm depend upon the value of decision parameters I,e parameter setting and DE require more explorative strategy during population evolution for large dimension problem

Wavelet-based Image Splicing Forgery Detection

Digital image processing is a progressive field which has made development over period of time in a way that it becomes easy to play with artifacts of image by manipulating them using transformation such as copy-paste, copy-move, rotation, smoothing of boundaries, scaling, color enhancing, resizing, addition of noise, blurring, compressing etc. Forgery performed with a digital image, raising a doubt about the authenticity of it. Image splicing is one of the most used method for tampering an image by compositing two or many image fragments to create a spliced image. In this paper, a wavelet-based mechanism is proposed to detect image splicing forgery by taking edge information of an image as a distinguishing feature by performing edge analysis using wavelet transform. Haar-based Discrete Wavelet Transform (DWT) is used for edge analysis that decompose an image into four sub-images and it followed by Speed-Up Robust Feature (SURF) method which is a keypoint-based feature extractor technique. SURF extracts features from the decomposed images of DWT and used that features for performing classification using SVM linear classifier